Recognition of Potential COVID-19 Drug Treatments through the Study of Existing Protein–Drug and Protein–Protein Structures: An Analysis of Kinetically Active Residues

We report the results of our in silico study of approved drugs as potential treatments for COVID-19. The study is based on the analysis of normal modes of proteins. The drugs studied include chloroquine, ivermectin, remdesivir, sofosbuvir, boceprevir, and α-difluoromethylornithine (DMFO). We applied the tools we developed and standard tools used in the structural biology community. Our results indicate that small molecules selectively bind to stable, kinetically active residues and residues adjoining them on the surface of proteins and inside protein pockets, and that some prefer hydrophobic sites over other active sites. Our approach is not restricted to viruses and can facilitate rational drug design, as well as improve our understanding of molecular interactions, in general.

Download Full-text

Binding site matching in rational drug design: algorithms and applications

Briefings in Bioinformatics ◽

10.1093/bib/bby078 ◽

2018 ◽

Vol 20 (6) ◽

pp. 2167-2184 ◽

Cited By ~ 3

Author(s):

Misagh Naderi ◽

Jeffrey Mitchell Lemoine ◽

Rajiv Gandhi Govindaraj ◽

Omar Zade Kana ◽

Wei Pan Feinstein ◽

...

Keyword(s):

Small Molecules ◽

Binding Site ◽

Protein Structures ◽

Chemical Properties ◽

Drug Repurposing ◽

Rational Drug Design ◽

Basic Knowledge ◽

Rational Drug ◽

Binding Pockets ◽

Local Protein

Abstract Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

Download Full-text

A structural keystone for drug design

Journal of Integrative Bioinformatics ◽

10.1515/jib-2006-19 ◽

2006 ◽

Vol 3 (1) ◽

pp. 21-31

Author(s):

Kristian Rother ◽

Mathias Dunkel ◽

Elke Michalsky ◽

Silke Trissl ◽

Andrean Goede ◽

...

Keyword(s):

Drug Design ◽

Small Molecules ◽

Protein Complexes ◽

Protein Structures ◽

Binding Activity ◽

Rational Drug Design ◽

Biologically Active ◽

Web Resource ◽

Fold Family ◽

Approved Drugs

Abstract 3D-structures of proteins and potential ligands are the cornerstones of rational drug design. The first brick to build upon is selecting a protein target and finding out whether biologically active compounds are known. Both tasks require more information than the structures themselves provide. For this purpose we have built a web resource bridging protein and ligand databases. It consists of three parts: i) A data warehouse on annotation of protein structures that integrates many well-known databases such as Swiss-Prot, SCOP, ENZYME and others. ii) A conformational library of structures of approved drugs. iii) A conformational library of ligands from the PDB, linking the realms of proteins and small molecules. The data collection contains structures of 30,000 proteins, 5,000 different ligands from 70,000 ligand-protein complexes, and 2,500 known drugs. Sets of protein structures can be refined by criteria like protein fold, family, metabolic pathway, resolution and textual annotation. The structures of organic compounds (drugs and ligands) can be searched considering chemical formula, trivial and trade names as well as medical classification codes for drugs (ATC). Retrieving structures by 2D-similarity has been implemented for all small molecules using Tanimoto coefficients. For the drug structures, 110,000 structural conformers have been calculated to account for structural flexibility. Two substances can be compared online by 3D-superimposition, where the pair of conformers that fits best is detected. Together, these web-accessible resources can be used to identify promising drug candidates. They have been used in-house to find alternatives to substances with a known binding activity but adverse side effects.

Download Full-text

Identification of protein pockets and cavities by Euclidean Distance Transform

10.7287/peerj.preprints.27314v1 ◽

2018 ◽

Author(s):

Sebastian Daberdaku

Keyword(s):

Ligand Binding ◽

Active Sites ◽

Euclidean Distance ◽

Protein Structures ◽

Ligand Docking ◽

Distance Transform ◽

Structure Based Drug Design ◽

Distance Map ◽

Euclidean Distance Transform ◽

Protein Pockets

Protein pockets and cavities usually coincide with the active sites of biological processes, and their identification is significant since it constitutes an important step for structure-based drug design and protein-ligand docking applications. This paper presents a novel purely geometric algorithm for the detection of ligand binding protein pockets and cavities based on the Euclidean Distance Transform (EDT). The EDT can be used to compute the Solvent-Excluded surface for any given probe sphere radius value at high resolutions and in a timely manner. The algorithm is adaptive to the specific candidate ligand: it computes two voxelised protein surfaces using two different probe sphere radii depending on the shape of the candidate ligand. The pocket regions consist of the voxels located between the two surfaces, which exhibit a certain minimum depth value from the outer surface. The distance map values computed by the EDT algorithm during the second surface computation can be used to elegantly determine the depth of each candidate pocket and to rank them accordingly. Cavities on the other hand, are identified by scanning the inside of the protein for voids. The algorithm determines and outputs the best k candidate pockets and cavities, i.e. the ones that are more likely to bind to the given ligand. The method was applied to a representative set of protein-ligand complexes and their corresponding unbound protein structures to evaluate its ligand binding site prediction capabilities, and was shown to outperform most of the previously developed purely geometric pocket and cavity search methods.

