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Advances in Computational Chemistry for Drug Design, Discovery and Screening

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 67755

Special Issue Editors


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Guest Editor
Research Center for Computer-Aided Drug Discovery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: computational drug discovery; molecular simulations; protein structure and functions

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Guest Editor
Biological and Chemical Research Centre, Faculty of Chemistry, University of Warsaw, 02-093 Warsaw, Poland
Interests: computational biology and chemistry; bioinformatics; medicinal chemistry; structural biology; molecular modeling; molecular dynamics; docking; G-protein-coupled receptors; amyloids; membranes
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Chemical Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan
Interests: molecular simulations; nanotechnolgy; nanoparticle; nano-bio intefaces

Special Issue Information

Dear Colleagues,

This Special Issue will provide up-to-date information on computational methods and technology in modern drug discovery. Current drug discovery is a long and risky task. It takes at least ten years and one billion dollars. How to speed up drug development and save costs has become an essential topic in the pharmaceutical industry. New advances in computational biology such as molecular modeling, molecular dynamics, virtual screening, and, more recently, artificial intelligence play more and more important roles. Many tedious and time-consuming steps can be replaced or facilitated by these technologies. This could lead to noticeable savings in the costs of modern drug discovery, in terms of both time and finance.

Contributions to this Special Issue may cover all advances related to computational drug discovery, including new target identification, virtual screening, drug design, lead optimization, properties prediction by artificial intelligence, binding energy calculation, WebGL based real-time simulation and data analysis, molecular dynamics simulation, and drug re-purposing. 

Dr. Shuguang Yuan
Prof. Dr. Sławomir Filipek
Dr. Hideya Nakamura
Guest Editors

Manuscript Submission Information

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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. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). 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.

Keywords

  • computational drug design
  • molecular modeling
  • virtual screening
  • protein–ligand interactions
  • molecular dynamics simulation
  • binding energy calculation
  • artificial intelligence

Published Papers (28 papers)

