Computational Methods Used in Phytocompound-Based Drug Discovery

Ion channels are widely expressed in living cells and play critical roles in various cellular biological functions. Dysfunctional ion channels can cause a variety of diseases, making ion channels attractive targets for drug discovery. Computational approaches, such as molecular docking and molecular dynamic simulations, provide economic and efficient tools for finding modulators of ion channels and for elucidating the action mechanisms of small molecules. In this review, we focus primarily on four types of ion channels (voltage-gated, ligand-gated, acid-sensing, and virus matrix 2 ion channels). The current advancements in computer-aided drug discovery and design targeting ion channels are summarized. First, ligand-based studies for drug design are briefly outlined. Then, we focus on the structurebased studies targeting pore domains, endogenous binding sites and allosteric sites of ion channels. Moreover, we also review the contribution of computational methods to the field of ligand binding and unbinding pathways of ion channels. Finally, we propose future developments for the field.

New drug discovery has been acknowledged as a complicated, expensive, time-consuming, and challenging project. It has been estimated that around 12 years and 2.7 billion USD, on average, are demanded for a new drug discovery via traditional drug development pipeline. How to reduce the research cost and speed up the development process of new drug discovery has become a challenging, urgent question for the pharmaceutical industry. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology for faster, cheaper and more effective drug design. Recently, the rapid growth of computational tools for drug discovery, including anticancer therapies, has exhibited a significant and outstanding impact on anticancer drug design, and has also provided fruitful insights into the area of cancer therapy. In this work, we discussed the Qualitative structure activity relationship, a computer-aided drug discovery process with a focus on anticancer drugs. Keywords: New drug discovery, Computer-aided drug discovery, the Qualitative structure activity relationship, Anticancer

A drug takes many years to develop and reach the market using the conventional drug discovery procedure. Computer-aided drug design (CADD) is an emerging technology that accelerates the process of drug discovery and minimizes the total expenditure associated with labour and resources. In the current scenario, the computational aided drug design (CADD) techniques play a significant role in the design and development of lead molecules for the treatment of various lethal pathological conditions. The prediction of the tertiary structure of a protein is a big concern in drug design and discovery. A typical drug discovery procedure starts with the tertiary structure of a protein. At present, a total of 184,407 protein structures are available in the protein data bank, which are determinedusing experimental methods. However, the procedures are difficult and time-consuming. A more advanced technique has been developed for the prediction of the 3D structure of a protein using a computational method. This technique has played a vital role in drug discovery. It has not only facilitated but also hastened the process of drug discovery. The method is named homology modeling since it involves the building of a protein model based on its homology to similar evolutionary proteins. The method is based on the fact that evolutionary related proteins have similar structures. In homology modelling, the 3D structure of a protein is derived from its primary sequence based on its similarity to the existing protein templates. There are many computational tools for homology modelling such as Modeller, Swiss model, Composer, 3D-JIGSAW, etc. The proposed book chapter will cover the introduction to homology modelling, step-by-step guide to building a protein model, various challenges and how to refine and validate the model, different algorithms related to sequence alignment, similarity search, and the applications of homology modelling in drug design and discovery. The chapter would be very fruitful to the readers to get insights into protein modelling, which will facilitate their research activities. It will be of great application in various disciplines,such as bioinformatics, physics, structural biology, and molecular biology. The content of the chapter will cover various research papers, review papers, and corresponding reference books.

Computational transformation in drug discovery: A comprehensive study on molecular docking and quantitative structure activity relationship (QSAR)

Protein engineering, the process by which novel proteins with desired properties are developed, has grown by leaps and bounds since the first examples of protein mutagenesis were described over three decades ago. In the last 30 years, protein engineers have successfully created a wide range of proteins tailored to specific industrial, medical, and research applications. Yet challenges remain that prevent the engineering of complex protein functions on demand. With the aim of creating a forum for the protein engineering community, the inaugural Protein Engineering Canada Conference was held on June 20–22, 2014, in Ottawa, Canada. Over the course of the 2-day conference, 115 protein scientists from over 30 institutions representing 5 countries shared ideas, networked with colleagues, and learned about exciting new research covering a broad array of topics such as enzyme engineering, computational protein design, X-ray crystallography, protein NMR, molecular modeling, and protein evolution. This special issue highlights the breadth of research that was covered during the Protein Engineering Canada Conference by including previously unpublished research articles submitted by speakers as well as some selections that were submitted directly to Protein Science.

Biologics and molecular recognition: Mike Brigham‐Burke memorial issue

Although many studies have addressed the significance of interactions in the dimeric structures of proteins, there is no dedicated database of these interactions. To this end, the Molecular Interactions in Protein Dimer Structure (MIPDS) database has been developed; it is an open-access repository containing 60 298 3D structures of dimeric proteins sourced from the Protein Data Bank. This helps researchers comprehend the types of interaction, which include those mediated by water, small molecules or ligands and direct interactions, in 3D structures at the molecular level. The database is accessible through a user-friendly interface, where users can conduct searches based on PDB accession number, interaction type and geometric parameters. It can be viewed in textual and graphical formats using the plug-in JSmol. MIPDS is updated weekly using programmed scripts to incorporate newly released dimeric structures and analyses of their interaction types. The database is intended for the scientific community working in structural biology, structural bioinformatics, drug discovery and development. MIPDS is freely accessible to users worldwide at http://dicsoft1.physics.iisc.ac.in/mipds.

