Leveraging nonstructural data to predict structures and affinities of protein–ligand complexes

AbstractOver the past fifty years, tremendous effort has been devoted to computational methods for predicting properties of ligands that bind macromolecular targets, a problem critical to rational drug design. Such methods generally fall into two categories: physics-based methods, which directly model ligand interactions with the target given the target’s three-dimensional (3D) structure, and ligand-based methods, which predict ligand properties given experimental measurements for similar ligands. Here we present a rigorous statistical framework to combine these two sources of information. We develop a method to predict a ligand’s pose—the 3D structure of the ligand bound to its protein target—that leverages a widely available source of information: a list of other ligands that are known to bind the same target but for which no 3D structure is available. This combination of physics-based and ligand-based modeling improves upon state-of-the-art pose prediction accuracy across all major families of drug targets. As an illustrative application, we predict binding poses of antipsychotics and validate the results experimentally. Our statistical framework and results suggest broad opportunities to predict diverse ligand properties using machine learning methods that draw on physical modeling and ligand data simultaneously.

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Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

Briefings in Bioinformatics ◽

10.1093/bib/bbaa034 ◽

2020 ◽

Cited By ~ 8

Author(s):

Natesh Singh ◽

Ludovic Chaput ◽

Bruno O Villoutreix

Keyword(s):

Virtual Screening ◽

Drug Repositioning ◽

Pharmaceutical Research ◽

Bioactive Molecules ◽

Screening Methods ◽

Web Servers ◽

Web Based ◽

Drug Candidates ◽

Screening Experiments ◽

Molecule Docking

Abstract The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

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Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Current Medicinal Chemistry ◽

10.2174/0929867324666170712115411 ◽

2019 ◽

Vol 26 (21) ◽

pp. 3874-3889 ◽

Cited By ~ 3

Author(s):

Jelica Vucicevic ◽

Katarina Nikolic ◽

John B.O. Mitchell

Keyword(s):

Virtual Screening ◽

Drug Design ◽

Antineoplastic Agents ◽

Drug Targets ◽

3D Qsar ◽

Pharmacophore Modeling ◽

Rational Drug Design ◽

Bioactive Molecules ◽

Computational Approaches ◽

Rational Drug

Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

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D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

10.26434/chemrxiv.11959323 ◽

2020 ◽

Cited By ~ 1

Author(s):

Zhengdan Zhu ◽

Xiaoyu Wang ◽

Yanqing Yang ◽

Xinben Zhang ◽

Kaijie Mu ◽

...

Keyword(s):

Protein Structure ◽

Virtual Screening ◽

Drug Targets ◽

False Negative ◽

Three Dimensional ◽

Target Prediction ◽

Molecular Structures ◽

Bioactive Molecules ◽

Active Compounds ◽

Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

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Supercomputer-aided Drug Repositioning at Scale: Virtual Screening for SARS-CoV-2 Protease Inhibitor

10.26434/chemrxiv.12101457.v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Sangjae Seo ◽

Jung Woo Park ◽

Dosik An ◽

Junwon Yoon ◽

Hyojung Paik ◽

...

Keyword(s):

Virtual Screening ◽

Protease Inhibitor ◽

Binding Affinity ◽

High Throughput ◽

Active Site ◽

High Throughput Screening ◽

Drug Repositioning ◽

Three Dimensional ◽

Dimensional Structure ◽

Drug Candidates

Coronavirus diseases (COVID-19) outbreak has been labelled a pandemic. For the prioritization of treatments to cope with COVID-19, it is important to conduct rapid high-throughput screening of chemical compounds to repurposing the approved drugs, such as repositioning of chloroquine (Malaria drug) for COVID-19. In this study, exploiting supercomputer resource, we conducted high-throughput virtual screening for potential repositioning candidates of the protease inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using the three dimensional structure of main protease (Mpro) of SARS-CoV-2, we evaluated binding affinity between Mpro and drug candidates listed in SWEETLEAD library and ChEMBL database. Docking scores of 19,168 drug molecules at the active site of Mpro were calculated using Autodock Vina package. Among the calculated result, we selected 43 drug candidates and ran molecular dynamics (MD) simulation to further investigate protein-drug interaction. Among compounds that bind to the active site of SARS-CoV-2, we finally selected the 8 drugs showing the highest binding affinity; asunaprevir, atazanavir, dasabuvir, doravirine, fosamprenavir, ritonavir, voxilaprevir and amprenavir, which are the antiviral drugs of hepatitis C virus or human immunodeficiency virus. We expect that the present study provides comprehensive insights into the development of antiviral medication, especially for the treatment of COVID-19.<div><br></div><div>* Attached excel file contains a full list of results of docking calculations</div>

