Software for molecular docking: a review

Publshed ArticleMolecular docking methodology explores the behavior of small molecules in the binding site of a target protein. As more protein structures are determined experimentally using X-ray crystallography or nuclear magnetic resonance (NMR) spectroscopy, molecular docking is increasingly used as a tool in drug discovery. Docking against homologymodeled targets also becomes possible for proteins whose structures are not known. With the docking strategies, the druggability of the compounds and their specificity against a particular target can be calculated for further lead optimization processes. Molecular docking programs perform a search algorithm in which the conformation of the ligand is evaluated recursively until the convergence to the minimum energy is reached. Finally, an affinity scoring function, ΔG [U total in kcal/mol], is employed to rank the candidate poses as the sum of the electrostatic and van der Waals energies. The driving forces for these specific interactions in biological systems aim toward complementarities between the shape and electrostatics of the binding site surfaces and the ligand or substrate

Towards Effective Consensus Scoring in Structure-Based Virtual Screening

Virtual screening (VS) is a computational strategy that uses in silico automated protein docking inter alia to rank potential ligands, or by extension rank protein–ligand pairs, identifying potential drug candidates. Most docking methods use preferred sets of physicochemical descriptors (PCDs) to model the interactions between host and guest molecules. Thus, conventional VS is often data-specific, method-dependent and with demonstrably differing utility in identifying candidate drugs. This study proposes four universality classes of novel consensus scoring (CS) algorithms that combine docking scores, derived from ten docking programs (ADFR, DOCK, Gemdock, Ledock, PLANTS, PSOVina, QuickVina2, Smina, Autodock Vina and VinaXB), using decoys from the DUD-E repository (http://dude.docking.org/) against 29 MRSA-oriented targets to create a general VS formulation that can identify active ligands for any suitable protein target. Our results demonstrate that CS provides improved ligand–protein docking fidelity when compared to individual docking platforms. This approach requires only a small number of docking combinations and can serve as a viable and parsimonious alternative to more computationally expensive docking approaches. Predictions from our CS algorithm are compared against independent machine learning evaluations using the same docking data, complementing the CS outcomes. Our method is a reliable approach for identifying protein targets and high-affinity ligands that can be tested as high-probability candidates for drug repositioning. Graphical Abstract

Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview

Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies

Antimicrobial Drug Repurposing Through Molecular Modelling: Acquisition, Analyis and Prediction

Antimicrobial resistance has sparked unprecedented medical crises around the world, not only increasing the mortality rate but also impacting nosocomial resources. Methicillin-resistant Staphylococcus aureus (MRSA) has consistently evaded the available range of antibiotics and is a typical case study for new generation drugs. Drug development has been conventionally suffering from exceedingly high costs and overdrawn timelines. Drug Repurposing can be a solution to alleviate those burdens. Put simply, DR is a mechanism to identify new usages of existing drugs, typically targeted to treat diseases different to the ones that these were initially intended for. This inherently interdisciplinary research targets to identify the best MRSA drug candidates analysing protein (BIG) data, in the process developing a combination of techniques from stochastic mathematics, statistics and data analytics that can generically identify drug targets from the databank. Structure-based virtual screening was used to repurpose an extensive range of marketed drugs and Phase I/II/III trials. Molecular docking methods were used for virtual screening against MRSA targets based on sequence alignment to match gene sequences against proteins in the Protein Data Bank (PDB). Ligands from the Database of Useful Decoys - Enhanced were docked against MRSA-oriented target proteins using 10 open-source docking programmes for benchmark. The novel consensus scoring methods prove superior to other reported consensus scores in terms of discrimination between decoys and active ligands concerning MRSA drug target identification. The consensus scoring predictions are then applied to docking data between MRSA targets and compounds from the Repurposing Hub to identify a list of potential drug candidates for anti-MRSA treatment. MRSA is currently an apocalypse across the world with limited prevention and medications. This study provided more potential candidates to help fight against MRSA. The consensus scoring developed in this study can be generically implemented to unlock other antimicrobial drug candidates

Exploring protein flexibility during docking to investigate ligand-target recognition

Ligand-protein binding models have experienced an evolution during time: from the lock-key model to induced-fit and conformational selection, the role of protein flexibility has become more and more relevant. Understanding binding mechanism is of great importance in drug-discovery, because it could help to rationalize the activity of known binders and to optimize them. The application of computational techniques to drug-discovery has been reported since the 1980s, with the advent computer-aided drug design. During the years several techniques have been developed to address the protein flexibility issue. The present work proposes a strategy to consider protein structure variability in molecular docking, through a ligand-based/structure-based integrated approach and through the development of a fully automatic cross-docking benchmark pipeline. Moreover, a full exploration of protein flexibility during the binding process is proposed through the Supervised Molecular Dynamics. The application of a tabu-like algorithm to classical molecular dynamics accelerates the binding process from the micro-millisecond to the nanosecond timescales. In the present work, an implementation of this algorithm has been performed to study peptide-protein recognition processes

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD