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Feature Papers in Molecular Informatics

A topical collection in International Journal of Molecular Sciences (ISSN 1422-0067). This collection belongs to the section "Molecular Informatics".

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Editors

Department of Drug Sciences, University of Catania, 95125 Catania, Italy
Interests: organic synthesis; computational chemistry; computer aided drug design; molecular modeling; computational studies of reaction mechanisms; molecular docking; QSAR; 1,3-dipolar cycloadditions
Special Issues, Collections and Topics in MDPI journals
1. Institute of Pharmaceutical Science, King’s College London, Stamford Street, London SE1 9NH, UK
2. Department of Drug Sciences, University of Catania, Catania, Italy
Interests: organic synthesis; computational chemistry; computer-aided drug design; gallium chelating agents; peptides chemistry
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

This Topical Collection “Feature Papers in Molecular Informatics” aims to collect high-quality research articles, short communications, and review articles in all the fields of molecular informatics applied to the understanding of the intricate world of biology and molecular research. We encourage the Editorial Board Members of the Molecular Informatics section of the International Journal of Molecular Sciences to contribute papers reflecting the latest progress in their research field and to invite relevant experts and colleagues to share their research. Topics of this collection include:

  • Bioinformatics
  • Cheminformatics
  • Chemical reactivity indexes
  • Coarse-grained models
  • Computational models for photodynamic therapy
  • Computational nanochemistry
  • Computational studies of enzyme mechanisms
  • Computational studies of reaction mechanisms
  • Computer-aided drug design
  • Density functional theory approaches
  • Free-energy calculations
  • Hybrid QM/MM studies and models
  • Ligand–receptor interactions
  • Modeling of reactions in confined spaces
  • Molecular docking
  • Molecular dynamics
  • Molecular modeling
  • QSAR and QSPR
  • Quantum chemistry
  • Virtual screening

Dr. Antonio Rescifina
Dr. Giuseppe Floresta
Collection Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (34 papers)

2024

Jump to: 2023, 2022, 2021

16 pages, 6163 KiB  
Article
Mlp4green: A Binary Classification Approach Specifically for Green Odor
by Jiuliang Yang, Zhiming Qian, Yi He, Minghao Liu, Wannan Li and Weiwei Han
Int. J. Mol. Sci. 2024, 25(6), 3515; https://doi.org/10.3390/ijms25063515 - 20 Mar 2024
Viewed by 296
Abstract
Fresh green leaves give off a smell known as “green odor.” It has antibacterial qualities and can be used to attract or repel insects. However, a common method for evaluating green odor molecules has never existed. Machine learning techniques are widely used in [...] Read more.
Fresh green leaves give off a smell known as “green odor.” It has antibacterial qualities and can be used to attract or repel insects. However, a common method for evaluating green odor molecules has never existed. Machine learning techniques are widely used in research to forecast molecular attributes for binary classification. In this work, the green odor molecules were first trained and learned using machine learning methods, and then clustering analysis and molecular docking were performed to further explore their molecular characteristics and mechanisms of action. For comparison, four algorithmic models were employed, MLP performed the best in all metrics, including Accuracy, Precision, Average Precision, Matthews coefficient, and Area under curve. We determined by difference analysis that, in comparison to non-green odor molecules, green odor molecules have a lower molecular mass and fewer electrons. Based on the MLP algorithm, we constructed a binary classification prediction website for green odors. The first application of deep learning techniques to the study of green odor molecules can be seen as a signal of a new era in which green odor research has advanced into intelligence and standardization. Full article
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2023

Jump to: 2024, 2022, 2021

12 pages, 1888 KiB  
Article
Towards a Long-Read Sequencing Approach for the Molecular Diagnosis of RPGRORF15 Genetic Variants
by Gabriele Bonetti, William Cozza, Andrea Bernini, Jurgen Kaftalli, Chiara Mareso, Francesca Cristofoli, Maria Chiara Medori, Leonardo Colombo, Salvatore Martella, Giovanni Staurenghi, Anna Paola Salvetti, Benedetto Falsini, Giorgio Placidi, Marcella Attanasio, Grazia Pertile, Mario Bengala, Francesca Bosello, Antonio Petracca, Fabiana D’Esposito, Benedetta Toschi, Paolo Lanzetta, Federico Ricci, Francesco Viola, Giuseppe Marceddu and Matteo Bertelliadd Show full author list remove Hide full author list
Int. J. Mol. Sci. 2023, 24(23), 16881; https://doi.org/10.3390/ijms242316881 - 28 Nov 2023
Viewed by 999
Abstract
Sequencing of the low-complexity ORF15 exon of RPGR, a gene correlated with retinitis pigmentosa and cone dystrophy, is difficult to achieve with NGS and Sanger sequencing. False results could lead to the inaccurate annotation of genetic variants in dbSNP and ClinVar databases, tools [...] Read more.
Sequencing of the low-complexity ORF15 exon of RPGR, a gene correlated with retinitis pigmentosa and cone dystrophy, is difficult to achieve with NGS and Sanger sequencing. False results could lead to the inaccurate annotation of genetic variants in dbSNP and ClinVar databases, tools on which HGMD and Ensembl rely, finally resulting in incorrect genetic variants interpretation. This paper aims to propose PacBio sequencing as a feasible method to correctly detect genetic variants in low-complexity regions, such as the ORF15 exon of RPGR, and interpret their pathogenicity by structural studies. Biological samples from 75 patients affected by retinitis pigmentosa or cone dystrophy were analyzed with NGS and repeated with PacBio. The results showed that NGS has a low coverage of the ORF15 region, while PacBio was able to sequence the region of interest and detect eight genetic variants, of which four are likely pathogenic. Furthermore, molecular modeling and dynamics of the RPGR Glu-Gly repeats binding to TTLL5 allowed for the structural evaluation of the variants, providing a way to predict their pathogenicity. Therefore, we propose PacBio sequencing as a standard procedure in diagnostic research for sequencing low-complexity regions such as RPGRORF15, aiding in the correct annotation of genetic variants in online databases. Full article
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24 pages, 14204 KiB  
Article
Molecular Dynamics and Docking Simulations of Homologous RsmE Methyltransferases Hints at a General Mechanism for Substrate Release upon Uridine Methylation on 16S rRNA
by Aaron Hernández-Cid, Jorge Lozano-Aponte and Thomas Scior
Int. J. Mol. Sci. 2023, 24(23), 16722; https://doi.org/10.3390/ijms242316722 - 24 Nov 2023
Viewed by 696
Abstract
In this study, molecular dynamics (MD) and docking simulations were carried out on the crystal structure of Neisseria Gonorrhoeae RsmE aiming at free energy of binding estimation (ΔGbinding) of the methyl transfer substrate S-adenosylmethionine (SAM), as well as its homocysteine precursor [...] Read more.
In this study, molecular dynamics (MD) and docking simulations were carried out on the crystal structure of Neisseria Gonorrhoeae RsmE aiming at free energy of binding estimation (ΔGbinding) of the methyl transfer substrate S-adenosylmethionine (SAM), as well as its homocysteine precursor S-adenosylhomocysteine (SAH). The mechanistic insight gained was generalized in view of existing homology to two other crystal structures of RsmE from Escherichia coli and Aquifex aeolicus. As a proof of concept, the crystal poses of SAM and SAH were reproduced reflecting a more general pattern of molecular interaction for bacterial RsmEs. Our results suggest that a distinct set of conserved residues on loop segments between β12, α6, and Met169 are interacting with SAM and SAH across these bacterial methyltransferases. Comparing molecular movements over time (MD trajectories) between Neisseria gonorrhoeae RsmE alone or in the presence of SAH revealed a hitherto unknown gatekeeper mechanism by two isoleucine residues, Ile171 and Ile219. The proposed gating allows switching from an open to a closed state, mimicking a double latch lock. Additionally, two key residues, Arg221 and Thr222, were identified to assist the exit mechanism of SAH, which could not be observed in the crystal structures. To the best of our knowledge, this study describes for the first time a general catalytic mechanism of bacterial RsmE on theoretical ground. Full article
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20 pages, 3408 KiB  
Article
Structural Space of the Duffy Antigen/Receptor for Chemokines’ Intrinsically Disordered Ectodomain 1 Explored by Temperature Replica-Exchange Molecular Dynamics Simulations
by Agata Kranjc, Tarun Jairaj Narwani, Sophie S. Abby and Alexandre G. de Brevern
Int. J. Mol. Sci. 2023, 24(17), 13280; https://doi.org/10.3390/ijms241713280 - 26 Aug 2023
Viewed by 984
Abstract
Plasmodium vivax malaria affects 14 million people each year. Its invasion requires interactions between the parasitic Duffy-binding protein (PvDBP) and the N-terminal extracellular domain (ECD1) of the host’s Duffy antigen/receptor for chemokines (DARC). ECD1 is highly flexible and intrinsically disordered, therefore [...] Read more.
Plasmodium vivax malaria affects 14 million people each year. Its invasion requires interactions between the parasitic Duffy-binding protein (PvDBP) and the N-terminal extracellular domain (ECD1) of the host’s Duffy antigen/receptor for chemokines (DARC). ECD1 is highly flexible and intrinsically disordered, therefore it can adopt different conformations. We computationally modeled the challenging ECD1 local structure. With T-REMD simulations, we sampled its dynamic behavior and collected its most representative conformations. Our results suggest that most of the DARC ECD1 domain remains in a disordered state during the simulated time. Globular local conformations are found in the analyzed local free-energy minima. These globular conformations share an α-helix spanning residues Ser18 to Ser29 and in many cases they comprise an antiparallel β-sheet, whose β-strands are formed around residues Leu10 and Ala49. The formation of a parallel β-sheet is almost negligible. So far, progress in understanding the mechanisms forming the basis of the P. vivax malaria infection of reticulocytes has been hampered by experimental difficulties, along with a lack of DARC structural information. Our collection of the most probable ECD1 structural conformations will help to advance modeling of the DARC structure and to explore DARC–ECD1 interactions with a range of physiological and pathological ligands. Full article
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18 pages, 739 KiB  
Review
Perspectives of Proteomics in Respiratory Allergic Diseases
by Miguel Ángel Galván-Morales
Int. J. Mol. Sci. 2023, 24(16), 12924; https://doi.org/10.3390/ijms241612924 - 18 Aug 2023
Viewed by 963
Abstract
Proteomics in respiratory allergic diseases has such a battery of techniques and programs that one would almost think there is nothing impossible to find, invent or mold. All the resources that we document here are involved in solving problems in allergic diseases, both [...] Read more.
Proteomics in respiratory allergic diseases has such a battery of techniques and programs that one would almost think there is nothing impossible to find, invent or mold. All the resources that we document here are involved in solving problems in allergic diseases, both diagnostic and prognostic treatment, and immunotherapy development. The main perspectives, according to this version, are in three strands and/or a lockout immunological system: (1) Blocking the diapedesis of the cells involved, (2) Modifications and blocking of paratopes and epitopes being understood by modifications to antibodies, antagonisms, or blocking them, and (3) Blocking FcεRI high-affinity receptors to prevent specific IgEs from sticking to mast cells and basophils. These tools and targets in the allergic landscape are, in our view, the prospects in the field. However, there are still many allergens to identify, including some homologies between allergens and cross-reactions, through the identification of structures and epitopes. The current vision of using proteomics for this purpose remains a constant; this is also true for the basis of diagnostic and controlled systems for immunotherapy. Ours is an open proposal to use this vision for treatment. Full article
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32 pages, 14670 KiB  
Review
Molecular Design of Magnetic Resonance Imaging Agents Binding to Amyloid Deposits
by Alena Nikiforova and Igor Sedov
Int. J. Mol. Sci. 2023, 24(13), 11152; https://doi.org/10.3390/ijms241311152 - 06 Jul 2023
Cited by 2 | Viewed by 1400
Abstract
The ability to detect and monitor amyloid deposition in the brain using non-invasive imaging techniques provides valuable insights into the early diagnosis and progression of Alzheimer’s disease and helps to evaluate the efficacy of potential treatments. Magnetic resonance imaging (MRI) is a widely [...] Read more.
The ability to detect and monitor amyloid deposition in the brain using non-invasive imaging techniques provides valuable insights into the early diagnosis and progression of Alzheimer’s disease and helps to evaluate the efficacy of potential treatments. Magnetic resonance imaging (MRI) is a widely available technique offering high-spatial-resolution imaging. It can be used to visualize amyloid deposits with the help of amyloid-binding diagnostic agents injected into the body. In recent years, a number of amyloid-targeted MRI probes have been developed, but none of them has entered clinical practice. We review the advances in the field and deduce the requirements for the molecular structure and properties of a diagnostic probe candidate. These requirements make up the base for the rational design of MRI-active small molecules targeting amyloid deposits. Particular attention is paid to the novel cryo-EM structures of the fibril aggregates and their complexes, with known binders offering the possibility to use computational structure-based design methods. With continued research and development, MRI probes may revolutionize the diagnosis and treatment of neurodegenerative diseases, ultimately improving the lives of millions of people worldwide. Full article
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12 pages, 777 KiB  
Article
The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models
by Alberto Danieli, Erika Colombo, Giuseppa Raitano, Anna Lombardo, Alessandra Roncaglioni, Alberto Manganaro, Alessio Sommovigo, Edoardo Carnesecchi, Jean-Lou C. M. Dorne and Emilio Benfenati
Int. J. Mol. Sci. 2023, 24(12), 9894; https://doi.org/10.3390/ijms24129894 - 08 Jun 2023
Cited by 2 | Viewed by 1563
Abstract
A sound assessment of in silico models and their applicability domain can support the use of new approach methodologies (NAMs) in chemical risk assessment and requires increasing the users’ confidence in this approach. Several approaches have been proposed to evaluate the applicability domain [...] Read more.
A sound assessment of in silico models and their applicability domain can support the use of new approach methodologies (NAMs) in chemical risk assessment and requires increasing the users’ confidence in this approach. Several approaches have been proposed to evaluate the applicability domain of such models, but their prediction power still needs a thorough assessment. In this context, the VEGA tool capable of assessing the applicability domain of in silico models is examined for a range of toxicological endpoints. The VEGA tool evaluates chemical structures and other features related to the predicted endpoints and is efficient in measuring applicability domain, enabling the user to identify less accurate predictions. This is demonstrated with many models addressing different endpoints, towards toxicity of relevance to human health, ecotoxicological endpoints, environmental fate, physicochemical and toxicokinetic properties, for both regression models and classifiers. Full article
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22 pages, 2596 KiB  
Article
MncR: Late Integration Machine Learning Model for Classification of ncRNA Classes Using Sequence and Structural Encoding
by Heiko Dunkel, Henning Wehrmann, Lars R. Jensen, Andreas W. Kuss and Stefan Simm
Int. J. Mol. Sci. 2023, 24(10), 8884; https://doi.org/10.3390/ijms24108884 - 17 May 2023
Cited by 2 | Viewed by 1593
Abstract
Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify [...] Read more.
Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify potential diagnostic and therapeutic biomarkers. To improve the classification of ncRNAs, we investigated different approaches of utilizing primary sequences and secondary structures as well as the late integration of both using machine learning models, including different neural network architectures. As input, we used the newest version of RNAcentral, focusing on six ncRNA classes, including lncRNA, rRNA, tRNA, miRNA, snRNA and snoRNA. The late integration of graph-encoded structural features and primary sequences in our MncR classifier achieved an overall accuracy of >97%, which could not be increased by more fine-grained subclassification. In comparison to the actual best-performing tool ncRDense, we had a minimal increase of 0.5% in all four overlapping ncRNA classes on a similar test set of sequences. In summary, MncR is not only more accurate than current ncRNA prediction tools but also allows the prediction of long ncRNA classes (lncRNAs, certain rRNAs) up to 12.000 nts and is trained on a more diverse ncRNA dataset retrieved from RNAcentral. Full article
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20 pages, 14831 KiB  
Article
Deep Semantic Segmentation of Angiogenesis Images
by Alisher Ibragimov, Sofya Senotrusova, Kseniia Markova, Evgeny Karpulevich, Andrei Ivanov, Elizaveta Tyshchuk, Polina Grebenkina, Olga Stepanova, Anastasia Sirotskaya, Anastasiia Kovaleva, Arina Oshkolova, Maria Zementova, Viktoriya Konstantinova, Igor Kogan, Sergey Selkov and Dmitry Sokolov
Int. J. Mol. Sci. 2023, 24(2), 1102; https://doi.org/10.3390/ijms24021102 - 06 Jan 2023
Cited by 1 | Viewed by 1717
Abstract
Angiogenesis is the development of new blood vessels from pre-existing ones. It is a complex multifaceted process that is essential for the adequate functioning of human organisms. The investigation of angiogenesis is conducted using various methods. One of the most popular and most [...] Read more.
Angiogenesis is the development of new blood vessels from pre-existing ones. It is a complex multifaceted process that is essential for the adequate functioning of human organisms. The investigation of angiogenesis is conducted using various methods. One of the most popular and most serviceable of these methods in vitro is the short-term culture of endothelial cells on Matrigel. However, a significant disadvantage of this method is the manual analysis of a large number of microphotographs. In this regard, it is necessary to develop a technique for automating the annotation of images of capillary-like structures. Despite the increasing use of deep learning in biomedical image analysis, as far as we know, there still has not been a study on the application of this method to angiogenesis images. To the best of our knowledge, this article demonstrates the first tool based on a convolutional Unet++ encoder–decoder architecture for the semantic segmentation of in vitro angiogenesis simulation images followed by the resulting mask postprocessing for data analysis by experts. The first annotated dataset in this field, AngioCells, is also being made publicly available. To create this dataset, participants were recruited into a markup group, an annotation protocol was developed, and an interparticipant agreement study was carried out. Full article
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2022

Jump to: 2024, 2023, 2021

14 pages, 2720 KiB  
Article
A Fucosylated Lactose-Presenting Tetravalent Glycocluster Acting as a Mutual Ligand of Pseudomonas aeruginosa Lectins A (PA-IL) and B (PA-IIL)—Synthesis and Interaction Studies
by Magdolna Csávás, László Kalmár, Petronella Szőke, László Bence Farkas, Bálint Bécsi, Zoltán Kónya, János Kerékgyártó, Anikó Borbás, Ferenc Erdődi and Katalin E. Kövér
Int. J. Mol. Sci. 2022, 23(24), 16194; https://doi.org/10.3390/ijms232416194 - 19 Dec 2022
Cited by 2 | Viewed by 1352
Abstract
The Gram-negative bacterium Pseudomonas aeruginosa is an important opportunistic human pathogen associated with cystic fibrosis. P. aeruginosa produces two soluble lectins, the d-galactose-specific lectin PA-IL (LecA) and the l-fucose-specific lectin PA-IIL (LecB), among other virulence factors. These lectins play an important [...] Read more.
The Gram-negative bacterium Pseudomonas aeruginosa is an important opportunistic human pathogen associated with cystic fibrosis. P. aeruginosa produces two soluble lectins, the d-galactose-specific lectin PA-IL (LecA) and the l-fucose-specific lectin PA-IIL (LecB), among other virulence factors. These lectins play an important role in the adhesion to host cells and biofilm formation. Moreover, PA-IL is cytotoxic to respiratory cells in the primary culture. Therefore, these lectins are promising therapeutic targets. Specifically, carbohydrate-based compounds could inhibit their activity. In the present work, a 3-O-fucosyl lactose-containing tetravalent glycocluster was synthesized and utilized as a mutual ligand of galactophilic and fucophilic lectins. Pentaerythritol equipped with azido ethylene glycol-linkers was chosen as a multivalent scaffold and the glycocluster was constructed by coupling the scaffold with propargyl 3-O-fucosyl lactoside using an azide-alkyne 1,3-dipolar cycloaddition reaction. The interactions between the glycocluster and PA-IL or PA-IIL were investigated by isothermal titration microcalorimetry and saturation transfer difference NMR spectroscopy. These results may assist in the development of efficient anti-adhesion therapy for the treatment of a P. aeruginosa infection. Full article
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14 pages, 2268 KiB  
Article
Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence
by Jesus Gonzalez-Bosquet, Sofia Gabrilovich, Megan E. McDonald, Brian J. Smith, Kimberly K. Leslie, David D. Bender, Michael J. Goodheart and Eric Devor
Int. J. Mol. Sci. 2022, 23(24), 16014; https://doi.org/10.3390/ijms232416014 - 16 Dec 2022
Cited by 3 | Viewed by 1284
Abstract
Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study [...] Read more.
Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case–control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone. Full article
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11 pages, 1668 KiB  
Article
Antiviral Activity and Crystal Structures of HIV-1 gp120 Antagonists
by Francesca Curreli, Young D. Kwon, Isabella Nicolau, Giancarla Burgos, Andrea Altieri, Alexander V. Kurkin, Raffaello Verardi, Peter D. Kwong and Asim K. Debnath
Int. J. Mol. Sci. 2022, 23(24), 15999; https://doi.org/10.3390/ijms232415999 - 15 Dec 2022
Cited by 3 | Viewed by 1337
Abstract
As part of our effort to discover drugs that target HIV-1 entry, we report the antiviral activity and crystal structures of two novel inhibitors in a complex with a gp120 core. NBD-14204 showed similar antiviral activity against all the clinical isolates tested. The [...] Read more.
As part of our effort to discover drugs that target HIV-1 entry, we report the antiviral activity and crystal structures of two novel inhibitors in a complex with a gp120 core. NBD-14204 showed similar antiviral activity against all the clinical isolates tested. The IC50 values were in the range of 0.24–0.9 µM with an overall mean of 0.47 ± 0.03 µM, showing slightly better activity against the clinical isolates than against the lab-adapted HIV-1HXB2 (IC50 = 0.96 ± 0.1 µM). Moreover, the antiviral activity of NBD-14208 was less consistent, showing a wider range of IC50 values (0.66–5.7 µM) with an overall mean of 3 ± 0.25 µM and better activity against subtypes B and D (Mean IC50 2.2–2.5 µM) than the A, C and Rec viruses (Mean IC50 2.9–3.9 µM). SI of NBD-14204 was about 10-fold higher than NBD-14208, making it a better lead compound for further optimization. In addition, we tested these compounds against S375Y and S375H mutants of gp120, which occurred in some clades and observed these to be sensitive to NBD-14204 and NBD-14208. These inhibitors also showed modest activity against HIV-1 reverse transcriptase. Furthermore, we determined the crystal structures of both inhibitors in complexes with gp120 cores. As expected, both NBD-14204 and NBD-14208 bind primarily within the Phe43 cavity. It is noteworthy that the electron density of the thiazole ring in both structures was poorly defined due to the flexibility of this scaffold, suggesting that these compounds maintain substantial entropy, even when bound to the Phe43 cavity. Full article
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17 pages, 3720 KiB  
Article
Deciphering the Genetic Crosstalk between Microglia and Oligodendrocyte Precursor Cells during Demyelination and Remyelination Using Transcriptomic Data
by Jennifer Enrich-Bengoa, Gemma Manich, Irene R. Dégano and Alex Perálvarez-Marín
Int. J. Mol. Sci. 2022, 23(23), 14868; https://doi.org/10.3390/ijms232314868 - 28 Nov 2022
Cited by 3 | Viewed by 2352
Abstract
Demyelinating disorders show impaired remyelination due to failure in the differentiation of oligodendrocyte progenitor cells (OPCs) into mature myelin-forming oligodendrocytes, a process driven by microglia–OPC crosstalk. Through conducting a transcriptomic analysis of microarray studies on the demyelination–remyelination cuprizone model and using human samples [...] Read more.