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Introduction of Advanced Methods for Structure-based Drug Discovery

Current Bioinformatics ◽

10.2174/1574893615999200703113200 ◽

2020 ◽

Vol 15 ◽

Author(s):

Bilal Shaker ◽

Kha Mong Tran ◽

Chanjin Jung ◽

Dokyun Na

Keyword(s):

Drug Discovery ◽

Pharmaceutical Industry ◽

Virtual Screening ◽

Molecular Basis ◽

3D Structure ◽

Protein Targets ◽

Drug Candidates ◽

Web Tools ◽

Conventional Drug ◽

Potent Drug

: Structure-based drug discovery has become a promising and efficient approach for identifying novel and potent drug candidates with less time and cost than conventional drug discovery approaches. It has been widely used in the pharmaceutical industry since it uses the 3D structure of biological protein targets and thereby to understand the molecular basis of diseases. For the virtual identification of drug candidates based on structure, there are a few steps for protein and compound preparations to obtain accurate results. In this review, the software and web-tools for the preparation and structure-based simulation are introduced. In addition, recent improvements in structure-based virtual screening, target library designing for virtual screening, docking, scoring, and postprocessing of top hits are also introduced.

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Positive Predictive Value Surfaces as a Complementary Tool to Assess the Performance of Virtual Screening Methods

Mini-Reviews in Medicinal Chemistry ◽

10.2174/1871525718666200219130229 ◽

2020 ◽

Vol 20 (14) ◽

pp. 1447-1460

Author(s):

Juan F. Morales ◽

Sara Chuguransky ◽

Lucas N. Alberca ◽

Juan I. Alice ◽

Sofía Goicoechea ◽

...

Keyword(s):

Virtual Screening ◽

Positive Predictive Value ◽

Ensemble Learning ◽

Predictive Value ◽

Experimental Testing ◽

Screening Tools ◽

Screening Methods ◽

Learning Approaches ◽

Classification Problems ◽

Screening Experiments

Background: Since their introduction in the virtual screening field, Receiver Operating Characteristic (ROC) curve-derived metrics have been widely used for benchmarking of computational methods and algorithms intended for virtual screening applications. Whereas in classification problems, the ratio between sensitivity and specificity for a given score value is very informative, a practical concern in virtual screening campaigns is to predict the actual probability that a predicted hit will prove truly active when submitted to experimental testing (in other words, the Positive Predictive Value - PPV). Estimation of such probability is however, obstructed due to its dependency on the yield of actives of the screened library, which cannot be known a priori. Objective: To explore the use of PPV surfaces derived from simulated ranking experiments (retrospective virtual screening) as a complementary tool to ROC curves, for both benchmarking and optimization of score cutoff values. Methods: The utility of the proposed approach is assessed in retrospective virtual screening experiments with four datasets used to infer QSAR classifiers: inhibitors of Trypanosoma cruzi trypanothione synthetase; inhibitors of Trypanosoma brucei N-myristoyltransferase; inhibitors of GABA transaminase and anticonvulsant activity in the 6 Hz seizure model. Results: Besides illustrating the utility of PPV surfaces to compare the performance of machine learning models for virtual screening applications and to select an adequate score threshold, our results also suggest that ensemble learning provides models with better predictivity and more robust behavior. Conclusion: PPV surfaces are valuable tools to assess virtual screening tools and choose score thresholds to be applied in prospective in silico screens. Ensemble learning approaches seem to consistently lead to improved predictivity and robustness.

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Screening of Natural Product and Natural Product like Molecules against SARS–CoV–2 Main Protease Using Molecular Modeling Methods

Journal of Pharmaceutical Research International ◽

10.9734/jpri/2020/v32i4831123 ◽

2021 ◽

pp. 36-51

Author(s):

Ismail Hakki Akgün

Keyword(s):

Natural Products ◽

Virtual Screening ◽

Natural Product ◽

Enzyme Inhibitors ◽

Three Dimensional ◽

Screening Methods ◽

Data Set ◽

Modeling Methods ◽

Main Protease ◽

Synthetic Accessibility

Objective: To determine possible MPro enzyme inhibitors by using structure-based virtual screening methods, in the ZINC Biogenic Data Set containing natural products and natural product-like molecules. Materials and Methods: QVina, an AutoDockVina derivative, was used in virtual screening operations, GROMACS in molecular dynamics studies and SwissAdme server in ADME (Absorption, Distribution, Metabolism, and Excretion) calculations. KNIME (Konstanz Information Miner) and ChemAxon software were used for filtering data and creating three-dimensional structures of the molecules. Results: Seven out of totally screened 51535 natural products or natural products like molecules were identified as possible candidate to be used as SARS–CoV–2 Main Protease (MPro) enzyme inhibitors based on the results obtained from structure based virtual screening and ADME models. Conclusion: Among the seven potent molecules, two of them (ZINC000604382012 and ZINC000514288074) were selected as candidate molecules for further studies according to the results obtained from g_mmpbsa simulations and synthetic accessibility models. In addition, a workflow has been established to identify novel or potent Mpro enzyme inhibitors.

