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Computational Answers to Biomolecular Recognition Problems

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 22445

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Prof. Dr. Pedro Silva

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I3ID/Faculdade de Ciências da Saúde, Universidade Fernando Pessoa, Porto, Portugal
Interests: computational chemistry; reaction mechanisms; enzymology; drug design
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Dear Colleagues,

By their very nature, all biochemical events require interaction between a macromolecule (usually a protein or a nucleic acid) and a target molecule. Selectivity is achieved through the geometric features of the binding pockets present in the macromolecules, their electrostatic profiles, and their ability to establish non-electrostatic interactions with precise ligand moieties. While experimental methods such as isothermal-titration calorimetry and the determination of binding constants can be used to quantify the strength of those interactions, structural insight is needed to enable the rational tailoring of ligand or macromolecule to improve drug binding, decrease amyloid formation or modulate gene expression through interaction with manipulated transcription factors. Quite often, the experimental acquisition of these structural data through NMR or X-ray crystallography is hampered by the insolubility of the resulting complexes or the inability to generate properly diffracting crystals. Computational methods offer the possibility of precisely describing all types of ligand–macromolecule interactions and are therefore a promising avenue to obtain that information, to test or discard a large variety of hypotheses regarding molecular recognition, and to select, among the vast chemical space of potential drug scaffolds, those which are most likely to meet success before committing expensive experimental resources to their synthesis and evaluation. In this Special Edition, we welcome manuscripts describing the application of computational methods to unravel the details of the interactions between proteins and their targets, as well as novel methodological approaches to protein–DNA, protein–protein, and protein–ligand docking or scoring.

Prof. Dr. Pedro Silva
Guest Editor

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Keywords

Ligand binding
Protein-protein docking
Reverse docking
Protein–DNA interactions
Molecular dynamics

Published Papers (6 papers)

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Research

21 pages, 56527 KiB

Open AccessArticle

Identification of New Potential Inhibitors of Quorum Sensing through a Specialized Multi-Level Computational Approach

by Fábio G. Martins, André Melo and Sérgio F. Sousa

Molecules 2021, 26(9), 2600; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26092600 - 29 Apr 2021

Cited by 21 | Viewed by 3247

Abstract

Biofilms are aggregates of microorganisms anchored to a surface and embedded in a self-produced matrix of extracellular polymeric substances and have been associated with 80% of all bacterial infections in humans. Because bacteria in biofilms are less amenable to antibiotic treatment, biofilms have been associated with developing antibiotic resistance, a problem that urges developing new therapeutic options and approaches. Interfering with quorum-sensing (QS), an important process of cell-to-cell communication by bacteria in biofilms is a promising strategy to inhibit biofilm formation and development. Here we describe and apply an in silico computational protocol for identifying novel potential inhibitors of quorum-sensing, using CviR—the quorum-sensing receptor from Chromobacterium violaceum—as a model target. This in silico approach combines protein-ligand docking (with 7 different docking programs/scoring functions), receptor-based virtual screening, molecular dynamic simulations, and free energy calculations. Particular emphasis was dedicated to optimizing the discrimination ability between active/inactive molecules in virtual screening tests using a target-specific training set. Overall, the optimized protocol was used to evaluate 66,461 molecules, including those on the ZINC/FDA-Approved database and to the Mu.Ta.Lig Virtual Chemotheca. Multiple promising compounds were identified, yielding good prospects for future experimental validation and for drug repurposing towards QS inhibition. Full article

(This article belongs to the Special Issue Computational Answers to Biomolecular Recognition Problems)

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22 pages, 9303 KiB

Open AccessArticle

In Silico Mining of Terpenes from Red-Sea Invertebrates for SARS-CoV-2 Main Protease (M^pro) Inhibitors

by Mahmoud A. A. Ibrahim, Alaa H. M. Abdelrahman, Tarik A. Mohamed, Mohamed A. M. Atia, Montaser A. M. Al-Hammady, Khlood A. A. Abdeljawaad, Eman M. Elkady, Mahmoud F. Moustafa, Faris Alrumaihi, Khaled S. Allemailem, Hesham R. El-Seedi, Paul W. Paré, Thomas Efferth and Mohamed-Elamir F. Hegazy

Molecules 2021, 26(7), 2082; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26072082 - 05 Apr 2021

Cited by 38 | Viewed by 4423

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for the COVID-19 pandemic, which generated more than 1.82 million deaths in 2020 alone, in addition to 83.8 million infections. Currently, there is no antiviral medication to treat COVID-19. In the search for drug leads, marine-derived metabolites are reported here as prospective SARS-CoV-2 inhibitors. Two hundred and twenty-seven terpene natural products isolated from the biodiverse Red-Sea ecosystem were screened for inhibitor activity against the SARS-CoV-2 main protease (M^pro) using molecular docking and molecular dynamics (MD) simulations combined with molecular mechanics/generalized Born surface area binding energy calculations. On the basis of in silico analyses, six terpenes demonstrated high potency as M^pro inhibitors with ΔG_binding ≤ −40.0 kcal/mol. The stability and binding affinity of the most potent metabolite, erylosides B, were compared to the human immunodeficiency virus protease inhibitor, lopinavir. Erylosides B showed greater binding affinity towards SARS-CoV-2 M^pro than lopinavir over 100 ns with ΔG_binding values of −51.9 vs. −33.6 kcal/mol, respectively. Protein–protein interactions indicate that erylosides B biochemical signaling shares gene components that mediate severe acute respiratory syndrome diseases, including the cytokine- and immune-signaling components BCL2L1, IL2, and PRKC. Pathway enrichment analysis and Boolean network modeling were performed towards a deep dissection and mining of the erylosides B target–function interactions. The current study identifies erylosides B as a promising anti-COVID-19 drug lead that warrants further in vitro and in vivo testing. Full article

(This article belongs to the Special Issue Computational Answers to Biomolecular Recognition Problems)

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14 pages, 4249 KiB

Open AccessArticle

Improving Blind Docking in DOCK6 through an Automated Preliminary Fragment Probing Strategy

by Paula Jofily, Pedro G. Pascutti and Pedro H. M. Torres

Molecules 2021, 26(5), 1224; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26051224 - 25 Feb 2021

Cited by 14 | Viewed by 3561

Abstract

Probing protein surfaces to accurately predict the binding site and conformation of a small molecule is a challenge currently addressed through mainly two different approaches: blind docking and cavity detection-guided docking. Although cavity detection-guided blind docking has yielded high success rates, it is less practical when a large number of molecules must be screened against many detected binding sites. On the other hand, blind docking allows for simultaneous search of the whole protein surface, which however entails the loss of accuracy and speed. To bridge this gap, in this study, we developed and tested BLinDPyPr, an automated pipeline which uses FTMap and DOCK6 to perform a hybrid blind docking strategy. Through our algorithm, FTMap docked probe clusters are converted into DOCK6 spheres for determining binding regions. Because these spheres are solely derived from FTMap probes, their locations are contained in and specific to multiple potential binding pockets, which become the regions that are simultaneously probed and chosen by the search algorithm based on the properties of each candidate ligand. This method yields pose prediction results (45.2–54.3% success rates) comparable to those of site-specific docking with the classic DOCK6 workflow (49.7–54.3%) and is half as time-consuming as the conventional blind docking method with DOCK6. Full article

(This article belongs to the Special Issue Computational Answers to Biomolecular Recognition Problems)

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30 pages, 7849 KiB

Open AccessArticle

In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2

by Sebastián A. Cuesta, José R. Mora and Edgar A. Márquez

Molecules 2021, 26(4), 1100; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26041100 - 19 Feb 2021

Cited by 15 | Viewed by 4434

Abstract

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC₅₀ values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R² = 0.897, Q²_LOO = 0.854, and Q²_ext = 0.876 and complying with all the parameters established in the validation Tropsha’s test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC₅₀ value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC₅₀ values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes. Full article

(This article belongs to the Special Issue Computational Answers to Biomolecular Recognition Problems)