Download Full-text

Protein Structures and Structure-Based Rational Drug Design

Applied Bioinformatics ◽

10.1007/978-3-540-72800-9_6 ◽

2008 ◽

pp. 117-137

Keyword(s):

Drug Design ◽

Protein Structures ◽

Rational Drug Design ◽

Rational Drug

Download Full-text

A Deep-Learning Proteomic-Scale Approach for Drug Design

Pharmaceuticals ◽

10.3390/ph14121277 ◽

2021 ◽

Vol 14 (12) ◽

pp. 1277

Author(s):

Brennan Overhoff ◽

Zackary Falls ◽

William Mangione ◽

Ram Samudrala

Keyword(s):

Deep Learning ◽

Drug Design ◽

Protein Structures ◽

Holistic Approach ◽

Rational Drug Design ◽

P Value ◽

Experimental Drugs ◽

Rational Drug ◽

Drug Compound ◽

Novel Drug

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

Download Full-text

Universal Architectural Concepts Underlying Protein Folding Patterns

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.612920 ◽

2021 ◽

Vol 7 ◽

Author(s):

Arun S. Konagurthu ◽

Ramanan Subramanian ◽

Lloyd Allison ◽

David Abramson ◽

Peter J. Stuckey ◽

...

Keyword(s):

Structure Prediction ◽

Protein Structures ◽

Data Bank ◽

Rational Drug Design ◽

Basis Set ◽

Sequence Structure ◽

Information Theoretic ◽

Protein Architecture ◽

Structure Correlations ◽

Rational Drug

What is the architectural “basis set” of the observed universe of protein structures? Using information-theoretic inference, we answer this question with a dictionary of 1,493 substructures—called concepts—typically at a subdomain level, based on an unbiased subset of known protein structures. Each concept represents a topologically conserved assembly of helices and strands that make contact. Any protein structure can be dissected into instances of concepts from this dictionary. We dissected the Protein Data Bank and completely inventoried all the concept instances. This yields many insights, including correlations between concepts and catalytic activities or binding sites, useful for rational drug design; local amino-acid sequence–structure correlations, useful for ab initio structure prediction methods; and information supporting the recognition and exploration of evolutionary relationships, useful for structural studies. An interactive site, Proçodic, at http://lcb.infotech.monash.edu.au/prosodic (click), provides access to and navigation of the entire dictionary of concepts and their usages, and all associated information. This report is part of a continuing programme with the goal of elucidating fundamental principles of protein architecture, in the spirit of the work of Cyrus Chothia.

Download Full-text

A blueprint for high affinity SARS-CoV-2 Mpro inhibitors from activity-based compound library screening guided by analysis of protein dynamics

10.1101/2020.12.14.422634 ◽

2020 ◽

Author(s):

Jonas Gossen ◽

Simone Albani ◽

Anton Hanke ◽

Benjamin P. Joseph ◽

Cathrine Bergh ◽

...

Keyword(s):

Ligand Binding ◽

Binding Site ◽

Active Sites ◽

Chemical Space ◽

Protein Structures ◽

Rational Drug Design ◽

In Vitro Assays ◽

Strong Basis ◽

High Affinity ◽

Compound Libraries

AbstractThe SARS-CoV-2 coronavirus outbreak continues to spread at a rapid rate worldwide. The main protease (Mpro) is an attractive target for anti-COVID-19 agents. Unfortunately, unexpected difficulties have been encountered in the design of specific inhibitors. Here, by analyzing an ensemble of ~30,000 SARS-CoV-2 Mpro conformations from crystallographic studies and molecular simulations, we show that small structural variations in the binding site dramatically impact ligand binding properties. Hence, traditional druggability indices fail to adequately discriminate between highly and poorly druggable conformations of the binding site. By performing ~200 virtual screenings of compound libraries on selected protein structures, we redefine the protein’s druggability as the consensus chemical space arising from the multiple conformations of the binding site formed upon ligand binding. This procedure revealed a unique SARS-CoV-2 Mpro blueprint that led to a definition of a specific structure-based pharmacophore. The latter explains the poor transferability of potent SARS-CoV Mpro inhibitors to SARS-CoV-2 Mpro, despite the identical sequences of the active sites. Importantly, application of the pharmacophore predicted novel high affinity inhibitors of SARS-CoV-2 Mpro, that were validated by in vitro assays performed here and by a newly solved X-ray crystal structure. These results provide a strong basis for effective rational drug design campaigns against SARS-CoV-2 Mpro and a new computational approach to screen protein targets with malleable binding sites.

Download Full-text

Mechanisms of Action for Small Molecules Revealed by Structural Biology in Drug Discovery

International Journal of Molecular Sciences ◽

10.3390/ijms21155262 ◽

2020 ◽

Vol 21 (15) ◽

pp. 5262 ◽

Cited By ~ 1

Author(s):

Qingxin Li ◽

CongBao Kang

Keyword(s):

Drug Discovery ◽

Small Molecules ◽

Structural Biology ◽

Small Molecule ◽

Molecular Interactions ◽

Mechanisms Of Action ◽

Rational Drug Design ◽

Binding Modes ◽

Small Molecule Drugs ◽

Rational Drug

Small-molecule drugs are organic compounds affecting molecular pathways by targeting important proteins. These compounds have a low molecular weight, making them penetrate cells easily. Small-molecule drugs can be developed from leads derived from rational drug design or isolated from natural resources. A target-based drug discovery project usually includes target identification, target validation, hit identification, hit to lead and lead optimization. Understanding molecular interactions between small molecules and their targets is critical in drug discovery. Although many biophysical and biochemical methods are able to elucidate molecular interactions of small molecules with their targets, structural biology is the most powerful tool to determine the mechanisms of action for both targets and the developed compounds. Herein, we reviewed the application of structural biology to investigate binding modes of orthosteric and allosteric inhibitors. It is exemplified that structural biology provides a clear view of the binding modes of protease inhibitors and phosphatase inhibitors. We also demonstrate that structural biology provides insights into the function of a target and identifies a druggable site for rational drug design.

Download Full-text