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Supercomputer-aided Drug Repositioning at Scale: Virtual Screening for SARS-CoV-2 Protease Inhibitor

10.26434/chemrxiv.12101457 ◽

2020 ◽

Author(s):

Sangjae Seo ◽

Jung Woo Park ◽

Dosik An ◽

Junwon Yoon ◽

Hyojung Paik ◽

...

Keyword(s):

Virtual Screening ◽

Protease Inhibitor ◽

Binding Affinity ◽

High Throughput ◽

Active Site ◽

High Throughput Screening ◽

Drug Repositioning ◽

Three Dimensional ◽

Dimensional Structure ◽

Drug Candidates

Coronavirus diseases (COVID-19) outbreak has been labelled a pandemic. For the prioritization of treatments to cope with COVID-19, it is important to conduct rapid high-throughput screening of chemical compounds to repurposing the approved drugs, such as repositioning of chloroquine (Malaria drug) for COVID-19. In this study, exploiting supercomputer resource, we conducted high-throughput virtual screening for potential repositioning candidates of the protease inhibitor of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Using the three dimensional structure of main protease (Mpro) of SARS-CoV-2, we evaluated binding affinity between Mpro and drug candidates listed in SWEETLEAD library and ChEMBL database. Docking scores of 19,168 drug molecules at the active site of Mpro were calculated using Autodock Vina package. Among the calculated result, we selected 43 drug candidates and ran molecular dynamics (MD) simulation to further investigate protein-drug interaction. Among compounds that bind to the active site of SARS-CoV-2, we finally selected the 8 drugs showing the highest binding affinity; asunaprevir, atazanavir, dasabuvir, doravirine, fosamprenavir, ritonavir, voxilaprevir and amprenavir, which are the antiviral drugs of hepatitis C virus or human immunodeficiency virus. We expect that the present study provides comprehensive insights into the development of antiviral medication, especially for the treatment of COVID-19.<div><br></div><div>* Attached excel file contains a full list of results of docking calculations</div>

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D3Similarity: A Ligand-Based Approach for Predicting Drug Targets and for Virtual Screening of Active Compounds Against COVID-19

10.26434/chemrxiv.11959323.v1 ◽

2020 ◽

Cited By ~ 2

Author(s):

Zhengdan Zhu ◽

Xiaoyu Wang ◽

Yanqing Yang ◽

Xinben Zhang ◽

Kaijie Mu ◽

...

Keyword(s):

Protein Structure ◽

Virtual Screening ◽

Drug Targets ◽

False Negative ◽

Three Dimensional ◽

Target Prediction ◽

Molecular Structures ◽

Bioactive Molecules ◽

Active Compounds ◽

Target Proteins

<p>Discovering efficient drugs and identifying target proteins are still an unmet but urgent need for curing COVID-19. Protein structure based docking is a widely applied approach for discovering active compounds against drug targets and for predicting potential targets of active compounds. However, this approach has its inherent deficiency caused by, e.g., various different conformations with largely varied binding pockets adopted by proteins, or the lack of true target proteins in the database. This deficiency may result in false negative results. As a complementary approach to the protein structure based platform for COVID-19, termed as D3Docking in our recent work, we developed the ligand-based method, named D3Similarity, which is based on the molecular similarity evaluation between the submitted molecule(s) and those in an active compound database. The database is constituted by all the reported bioactive molecules against the coronaviruses SARS, MERS and SARS-CoV-2, some of which have target or mechanism information but some don’t. Based on the two-dimensional and three-dimensional similarity evaluation of molecular structures, virtual screening and target prediction could be performed according to similarity ranking results. With two examples, we demonstrated the reliability and efficiency of D3Similarity for drug discovery and target prediction against COVID-19. D3Similarity is available free of charge at <a href="https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php">https://www.d3pharma.com/D3Targets-2019-nCoV/D3Similarity/index.php</a>.</p>

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