Structural Bioinformatics: Applications in Preclinical Drug Discovery Process

In this chapter, we present an overview of various computational methods, particularly, those that are used to compute the free energy of binding to understand target site mutations that will enable us to foresee mutations that could significantly affect drug binding. We begin by looking at the driving forces that lead to drug resistance and throw some light on the various mechanisms by which drugs can be rendered ineffective. Next, we studied molecular dynamic simulations and its use to understand the thermodynamics of protein–ligand interactions. Building on these fundamentals, we discuss various methods that are available to compute the free energy binding, their mathematical formulations, the practical aspects of each these methods and finally their use in understanding drug resistance.

Computational design of molecules with desired properties has become indispensable in many areas of research, particularly in the pharmaceutical industry and academia. Pharmacophore is one of the essential state-of-the-art techniques widely used in various ways in the computer-aided drug design projects. The pharmacophore modelling approaches have been an important part of many drug discovery strategies due to its simple yet diverse usage. It has been extensively applied for virtual screening, lead optimization, target identification, toxicity prediction and de novo lead design and has a huge scope for application in fragment-based drug design and lead design targeting protein–protein interaction interfaces and target-based classification of chemical space. In this chapter, we have briefly discussed the basic concepts and methods of generation of pharmacophore models. The diverse applications of the pharmacophore approaches have been discussed using number of case studies. We conclude with the limitations of the approaches and its wide scope for the future application depending on the research problem and the type of initial data available.

The network description is widely used to analyze the topology and the dynamics of complex systems. Residue interaction network (RIN) represents three-dimensional structure of protein as a set of nodes (residues) with their connections (edges). Calculated topological parameters from RIN correlate with various aspects of protein structure and function. Here, we reviewed the applications of RIN for the analysis and prediction of functionally important residues and ligand binding sites, protein–protein interactions, allosteric regulation, influence of point mutations on structure and dynamics of proteins.

A newly developed drug discovery method composed of graph theoretical approaches for generating structures combinatorially from an activity-related root vertex, prediction of activity using topological distance-based vertex index and a rule-based algorithm and prioritization of putative active compounds using a newly defined Molecular Priority Score (MPS) has been described in this chapter. The rule-based method is also used for identifying suitable activity-related vertices (atoms) present in the active compounds of a data set, and identified vertex is used for combinatorial generation of structures. An algorithm has also been described for identifying suitable training set–test set splits (combinations) for a given data set since getting a suitable training set is of utmost importance for getting acceptable activity prediction. The method has also been used, to our knowledge for the first time, for matching and searching rooted trees and sub-trees in the compounds of a data set to discover novel drug candidates. The performance of different modules of the proposed method has been investigated by considering two different series of bioactive compounds: (1) convulsant and anticonvulsant barbiturates and (2) nucleoside analogues with their activities against HIV and a data set of 3779 potential antitubercular compounds. While activity prediction, compound prioritization and structure generation studies have been carried out for barbiturates and nucleoside analogues, activity-related tree–sub-tree searching in the said data set has been carried for screening potential antitubercular compounds. All the results show a high level of success rate. The possible relation of this work with scaffold hopping and inverse quantitative structure–activity relationship (iQSAR) problem has also been discussed. This newly developed method seems to hold promise for discovering novel therapeutic candidates.

