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Biomolecules
Special Issues
Protein Structure Prediction in Drug Discovery
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Protein Structure Prediction in Drug Discovery

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 15209

Special Issue Editor

Dr. Alessandro Paiardini

E-Mail Website
Guest Editor
Department of Biochemical Sciences “A. Rossi Fanelli”, Sapienza University of Rome, P.le Aldo Moro 5, 00185 Rome, Italy
Interests: drug design; molecular docking and virtual screening; protein structure prediction and homology modeling; protein structure and evolution
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

When the results of DeepMind's AlphaFold2 at CASP were announced in 2020, the scientific world was so amazed by how effectively it performed that "it will change everything" became the motto for this revolution. As a result, it should come as no surprise that "Protein Structure Prediction" was named Nature's Method of the Year 2021. Structure-based drug discovery (SBDD) is the one area of biology and medicine that is most expected to benefit and make a huge leap as a result of the developments of AlphaFold2 and comparable tools, such as RoseTTAFold. However, since the accuracy of the residues’ conformations at the active sites remains a key limitation in SBDD, as does the inability to guess which conformational state of a protein these tools will predict, it is still necessary to associate and integrate previous physically based models and expert-driven knowledge with new machine-learning approaches, as well as experimentally derived structural data. 

We encourage articles at the confluence of the promising fields of protein structure prediction and drug development for this timely Special Issue of Biomolecules. New machine-learning approaches and tools, as well as developments and applications in previously existing techniques, such as threading and homology modeling, for protein structure prediction of therapeutic intervention targets, are all areas of interest. Furthermore, scientists working in the broad field of drug discovery are encouraged to submit original research and review articles describing new tools or solutions, the characterization and/or refinement of novel structures, and the design of small molecules, peptides, or peptidomimetics that were discovered using protein structure prediction methods.

Dr. Alessandro Paiardini
Guest Editor

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 special issue 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. Biomolecules is an international peer-reviewed open access monthly 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

  • protein structure prediction
  • drug design
  • docking
  • virtual screening
  • drug discovery
  • machine-learning
  • alphafold

Related Special Issue

Published Papers (6 papers)

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Editorial

Jump to: Research, Review

3 pages, 181 KiB  
Editorial
Protein Structure Prediction in Drug Discovery
by Alessandro Paiardini
Biomolecules 2023, 13(8), 1258; https://doi.org/10.3390/biom13081258 - 17 Aug 2023
Cited by 1 | Viewed by 1045
Abstract
When the results of DeepMind’s AlphaFold2 at CASP were announced in 2020, the scientific world was so amazed by how effectively it performed that “it will change everything” became the motto for this revolution [...] Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery)