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Research

21 pages, 14119 KiB  
Article
Identification of Dual-Target Inhibitors for Epidermal Growth Factor Receptor and AKT: Virtual Screening Based on Structure and Molecular Dynamics Study
by Hanyu Yang, Zhiwei Zhang, Qian Liu, Jie Yu, Chongjin Liu and Wencai Lu
Molecules 2023, 28(22), 7607; https://doi.org/10.3390/molecules28227607 - 15 Nov 2023
Viewed by 974
Abstract
Epidermal growth factor EGFR is an important target for non-small cell lung (NSCL) cancer, and inhibitors of the AKT protein have been used in many cancer treatments, including those for NSCL cancer. Therefore, searching small molecular inhibitors which can target both EGFR and [...] Read more.
Epidermal growth factor EGFR is an important target for non-small cell lung (NSCL) cancer, and inhibitors of the AKT protein have been used in many cancer treatments, including those for NSCL cancer. Therefore, searching small molecular inhibitors which can target both EGFR and AKT may help cancer treatment. In this study, we applied a ligand-based pharmacophore model, molecular docking, and MD simulation methods to search for potential inhibitors of EGFR and then studied dual-target inhibitors of EGFR and AKT by screening the immune-oncology Chinese medicine (TCMIO) database and the human endogenous database (HMDB). It was found that TCMIO89212, TCMIO90156, and TCMIO98874 had large binding free energies with EGFR and AKT, and HMDB0012243 also has the ability to bind to EGFR and AKT. These results may provide valuable information for further experimental study. Full article
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14 pages, 2843 KiB  
Article
A Bioinformatic Study on the Potential Anti-Vitiligo Activity of a Carpobrotus edulis Compound
by Emna Trigui, Hanen Ben Hassen, Hatem Zaghden, Maher Trigui and Sami Achour
Molecules 2023, 28(22), 7545; https://doi.org/10.3390/molecules28227545 - 11 Nov 2023
Viewed by 1055
Abstract
The plant Carpobrotus edulis has traditionally been known for its wide applications in diseases, especially vitiligo, which is characterized by patches and white macules caused by the loss of melanocytes. One of the chemical treatments for vitiligo consists mainly of skin repigmentation and [...] Read more.
The plant Carpobrotus edulis has traditionally been known for its wide applications in diseases, especially vitiligo, which is characterized by patches and white macules caused by the loss of melanocytes. One of the chemical treatments for vitiligo consists mainly of skin repigmentation and usually leads to a non-durable effect by inhibiting the Janus kinase (JAK) signal transduction (STAT pathway). JAK inhibitors generally block multiple JAK tyrosine kinases, which leads to secondary effects. In this study, natural molecules from Carpobrotus edulis were extracted and tested using a structure-based drug-design approach and pharmacophore modeling. The best-fit candidate from the extracted molecules was compared to the chemical molecules used. The results indicated a similarity between the chemical and natural ligands which suggested the potential use of the natural product against vitiligo. The main finding of this research work was the discovery of a new molecule extracted from a natural plant and the detection of its anti-vitiligo activity using an in-silico approach. This method can significantly reduce the cost of searching for potential medicinal molecules. Full article
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17 pages, 6441 KiB  
Article
A Biological Feature and Heterogeneous Network Representation Learning-Based Framework for Drug–Target Interaction Prediction
by Liwei Liu, Qi Zhang, Yuxiao Wei, Qi Zhao and Bo Liao
Molecules 2023, 28(18), 6546; https://doi.org/10.3390/molecules28186546 - 09 Sep 2023
Cited by 1 | Viewed by 1376
Abstract
The prediction of drug–target interaction (DTI) is crucial to drug discovery. Although the interactions between the drug and target can be accurately verified by traditional biochemical experiments, the determination of DTI through biochemical experiments is a time-consuming, laborious, and expensive process. Therefore, we [...] Read more.
The prediction of drug–target interaction (DTI) is crucial to drug discovery. Although the interactions between the drug and target can be accurately verified by traditional biochemical experiments, the determination of DTI through biochemical experiments is a time-consuming, laborious, and expensive process. Therefore, we propose a learning-based framework named BG-DTI for drug–target interaction prediction. Our model combines two main approaches based on biological features and heterogeneous networks to identify interactions between drugs and targets. First, we extract original features from the sequence to encode each drug and target. Later, we further consider the relationships among various biological entities by constructing drug–drug similarity networks and target–target similarity networks. Furthermore, a graph convolutional network and a graph attention network in the graph representation learning module help us learn the features representation of drugs and targets. After obtaining the features from graph representation learning modules, these features are combined into fusion descriptors for drug–target pairs. Finally, we send the fusion descriptors and labels to a random forest classifier for predicting DTI. The evaluation results show that BG-DTI achieves an average AUC of 0.938 and an average AUPR of 0.930, which is better than those of five existing state-of-the-art methods. We believe that BG-DTI can facilitate the development of drug discovery or drug repurposing. Full article
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18 pages, 631 KiB  
Article
Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction
by Ping Xuan, Peiru Li, Hui Cui, Meng Wang, Toshiya Nakaguchi and Tiangang Zhang
Molecules 2023, 28(18), 6544; https://doi.org/10.3390/molecules28186544 - 09 Sep 2023
Viewed by 830
Abstract
Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks for side-effect-drug-association prediction (TCSD). The method [...] Read more.
Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks for side-effect-drug-association prediction (TCSD). The method encodes and integrates attributes from multiple types of neighbor nodes, connection semantics, and multi-view pairwise information. In each drug-side-effect heterogeneous graph, a target node has two types of neighbor nodes, the drug nodes and the side-effect ones. We proposed a new heterogeneous graph transformer-based context representation learning module. The module is able to encode specific topology and the contextual relations among multiple kinds of nodes. There are similarity and association connections between the target node and its various types of neighbor nodes, and these connections imply semantic diversity. Therefore, we designed a new strategy to measure the importance of a neighboring node to the target node and incorporate different semantics of the connections between the target node and its multi-type neighbors. Furthermore, we designed attentions at the neighbor node type level and at the graph level, respectively, to obtain enhanced informative neighbor node features and multi-graph features. Finally, a pairwise multi-view feature learning module based on capsule networks was built to learn the pairwise attributes from the heterogeneous graphs. Our prediction model was evaluated using a public dataset, and the cross-validation results showed it achieved superior performance to several state-of-the-art methods. Ablation experiments undertaken demonstrated the effectiveness of heterogeneous graph transformer-based context encoding, the position enhanced pairwise attribute learning, and the neighborhood node category-level attention. Case studies on five drugs further showed TCSD’s ability in retrieving potential drug-related side-effect candidates, and TCSD inferred the candidate side-effects for 708 drugs. Full article
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10 pages, 2379 KiB  
Communication
Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties
by Masahito Ohue, Yuki Kojima and Takatsugu Kosugi
Molecules 2023, 28(15), 5652; https://doi.org/10.3390/molecules28155652 - 26 Jul 2023
Cited by 2 | Viewed by 1522
Abstract
Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the ”rule of five (RO5)”. Therefore, [...] Read more.