Structure-based in silico studies aiming to predict affinity of a set of ligands to their cognate receptor have been enjoying keen interest and attention of researchers in drug design around the globe since many decades, and made significant progress to increase its predictive power, even it has emerged as a complementary field to in vivo and in vitro studies in recent years. Structure-based drug discovery (SBDD) process whose success heavily relies on a careful selection of structure of receptor and ligands and its accuracy, completeness, and rigor of chosen model, imitation of the physiological condition in such in silico models, e.g., pH and solvation. Appropriateness of selected mechanism of binding concept and the realization in mathematical terms used in scoring methods have a strong influence on the accuracy too. However, constant identification of new targets using systems approach like genomics, proteomics, metabolomics, and network biology has led a paradigm shift from single or a couple of targets toward the appreciation of emerging role of a network of targets. The application of such strategies in study of complex diseases is gaining attention. Identification of binding sites of receptor and their characterization is important to be able to portray its interacting features. It involves the search of ligands which are able to possess the features, present them complementary to the binding site, so by docking the set of ligands to the binding pocket of the receptor, activity can be evaluated. In silico receptor–ligand binding affinity prediction from docking has witnessed rigid-receptor rigid-ligand to flexible-ligand rigid-receptor treatment, and nowadays docking studies, through sampling side chain rotations of the binding site residues, also account for the flexibility of binding pocket of the receptor in indirect way. Literature survey has shown progress in ranking ligands in order of affinity using reliable scoring functions to find potent scaffolds which can be further optimized to gain more affinity. Many methods include effect of solvation in binding processes, like considering conserved water positions in active sites (water maps), explicit water simulation in presence of ligand with receptor, free energy perturbation, and thermodynamic integration. Availability of many conformers of receptors and ligands in solution suggests the importance of entropy in estimation of binding affinity, but entropy component of binding free energy directly is not included in such studies. In spite of unprecedented advancement of computational modeling, faster simulation techniques, accurate solvation models and current best practices, the dependence of binding affinity on pH, estimation of entropy along with enthalpy in binding affinity, inclusion of conformational entropy of ligand and receptor, and modulation of flexibilities during complex formation are important challenges lying ahead. Therefore, an account of prowess and challenges in structure-based prediction of binding affinity addressed in present review will provide directions for its appropriate application, understanding its limitations and getting important feedbacks for its betterment.

Structure-based drug design (SBDD) is being efficiently used for the design of antimalarial agents. It is a very effective tool for challenges like drug selectivity and resistance. Over the past decade, a considerable number of druggable targets have been explored—these include Na⁺ ATPase 4 ion channel, cytochrome bc1, mitochondrial electron transport chain, phosphatidylinositol 4-kinase (PfPI4 K), dihydroorotate dehydrogenase, hemozoin formation, dihydrofolate reductase inhibitors, etc. Among these, Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) is a new and very promising target. PfDHODH has shown considerable potential in arresting growth of the parasite at blood stage by inhibiting pyrimidine biosynthesis. This chapter provides a review of all the SBDD efforts for the development of inhibitors against PfDHODH.

In recent times, our healthcare system is being challenged by many drug-resistant microorganisms and ageing-associated diseases for which we do not have any drugs or drugs with poor therapeutic profile. With pharmaceutical technological advancements, increasing computational power and growth of related biomedical fields, there have been dramatic increase in the number of drugs approved in general, but still way behind in drug discovery for certain class of diseases. Now, we have access to bigger genomics database, better biophysical methods,  and knowledge about chemical space with which we should be able to easily explore and predict synthetically feasible compounds for the lead optimization process. In this chapter, we discuss the limitations and highlights of currently available computational methods used for protein–ligand binding affinities estimation and this includes force-field, ab initio electronic structure theory and machine learning approaches. Since the electronic structure-based approach cannot be applied to systems of larger length scale, the free energy methods based on this employ certain approximations, and these have been discussed in detail in this chapter. Recently, the methods based on electronic structure theory and machine learning approaches also are successfully being used to compute protein–ligand binding affinities and other pharmacokinetic and pharmacodynamic properties and so have greater potential to take forward computer-aided drug discovery to newer heights.

Epigenetics has become an important field of research in drug discovery. Epigenetic mechanisms are dynamic in nature and play a fundamental role in cellular processes. Dysregulation of epigenetic events, including cross-talk between DNA methylation and histone modifications, not only affects gene expression but also causes pathophysiological effects leading to cancer, aging, cardiovascular, neurological, and metabolic disorders. Epigenetic targets have captured the attention of researchers from diverse backgrounds to identify potential drugs for various diseases. However, drug development is a complex, time-consuming process and challenged by the high attrition rate. As with many chemotherapeutics, it is pertinent to avoid possible risk factors in epigenetic drug discovery. In this context, computational approaches can rationally guide the search for active compounds by utilizing the accumulated epigenetics knowledge base. In this chapter, we have described the chemoinformatic strategies that can be applied to facilitate the early-stage lead discovery in epigenetics, based on current best practices.