Demyelinating disorders show impaired remyelination due to failure in the differentiation of oligodendrocyte progenitor cells (OPCs) into mature myelin-forming oligodendrocytes, a process driven by microglia–OPC crosstalk. Through conducting a transcriptomic analysis of microarray studies on the demyelination–remyelination cuprizone model and using human samples of multiple sclerosis (MS), we identified molecules involved in this crosstalk. Differentially expressed genes (DEGs) of specific regions/cell types were detected in GEO transcriptomic raw data after cuprizone treatment and in MS samples, followed by functional analysis with GO terms and WikiPathways. Additionally, microglia–OPC crosstalk between microglia ligands, OPC receptors and target genes was examined with the NicheNet model. We identified 108 and 166 DEGs in the demyelinated corpus callosum (CC) at 2 and 4 weeks of cuprizone treatment; 427 and 355 DEGs in the remyelinated (4 weeks of cuprizone treatment + 14 days of normal diet) compared to 2- and 4-week demyelinated CC; 252 DEGs in MS samples and 2730 and 12 DEGs in OPC and microglia of 4-week demyelinated CC. At this time point, we found 95 common DEGs in the CC and OPCs, and one common DEG in microglia and OPCs, mostly associated with myelin and lipid metabolism. Crosstalk analysis identified 47 microglia ligands, 43 OPC receptors and 115 OPC target genes, all differentially expressed in cuprizone-treated samples and associated with myelination. Our differential expression pipeline identified demyelination/remyelination transcriptomic biomarkers in studies using diverse platforms and cell types/tissues. Cellular crosstalk analysis yielded novel markers of microglia ligands, OPC receptors and target genes. Full article
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16 pages, 3167 KiB  
Article
Probing the Efficiency of 13-Pyridylalkyl Berberine Derivatives to Human Telomeric G-Quadruplexes Binding: Spectroscopic, Solid State and In Silico Analysis
by Carla Bazzicalupi, Alessandro Bonardi, Tarita Biver, Marta Ferraroni, Francesco Papi, Matteo Savastano, Paolo Lombardi and Paola Gratteri
Int. J. Mol. Sci. 2022, 23(22), 14061; https://doi.org/10.3390/ijms232214061 - 14 Nov 2022
Cited by 3 | Viewed by 1452
Abstract
The interaction between the series of berberine derivatives 15 (NAX071, NAX120, NAX075, NAX077 and NAX079) and human telomeric G-quadruplexes (G4), which are able to inhibit the Telomerase enzyme’s activity in malignant cells, was investigated. The derivatives bear a pyridine moiety connected [...] Read more.
The interaction between the series of berberine derivatives 15 (NAX071, NAX120, NAX075, NAX077 and NAX079) and human telomeric G-quadruplexes (G4), which are able to inhibit the Telomerase enzyme’s activity in malignant cells, was investigated. The derivatives bear a pyridine moiety connected by a hydrocarbon linker of varying length (n = 1–5, with n number of aliphatic carbon atoms) to the C13 position of the parent berberine. As for the G4s, both bimolecular 5′-TAGGGTTAGGGT-3′ (Tel12) and monomolecular 5′-TAGGGTTAGGGTTAGGGTTAGGG-3′ (Tel23) DNA oligonucleotides were considered. Spectrophotometric titrations, melting tests, X-ray diffraction solid state analysis and in silico molecular dynamics (MD) simulations were used to describe the different systems. The results were compared in search of structure–activity relationships. The analysis pointed out the formation of 1:1 complexes between Tel12 and all ligands, whereas both 1:1 and 2:1 ligand/G4 stoichiometries were found for the adduct formed by NAX071 (n = 1). Tel12, with tetrads free from the hindrance by the loop, showed a higher affinity. The details of the different binding geometries were discussed, highlighting the importance of H-bonds given by the berberine benzodioxole group and a correlation between the strength of binding and the hydrocarbon linker length. Theoretical (MD) and experimental (X-ray) structural studies evidence the possibility for the berberine core to interact with one or both G4 strands, depending on the constraints given by the linker length, thus affecting the G4 stabilization effect. Full article
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24 pages, 11743 KiB  
Article
Identification of Selective BRD9 Inhibitor via Integrated Computational Approach
by Maria Mushtaq Ali, Sajda Ashraf, Mohammad Nure-e-Alam, Urooj Qureshi, Khalid Mohammed Khan and Zaheer Ul-Haq
Int. J. Mol. Sci. 2022, 23(21), 13513; https://doi.org/10.3390/ijms232113513 - 04 Nov 2022
Cited by 4 | Viewed by 1564
Abstract
Bromodomain-containing protein 9 (BRD9), a member of the bromodomain and extra terminal domain (BET) protein family, works as an epigenetic reader. BRD9 has been considered an essential drug target for cancer, inflammatory diseases, and metabolic disorders. Due to its high similarity among other [...] Read more.
Bromodomain-containing protein 9 (BRD9), a member of the bromodomain and extra terminal domain (BET) protein family, works as an epigenetic reader. BRD9 has been considered an essential drug target for cancer, inflammatory diseases, and metabolic disorders. Due to its high similarity among other isoforms, no effective treatment of BRD9-associated disorders is available. For the first time, we performed a detailed comparative analysis among BRD9, BRD7, and BRD4. The results indicate that residues His42, Gly43, Ala46, Ala54, Val105, and Leu109 can confer the BRD9 isoform selectivity. The predicted crucial residues were further studied. The pharmacophore model’s features were precisely mapped with some key residues including, Gly43, Phe44, Phe45, Asn100, and Tyr106, all of which play a crucial role in BRD9 inhibition. Docking-based virtual screening was utilized with the consideration of the conserved water network in the binding cavity to identify the potential inhibitors of BRD9. In this workflow, 714 compounds were shortlisted. To attain selectivity, 109 compounds were re-docked to BRD7 for negative selection. Finally, four compounds were selected for molecular dynamics studies. Our studies pave the way for the identification of new compounds and their role in causing noticeable, functional differences in isoforms and between orthologues. Full article
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24 pages, 4499 KiB  
Article
The Role of Oxidation Pattern and Water Content in the Spatial Arrangement and Dynamics of Oxidized Graphene-Based Aqueous Dispersions
by Anastassia Rissanou, Ioannis Karnis, Fanourios Krasanakis, Kiriaki Chrissopoulou and Konstantinos Karatasos
Int. J. Mol. Sci. 2022, 23(21), 13459; https://doi.org/10.3390/ijms232113459 - 03 Nov 2022
Cited by 4 | Viewed by 1121
Abstract
In this work, we employ fully atomistic molecular dynamics simulations to elucidate the effects of the oxidation pattern and of the water content on the organization of graphene sheets in aqueous dispersions and on the dynamic properties of the different moieties at neutral [...] Read more.
In this work, we employ fully atomistic molecular dynamics simulations to elucidate the effects of the oxidation pattern and of the water content on the organization of graphene sheets in aqueous dispersions and on the dynamic properties of the different moieties at neutral pH conditions. Analysis of the results reveals the role of the oxidation motif (peripherally or fully oxidized flakes) in the tendency of the flakes to self-assemble and in the control of key structural characteristics, such as the interlayer distance between the sheets and the average size and the distribution of the formed aggregates. In certain cases, the results are compared to a pertinent experimental system, validating further the relevant computational models. Examination of the diffusional motion of the oxidized flakes shows that different degrees of spatial restriction are imposed upon the decrease in the water content and elucidates the conditions under which a motional arrest of the flakes takes place. At constant water content, the structural differences between the formed aggregates appear to additionally impart distinct diffusional characteristics of a water molecule. A detailed examination of the counterion dynamics describes their interaction with the oxidized flakes and their dependence on the water content and on the oxidation pattern, offering new insight into the expected electrical properties of the dispersions. The detailed information provided by this work will be particularly useful in applications such as molecular sieving, nanofiltration, and in cases where conductive membranes based on oxidized forms of graphene are used. Full article
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17 pages, 3311 KiB  
Article
Toolbox Accelerating Glycomics (TAG): Improving Large-Scale Serum Glycomics and Refinement to Identify SALSA-Modified and Rare Glycans
by Nobuaki Miura, Hisatoshi Hanamatsu, Ikuko Yokota, Keiko Akasaka-Manya, Hiroshi Manya, Tamao Endo, Yasuro Shinohara and Jun-ichi Furukawa
Int. J. Mol. Sci. 2022, 23(21), 13097; https://doi.org/10.3390/ijms232113097 - 28 Oct 2022
Cited by 2 | Viewed by 1551
Abstract
Glycans are involved in many fundamental cellular processes such as growth, differentiation, and morphogenesis. However, their broad structural diversity makes analysis difficult. Glycomics via mass spectrometry has focused on the composition of glycans, but informatics analysis has not kept pace with the development [...] Read more.