Research

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12 pages, 1510 KiB  
Article
Analysis of CDR3 Sequences from T-Cell Receptor β in Acute Respiratory Distress Syndrome
by Sara Hey, Dayjah Whyte, Minh-Chau Hoang, Nick Le, Joseph Natvig, Claire Wingfield, Charles Onyeama, Judie Howrylak and Inimary T. Toby
Biomolecules 2023, 13(5), 825; https://doi.org/10.3390/biom13050825 - 12 May 2023
Cited by 1 | Viewed by 1746
Abstract
Acute Respiratory Distress Syndrome (ARDS) is an illness that typically develops in people who are significantly ill or have serious injuries. ARDS is characterized by fluid build-up that occurs in the alveoli. T-cells are implicated as playing a role in the modulation of [...] Read more.
Acute Respiratory Distress Syndrome (ARDS) is an illness that typically develops in people who are significantly ill or have serious injuries. ARDS is characterized by fluid build-up that occurs in the alveoli. T-cells are implicated as playing a role in the modulation of the aberrant response leading to excessive tissue damage and, eventually, ARDS. Complementarity Determining Region 3 (CDR3) sequences derived from T-cells are key players in the adaptive immune response. This response is governed by an elaborate specificity for distinct molecules and the ability to recognize and vigorously respond to repeated exposures to the same molecules. Most of the diversity in T-cell receptors (TCRs) is contained in the CDR3 regions of the heterodimeric cell-surface receptors. For this study, we employed the novel technology of immune sequencing to assess lung edema fluid. Our goal was to explore the landscape of CDR3 clonal sequences found within these samples. We obtained more than 3615 CDR3 sequences across samples in the study. Our data demonstrate that: (1) CDR3 sequences from lung edema fluid exhibit distinct clonal populations, and (2) CDR3 sequences can be further characterized based on biochemical features. Analysis of these CDR3 sequences offers insight into the CDR3-driven T-cell repertoire of ARDS. These findings represent the first step towards applications of this technology with these types of biological samples in the context of ARDS. Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery)
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17 pages, 2526 KiB  
Article
Druggable Pockets at the RNA Interface Region of Influenza A Virus NS1 Protein Are Conserved across Sequence Variants from Distinct Subtypes
by Sarah Naceri, Daniel Marc, Rachel Blot, Delphine Flatters and Anne-Claude Camproux
Biomolecules 2023, 13(1), 64; https://doi.org/10.3390/biom13010064 - 29 Dec 2022
Cited by 2 | Viewed by 1342
Abstract
Influenza A viruses still represent a major health issue, for both humans and animals. One of the main viral proteins of interest to target is the NS1 protein, which counters the host immune response and promotes viral replication. NS1 is a homodimer composed [...] Read more.
Influenza A viruses still represent a major health issue, for both humans and animals. One of the main viral proteins of interest to target is the NS1 protein, which counters the host immune response and promotes viral replication. NS1 is a homodimer composed of a dimeric RNA-binding domain (RBD), which is structurally stable and conserved in sequence, and two effector domains that are tethered to the RBD by linker regions. This linker flexibility leads to NS1 polymorphism and can therefore exhibit different forms. Previously, we identified a putative drug-binding site, located in the RBD interface in a crystal structure of NS1. This pocket could be targeted to block RNA binding and inhibit NS1 activities. The objective of the present study is to confirm the presence of this druggable site, whatever the sequence variants, in order to develop a universal therapeutic compound that is insensitive to sequence variations and structural flexibility. Using a set of four NS1 full-length structures, we combined different bioinformatics approaches such as pocket tracking along molecular dynamics simulations, druggability prediction and classification. This protocol successfully confirmed a frequent large binding-site that is highly druggable and shared by different NS1 forms, which is promising for developing a robust NS1-targeted therapy. Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery)
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17 pages, 2581 KiB  
Article
In Silico Protein Folding Prediction of COVID-19 Mutations and Variants
by Sumana Bhowmick, Tim Jing, Wei Wang, Elena Y. Zhang, Frank Zhang and Yanmin Yang
Biomolecules 2022, 12(11), 1665; https://doi.org/10.3390/biom12111665 - 10 Nov 2022
Cited by 4 | Viewed by 2446
Abstract
With its fast-paced mutagenesis, the SARS-CoV-2 Omicron variant has threatened many societies worldwide. Strategies for predicting mutagenesis such as the computational prediction of SARS-CoV-2 structural diversity and its interaction with the human receptor will greatly benefit our understanding of the virus and help [...] Read more.
With its fast-paced mutagenesis, the SARS-CoV-2 Omicron variant has threatened many societies worldwide. Strategies for predicting mutagenesis such as the computational prediction of SARS-CoV-2 structural diversity and its interaction with the human receptor will greatly benefit our understanding of the virus and help develop therapeutics against it. We aim to use protein structure prediction algorithms along with molecular docking to study the effects of various mutations in the Receptor Binding Domain (RBD) of the SARS-CoV-2 and its key interactions with the angiotensin-converting enzyme 2 (ACE-2) receptor. The RBD structures of the naturally occurring variants of SARS-CoV-2 were generated from the WUHAN-Hu-1 using the trRosetta algorithm. Docking (HADDOCK) and binding analysis (PRODIGY) between the predicted RBD sequences and ACE-2 highlighted key interactions at the Receptor-Binding Motif (RBM). Further mutagenesis at conserved residues in the Original, Delta, and Omicron variants (P499S and T500R) demonstrated stronger binding and interactions with the ACE-2 receptor. The predicted T500R mutation underwent some preliminary tests in vitro for its binding and transmissibility in cells; the results correlate with the in-silico analysis. In summary, we suggest conserved residues P499 and T500 as potential mutation sites that could increase the binding affinity and yet do not exist in nature. This work demonstrates the use of the trRosetta algorithm to predict protein structure and future mutations at the RBM of SARS-CoV-2, followed by experimental testing for further efficacy verification. It is important to understand the protein structure and folding to help develop potential therapeutics. Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery)
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23 pages, 8723 KiB  
Article
Clade-Specific Alterations within the HIV-1 Capsid Protein with Implications for Nuclear Translocation
by Alexej Dick, Megan E. Meuser and Simon Cocklin
Biomolecules 2022, 12(5), 695; https://doi.org/10.3390/biom12050695 - 12 May 2022
Cited by 3 | Viewed by 2162
Abstract
The HIV-1 capsid (CA) protein has emerged as an attractive therapeutic target. However, all inhibitor designs and structural analyses for this essential HIV-1 protein have focused on the clade B HIV-1 (NL4-3) variant. This study creates, overproduces, purifies, and characterizes the CA proteins [...] Read more.
The HIV-1 capsid (CA) protein has emerged as an attractive therapeutic target. However, all inhibitor designs and structural analyses for this essential HIV-1 protein have focused on the clade B HIV-1 (NL4-3) variant. This study creates, overproduces, purifies, and characterizes the CA proteins from clade A1, A2, B, C, and D isolates. These new CA constructs represent novel reagents that can be used in future CA-targeted inhibitor design and to investigate CA proteins’ structural and biochemical properties from genetically diverse HIV-1 subtypes. Moreover, we used surface plasmon resonance (SPR) spectrometry and computational modeling to examine inter-clade differences in CA assembly and binding of PF-74, CPSF-6, and NUP-153. Interestingly, we found that HIV-1 CA from clade A1 does not bind to NUP-153, suggesting that the import of CA core structures through the nuclear pore complex may be altered for viruses from this clade. Overall, we have demonstrated that in silico generated models of the HIV-1 CA protein from clades other than the prototypically used clade B have utility in understanding and predicting biology and antiviral drug design and mechanism of action. Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery)
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Review

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16 pages, 2563 KiB  
Review
Boosting the Full Potential of PyMOL with Structural Biology Plugins
by Serena Rosignoli and Alessandro Paiardini
Biomolecules 2022, 12(12), 1764; https://doi.org/10.3390/biom12121764 - 27 Nov 2022
Cited by 22 | Viewed by 5441
Abstract
Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, [...] Read more.
Over the past few decades, the number of available structural bioinformatics pipelines, libraries, plugins, web resources and software has increased exponentially and become accessible to the broad realm of life scientists. This expansion has shaped the field as a tangled network of methods, algorithms and user interfaces. In recent years PyMOL, widely used software for biomolecules visualization and analysis, has started to play a key role in providing an open platform for the successful implementation of expert knowledge into an easy-to-use molecular graphics tool. This review outlines the plugins and features that make PyMOL an eligible environment for supporting structural bioinformatics analyses. Full article
(This article belongs to the Special Issue Protein Structure Prediction in Drug Discovery)
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