Protein-protein interactions (PPIs) are associated with various diseases; hence, they are important targets in drug discovery. However, the physicochemical empirical properties of PPI-targeted drugs are distinct from those of conventional small molecule oral pharmaceuticals, which adhere to the ”rule of five (RO5)”. Therefore, developing PPI-targeted drugs using conventional methods, such as molecular generation models, is challenging. In this study, we propose a molecular generation model based on deep reinforcement learning that is specialized for the production of PPI inhibitors. By introducing a scoring function that can represent the properties of PPI inhibitors, we successfully generated potential PPI inhibitor compounds. These newly constructed virtual compounds possess the desired properties for PPI inhibitors, and they show similarity to commercially available PPI libraries. The virtual compounds are freely available as a virtual library. Full article
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15 pages, 4787 KiB  
Article
Identification of Potent Inhibitors Targeting EGFR and HER3 for Effective Treatment of Chemoresistance in Non-Small Cell Lung Cancer
by Ayed A. Dera, Sumera Zaib, Areeba, Nadia Hussain, Nehal Rana, Hira Javed and Imtiaz Khan
Molecules 2023, 28(12), 4850; https://doi.org/10.3390/molecules28124850 - 19 Jun 2023
Cited by 3 | Viewed by 1446
Abstract
Non-small cell lung cancer (NSCLC) is the most common form of lung cancer. Despite the existence of various therapeutic options, NSCLC is still a major health concern due to its aggressive nature and high mutation rate. Consequently, HER3 has been selected as a [...] Read more.
Non-small cell lung cancer (NSCLC) is the most common form of lung cancer. Despite the existence of various therapeutic options, NSCLC is still a major health concern due to its aggressive nature and high mutation rate. Consequently, HER3 has been selected as a target protein along with EGFR because of its limited tyrosine kinase activity and ability to activate PI3/AKT pathway responsible for therapy failure. We herein used a BioSolveIT suite to identify potent inhibitors of EGFR and HER3. The schematic process involves screening of databases for constructing compound library comprising of 903 synthetic compounds (602 for EGFR and 301 for HER3) followed by pharmacophore modeling. The best docked poses of compounds with the druggable binding site of respective proteins were selected according to pharmacophore designed by SeeSAR version 12.1.0. Subsequently, preclinical analysis was performed via an online server SwissADME and potent inhibitors were selected. Compound 4k and 4m were the most potent inhibitors of EGFR while 7x effectively inhibited the binding site of HER3. The binding energies of 4k, 4m, and 7x were −7.7, −6.3 and −5.7 kcal/mol, respectively. Collectively, 4k, 4m and 7x showed favorable interactions with the most druggable binding sites of their respective proteins. Finally, in silico pre-clinical testing by SwissADME validated the non-toxic nature of compounds 4k, 4m and 7x providing a promising treatment option for chemoresistant NSCLC. Full article
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20 pages, 8203 KiB  
Article
Novel Potential Janus Kinase Inhibitors with Therapeutic Prospects in Rheumatoid Arthritis Addressed by In Silico Studies
by Andrei-Flavius Radu, Simona Gabriela Bungau, Andrei Paul Negru, Bogdan Uivaraseanu and Mihaela Alexandra Bogdan
Molecules 2023, 28(12), 4699; https://doi.org/10.3390/molecules28124699 - 12 Jun 2023
Cited by 7 | Viewed by 1387
Abstract
Rheumatoid arthritis (RA) is a debilitating autoimmune disorder with an inflammatory condition targeting the joints that affects millions of patients worldwide. Several unmet needs still need to be addressed despite recent improvements in the management of RA. Although current RA therapies can diminish [...] Read more.
Rheumatoid arthritis (RA) is a debilitating autoimmune disorder with an inflammatory condition targeting the joints that affects millions of patients worldwide. Several unmet needs still need to be addressed despite recent improvements in the management of RA. Although current RA therapies can diminish inflammation and alleviate symptoms, many patients remain unresponsive or experience flare-ups of their ailment. The present study aims to address these unmet needs through in silico research, with a focus on the identification of novel, potentially active molecules. Therefore, a molecular docking analysis has been conducted using AutoDockTools 1.5.7 on Janus kinase (JAK) inhibitors that are either approved for RA or in advanced phases of research. The binding affinities of these small molecules against JAK1, JAK2, and JAK3, which are target proteins implicated in the pathophysiology of RA, have been assessed. Subsequent to identifying the ligands with the highest affinity for these target proteins, a ligand-based virtual screening was performed utilizing SwissSimilarity, starting with the chemical structures of the previously identified small molecules. ZINC252492504 had the highest binding affinity (−9.0 kcal/mol) for JAK1, followed by ZINC72147089 (−8.6 kcal/mol) for JAK2, and ZINC72135158 (−8.6 kcal/mol) for JAK3. Using SwissADME, an in silico pharmacokinetic evaluation showed that oral administration of the three small molecules may be feasible. Based on the preliminary results of the present study, additional extensive research is required for the most promising candidates to be conducted so their efficacy and safety profiles can be thoroughly characterized, and they can become medium- and long-term pharmacotherapeutic solutions for the treatment of RA. Full article
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26 pages, 22354 KiB  
Article
Binding Mechanism of CD47 with SIRPα Variants and Its Antibody: Elucidated by Molecular Dynamics Simulations
by Kaisheng Huang, Yi Liu, Shuixiu Wen, Yuxin Zhao, Hanjing Ding, Hui Liu and De-Xin Kong
Molecules 2023, 28(12), 4610; https://doi.org/10.3390/molecules28124610 - 07 Jun 2023
Viewed by 1596
Abstract
The intricate complex system of the differentiation 47 (CD47) and the signal-regulatory protein alpha (SIRPα) cluster is a crucial target for cancer immunotherapy. Although the conformational state of the CD47-SIRPα complex has been revealed through crystallographic studies, further characterization is needed to fully [...] Read more.
The intricate complex system of the differentiation 47 (CD47) and the signal-regulatory protein alpha (SIRPα) cluster is a crucial target for cancer immunotherapy. Although the conformational state of the CD47-SIRPα complex has been revealed through crystallographic studies, further characterization is needed to fully understand the binding mechanism and to identify the hot spot residues involved. In this study, molecular dynamics (MD) simulations were carried out for the complexes of CD47 with two SIRPα variants (SIRPαv1, SIRPαv2) and the commercially available anti-CD47 monoclonal antibody (B6H12.2). The calculated binding free energy of CD47-B6H12.2 is lower than that of CD47-SIRPαv1 and CD47-SIRPαv2 in all the three simulations, indicating that CD47-B6H12.2 has a higher binding affinity than the other two complexes. Moreover, the dynamical cross-correlation matrix reveals that the CD47 protein shows more correlated motions when it binds to B6H12.2. Significant effects were observed in the energy and structural analyses of the residues (Glu35, Tyr37, Leu101, Thr102, Arg103) in the C strand and FG region of CD47 when it binds to the SIRPα variants. The critical residues (Leu30, Val33, Gln52, Lys53, Thr67, Arg69, Arg95, and Lys96) were identified in SIRPαv1 and SIRPαv2, which surround the distinctive groove regions formed by the B2C, C’D, DE, and FG loops. Moreover, the crucial groove structures of the SIRPα variants shape into obvious druggable sites. The C’D loops on the binding interfaces undergo notable dynamical changes throughout the simulation. For B6H12.2, the residues Tyr32LC, His92LC, Arg96LC, Tyr32HC, Thr52HC, Ser53HC, Ala101HC, and Gly102HC in its initial half of the light and heavy chains exhibit obvious energetic and structural impacts upon binding with CD47. The elucidation of the binding mechanism of SIRPαv1, SIRPαv2, and B6H12.2 with CD47 could provide novel perspectives for the development of inhibitors targeting CD47-SIRPα. Full article
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14 pages, 4521 KiB  
Article
Binding Affinity and Mechanisms of Potential Antidepressants Targeting Human NMDA Receptors
by Simin Ye, Yanqiang Han, Zhiyun Wei and Jinjin Li
Molecules 2023, 28(11), 4346; https://doi.org/10.3390/molecules28114346 - 25 May 2023
Viewed by 1840
Abstract
