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The Future of Force Fields in Computational Medicinal Chemistry
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The Future of Force Fields in Computational Medicinal Chemistry

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biophysics".

Deadline for manuscript submissions: closed (29 June 2021) | Viewed by 8970

Special Issue Editors

Dr. Agnieszka Bronowska

E-Mail Website
Guest Editor
Chemistry – School of Natural and Environmental Sciences, Newcastle University, Newcastle, UK
Interests: molecular dynamics simulations of macromolecules; protein-ligand interactions; structure-guided drug design; force field development; multiscale protein modelling; modelling of redox switches; modelling of intrinsically disordered region; liquid-liquid phase separation in biological systems
Dr. Dominik Gront

E-Mail Website
Guest Editor
Department of Chemistry, University of Warsaw, Warsaw, Poland
Interests: molecular simulations; coarse-grained modeling

Special Issue Information

Dear Colleagues,

Molecular force fields are the cornerstone of modern biomolecular simulations, enabling structure-guided drug design; multiscale molecular modeling; molecular dynamics (MD) simulations of macromolecular complexes; studies of protein folding, misfolding, and aggregation; and the discovery of novel “druggable” sites. Empirical force fields, traditionally used in atomistic MD simulations and molecular docking algorithms, are undergoing continuing improvements, and their accuracy and performance are systematically increasing. However, existing limitations and inaccuracies of contemporary force fields limit their applicability, and some legacy issues may hamper improvements. 

This Special Issue will focus on some of the approaches crucial for the successful design of next-generation force fields. Recent improvements in protein force fields will be overviewed, including polarizable and reactive force fields, and scoring functions suitable for ensemble, adaptive, and covalent docking. Improved parameters, electrostatics, and solvation modelling will be included, regarding their accuracy in modeling challenging systems such as intrinsically disordered proteins, protein–protein interaction interfaces, and crowded environments. Studies involving theoretical underpinning, applications of these new force fields, and some recent benchmarks will be covered.

Dr. Agnieszka Bronowska
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. 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.

Keywords

  • molecular mechanics
  • multiscale modelling
  • force fields
  • scoring functions
  • solvation models
  • development of parameters
  • intrinsically disordered proteins

Published Papers (2 papers)

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Research

21 pages, 2375 KiB  
Article
QSAR Implementation for HIC Retention Time Prediction of mAbs Using Fab Structure: A Comparison between Structural Representations
by Micael Karlberg, João Victor de Souza, Lanyu Fan, Arathi Kizhedath, Agnieszka K. Bronowska and Jarka Glassey
Int. J. Mol. Sci. 2020, 21(21), 8037; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21218037 - 28 Oct 2020
Cited by 6 | Viewed by 2463
Abstract
Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by high failure rates originating from failed clinical trials and developability issues in process development. There is, therefore, a growing need for better in silico tools to aid in [...] Read more.
Monoclonal antibodies (mAbs) constitute a rapidly growing biopharmaceutical sector. However, their growth is impeded by high failure rates originating from failed clinical trials and developability issues in process development. There is, therefore, a growing need for better in silico tools to aid in risk assessment of mAb candidates to promote early-stage screening of potentially problematic mAb candidates. In this study, a quantitative structure–activity relationship (QSAR) modelling workflow was designed for the prediction of hydrophobic interaction chromatography (HIC) retention times of mAbs. Three novel descriptor sets derived from primary sequence, homology modelling, and atomistic molecular dynamics (MD) simulations were developed and assessed to determine the necessary level of structural resolution needed to accurately capture the relationship between mAb structures and HIC retention times. The results showed that descriptors derived from 3D structures obtained after MD simulations were the most suitable for HIC retention time prediction with a R2 = 0.63 in an external test set. It was found that when using homology modelling, the resulting 3D structures became biased towards the used structural template. Performing an MD simulation therefore proved to be a necessary post-processing step for the mAb structures in order to relax the structures and allow them to attain a more natural conformation. Based on the results, the proposed workflow in this paper could therefore potentially contribute to aid in risk assessment of mAb candidates in early development. Full article
(This article belongs to the Special Issue The Future of Force Fields in Computational Medicinal Chemistry)
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15 pages, 2498 KiB  
Article
Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein–Inhibitor Complexes in Relevance to Developing Parkinson’s Therapeutics
by Natarajan Arul Murugan, Charuvaka Muvva, Chitra Jeyarajpandian, Jeyaraman Jeyakanthan and Venkatesan Subramanian
Int. J. Mol. Sci. 2020, 21(20), 7648; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21207648 - 16 Oct 2020
Cited by 10 | Viewed by 5964
Abstract
Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as benzylamine, phenethylamine and dopamine with the help of Flavin adenine dinucleotide (FAD) cofactor. The Parkinson’s disease associated symptoms can be [...] Read more.
Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as benzylamine, phenethylamine and dopamine with the help of Flavin adenine dinucleotide (FAD) cofactor. The Parkinson’s disease associated symptoms can be treated using inhibitors of MAO-B as the dopamine degradation can be reduced. Currently, many inhibitors are available having micromolar to nanomolar binding affinities. However, still there is demand for compounds with superior binding affinity and binding specificity with favorable pharmacokinetic properties for treating Parkinson’s disease and computational screening methods can be majorly recruited for this. However, the accuracy of currently available force-field methods for ranking the inhibitors or lead drug-like compounds should be improved and novel methods for screening compounds need to be developed. We studied the performance of various force-field-based methods and data driven approaches in ranking about 3753 compounds having activity against the MAO-B target. The binding affinities computed using autodock and autodock-vina are shown to be non-reliable. The force-field-based MM-GBSA also under-performs. However, certain machine learning approaches, in particular KNN, are found to be superior, and we propose KNN as the most reliable approach for ranking the complexes to reasonable accuracy. Furthermore, all the employed machine learning approaches are also computationally less demanding. Full article
(This article belongs to the Special Issue The Future of Force Fields in Computational Medicinal Chemistry)
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