Structure-based drug design (SBDD) is a computational analysis of identifying ligands which can potentially inhibit the target. SBDD is a cluster of methods and modules which reduces the cost and time spent on experimental procedures. SBDD plays a crucial role in preclinical drug development procedures. There is a vast development in techniques and methods related to theoretical physics and chemistry, computers processers, and pharmacokinetic analysis which helps in elucidating the biological role of ligands and their receptors. Here, the general theoretical backgrounds of various SBDD and simulation approaches employed are discussed. These methods are also discussed with respect to the identification of potential drug-like molecules from natural sources to control human ailments.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) and is a major public health concern. According to the 2017 WHO report, global burden of TB infection was 10.4 million people causing the mortality rate of ~1.6 million. The rapid emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB is of major concern in anti-TB drug discovery. There are different druggable targets and its pathways involved in the virulence, which include Mtb cell wall, replication and transcription, regulatory, protein synthesis, membrane transport, and energy production which need to be explored for efficient killing of the bacteria. The ability of the tubercle bacilli to remain within the host intracellular compartment is of other major concern in TB therapy. Thus, to tackle the TB drug resistance, potent inhibitors with novel mechanism of action of different Mtb druggable targets need to be discovered. Three-dimensional structure of different Mtb target was solved for structure-based drug design. The current chapter focuses on some of the key druggable targets in Mtb and also the recent advances in target-based drug designing in the area of anti-tubercular drug discovery.

In this current age of data-driven science, perceptive research is being carried out in the areas of genomics, network and metabolic biology, human, animal, organ and tissue models of drug toxicity, witnessing or capturing key biological events or interactions for drug discovery. Drug designing and repurposing involves understanding of ligand orientations for proper binding to the target molecules. The crucial requirement of finding right pose of small molecule in ligand–protein complex is done using drug docking and simulation methods. The domains of biology like genomics, biomolecular structure dynamics, and drug discovery are capable of generating vast molecular data in range of terabytes to petabytes. The analysis and visualization of this data pose a great challenge to the researchers and needs to be addressed in an accelerated and efficient way. So there is continuous need to have advanced analytics platform and algorithms which can perform analysis of this data in a faster way. Big data technologies may help to provide solutions for these problems of molecular docking and simulations.

Single-particle cryo-electron microscopy (cryo-EM) and three-dimensional (3D) image processing have gained importance in the last few years to obtain atomic structures of drug targets. Obtaining atomic-resolution 3D structure better than ~2.5 Å is a standard approach in pharma companies to design and optimize therapeutic compounds against drug targets like proteins. Protein crystallography is the main technique in solving the structures of drug targets at atomic resolution. However, this technique requires protein crystals which in turn is a major bottleneck. It was not possible to obtain the structure of proteins better than 2.5 Å resolution by any other methods apart from protein crystallography until 2015. Recent advances in single-particle cryo-EM and 3D image processing have led to a resolution revolution in the field of structural biology that has led to high-resolution protein structures, thus breaking the cryo-EM resolution barriers to facilitate drug discovery. There are 24 structures solved by single-particle cryo-EM with resolution 2.5 Å or better in the EMDataBank (EMDB) till date. Among these, five cryo-EM 3D reconstructions of proteins in the EMDB have their associated coordinates deposited in Protein Data Bank (PDB), with bound inhibitor/ligand. Thus, for the first time, single-particle cryo-EM was included in the structure-based drug design (SBDD) pipeline for solving protein structures independently or where crystallography has failed to crystallize the protein. Further, this technique can be complementary and supplementary to protein crystallography field in solving 3D structures. Thus, single-particle cryo-EM can become a standard approach in pharmaceutical industry in the design, validation, and optimization of therapeutic compounds targeting therapeutically important protein molecules during preclinical drug discovery research. The present chapter will describe briefly the history and the principles of single-particle cryo-EM and 3D image processing to obtain atomic-resolution structure of proteins and their complex with their drug targets/ligands.