Depression, a mental disorder that plagues the world, is a burden on many families. There is a great need for new, fast-acting antidepressants to be developed. N-methyl-D-aspartic acid (NMDA) is an ionotropic glutamate receptor that plays an important role in learning and memory [...] Read more.
Depression, a mental disorder that plagues the world, is a burden on many families. There is a great need for new, fast-acting antidepressants to be developed. N-methyl-D-aspartic acid (NMDA) is an ionotropic glutamate receptor that plays an important role in learning and memory processes and its TMD region is considered as a potential target to treat depression. However, due to the unclear binding sites and pathways, the mechanism of drug binding lacks basic explanation, which brings great complexity to the development of new drugs. In this study, we investigated the binding affinity and mechanisms of an FDA-approved antidepressant (S-ketamine) and seven potential antidepressants (R-ketamine, memantine, lanicemine, dextromethorphan, Ro 25-6981, ifenprodil, and traxoprodil) targeting the NMDA receptor by ligand–protein docking and molecular dynamics simulations. The results indicated that Ro 25-6981 has the strongest binding affinity to the TMD region of the NMDA receptor among the eight selected drugs, suggesting its potential effective inhibitory effect. We also calculated the critical binding-site residues at the active site and found that residues Leu124 and Met63 contributed the most to the binding energy by decomposing the free energy contributions on a per-residue basis. We further compared S-ketamine and its chiral molecule, R-ketamine, and found that R-ketamine had a stronger binding capacity to the NMDA receptor. This study provides a computational reference for the treatment of depression targeting NMDA receptors, and the proposed results will provide potential strategies for further antidepressant development and is a useful resource for the future discovery of fast-acting antidepressant candidates. Full article
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31 pages, 8709 KiB  
Article
Study of MDM2 as Prognostic Biomarker in Brain-LGG Cancer and Bioactive Phytochemicals Inhibit the p53-MDM2 Pathway: A Computational Drug Development Approach
by Partha Biswas, Shabana Bibi, Qudsia Yousafi, Asim Mehmood, Shahzad Saleem, Awais Ihsan, Dipta Dey, Md. Nazmul Hasan Zilani, Md. Nazmul Hasan, Rasha Saleem, Aeshah A. Awaji, Usama A. Fahmy and Mohamed M. Abdel-Daim
Molecules 2023, 28(7), 2977; https://doi.org/10.3390/molecules28072977 - 27 Mar 2023
Cited by 6 | Viewed by 2740
Abstract
An evaluation of the expression and predictive significance of the MDM2 gene in brain lower-grade glioma (LGG) cancer was carried out using onco-informatics pipelines. Several transcriptome servers were used to measure the differential expression of the targeted MDM2 gene and search mutations and [...] Read more.
An evaluation of the expression and predictive significance of the MDM2 gene in brain lower-grade glioma (LGG) cancer was carried out using onco-informatics pipelines. Several transcriptome servers were used to measure the differential expression of the targeted MDM2 gene and search mutations and copy number variations. GENT2, Gene Expression Profiling Interactive Analysis, Onco-Lnc, and PrognoScan were used to figure out the survival rate of LGG cancer patients. The protein–protein interaction networks between MDM2 gene and its co-expressed genes were constructed by Gene-MANIA tool. Identified bioactive phytochemicals were evaluated through molecular docking using Schrödinger Suite Software, with the MDM2 (PDB ID: 1RV1) target. Protein–ligand interactions were observed with key residues of the macromolecular target. A molecular dynamics simulation of the novel bioactive compounds with the targeted protein was performed. Phytochemicals targeting MDM2 protein, such as Taxifolin and (-)-Epicatechin, have been shown with more highly stable results as compared to the control drug, and hence, concluded that phytochemicals with bioactive potential might be alternative therapeutic options for the management of LGG patients. Our once informatics-based designed pipeline has indicated that the MDM2 gene may have been a predictive biomarker for LGG cancer and selected phytochemicals possessed outstanding interaction results within the macromolecular target’s active site after utilizing in silico approaches. In vitro and in vivo experiments are recommended to confirm these outcomes. Full article
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17 pages, 8803 KiB  
Article
Identification of Potential Inhibitors for the Treatment of Alkaptonuria Using an Integrated In Silico Computational Strategy
by Sumera Zaib, Nehal Rana, Nadia Hussain, Hanan A. Ogaly, Ayed A. Dera and Imtiaz Khan
Molecules 2023, 28(6), 2623; https://doi.org/10.3390/molecules28062623 - 14 Mar 2023
Cited by 6 | Viewed by 4009
Abstract
Alkaptonuria (AKU) is a rare genetic autosomal recessive disorder characterized by elevated serum levels of homogentisic acid (HGA). In this disease, tyrosine metabolism is interrupted because of the alterations in homogentisate dioxygenase (HGD) gene. The patient suffers from ochronosis, fractures, and tendon ruptures. [...] Read more.
Alkaptonuria (AKU) is a rare genetic autosomal recessive disorder characterized by elevated serum levels of homogentisic acid (HGA). In this disease, tyrosine metabolism is interrupted because of the alterations in homogentisate dioxygenase (HGD) gene. The patient suffers from ochronosis, fractures, and tendon ruptures. To date, no medicine has been approved for the treatment of AKU. However, physiotherapy and strong painkillers are administered to help mitigate the condition. Recently, nitisinone, an FDA-approved drug for type 1 tyrosinemia, has been given to AKU patients in some countries and has shown encouraging results in reducing the disease progression. However, this drug is not the targeted treatment for AKU, and causes keratopathy. Therefore, the foremost aim of this study is the identification of potent and druggable inhibitors of AKU with no or minimal side effects by targeting 4-hydroxyphenylpyruvate dioxygenase. To achieve our goal, we have performed computational modelling using BioSolveIT suit. The library of ligands for molecular docking was acquired by fragment replacement of reference molecules by ReCore. Subsequently, the hits were screened on the basis of estimated affinities, and their pharmacokinetic properties were evaluated using SwissADME. Afterward, the interactions between target and ligands were investigated using Discovery Studio. Ultimately, compounds c and f were identified as potent inhibitors of 4-hydroxyphenylpyruvate dioxygenase. Full article
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29 pages, 16366 KiB  
Article
Rational Computational Approaches in Drug Discovery: Potential Inhibitors for Allosteric Regulation of Mutant Isocitrate Dehydrogenase-1 Enzyme in Cancers
by Masthan Thamim, Ashish Kumar Agrahari, Pawan Gupta and Krishnan Thirumoorthy
Molecules 2023, 28(5), 2315; https://doi.org/10.3390/molecules28052315 - 02 Mar 2023
Cited by 2 | Viewed by 2267
Abstract
Mutations in homodimeric isocitrate dehydrogenase (IDH) enzymes at specific arginine residues result in the abnormal activity to overproduce D-2 hydroxyglutarate (D-2HG), which is often projected as solid oncometabolite in cancers and other disorders. As a result, depicting the potential inhibitor [...] Read more.
Mutations in homodimeric isocitrate dehydrogenase (IDH) enzymes at specific arginine residues result in the abnormal activity to overproduce D-2 hydroxyglutarate (D-2HG), which is often projected as solid oncometabolite in cancers and other disorders. As a result, depicting the potential inhibitor for D-2HG formation in mutant IDH enzymes is a challenging task in cancer research. The mutation in the cytosolic IDH1 enzyme at R132H, especially, may be associated with higher frequency of all types of cancers. So, the present work specifically focuses on the design and screening of allosteric site binders to the cytosolic mutant IDH1 enzyme. The 62 reported drug molecules were screened along with biological activity to identify the small molecular inhibitors using computer-aided drug design strategies. The designed molecules proposed in this work show better binding affinity, biological activity, bioavailability, and potency toward the inhibition of D-2HG formation compare to the reported drugs in the in silico approach. Full article
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17 pages, 3363 KiB  