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12 pages, 2976 KiB

Open AccessArticle

Does Antibody Stabilize the Ligand Binding in GP120 of HIV-1 Envelope Protein? Evidence from MD Simulation

by Shalini Yadav, Vishnudatt Pandey, Rakesh Kumar Tiwari, Rajendra Prasad Ojha and Kshatresh Dutta Dubey

Molecules 2021, 26(1), 239; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules26010239 - 05 Jan 2021

Cited by 1 | Viewed by 2464

Abstract

CD4-mimetic HIV-1 entry inhibitors are small sized molecules which imitate similar conformational flexibility, in gp120, to the CD4 receptor. However, the mechanism of the conformational flexibility instigated by these small sized inhibitors is little known. Likewise, the effect of the antibody on the function of these inhibitors is also less studied. In this study, we present a thorough inspection of the mechanism of the conformational flexibility induced by a CD4-mimetic inhibitor, NBD-557, using Molecular Dynamics Simulations and free energy calculations. Our result shows the functional importance of Asn425 in substrate induced conformational dynamics in gp120. The MD simulations of Asn425Gly mutant provide a less dynamic gp120 in the presence of NBD-557 without incapacitating the binding enthalpy of NBD-557. The MD simulations of complexes with the antibody clearly show the enhanced affinity of NBD-557 due to the presence of the antibody, which is in good agreement with experimental Isothermal Titration Calorimetry results (Biochemistry2006, 45, 10973–10980). Full article

(This article belongs to the Special Issue Computational Answers to Biomolecular Recognition Problems)

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18 pages, 3857 KiB

Open AccessArticle

Deciphering the Role of Filamin B Calponin-Homology Domain in Causing the Larsen Syndrome, Boomerang Dysplasia, and Atelosteogenesis Type I Spectrum Disorders via a Computational Approach

by Udhaya Kumar S., Srivarshini Sankar, Salma Younes, Thirumal Kumar D., Muneera Naseer Ahmad, Sarah Samer Okashah, Balu Kamaraj, Abeer Mohammed Al-Subaie, George Priya Doss C. and Hatem Zayed

Molecules 2020, 25(23), 5543; https://0-doi-org.brum.beds.ac.uk/10.3390/molecules25235543 - 26 Nov 2020

Cited by 11 | Viewed by 3049

Abstract

Filamins (FLN) are a family of actin-binding proteins involved in regulating the cytoskeleton and signaling phenomenon by developing a network with F-actin and FLN-binding partners. The FLN family comprises three conserved isoforms in mammals: FLNA, FLNB, and FLNC. FLNB is a multidomain monomer protein with domains containing an actin-binding N-terminal domain (ABD 1–242), encompassing two calponin-homology domains (assigned CH1 and CH2). Primary variants in FLNB mostly occur in the domain (CH2) and surrounding the hinge-1 region. The four autosomal dominant disorders that are associated with FLNB variants are Larsen syndrome, atelosteogenesis type I (AOI), atelosteogenesis type III (AOIII), and boomerang dysplasia (BD). Despite the intense clustering of FLNB variants contributing to the LS-AO-BD disorders, the genotype-phenotype correlation is still enigmatic. In silico prediction tools and molecular dynamics simulation (MDS) approaches have offered the potential for variant classification and pathogenicity predictions. We retrieved 285 FLNB missense variants from the UniProt, ClinVar, and HGMD databases in the current study. Of these, five and 39 variants were located in the CH1 and CH2 domains, respectively. These variants were subjected to various pathogenicity and stability prediction tools, evolutionary and conservation analyses, and biophysical and physicochemical properties analyses. Molecular dynamics simulation (MDS) was performed on the three candidate variants in the CH2 domain (W148R, F161C, and L171R) that were predicted to be the most pathogenic. The MDS analysis results showed that these three variants are highly compact compared to the native protein, suggesting that they could affect the protein on the structural and functional levels. The computational approach demonstrates the differences between the FLNB mutants and the wild type in a structural and functional context. Our findings expand our knowledge on the genotype-phenotype correlation in FLNB-related LS-AO-BD disorders on the molecular level, which may pave the way for optimizing drug therapy by integrating precision medicine. Full article

(This article belongs to the Special Issue Computational Answers to Biomolecular Recognition Problems)

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