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Biomolecules
Special Issues
Machine Learning Approach to Protein Structure, Dynamics, and Function
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Machine Learning Approach to Protein Structure, Dynamics, and Function

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Structure and Dynamics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 20272

Special Issue Editor

Prof. Dr. Irina Nesmelova

E-Mail Website
Guest Editor
Department of Physics and Optical Science, University of North Carolina Charlotte, 9201 University City Blvd., Charlotte, NC 28223, USA
Interests: protein biophysics; NMR spectroscopy; biomolecular interactions; protein assembly and aggregation

Special Issue Information

Dear Colleagues,

We are preparing a Special Issue on “Machine Learning Approach to Protein Structure, Dynamics, and Function” for the MPDI journal Biomolecules. In recent years, the number of publications related to this topic has grown exponentially. The development of sophisticated algorithms and the accumulation of large amounts of structural and functional data on proteins enables the extraction of complex patterns and non-trivial relationships, deepening our understanding of protein biology and paving the way for the design of novel, safer therapeutics. Thus, the review of the present state of machine learning applications to proteins seems timely. We welcome original manuscripts and reviews dealing with any aspect of machine learning approaches to studying protein structures, dynamics, and function.

Dr. Irina Nesmelova
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

  • machine learning
  • artificial intelligence
  • proteins
  • protein structure
  • protein dynamics
  • protein design
  • protein function
  • drug design

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Published Papers (3 papers)

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Research

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18 pages, 5286 KiB  
Article
AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2
by Bowen Tang, Fengming He, Dongpeng Liu, Fei He, Tong Wu, Meijuan Fang, Zhangming Niu, Zhen Wu and Dong Xu
Biomolecules 2022, 12(6), 746; https://doi.org/10.3390/biom12060746 - 25 May 2022
Cited by 63 | Viewed by 6692
Abstract
The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or [...] Read more.
The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN–FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2. Full article
(This article belongs to the Special Issue Machine Learning Approach to Protein Structure, Dynamics, and Function)
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Review

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50 pages, 5658 KiB  
Review
Protein Function Analysis through Machine Learning
by Chris Avery, John Patterson, Tyler Grear, Theodore Frater and Donald J. Jacobs
Biomolecules 2022, 12(9), 1246; https://doi.org/10.3390/biom12091246 - 6 Sep 2022
Cited by 15 | Viewed by 8502
Abstract
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein [...] Read more.
Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML approaches have been incorporated into many areas of computational biology dealing with protein function. We examine how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function. The applications discussed are protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, molecular docking in terms of protein–ligand binding, including allosteric effects, protein–protein interactions and protein-centric drug discovery. To quantify the mechanisms underlying protein function, a holistic approach that takes structure, flexibility, stability, and dynamics into account is required, as these aspects become inseparable through their interdependence. Another key component of protein function is conformational dynamics, which often manifest as protein kinetics. Computational methods that use ML to generate representative conformational ensembles and quantify differences in conformational ensembles important for function are included in this review. Future opportunities are highlighted for each of these topics. Full article
(This article belongs to the Special Issue Machine Learning Approach to Protein Structure, Dynamics, and Function)
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13 pages, 1473 KiB  
Review
Single-Stranded DNA Binding Proteins and Their Identification Using Machine Learning-Based Approaches
by Jun-Tao Guo and Fareeha Malik
Biomolecules 2022, 12(9), 1187; https://doi.org/10.3390/biom12091187 - 26 Aug 2022
Cited by 11 | Viewed by 3804
Abstract
Single-stranded DNA (ssDNA) binding proteins (SSBs) are critical in maintaining genome stability by protecting the transient existence of ssDNA from damage during essential biological processes, such as DNA replication and gene transcription. The single-stranded region of telomeres also requires protection by ssDNA binding [...] Read more.
Single-stranded DNA (ssDNA) binding proteins (SSBs) are critical in maintaining genome stability by protecting the transient existence of ssDNA from damage during essential biological processes, such as DNA replication and gene transcription. The single-stranded region of telomeres also requires protection by ssDNA binding proteins from being attacked in case it is wrongly recognized as an anomaly. In addition to their critical roles in genome stability and integrity, it has been demonstrated that ssDNA and SSB–ssDNA interactions play critical roles in transcriptional regulation in all three domains of life and viruses. In this review, we present our current knowledge of the structure and function of SSBs and the structural features for SSB binding specificity. We then discuss the machine learning-based approaches that have been developed for the prediction of SSBs from double-stranded DNA (dsDNA) binding proteins (DSBs). Full article
(This article belongs to the Special Issue Machine Learning Approach to Protein Structure, Dynamics, and Function)
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