Article
Virtual Screening of Hepatitis B Virus Pre-Genomic RNA as a Novel Therapeutic Target
by Lukasz T. Olenginski, Wojciech K. Kasprzak, Solomon K. Attionu, Bruce A. Shapiro and Theodore K. Dayie
Molecules 2023, 28(4), 1803; https://doi.org/10.3390/molecules28041803 - 14 Feb 2023
Cited by 2 | Viewed by 2365
Abstract
The global burden imposed by hepatitis B virus (HBV) infection necessitates the discovery and design of novel antiviral drugs to complement existing treatments. One attractive and underexploited therapeutic target is ε, an ~85-nucleotide (nt) cis-acting regulatory stem-loop RNA located at the 3′- [...] Read more.
The global burden imposed by hepatitis B virus (HBV) infection necessitates the discovery and design of novel antiviral drugs to complement existing treatments. One attractive and underexploited therapeutic target is ε, an ~85-nucleotide (nt) cis-acting regulatory stem-loop RNA located at the 3′- and 5′-ends of the pre-genomic RNA (pgRNA). Binding of the 5′-end ε to the viral polymerase protein (P) triggers two early events in HBV replication: pgRNA and P packaging and reverse transcription. Our recent solution nuclear magnetic resonance spectroscopy structure of ε permits structure-informed drug discovery efforts that are currently lacking for P. Here, we employ a virtual screen against ε using a Food and Drug Administration (FDA)-approved compound library, followed by in vitro binding assays. This approach revealed that the anti-hepatitis C virus drug Daclatasvir is a selective ε-targeting ligand. Additional molecular dynamics simulations demonstrated that Daclatasvir targets ε at its flexible 6-nt priming loop (PL) bulge and modulates its dynamics. Given the functional importance of the PL, our work supports the notion that targeting ε dynamics may be an effective anti-HBV therapeutic strategy. Full article
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26 pages, 10088 KiB  
Article
Theoretical Studies of Leu-Pro-Arg-Asp-Ala Pentapeptide (LPRDA) Binding to Sortase A of Staphylococcus aureus
by Dmitry A. Shulga and Konstantin V. Kudryavtsev
Molecules 2022, 27(23), 8182; https://doi.org/10.3390/molecules27238182 - 24 Nov 2022
Cited by 3 | Viewed by 1356
Abstract
Sortase A (SrtA) of Staphylococcus aureus is a well-defined molecular target to combat the virulence of these clinically important bacteria. However up to now no efficient drugs or even clinical candidates are known, hence the search for such drugs is still relevant and [...] Read more.
Sortase A (SrtA) of Staphylococcus aureus is a well-defined molecular target to combat the virulence of these clinically important bacteria. However up to now no efficient drugs or even clinical candidates are known, hence the search for such drugs is still relevant and necessary. SrtA is a complex target, so many straight-forward techniques for modeling using the structure-based drug design (SBDD) fail to produce the results they used to bring for other, simpler, targets. In this work we conduct theoretical studies of the binding/activity of Leu-Pro-Arg-Asp-Ala (LPRDA) polypeptide, which was recently shown to possess antivirulence activity against S. aureus. Our investigation was aimed at establishing a framework for the estimation of the key interactions and subsequent modification of LPRDA, targeted at non-peptide molecules, with better drug-like properties than the original polypeptide. Firstly, the available PDB structures are critically analyzed and the criteria to evaluate the quality of the ligand–SrtA complex geometry are proposed. Secondly, the docking protocol was investigated to establish its applicability to the LPRDA–SrtA complex prediction. Thirdly, the molecular dynamics studies were carried out to refine the geometries and estimate the stability of the complexes, predicted by docking. The main finding is that the previously reported partially chaotic movement of the β6/β7 and β7/β8 loops of SrtA (being the intrinsically disordered parts related to the SrtA binding site) is exaggerated when SrtA is complexed with LPRDA, which in turn reveals all the signs of the flexible and structurally disordered molecule. As a result, a wealth of plausible LPRDA–SrtA complex conformations are hard to distinguish using simple modeling means, such as docking. The use of more elaborate modeling approaches may help to model the system reliably but at the cost of computational efficiency. Full article
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11 pages, 2344 KiB  
Article
Computational Analysis of Triazole-Based Kojic Acid Analogs as Tyrosinase Inhibitors by Molecular Dynamics and Free Energy Calculations
by Lucas Sousa Martins, Reinaldo W. A. Gonçalves, Joana J. S. Moraes, Cláudio Nahum Alves and José Rogério A. Silva
Molecules 2022, 27(23), 8141; https://doi.org/10.3390/molecules27238141 - 23 Nov 2022
Viewed by 1115
Abstract
Molecular docking, molecular dynamics (MD) simulations and the linear interaction energy (LIE) method were used here to predict binding modes and free energy for a set of 1,2,3-triazole-based KA analogs as potent inhibitors of Tyrosinase (TYR), a key metalloenzyme of the melanogenesis process. [...] Read more.
Molecular docking, molecular dynamics (MD) simulations and the linear interaction energy (LIE) method were used here to predict binding modes and free energy for a set of 1,2,3-triazole-based KA analogs as potent inhibitors of Tyrosinase (TYR), a key metalloenzyme of the melanogenesis process. Initially, molecular docking calculations satisfactorily predicted the binding mode of evaluated KA analogs, where the KA part overlays the crystal conformation of the KA inhibitor into the catalytic site of TYR. The MD simulations were followed by the LIE method, which reproduced the experimental binding free energies for KA analogs with an r2 equal to 0.97, suggesting the robustness of our theoretical model. Moreover, the van der Waals contributions performed by some residues such as Phe197, Pro201, Arg209, Met215 and Val218 are responsible for the binding recognition of 1,2,3-triazole-based KA analogs in TYR catalytic site. Finally, our calculations provide suitable validation of the combination of molecular docking, MD, and LIE approaches as a powerful tool in the structure-based drug design of new and potent TYR inhibitors. Full article
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20 pages, 4482 KiB  
Article
Comparison of Intermolecular Interactions of Irreversible and Reversible Inhibitors with Bruton’s Tyrosine Kinase via Molecular Dynamics Simulations
by Xiangfan Yu, Simei Qiu, Dongshan Sun, Pei Guo and Quhuan Li
Molecules 2022, 27(21), 7451; https://doi.org/10.3390/molecules27217451 - 02 Nov 2022
Cited by 1 | Viewed by 2009
Abstract
Bruton’s tyrosine kinase (BTK) is a key protein from the TEC family and is involved in B-cell lymphoma occurrence and development. Targeting BTK is therefore an effective strategy for B-cell lymphoma treatment. Since previous studies on BTK have been limited to structure-function analyses [...] Read more.
Bruton’s tyrosine kinase (BTK) is a key protein from the TEC family and is involved in B-cell lymphoma occurrence and development. Targeting BTK is therefore an effective strategy for B-cell lymphoma treatment. Since previous studies on BTK have been limited to structure-function analyses of static protein structures, the dynamics of conformational change of BTK upon inhibitor binding remain unclear. Here, molecular dynamics simulations were conducted to investigate the molecular mechanisms of association and dissociation of a reversible (ARQ531) and irreversible (ibrutinib) small-molecule inhibitor to/from BTK. The results indicated that the BTK kinase domain was found to be locked in an inactive state through local conformational changes in the DFG motif, and P-, A-, and gatekeeper loops. The binding of the inhibitors drove the outward rotation of the C-helix, resulting in the upfolded state of Trp395 and the formation of the salt bridge of Glu445-Arg544, which maintained the inactive conformation state. Met477 and Glu475 in the hinge region were found to be the key residues for inhibitor binding. These findings can be used to evaluate the inhibitory activity of the pharmacophore and applied to the design of effective BTK inhibitors. In addition, the drug resistance to the irreversible inhibitor Ibrutinib was mainly from the strong interaction of Cys481, which was evidenced by the mutational experiment, and further confirmed by the measurement of rupture force and rupture times from steered molecular dynamics simulation. Our results provide mechanistic insights into resistance against BTK-targeting drugs and the key interaction sites for the development of high-quality BTK inhibitors. The steered dynamics simulation also offers a means to rapidly assess the binding capacity of newly designed inhibitors. Full article
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21 pages, 2208 KiB  
Article
Mycobacterium Time-Series Genome Analysis Identifies AAC2′ as a Potential Drug Target with Naloxone Showing Potential Bait Drug Synergism
by Vidya Niranjan, Akshay Uttarkar, Keerthana Murali, Swarna Niranjan, Jayalatha Gopal and Jitendra Kumar
Molecules 2022, 27(19), 6150; https://doi.org/10.3390/molecules27196150 - 20 Sep 2022
Cited by 7 | Viewed by 1924
Abstract
The World Health Organization has put drug resistance in tuberculosis on its list of significant threats, with a critical emphasis on resolving the genetic differences in Mycobacterium tuberculosis. This provides an opportunity for a better understanding of the evolutionary progression leading to anti-microbial [...] Read more.
The World Health Organization has put drug resistance in tuberculosis on its list of significant threats, with a critical emphasis on resolving the genetic differences in Mycobacterium tuberculosis. This provides an opportunity for a better understanding of the evolutionary progression leading to anti-microbial resistance. Anti-microbial resistance has a great impact on the economic stability of the global healthcare sector. We performed a timeline genomic analysis from 2003 to 2021 of 578 mycobacterium genomes to understand the pattern underlying genomic variations. Potential drug targets based on functional annotation was subjected to pharmacophore-based screening of FDA-approved phyto-actives. Reaction search, MD simulations, and metadynamics studies were performed. A total of 4,76,063 mutations with a transition/transversion ratio of 0.448 was observed. The top 10 proteins with the least number of mutations were high-confidence drug targets. Aminoglycoside 2′-N-acetyltransferase protein (AAC2′), conferring resistance to aminoglycosides, was shortlisted as a potential drug target based on its function and role in bait drug synergism. Gentamicin-AAC2′ binding pose was used as a pharmacophore template to screen 10,570 phyto-actives. A total of 66 potential hits were docked to obtain naloxone as a lead—active with a docking score of −6.317. Naloxone is an FDA-approved drug that rapidly reverses opioid overdose. This is a classic case of a repurposed phyto-active. Naloxone consists of an amine group, but the addition of the acetyl group is unfavorable, with a reaction energy of 612.248 kcal/mol. With gentamicin as a positive control, molecular dynamic simulation studies were performed for 200 ns to check the stability of binding. Metadynamics-based studies were carried out to compare unbinding energy with gentamicin. The unbinding energies were found to be −68 and −74 kcal/mol for naloxone and gentamycin, respectively. This study identifies naloxone as a potential drug candidate for a bait drug synergistic approach against Mycobacterium tuberculosis. Full article
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14 pages, 5945 KiB  
Article
High Throughput 3D Cell Migration Assay Using Micropillar/Microwell Chips
by Sang-Yun Lee, Lily M. Park, Yoo Jung Oh, Dong Hyuk Choi and Dong Woo Lee
Molecules 2022, 27(16), 5306; https://doi.org/10.3390/molecules27165306 - 19 Aug 2022
Cited by 3 | Viewed by 2854
Abstract
The 3D cell migration assay was developed for the evaluation of drugs that inhibit cell migration using high throughput methods. Wound-healing assays have commonly been used for cell migration assays. However, these assays have limitations in mimicking the in vivo microenvironment of the [...] Read more.
The 3D cell migration assay was developed for the evaluation of drugs that inhibit cell migration using high throughput methods. Wound-healing assays have commonly been used for cell migration assays. However, these assays have limitations in mimicking the in vivo microenvironment of the tumor and measuring cell viability for evaluation of cell migration inhibition without cell toxicity. As an attempt to manage these limitations, cells were encapsulated with Matrigel on the surface of the pillar, and an analysis of the morphology of cells attached to the pillar through Matrigel was performed for the measurement of cell migration. The micropillar/microwell chips contained 532 pillars and wells, which measure the migration and viability of cells by analyzing the roundness and size of the cells, respectively. Cells seeded in Matrigel have a spherical form. Over time, cells migrate through the Matrigel and attach to the surface of the pillar. Cells that have migrated and adhered have a diffused shape that is different from the initial spherical shape. Based on our analysis of the roundness of the cells, we were able to distinguish between the diffuse and spherical shapes. Cells in Matrigel on the pillar that were treated with migration-inhibiting drugs did not move to the surface of the pillar and remained in spherical forms. During the conduct of experiments, 70 drugs were tested in single chips and migration-inhibiting drugs without cell toxicity were identified. Conventional migration assays were performed using transwell for verification of the four main migration-inhibiting drugs found on the chip. Full article
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22 pages, 6927 KiB  
Article
Aphrodisiac Performance of Bioactive Compounds from Mimosa pudica Linn.: In Silico Molecular Docking and Dynamics Simulation Approach
by Chandrasekar Palanichamy, Parasuraman Pavadai, Theivendren Panneerselvam, Sankarganesh Arunachalam, Ewa Babkiewicz, Sureshbabu Ram Kumar Pandian, Kabilan Shanmugampillai Jeyarajaguru, Damodar Nayak Ammunje, Suthendran Kannan, Jaikanth Chandrasekaran, Krishnan Sundar, Piotr Maszczyk and Selvaraj Kunjiappan
Molecules 2022, 27(12), 3799; https://doi.org/10.3390/molecules27123799 - 13 Jun 2022
Cited by 15 | Viewed by 4853
Abstract
Plants and their derived molecules have been traditionally used to manage numerous pathological complications, including male erectile dysfunction (ED). Mimosa pudica Linn. commonly referred to as the touch-me-not plant, and its extract are important sources of new lead molecules in drug discovery research. [...] Read more.
Plants and their derived molecules have been traditionally used to manage numerous pathological complications, including male erectile dysfunction (ED). Mimosa pudica Linn. commonly referred to as the touch-me-not plant, and its extract are important sources of new lead molecules in drug discovery research. The main goal of this study was to predict highly effective molecules from M. pudica Linn. for reaching and maintaining penile erection before and during sexual intercourse through in silico molecular docking and dynamics simulation tools. A total of 28 bioactive molecules were identified from this target plant through public repositories, and their chemical structures were drawn using Chemsketch software. Graph theoretical network principles were applied to identify the ideal target (phosphodiesterase type 5) and rebuild the network to visualize the responsible signaling genes, proteins, and enzymes. The 28 identified bioactive molecules were docked against the phosphodiesterase type 5 (PDE5) enzyme and compared with the standard PDE5 inhibitor (sildenafil). Pharmacokinetics (ADME), toxicity, and several physicochemical properties of bioactive molecules were assessed to confirm their drug-likeness property. Molecular dynamics (MD) simulation modeling was performed to investigate the stability of PDE5–ligand complexes. Four bioactive molecules (Bufadienolide (−12.30 kcal mol−1), Stigmasterol (−11.40 kcal mol−1), Isovitexin (−11.20 kcal mol−1), and Apigetrin (−11.20 kcal mol−1)) showed the top binding affinities with the PDE5 enzyme, much more powerful than the standard PDE5 inhibitor (−9.80 kcal mol−1). The four top binding bioactive molecules were further validated for a stable binding affinity with the PDE5 enzyme and conformation during the MD simulation period as compared to the apoprotein and standard PDE5 inhibitor complexes. Further, the four top binding bioactive molecules demonstrated significant drug-likeness characteristics with lower toxicity profiles. According to the findings, the four top binding molecules may be used as potent and safe PDE5 inhibitors and could potentially be used in the treatment of ED. Full article
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11 pages, 1493 KiB  