This review provides a detailed analysis of the use of virtual library screening (VLS) in the drug discovery process. The first part is intended as a larger overview of the integrated VLS process. Small molecule and target macromolecule considerations will be described separately and will be subsequently integrated in a discussion of docking, scoring and evaluation. The second half of the review will focus on recent case studies that use VLS as part of an integrated drug discovery program. The case studies will illustrate the range of possible targets in VLS, provide an account of inclusive methodology and reveal the expectations for realistic goals. Recent efforts provide compelling evidence that VLS is successful when practiced in an integrated fashion involving synthetic, structural and computational expertise. Keywords: virtual screening, structure-based drug design, library design, drug discovery, diversity, filtering, ligand binding, docking

The Protein Data Bank (PDB) provides open access to >180,000 biological macromolecules that facilitate understanding of the structure and its function in biology. wwPDB collaboration and expert biocuration is a key to provide the best-curated biological data resource. wwPDB biocurators are at the very front line of structural biology who bridge between the scientists who submit PDB structures and the public who use these data. Our ultimate goal is to support research, training, and education worldwide and facilitate public awareness and understanding of these scientific discoveries. The key to long-term success and sustainability is forming the wwPDB partnership. Currently we have 15 biocurators worldwide working at RCSB PDB, PDBe and PDBj. We maintain a single global archive, no matter which PDB site users download data files from, we ensure identical data files are provided. wwPDB biocurators use a unified OneDep system to curate data. This system provides geographical workload distribution. wwPDB Biocurators setup standard procedures and follow these practices to enforce data standardization among PDB partners. We also review PDB archive and bring the data up to modern standards by conducting remediation effort. Biocuration at wwPDB is important. We manage data deposition, quality, and integrity, and provide integral support to the research community worldwide. To ensure data are Findable, Accessible, Interoperable, and Reusable (FAIR), biocuration at wwPDB focuses on data standardization and quality control. For data standardization, each PDB entry is assigned a unique, persistent identifier, data files are brought to semantic integrity and a common format that can enable large-scale data analysis and distribution. For data quality control, we cross-check with external database references such as UniProt, NCBI, or PubMed. Whatever possible, metadata are conformed to controlled vocabularies and range limits are set to catch data outliers. At the deposition time, biocuration is provided by data depositors on the sequences and taxonomy for polymers and chemical information for ligands if ligands do not match to PDB Chemical Component Dictionary. After data submission, wwPDB biocurators cross-check author’s sequence/taxonomy with UNP/NCBI references, assign existing ligands or add new ligand definitions based on author provided information, provide secondary and quaternary structure as value-added annotation. More importantly, wwPDB biocurators validate structures against experimental data adopting community-recommended validation software and provide depositors an official validation report for their manuscript submission which is now required by many scientific journals. Throughout the COVID-19 pandemic, wwPDB biocuration staff have continued to support research, training and education worldwide. All biocurators have been working remotely since March 2019. The use of unified OneDep system makes this possible with easy transition. We have record high on number of depositions with SARS-CoV-2 structures accounted for 7% of 2020 depositions. For the benefit of global health, as the structural biology community stepped up to provide much-needed insights into SARS-CoV-2, wwPDB biocurators have prioritized SARS-CoV-2 structures and working with depositors closely to release their structures immediately. As of August 1 st 2021, more than 1400 SARS-CoV-2 structures are now freely available from the PDB, reflecting enormous efforts made by the structural biology community in fighting the pandemic. The first structure with PDB ID 6LU7 was deposited on January 26 th 2020 and quickly released in about one week. PDB has recently provided coordinate versioning to allow depositors of the record to update their released structures without change of PDB IDs. Occasionally, rapid PDB data deposition and publication of SARS-CoV-2 structures driven by an understandable sense of urgency has resulted errors in public released PDB structures. The wwPDB coordinate versioning feature has enabled rapid correction of SARS-CoV-2 related structures archived in the PDB. To make the data findable, SARS-CoV-2 structures were updated with standard molecular name, taxonomy, and EC number when UNP references became available.

The recent COVID-19 outbreak has caused a high threat to the medical sciences across the world. About 39,596,858 confirmed cases and 1,107,374 deaths were witnessed as of 18 October 2020 by the World Health Organization. Although several strategies have been adopted for drug discovery, a promising drug/vaccine has not been developed. Conventional drug discovery is a time-consuming and expensive process and therefore adopting computational predictions would contribute to quicker and cost-effective drug discovery. Computational intelligence has played a significant part during the pandemic by designing applications for tracking of the community spread of the infection, processing and analyzing chest images and also developing computational tools and software for predictive analysis that contribute to the drug discovery channel. In this chapter, we provide a brief overview of the applications of computational intelligence and predictive analytics in screening potential lead molecules against COVID-19 with an emphasis on the drug repurposing. Bioinformatic tools and software are associated with the molecular targets, especially protein structure prediction, energy minimization of the predicted structures, binding site predictions, different ligand databases, virtual screening based on drug likeliness, pharmacokinetic and toxicity predictions, molecular docking and interaction modeling analysis along with various computational biology tools and software. This chapter also highlights the predictions of the binding potential of the few antibiotics that can be repurposed against the severe acute respiratory syndrome-coronavirus-2 targets by several computational biology tools and resources. Therefore, this chapter certainly enlightens with various approaches to computational intelligence and predictive analytics that would aid in fastening the process of drug discovery.