Glycans are involved in many fundamental cellular processes such as growth, differentiation, and morphogenesis. However, their broad structural diversity makes analysis difficult. Glycomics via mass spectrometry has focused on the composition of glycans, but informatics analysis has not kept pace with the development of instrumentation and measurement techniques. We developed Toolbox Accelerating Glycomics (TAG), in which glycans can be added manually to the glycan list that can be freely designed with labels and sialic acid modifications, and fast processing is possible. In the present work, we improved TAG for large-scale analysis such as cohort analysis of serum samples. The sialic acid linkage-specific alkylamidation (SALSA) method converts differences in linkages such as α2,3- and α2,6-linkages of sialic acids into differences in mass. Glycans modified by SALSA and several structures discovered in recent years were added to the glycan list. A routine to generate calibration curves has been implemented to explore quantitation. These improvements are based on redefinitions of residues and glycans in the TAG List to incorporate information on glycans that could not be attributed because it was not assumed in the previous version of TAG. These functions were verified through analysis of purchased sera and 74 spectra with linearity at the level of R2 > 0.8 with 81 estimated glycan structures obtained including some candidate of rare glycans such as those with the N,N’-diacetyllactosediamine structure, suggesting they can be applied to large-scale analyses. Full article
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12 pages, 2036 KiB  
Communication
Novel Class of Proteasome Inhibitors: In Silico and In Vitro Evaluation of Diverse Chloro(trifluoromethyl)aziridines
by Laura Ielo, Vincenzo Patamia, Andrea Citarella, Thomas Efferth, Nasim Shahhamzehei, Tanja Schirmeister, Claudio Stagno, Thierry Langer, Antonio Rescifina, Nicola Micale and Vittorio Pace
Int. J. Mol. Sci. 2022, 23(20), 12363; https://doi.org/10.3390/ijms232012363 - 15 Oct 2022
Cited by 8 | Viewed by 2461
Abstract
The ubiquitin-proteasome pathway (UPP) is the major proteolytic system in the cytosol and nucleus of all eukaryotic cells. The role of proteasome inhibitors (PIs) as critical agents for regulating cancer cell death has been established. Aziridine derivatives are well-known alkylating agents employed against [...] Read more.
The ubiquitin-proteasome pathway (UPP) is the major proteolytic system in the cytosol and nucleus of all eukaryotic cells. The role of proteasome inhibitors (PIs) as critical agents for regulating cancer cell death has been established. Aziridine derivatives are well-known alkylating agents employed against cancer. However, to the best of our knowledge, aziridine derivatives showing inhibitory activity towards proteasome have never been described before. Herein we report a new class of selective and nonPIs bearing an aziridine ring as a core structure. In vitro cell-based assays (two leukemia cell lines) also displayed anti-proliferative activity for some compounds. In silico studies indicated non-covalent binding mode and drug-likeness for these derivatives. Taken together, these results are promising for developing more potent PIs. Full article
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14 pages, 2613 KiB  
Review
A Perspective on the (Rise and Fall of) Protein β-Turns
by Alexandre G. de Brevern
Int. J. Mol. Sci. 2022, 23(20), 12314; https://doi.org/10.3390/ijms232012314 - 14 Oct 2022
Cited by 5 | Viewed by 2373
Abstract
The β-turn is the third defined secondary structure after the α-helix and the β-sheet. The β-turns were described more than 50 years ago and account for more than 20% of protein residues. Nonetheless, they are often overlooked or even misunderstood. This poor knowledge [...] Read more.
The β-turn is the third defined secondary structure after the α-helix and the β-sheet. The β-turns were described more than 50 years ago and account for more than 20% of protein residues. Nonetheless, they are often overlooked or even misunderstood. This poor knowledge of these local protein conformations is due to various factors, causes that I discuss here. For example, confusion still exists about the assignment of these local protein structures, their overlaps with other structures, the potential absence of a stabilizing hydrogen bond, the numerous types of β-turns and the software’s difficulty in assigning or visualizing them. I also propose some ideas to potentially/partially remedy this and present why β-turns can still be helpful, even in the AlphaFold 2 era. Full article
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15 pages, 4996 KiB  
Article
Analysis of Host–Bacteria Protein Interactions Reveals Conserved Domains and Motifs That Mediate Fundamental Infection Pathways
by Jordi Gómez Borrego and Marc Torrent Burgas
Int. J. Mol. Sci. 2022, 23(19), 11489; https://doi.org/10.3390/ijms231911489 - 29 Sep 2022
Cited by 5 | Viewed by 1618
Abstract
Adhesion and colonization of host cells by pathogenic bacteria depend on protein–protein interactions (PPIs). These interactions are interesting from the pharmacological point of view since new molecules that inhibit host-pathogen PPIs would act as new antimicrobials. Most of these interactions are discovered using [...] Read more.
Adhesion and colonization of host cells by pathogenic bacteria depend on protein–protein interactions (PPIs). These interactions are interesting from the pharmacological point of view since new molecules that inhibit host-pathogen PPIs would act as new antimicrobials. Most of these interactions are discovered using high-throughput methods that may display a high false positive rate. The absence of curation of these databases can make the available data unreliable. To address this issue, a comprehensive filtering process was developed to obtain a reliable list of domains and motifs that participate in PPIs between bacteria and human cells. From a structural point of view, our analysis revealed that human proteins involved in the interactions are rich in alpha helix and disordered regions and poorer in beta structure. Disordered regions in human proteins harbor short sequence motifs that are specifically recognized by certain domains in pathogenic proteins. The most relevant domain–domain interactions were validated by AlphaFold, showing that a proper analysis of host-pathogen PPI databases can reveal structural conserved patterns. Domain–motif interactions, on the contrary, were more difficult to validate, since unstructured regions were involved, where AlphaFold could not make a good prediction. Moreover, these interactions are also likely accommodated by post-translational modifications, especially phosphorylation, which can potentially occur in 25–50% of host proteins. Hence, while common structural patterns are involved in host–pathogen PPIs and can be retrieved from available databases, more information is required to properly infer the full interactome. By resolving these issues, and in combination with new prediction tools like Alphafold, new classes of antimicrobials could be discovered from a more detailed understanding of these interactions. Full article
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22 pages, 6021 KiB  
Article
Genetic Mechanism Study of Auditory Phoenix Spheres and Transcription Factors Prediction for Direct Reprogramming by Bioinformatics
by Jishizhan Chen, Ziyu Liu and Jinke Chang
Int. J. Mol. Sci. 2022, 23(18), 10287; https://doi.org/10.3390/ijms231810287 - 07 Sep 2022
Cited by 2 | Viewed by 1597
Abstract
Background: Hearing loss is the most common irreversible sensory disorder. By delivering regenerative cells into the cochlea, cell-based therapy provides a novel strategy for hearing restoration. Recently, newly-identified phoenix cells have drawn attention due to their nearly unlimited self-renewal and neural differentiation capabilities. [...] Read more.
Background: Hearing loss is the most common irreversible sensory disorder. By delivering regenerative cells into the cochlea, cell-based therapy provides a novel strategy for hearing restoration. Recently, newly-identified phoenix cells have drawn attention due to their nearly unlimited self-renewal and neural differentiation capabilities. They are a promising cell source for cell therapy and a potential substitute for induced pluripotent stem cells (iPSCs) in many in vitro applications. However, the underlying genomic mechanism of their self-renewal capabilities is largely unknown. The aim of this study was to identify hub genes and potential molecular mechanisms between differentiated and undifferentiated phoenix cells and predict transcription factors (TFs) for direct reprogramming. Material and Methods: The datasets were downloaded from the ArrayExpress database. Samples of differentiated and undifferentiated phoenix cells with three biological replicates were utilised for bioinformatic analysis. Differentially expressed genes (DEGs) were screened and the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The gene set enrichment analysis (GSEA) was conducted to verify the enrichment of four self-defined gene set collections, followed by protein-protein interaction (PPI) network construction and subcluster analysis. The prediction of TFs for direct reprogramming was performed based on the TRANSFAC database. Results: Ten hub genes were identified to be the key candidates for self-renewal. Ten TFs were predicted as the direct reprogramming factors. This study provides a theoretical foundation for understanding phoenix cells and clues for direct reprogramming, which would stimulate further experiments and clinical applications in hearing research and treatment. Full article
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20 pages, 6995 KiB  
Article
Identification of the WRKY Gene Family and Characterization of Stress-Responsive Genes in Taraxacum kok-saghyz Rodin
by Yifeng Cheng, Jinxue Luo, Hao Li, Feng Wei, Yuqi Zhang, Haiyang Jiang and Xiaojian Peng
Int. J. Mol. Sci. 2022, 23(18), 10270; https://doi.org/10.3390/ijms231810270 - 07 Sep 2022
Cited by 13 | Viewed by 2169
Abstract
WRKY transcription factors present unusual research value because of their critical roles in plant physiological processes and stress responses. Taraxacum kok-saghyz Rodin (TKS) is a perennial herb of dandelion in the Asteraceae family. However, the research on TKS WRKY TFs is limited. In [...] Read more.
WRKY transcription factors present unusual research value because of their critical roles in plant physiological processes and stress responses. Taraxacum kok-saghyz Rodin (TKS) is a perennial herb of dandelion in the Asteraceae family. However, the research on TKS WRKY TFs is limited. In this study, 72 TKS WRKY TFs were identified and named. Further comparison of the core motifs and the structure of the WRKY motif was analyzed. These TFs were divided into three groups through phylogenetic analysis. Genes in the same group of TkWRKY usually exhibit a similar exon-intron structure and motif composition. In addition, virtually all the TKS WRKY genes contained several cis-elements related to stress response. Expression profiling of the TkWRKY genes was assessed using transcriptome data sets and Real-Time RT-PCR data in tissues during physiological development, under abiotic stress and hormonal treatments. For instance, the TkWRKY18, TkWRKY23, and TkWRKY38 genes were significantly upregulated during cold stress, whereas the TkWRKY21 gene was upregulated under heat-stress conditions. These results could provide a basis for further studies on the function of the TKS WRKY gene family and genetic amelioration of TKS germplasm. Full article
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23 pages, 16129 KiB  
Article
A Uniquely Stable Trimeric Model of SARS-CoV-2 Spike Transmembrane Domain
by Elena T. Aliper, Nikolay A. Krylov, Dmitry E. Nolde, Anton A. Polyansky and Roman G. Efremov
Int. J. Mol. Sci. 2022, 23(16), 9221; https://doi.org/10.3390/ijms23169221 - 17 Aug 2022
Cited by 3 | Viewed by 2068
Abstract
Understanding fusion mechanisms employed by SARS-CoV-2 spike protein entails realistic transmembrane domain (TMD) models, while no reliable approaches towards predicting the 3D structure of transmembrane (TM) trimers exist. Here, we propose a comprehensive computational framework to model the spike TMD only based on [...] Read more.
Understanding fusion mechanisms employed by SARS-CoV-2 spike protein entails realistic transmembrane domain (TMD) models, while no reliable approaches towards predicting the 3D structure of transmembrane (TM) trimers exist. Here, we propose a comprehensive computational framework to model the spike TMD only based on its primary structure. We performed amino acid sequence pattern matching and compared the molecular hydrophobicity potential (MHP) distribution on the helix surface against TM homotrimers with known 3D structures and selected an appropriate template for homology modeling. We then iteratively built a model of spike TMD, adjusting “dynamic MHP portraits” and residue variability motifs. The stability of this model, with and without palmitoyl modifications downstream of the TMD, and several alternative configurations (including a recent NMR structure), was tested in all-atom molecular dynamics simulations in a POPC bilayer mimicking the viral envelope. Our model demonstrated unique stability under the conditions applied and conforms to known basic principles of TM helix packing. The original computational framework looks promising and could potentially be employed in the construction of 3D models of TM trimers for a wide range of membrane proteins. Full article
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17 pages, 2382 KiB  
Article
The Ability of Chlorophyll to Trap Carcinogen Aflatoxin B1: A Theoretical Approach
by Alma Vázquez-Durán, Guillermo Téllez-Isaías, Maricarmen Hernández-Rodríguez, René Miranda Ruvalcaba, Joel Martínez, María Inés Nicolás-Vázquez, Juan Manuel Aceves-Hernández and Abraham Méndez-Albores
Int. J. Mol. Sci. 2022, 23(11), 6068; https://doi.org/10.3390/ijms23116068 - 28 May 2022
Cited by 3 | Viewed by 1944
Abstract
The coordination of one and two aflatoxin B1 (AFB1, a potent carcinogen) molecules with chlorophyll a (chl a) was studied at a theoretical level. Calculations were performed using the M06-2X method in conjunction with the 6-311G(d,p) basis set, [...] Read more.
The coordination of one and two aflatoxin B1 (AFB1, a potent carcinogen) molecules with chlorophyll a (chl a) was studied at a theoretical level. Calculations were performed using the M06-2X method in conjunction with the 6-311G(d,p) basis set, in both gas and water phases. The molecular electrostatic potential map shows the chemical activity of various sites of the AFB1 and chl a molecules. The energy difference between molecular orbitals of AFB1 and chl a allowed for the establishment of an intermolecular interaction. A charge transfer from AFB1 to the central cation of chl a was shown. The energies of the optimized structures for chl a show two configurations, unfolded and folded, with a difference of 15.41 kcal/mol. Chl a appeared axially coordinated to the plane (α-down or β-up) of the porphyrin moiety, either with the oxygen atom of the ketonic group, or with the oxygen atom of the lactone moiety of AFB1. The complexes of maximum stability were chl a 1-α-E-AFB1 and chl a 2-β-E-AFB1, at −36.4 and −39.2 kcal/mol, respectively. Additionally, with two AFB1 molecules were chl a 1-D-2AFB1 and chl a 2-E-2AFB1, at −60.0 and −64.8 kcal/mol, respectively. Finally, biosorbents containing chlorophyll could improve AFB1 adsorption. Full article
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21 pages, 11468 KiB  
Review
Artificial Intelligence Technologies for COVID-19 De Novo Drug Design
by Giuseppe Floresta, Chiara Zagni, Davide Gentile, Vincenzo Patamia and Antonio Rescifina
Int. J. Mol. Sci. 2022, 23(6), 3261; https://doi.org/10.3390/ijms23063261 - 17 Mar 2022
Cited by 25 | Viewed by 3945
Abstract
The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, [...] Read more.
The recent covid crisis has provided important lessons for academia and industry regarding digital reorganization. Among the fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and beyond. Drug development is a costly and time-consuming business, and only a minority of approved drugs generate returns exceeding the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper reviews the most significant research on artificial intelligence in de novo drug design for COVID-19 pharmaceutical research. Full article
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16 pages, 3195 KiB  
Article
A Transfer-Learning-Based Deep Convolutional Neural Network for Predicting Leukemia-Related Phosphorylation Sites from Protein Primary Sequences
by Jian He, Yanling Wu, Xuemei Pu, Menglong Li and Yanzhi Guo
Int. J. Mol. Sci. 2022, 23(3), 1741; https://doi.org/10.3390/ijms23031741 - 03 Feb 2022
Cited by 5 | Viewed by 1973
Abstract
As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Abnormal phosphorylation has been [...] Read more.