Article
Accurate Physical Property Predictions via Deep Learning
by Yuanyuan Hou, Shiyu Wang, Bing Bai, H. C. Stephen Chan and Shuguang Yuan
Molecules 2022, 27(5), 1668; https://doi.org/10.3390/molecules27051668 - 03 Mar 2022
Cited by 13 | Viewed by 3625
Abstract
Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture [...] Read more.
Neural networks and deep learning have been successfully applied to tackle problems in drug discovery with increasing accuracy over time. There are still many challenges and opportunities to improve molecular property predictions with satisfactory accuracy even further. Here, we proposed a deep-learning architecture model, namely Bidirectional long short-term memory with Channel and Spatial Attention network (BCSA), of which the training process is fully data-driven and end to end. It is based on data augmentation and SMILES tokenization technology without relying on auxiliary knowledge, such as complex spatial structure. In addition, our model takes the advantages of the long- and short-term memory network (LSTM) in sequence processing. The embedded channel and spatial attention modules in turn specifically identify the prime factors in the SMILES sequence for predicting properties. The model was further improved by Bayesian optimization. In this work, we demonstrate that the trained BSCA model is capable of predicting aqueous solubility. Furthermore, our proposed method shows noticeable superiorities and competitiveness in predicting oil–water partition coefficient, when compared with state-of-the-art graphs models, including graph convoluted network (GCN), message-passing neural network (MPNN), and AttentiveFP. Full article
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13 pages, 5584 KiB  
Article
Molecular Dynamic Simulations of Bromodomain and Extra-Terminal Protein 4 Bonded to Potent Inhibitors
by Siao Chen, Yi He, Yajiao Geng, Zhi Wang, Lu Han and Weiwei Han
Molecules 2022, 27(1), 118; https://doi.org/10.3390/molecules27010118 - 26 Dec 2021
Cited by 5 | Viewed by 3044
Abstract
Bromodomain and extra-terminal domain (BET) subfamily is the most studied subfamily of bromodomain-containing proteins (BCPs) family which can modulate acetylation signal transduction and produce diverse physiological functions. Thus, the BET family can be treated as an alternative strategy for targeting androgen-receptor (AR)-driven cancers. [...] Read more.
Bromodomain and extra-terminal domain (BET) subfamily is the most studied subfamily of bromodomain-containing proteins (BCPs) family which can modulate acetylation signal transduction and produce diverse physiological functions. Thus, the BET family can be treated as an alternative strategy for targeting androgen-receptor (AR)-driven cancers. In order to explore the effect of inhibitors binding to BRD4 (the most studied member of BET family), four 150 ns molecular dynamic simulations were performed (free BRD4, Cpd4-BRD4, Cpd9-BRD4 and Cpd19-BRD4). Docking studies showed that Cpd9 and Cpd19 were located at the active pocket, as well as Cpd4. Molecular dynamics (MD) simulations indicated that only Cpd19 binding to BRD4 can induce residue Trp81-Ala89 partly become α-helix during MD simulations. MM-GBSA calculations suggested that Cpd19 had the best binding effect with BRD4 followed by Cpd4 and Cpd9. Computational alanine scanning results indicated that mutations in Phe83 made the greatest effects in Cpd9-BRD4 and Cpd19-BRD4 complexes, showing that Phe83 may play crucial roles in Cpd9 and Cpd19 binding to BRD4. Our results can provide some useful clues for further BCPs family search. Full article
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10 pages, 2353 KiB  
Communication
High-Throughput Screening Campaign Identified a Potential Small Molecule RXFP3/4 Agonist
by Guangyao Lin, Yang Feng, Xiaoqing Cai, Caihong Zhou, Lijun Shao, Yan Chen, Linhai Chen, Qing Liu, Qingtong Zhou, Ross A.D. Bathgate, Dehua Yang and Ming-Wei Wang
Molecules 2021, 26(24), 7511; https://doi.org/10.3390/molecules26247511 - 11 Dec 2021
Cited by 4 | Viewed by 2429
Abstract
Relaxin/insulin-like family peptide receptor 3 (RXFP3) belongs to class A G protein-coupled receptor family. RXFP3 and its endogenous ligand relaxin-3 are mainly expressed in the brain with important roles in the regulation of appetite, energy metabolism, endocrine homeostasis and emotional processing. It is [...] Read more.
Relaxin/insulin-like family peptide receptor 3 (RXFP3) belongs to class A G protein-coupled receptor family. RXFP3 and its endogenous ligand relaxin-3 are mainly expressed in the brain with important roles in the regulation of appetite, energy metabolism, endocrine homeostasis and emotional processing. It is therefore implicated as a potential target for treatment of various central nervous system diseases. Since selective agonists of RXFP3 are restricted to relaxin-3 and its analogs, we conducted a high-throughput screening campaign against 32,021 synthetic and natural product-derived compounds using a cyclic adenosine monophosphate (cAMP) measurement-based method. Only one compound, WNN0109-C011, was identified following primary screening, secondary screening and dose-response studies. Although displayed agonistic effect in cells overexpressing the human RXFP3, it also showed cross-reactivity with the human RXFP4. This hit compound may provide not only a chemical probe to investigate the function of RXFP3/4, but also a novel scaffold for the development of RXFP3/4 agonists. Full article
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15 pages, 3014 KiB  
Article
Don’t Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models
by Lina Humbeck, Tobias Morawietz, Noe Sturm, Adam Zalewski, Simon Harnqvist, Wouter Heyndrickx, Matthew Holmes and Bernd Beck
Molecules 2021, 26(22), 6959; https://doi.org/10.3390/molecules26226959 - 18 Nov 2021
Cited by 6 | Viewed by 3094
Abstract
Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amounts of data from diverse sources, which [...] Read more.
Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the standard tools of cheminformaticians and computational medicinal chemists. Multi-task and federated learning are promising machine learning approaches that allow privacy-preserving usage of large amounts of data from diverse sources, which is crucial for achieving good generalization and high-performance results. Using large, real world data sets from six pharmaceutical companies, here we investigate different strategies for averaging weighted task loss functions to train multi-task bioactivity classification models. The weighting strategies shall be suitable for federated learning and ensure that learning efforts are well distributed even if data are diverse. Comparing several approaches using weights that depend on the number of sub-tasks per assay, task size, and class balance, respectively, we find that a simple sub-task weighting approach leads to robust model performance for all investigated data sets and is especially suited for federated learning. Full article
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21 pages, 5246 KiB  
Article
Virtual Screening for Potential Phytobioactives as Therapeutic Leads to Inhibit NQO1 for Selective Anticancer Therapy
by Bhargav Shreevatsa, Chandan Dharmashekara, Vikas Halasumane Swamy, Meghana V. Gowda, Raghu Ram Achar, Vivek Hamse Kameshwar, Rajesh Kumar Thimmulappa, Asad Syed, Abdallah M. Elgorban, Salim S. Al-Rejaie, Joaquín Ortega-Castro, Juan Frau, Norma Flores-Holguín, Chandan Shivamallu, Shiva Prasad Kollur and Daniel Glossman-Mitnik
Molecules 2021, 26(22), 6863; https://doi.org/10.3390/molecules26226863 - 14 Nov 2021
Cited by 6 | Viewed by 3161
Abstract
NAD(P)H:quinone acceptor oxidoreductase-1 (NQO1) is a ubiquitous flavin adenine dinucleotide-dependent flavoprotein that promotes obligatory two-electron reductions of quinones, quinonimines, nitroaromatics, and azo dyes. NQO1 is a multifunctional antioxidant enzyme whose expression and deletion are linked to reduced and increased oxidative stress susceptibilities. NQO1 [...] Read more.