Virtual screening of large libraries of small molecules against proteins is a computational approach used in drug discovery and molecular biology to identify potential drug candidates or ligands that can bind to a specific target protein. The goal of this process is to predict the ab initio binding affinities and prioritize molecules that have the potential to interact with the target protein and modulate its activity. This is a crucial step in drug development because it can significantly reduce the time and cost associated with experimental compound screening. In this chapter, we provide an overview of structure-based virtual screening, which involves various steps, including curating small molecule libraries and protein structures from chemical and protein databases, preparing and refining structures, conducting high-throughput and automated binding simulations of ligands on receptor proteins using state-of-the-art docking software, scoring and ranking the binding affinities, and analyzing the results. As an example, we use RNA-dependent RNA polymerase (NS5B) enzyme of the hepatitis C virus (HCV) and demonstrate screening of a spectrum of small molecules available at the PubChem database to identify its potential modulators. In general, this process relies on systematic and data-driven methods that leverage swift identification of potential small molecule modulators for specific viral proteins, expediting drug discovery in pharmaceutical research.

Computer-aided drug discovery (CADD) is a growing scientific field that encompasses several areas. CADD provides tools and software that aid in various stages of drug development, reducing the cost of inventive work time for the treatment. CADD is recognized as a time and cost-efficient process and they can identify drugs specific to the protein site. CADD can be performed either by the structure-based method (SBDD) or the ligand-based method. SBDD is the most commonly used method. SBDD includes interactions such as protein-ligand, protein-protein (PPi), and protein-nucleic acid (PNi). The protein-ligand interaction studies are the most commonly explored field whereas PPi and PNi are not given much importance. Many biological processes depend on protein-protein interactions, and they significantly contribute to vaccine development. Numerous biological activities, such as DNA replication, RNA transcription, RNA splicing, nucleic acid degradation, and protein synthesis, depend on protein-nucleic acid interactions. Therapies based on PNi are shown to be a successful strategy for inhibiting disease conditions, but unfortunately, these strategies are left out due to lack of exploration. Effective computer methods with high accuracy for examining PPi and PNi are given in this work.

Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach

Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project

Mycobacterium tuberculosis, the causative agent of tuberculosis, has been a serious threat to public health. The recent emergence of extreme drug-resistant strains further worsened the situation and required more serious efforts to find a better cure for TB. Availability of the complete genome map of various Mycobacterium tuberculosis strains serves a basis to propose new therapeutic targets against Mycobacterium tuberculosis. The present study aimed to identify and characterize potential and novel drug target against Mycobacterium tuberculosis by using an in silico approach. The study consisted of three phases. In phase I, subtractive/comparative genomics was performed on genomic data of 17 strains of Mycobacterium tuberculosis comprised of 66,988 protein sequences. The analysis resulted in three identical hypothetical proteins of distinct origin. The shortlisted sequences were non-homologous to the host, a prerequisite characteristic of potential drug target. One of the sequences, i.e., WP_003901628.1 (the locus tag name is CFBR_RS17985) from the CDC5180 strain was further subjected to other bioinformatics methods in phase II. In phase II, the shortlisted protein sequence was qualitatively characterized for subcellular localization, functional family prediction and evolutionary relationships. The predicted subcellular localization of the query protein was found in cytoplasmic region. The functional family classification resulted in classifying the protein in transferase superfamily. Finally, in phase III of the study, the structure-based methods (i.e., homology modeling and docking) were performed to build the three-dimensional (3D) structure and to predict the putative binding site. The PDB-ID 3DR4 was found to be a suitable template having 30.1% sequence identity. The binding site of modeled protein was deciphered with DoGSite scorer tool. Later on, the ProBis tool was used to probe structurally similar binding sites in Protein Data Bank. Subsequently, the co-crystal ligands of the Protein Data Bank structures (prioritized by high-rank Z scores) were extracted and cross-docked into the binding site of the modeled protein. The cross-docking results revealed two ligands, i.e., 4′-deoxy-4′-aminopyridoxal-5′-phosphate and OG1 from two Protein Data Bank structures, i.e., 1MDO and 4K2M as best docked with RMSDs 2.8 and 2.5 A, respectively. It is inferred from the protein structure that the identified protein is alanine-rich with a PI < 7. Current study convincingly proposed that the shortlisted hypothetical protein (WP_003901628.1) is a potential drug target against which new drug candidates could be designed in a hope to cure tuberculosis.