As one of the most important post-translational modifications (PTMs), phosphorylation refers to the binding of a phosphate group with amino acid residues like Ser (S), Thr (T) and Tyr (Y) thus resulting in diverse functions at the molecular level. Abnormal phosphorylation has been proved to be closely related with human diseases. To our knowledge, no research has been reported describing specific disease-associated phosphorylation sites prediction which is of great significance for comprehensive understanding of disease mechanism. In this work, focusing on three types of leukemia, we aim to develop a reliable leukemia-related phosphorylation site prediction models by combing deep convolutional neural network (CNN) with transfer-learning. CNN could automatically discover complex representations of phosphorylation patterns from the raw sequences, and hence it provides a powerful tool for improvement of leukemia-related phosphorylation site prediction. With the largest dataset of myelogenous leukemia, the optimal models for S/T/Y phosphorylation sites give the AUC values of 0.8784, 0.8328 and 0.7716 respectively. When transferred learning on the small size datasets, the models for T-cell and lymphoid leukemia also give the promising performance by common sharing the optimal parameters. Compared with other five machine-learning methods, our CNN models reveal the superior performance. Finally, the leukemia-related pathogenesis analysis and distribution analysis on phosphorylated proteins along with K-means clustering analysis and position-specific conversation profiles on the phosphorylation site all indicate the strong practical feasibility of our easy-to-use CNN models. Full article
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13 pages, 2221 KiB  
Article
Docking and Molecular Dynamic of Microalgae Compounds as Potential Inhibitors of Beta-Lactamase
by Roberto Pestana-Nobles, Yani Aranguren-Díaz, Elwi Machado-Sierra, Juvenal Yosa, Nataly J. Galan-Freyle, Laura X. Sepulveda-Montaño, Daniel G. Kuroda and Leonardo C. Pacheco-Londoño
Int. J. Mol. Sci. 2022, 23(3), 1630; https://doi.org/10.3390/ijms23031630 - 31 Jan 2022
Cited by 7 | Viewed by 2780
Abstract
Bacterial resistance is responsible for a wide variety of health problems, both in children and adults. The persistence of symptoms and infections are mainly treated with β-lactam antibiotics. The increasing resistance to those antibiotics by bacterial pathogens generated the emergence of extended-spectrum β-lactamases [...] Read more.
Bacterial resistance is responsible for a wide variety of health problems, both in children and adults. The persistence of symptoms and infections are mainly treated with β-lactam antibiotics. The increasing resistance to those antibiotics by bacterial pathogens generated the emergence of extended-spectrum β-lactamases (ESBLs), an actual public health problem. This is due to rapid mutations of bacteria when exposed to antibiotics. In this case, β-lactamases are enzymes used by bacteria to hydrolyze the beta-lactam rings present in the antibiotics. Therefore, it was necessary to explore novel molecules as potential β-lactamases inhibitors to find antibacterial compounds against infection caused by ESBLs. A computational methodology based on molecular docking and molecular dynamic simulations was used to find new microalgae metabolites inhibitors of β-lactamase. Six 3D β-lactamase proteins were selected, and the molecular docking revealed that the metabolites belonging to the same structural families, such as phenylacridine (4-Ph), quercetin (Qn), and cryptophycin (Cryp), exhibit a better binding score and binding energy than commercial clinical medicine β-lactamase inhibitors, such as clavulanic acid, sulbactam, and tazobactam. These results indicate that 4-Ph, Qn, and Cryp molecules, homologous from microalgae metabolites, could be used, likely as novel β-lactamase inhibitors or as structural templates for new in-silico pharmaceutical designs, with the possibility of combatting β-lactam resistance Full article
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12 pages, 2182 KiB  
Article
More Is Not Always Better: Local Models Provide Accurate Predictions of Spectral Properties of Porphyrins
by Aleksey I. Rusanov, Olga A. Dmitrieva, Nugzar Zh. Mamardashvili and Igor V. Tetko
Int. J. Mol. Sci. 2022, 23(3), 1201; https://doi.org/10.3390/ijms23031201 - 21 Jan 2022
Cited by 9 | Viewed by 1923
Abstract
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We [...] Read more.
The development of new functional materials based on porphyrins requires fast and accurate prediction of their spectral properties. The available models in the literature for absorption wavelength and extinction coefficient of the Soret band have low accuracy for this class of compounds. We collected spectral data for porphyrins to extend the literature set and compared the performance of global and local models for their modelling using different machine learning methods. Interestingly, extension of the public database contributed models with lower accuracies compared to the models, which we built using porphyrins only. The later model calculated acceptable RMSE = 2.61 for prediction of the absorption band of 335 porphyrins synthesized in our laboratory, but had a low accuracy (RMSE = 0.52) for extinction coefficient. A development of models using only compounds from our laboratory significantly decreased errors for these compounds (RMSE = 0.5 and 0.042 for absorption band and extinction coefficient, respectively), but limited their applicability only to these homologous series. When developing models, one should clearly keep in mind their potential use and select a strategy that could contribute the most accurate predictions for the target application. The models and data are publicly available. Full article
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12 pages, 1113 KiB  
Article
TLCrys: Transfer Learning Based Method for Protein Crystallization Prediction
by Chen Jin, Zhuangwei Shi, Chuanze Kang, Ken Lin and Han Zhang
Int. J. Mol. Sci. 2022, 23(2), 972; https://doi.org/10.3390/ijms23020972 - 16 Jan 2022
Cited by 4 | Viewed by 1836
Abstract
X-ray diffraction technique is one of the most common methods of ascertaining protein structures, yet only 2–10% of proteins can produce diffraction-quality crystals. Several computational methods have been proposed so far to predict protein crystallization. Nevertheless, the current state-of-the-art computational methods are limited [...] Read more.
X-ray diffraction technique is one of the most common methods of ascertaining protein structures, yet only 2–10% of proteins can produce diffraction-quality crystals. Several computational methods have been proposed so far to predict protein crystallization. Nevertheless, the current state-of-the-art computational methods are limited by the scarcity of experimental data. Thus, the prediction accuracy of existing models hasn’t reached the ideal level. To address the problems above, we propose a novel transfer-learning-based framework for protein crystallization prediction, named TLCrys. The framework proceeds in two steps: pre-training and fine-tuning. The pre-training step adopts attention mechanism to extract both global and local information of the protein sequences. The representation learned from the pre-training step is regarded as knowledge to be transferred and fine-tuned to enhance the performance of crystalization prediction. During pre-training, TLCrys adopts a multi-task learning method, which not only improves the learning ability of protein encoding, but also enhances the robustness and generalization of protein representation. The multi-head self-attention layer guarantees that different levels of the protein representation can be extracted by the fine-tuned step. During transfer learning, the fine-tuning strategy used by TLCrys improves the task-specialized learning ability of the network. Our method outperforms all previous predictors significantly in five crystallization stages of prediction. Furthermore, the proposed methodology can be well generalized to other protein sequence classification tasks. Full article
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21 pages, 6796 KiB  
Article
Analysis of Integrin αIIb Subunit Dynamics Reveals Long-Range Effects of Missense Mutations on Calf Domains
by Sali Anies, Vincent Jallu, Julien Diharce, Tarun J. Narwani and Alexandre G. de Brevern
Int. J. Mol. Sci. 2022, 23(2), 858; https://doi.org/10.3390/ijms23020858 - 13 Jan 2022
Cited by 6 | Viewed by 1688
Abstract
Integrin αIIbβ3, a glycoprotein complex expressed at the platelet surface, is involved in platelet aggregation and contributes to primary haemostasis. Several integrin αIIbβ3 polymorphisms prevent the aggregation that causes haemorrhagic syndromes, such as Glanzmann thrombasthenia (GT). [...] Read more.
Integrin αIIbβ3, a glycoprotein complex expressed at the platelet surface, is involved in platelet aggregation and contributes to primary haemostasis. Several integrin αIIbβ3 polymorphisms prevent the aggregation that causes haemorrhagic syndromes, such as Glanzmann thrombasthenia (GT). Access to 3D structure allows understanding the structural effects of polymorphisms related to GT. In a previous analysis using Molecular Dynamics (MD) simulations of αIIbCalf-1 domain structure, it was observed that GT associated with single amino acid variation affects distant loops, but not the mutated position. In this study, experiments are extended to Calf-1, Thigh, and Calf-2 domains. Two loops in Calf-2 are unstructured and therefore are modelled expertly using biophysical restraints. Surprisingly, MD revealed the presence of rigid zones in these loops. Detailed analysis with structural alphabet, the Proteins Blocks (PBs), allowed observing local changes in highly flexible regions. The variant P741R located at C-terminal of Calf-1 revealed that the Calf-2 presence did not affect the results obtained with isolated Calf-1 domain. Simulations for Calf-1 + Calf-2, and Thigh + Calf-1 variant systems are designed to comprehend the impact of five single amino acid variations in these domains. Distant conformational changes are observed, thus highlighting the potential role of allostery in the structural basis of GT. Full article
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17 pages, 118645 KiB  
Article
Excited States Computation of Models of Phenylalanine Protein Chains: TD-DFT and Composite CC2/TD-DFT Protocols
by Marine Lebel, Thibaut Very, Eric Gloaguen, Benjamin Tardivel, Michel Mons and Valérie Brenner
Int. J. Mol. Sci. 2022, 23(2), 621; https://doi.org/10.3390/ijms23020621 - 06 Jan 2022
Cited by 2 | Viewed by 1549
Abstract
The present benchmark calculations testify to the validity of time-dependent density functional theory (TD-DFT) when exploring the low-lying excited states potential energy surfaces of models of phenylalanine protein chains. Among three functionals suitable for systems exhibiting charge-transfer excited states, LC-ωPBE, CAM-B3LYP, and ωB97X-D, [...] Read more.
The present benchmark calculations testify to the validity of time-dependent density functional theory (TD-DFT) when exploring the low-lying excited states potential energy surfaces of models of phenylalanine protein chains. Among three functionals suitable for systems exhibiting charge-transfer excited states, LC-ωPBE, CAM-B3LYP, and ωB97X-D, which were tested on a reference peptide system, we selected the ωB97X-D functional, which gave the best results compared to the approximate coupled-cluster singles and doubles (CC2) method. A quantitative agreement for both the geometrical parameters and the vibrational frequencies was obtained for the lowest singlet excited state (a ππ* state) of the series of capped peptides. In contrast, only a qualitative agreement was met for the corresponding adiabatic zero-point vibrational energy (ZPVE)-corrected excitation energies. Two composite protocols combining CC2 and DFT/TD-DFT methods were then developed to improve these calculations. Both protocols substantially reduced the error compared to CC2 and experiment, and the best of both even led to results of CC2 quality at a lower cost, thus providing a reliable alternative to this method for very large systems. Full article
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18 pages, 3266 KiB  
Article
Synthesis of Nucleoside-like Molecules from a Pyrolysis Product of Cellulose and Their Computational Prediction as Potential SARS-CoV-2 RNA-Dependent RNA Polymerase Inhibitors
by Andrea Defant, Federico Dosi, Nicole Innocenti and Ines Mancini
Int. J. Mol. Sci. 2022, 23(1), 518; https://doi.org/10.3390/ijms23010518 - 04 Jan 2022
Cited by 4 | Viewed by 2079
Abstract
(1R,5S)-1-Hydroxy-3,6-dioxa-bicyclo[3.2.1]octan-2-one, available by an efficient catalytic pyrolysis of cellulose, has been applied as a chiral building block in the synthesis of seven new nucleoside analogues, with structural modifications on the nucleobase moiety and on the carboxyl- derived unit. The [...] Read more.
(1R,5S)-1-Hydroxy-3,6-dioxa-bicyclo[3.2.1]octan-2-one, available by an efficient catalytic pyrolysis of cellulose, has been applied as a chiral building block in the synthesis of seven new nucleoside analogues, with structural modifications on the nucleobase moiety and on the carboxyl- derived unit. The inverted configuration by Mitsunobu reaction used in their synthesis was verified by 2D-NOESY correlations, supported by the optimized structure employing the DFT methods. An in silico screening of these compounds as inhibitors of SARS-CoV-2 RNA-dependent RNA polymerase has been carried out in comparison with both remdesivir, a mono-phosphoramidate prodrug recently approved for COVID-19 treatment, and its ribonucleoside metabolite GS-441524. Drug-likeness prediction and data by docking calculation indicated compound 6 [=(3S,5S)-methyl 5-(hydroxymethyl)-3-(6-(4-methylpiperazin-1-yl)-9H-purin-9-yl)tetrahydrofuran-3-carboxylate] as the best candidate. Furthermore, molecular dynamics simulation showed a stable interaction of structure 6 in RNA-dependent RNA polymerase (RdRp) complex and a lower average atomic fluctuation than GS-441524, suggesting a well accommodation in the RdRp binding pocket. Full article
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2021

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8 pages, 6215 KiB  
Communication
Green Efficient One-Pot Synthesis and Separation of Nitrones in Water Assisted by a Self-Assembled Nanoreactor
by Vincenzo Patamia, Giuseppe Floresta, Venerando Pistarà and Antonio Rescifina
Int. J. Mol. Sci. 2022, 23(1), 236; https://doi.org/10.3390/ijms23010236 - 26 Dec 2021
Cited by 5 | Viewed by 1876
Abstract
This article reports an alternative method for preparing nitrones using a tetrahedral capsule as a nanoreactor in water. Using the hydrophobic cavity of the capsule allowed us to reduce the reaction times and easily separate the nitrones from the reaction mixture, obtaining reaction [...] Read more.
This article reports an alternative method for preparing nitrones using a tetrahedral capsule as a nanoreactor in water. Using the hydrophobic cavity of the capsule allowed us to reduce the reaction times and easily separate the nitrones from the reaction mixture, obtaining reaction yields equal or comparable to those obtained with the methods already reported. Furthermore, at the basis of this methodology, there is an eco-friendly approach carried out that can certainly be extended to other synthesis methods for the preparation of other substrates by exploiting various types of macrocyclic hosts, suitably designed and widely used in supramolecular chemistry. Full article
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15 pages, 3898 KiB  
Article
Meta-Analysis and Bioinformatics Detection of Susceptibility Genes in Diabetic Nephropathy
by Maria Tziastoudi, Christos Cholevas, Theoharis C. Theoharides and Ioannis Stefanidis
Int. J. Mol. Sci. 2022, 23(1), 20; https://doi.org/10.3390/ijms23010020 - 21 Dec 2021
Cited by 4 | Viewed by 2620
Abstract
The latest meta-analysis of genome-wide linkage studies (GWLS) identified nine cytogenetic locations suggestive of a linkage with diabetic nephropathy (DN) due to type 1 diabetes mellitus (T1DM) and seven locations due to type 2 diabetes mellitus (T2DM). In order to gain biological insight [...] Read more.
The latest meta-analysis of genome-wide linkage studies (GWLS) identified nine cytogenetic locations suggestive of a linkage with diabetic nephropathy (DN) due to type 1 diabetes mellitus (T1DM) and seven locations due to type 2 diabetes mellitus (T2DM). In order to gain biological insight about the functional role of the genes located in these regions and to prioritize the most significant genetic loci for further research, we conducted a gene ontology analysis with an over representation test for the functional annotation of the protein coding genes. Protein analysis through evolutionary relationships (PANTHER) version 16.0 software and Cytoscape with the relevant plugins were used for the gene ontology analysis, and the overrepresentation test and STRING database were used for the construction of the protein network. The findings of the over-representation test highlight the contribution of immune related molecules like immunoglobulins, cytokines, and chemokines with regard to the most overrepresented protein classes, whereas the most enriched signaling pathways include the VEGF signaling pathway, the Cadherin pathway, the Wnt pathway, the angiogenesis pathway, the p38 MAPK pathway, and the EGF receptor signaling pathway. The common section of T1DM and T2DM results include the significant over representation of immune related molecules, and the Cadherin and Wnt signaling pathways that could constitute potential therapeutic targets for the treatment of DN, irrespective of the type of diabetes. Full article
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