NAD(P)H:quinone acceptor oxidoreductase-1 (NQO1) is a ubiquitous flavin adenine dinucleotide-dependent flavoprotein that promotes obligatory two-electron reductions of quinones, quinonimines, nitroaromatics, and azo dyes. NQO1 is a multifunctional antioxidant enzyme whose expression and deletion are linked to reduced and increased oxidative stress susceptibilities. NQO1 acts as both a tumor suppressor and tumor promoter; thus, the inhibition of NQO1 results in less tumor burden. In addition, the high expression of NQO1 is associated with a shorter survival time of cancer patients. Inhibiting NQO1 also enables certain anticancer agents to evade the detoxification process. In this study, a series of phytobioactives were screened based on their chemical classes such as coumarins, flavonoids, and triterpenoids for their action on NQO1. The in silico evaluations were conducted using PyRx virtual screening tools, where the flavone compound, Orientin showed a better binding affinity score of −8.18 when compared with standard inhibitor Dicumarol with favorable ADME properties. An MD simulation study found that the Orientin binding to NQO1 away from the substrate-binding site induces a potential conformational change in the substrate-binding site, thereby inhibiting substrate accessibility towards the FAD-binding domain. Furthermore, with this computational approach we are offering a scope for validation of the new therapeutic components for their in vitro and in vivo efficacy against NQO1. Full article
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15 pages, 4321 KiB  
Article
Finding the First Potential Inhibitors of Shikimate Kinase from Methicillin Resistant Staphylococcus aureus through Computer-Assisted Drug Design
by Lluvia Rios-Soto, Alfredo Téllez-Valencia, Erick Sierra-Campos, Mónica Valdez-Solana, Jorge Cisneros-Martínez, Marcelo Gómez Palacio-Gastélum, Adriana Castillo-Villanueva and Claudia Avitia-Domínguez
Molecules 2021, 26(21), 6736; https://doi.org/10.3390/molecules26216736 - 08 Nov 2021
Cited by 2 | Viewed by 2028
Abstract
Methicillin-resistant Staphylococcus aureus (MRSA) is an important threat as it causes serious hospital and community acquired infections with deathly outcomes oftentimes, therefore, development of new treatments against this bacterium is a priority. Shikimate kinase, an enzyme in the shikimate pathway, is considered a [...] Read more.
Methicillin-resistant Staphylococcus aureus (MRSA) is an important threat as it causes serious hospital and community acquired infections with deathly outcomes oftentimes, therefore, development of new treatments against this bacterium is a priority. Shikimate kinase, an enzyme in the shikimate pathway, is considered a good target for developing antimicrobial drugs; this is given because of its pathway, which is essential in bacteria whereas it is absent in mammals. In this work, a computer-assisted drug design strategy was used to report the first potentials inhibitors for Shikimate kinase from methicillin-resistant Staphylococcus aureus (SaSK), employing approximately 5 million compounds from ZINC15 database. Diverse filtering criteria, related to druglike characteristics and virtual docking screening in the shikimate binding site, were performed to select structurally diverse potential inhibitors from SaSK. Molecular dynamics simulations were performed to elucidate the dynamic behavior of each SaSK–ligand complex. The potential inhibitors formed important interactions with residues that are crucial for enzyme catalysis, such as Asp37, Arg61, Gly82, and Arg138. Therefore, the compounds reported provide valuable information and can be seen as the first step toward developing SaSK inhibitors in the search of new drugs against MRSA. Full article
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15 pages, 5376 KiB  
Article
Molecular Dynamics Simulations Study of the Interactions between Human Dipeptidyl-Peptidase III and Two Substrates
by Shitao Zhang, Shuai Lv, Xueqi Fu, Lu Han, Weiwei Han and Wannan Li
Molecules 2021, 26(21), 6492; https://doi.org/10.3390/molecules26216492 - 27 Oct 2021
Cited by 2 | Viewed by 1940
Abstract
Human dipeptidyl-peptidase III (hDPP III) is capable of specifically cleaving dipeptides from the N-terminal of small peptides with biological activity such as angiotensin II (Ang II, DRVYIHPF), and participates in blood pressure regulation, pain modulation, and the development of cancers in human biological [...] Read more.
Human dipeptidyl-peptidase III (hDPP III) is capable of specifically cleaving dipeptides from the N-terminal of small peptides with biological activity such as angiotensin II (Ang II, DRVYIHPF), and participates in blood pressure regulation, pain modulation, and the development of cancers in human biological activities. In this study, 500 ns molecular dynamics simulations were performed on free-hDPP III (PDB code: 5E33), hDPP III-Ang II (PDB code: 5E2Q), and hDPP III-IVYPW (PDB code: 5E3C) to explore how these two peptides affect the catalytic efficiency of enzymes in terms of the binding mode and the conformational changes. Our results indicate that in the case of the hDPP III-Ang II complex, subsite S1 became small and hydrophobic, which might be propitious for the nucleophile to attack the substrate. The structures of the most stable conformations of the three systems revealed that Arg421-Lys423 could form an α-helix with the presence of Ang II, but only part of the α-helix was produced in hDPP III-IVYPW. As the hinge structure in hDPP III, the conformational changes that took place in the Arg421-Lys423 residue could lead to the changes in the shape and space of the catalytic subsites, which might allow water to function as a nucleophile to attack the substrate. Our results may provide new clues to enable the design of new inhibitors for hDPP III in the future. Full article
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21 pages, 7784 KiB  
Article
Insights into Conformational Dynamics and Allostery in DNMT1-H3Ub/USP7 Interactions
by Yu Zhu, Fei Ye, Ziyun Zhou, Wanlin Liu, Zhongjie Liang and Guang Hu
Molecules 2021, 26(17), 5153; https://doi.org/10.3390/molecules26175153 - 25 Aug 2021
Cited by 6 | Viewed by 2778
Abstract
DNA methyltransferases (DNMTs) including DNMT1 are a conserved family of cytosine methylases that play crucial roles in epigenetic regulation. The versatile functions of DNMT1 rely on allosteric networks between its different interacting partners, emerging as novel therapeutic targets. In this work, based on [...] Read more.
DNA methyltransferases (DNMTs) including DNMT1 are a conserved family of cytosine methylases that play crucial roles in epigenetic regulation. The versatile functions of DNMT1 rely on allosteric networks between its different interacting partners, emerging as novel therapeutic targets. In this work, based on the modeling structures of DNMT1-ubiquitylated H3 (H3Ub)/ubiquitin specific peptidase 7 (USP7) complexes, we have used a combination of elastic network models, molecular dynamics simulations, structural residue perturbation, network modeling, and pocket pathway analysis to examine their molecular mechanisms of allosteric regulation. The comparative intrinsic and conformational dynamics analysis of three DNMT1 systems has highlighted the pivotal role of the RFTS domain as the dynamics hub in both intra- and inter-molecular interactions. The site perturbation and network modeling approaches have revealed the different and more complex allosteric interaction landscape in both DNMT1 complexes, involving the events caused by mutational hotspots and post-translation modification sites through protein-protein interactions (PPIs). Furthermore, communication pathway analysis and pocket detection have provided new mechanistic insights into molecular mechanisms underlying quaternary structures of DNMT1 complexes, suggesting potential targeting pockets for PPI-based allosteric drug design. Full article
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10 pages, 4072 KiB  
Article
MolADI: A Web Server for Automatic Analysis of Protein–Small Molecule Dynamic Interactions
by Bing Bai, Rongfeng Zou, H. C. Stephen Chan, Hongchun Li and Shuguang Yuan
Molecules 2021, 26(15), 4625; https://doi.org/10.3390/molecules26154625 - 30 Jul 2021
Cited by 9 | Viewed by 4940
Abstract
Protein–ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. [...] Read more.
Protein–ligand interaction analysis is important for drug discovery and rational protein design. The existing online tools adopt only a single conformation of the complex structure for calculating and displaying the interactions, whereas both protein residues and ligand molecules are flexible to some extent. The interactions evolved with time in the trajectories are of greater interest. MolADI is a user-friendly online tool which analyzes the protein–ligand interactions in detail for either a single structure or a trajectory. Interactions can be viewed easily with both 2D graphs and 3D representations. MolADI is available as a web application. Full article
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