Bioinformatics and variability in drug response: a protein structural perspective

Abstract

Marketed drugs frequently perform worse in clinical practice than in the clinical trials on which their approval is based. Many therapeutic compounds are ineffective for a large subpopulation of patients to whom they are prescribed; worse, a significant fraction of patients experience adverse effects more severe than anticipated. The unacceptable risk -benefit profile for many drugs mandates a paradigm shift towards personalized medicine. However, prior to adoption of patient-specific approaches, it is useful to understand the molecular details underlying variable drug response among diverse patient populations. Over the past decade, progress in structural genomics led to an explosion of available three-dimensional structures of drug target proteins while efforts in pharmacogenetics offered insights into polymorphisms correlated with differential therapeutic outcomes. Together these advances provide the opportunity to examine how altered protein structures arising from genetic differences affect protein -drug interactions and, ultimately, drug response. In this review, we first summarize structural characteristics of protein targets and common mechanisms of drug interactions. Next, we describe the impact of coding mutations on protein structures and drug response. Finally, we highlight tools for analysing protein structures and protein -drug interactions and discuss their application for understanding altered drug responses associated with protein structural variants.

Recent Progress on Structural Bioinformatics Research of Cytochrome P450 and Its Impact on Drug Discovery

“…Regarding possible structural mechanisms by which nsSNP and resultant mutation alter CYP activity, there are mainly several possible interpretations such as altering the physicochemical and geometric properties of CYP active site and disrupting the stability and folding of CYP enzyme [21]. Although the computational methods for predicting the effect of nsSNPs on protein function can be classified into two major types, sequence-based and structure-based methods, the latter type of method seems more rational.…”

“…More recently, the impressive progress in genome sequencing, protein expression and high-throughput crystallography and NMR has significantly accelerated drug development [19]. In addition, protein structure is considered to play influential role in each stage throughout whole drug development process [21]. The methods of structural bioinformatics focused on macromolecular structure particularly protein structure also efficiently facilitated target identification and lead discovery [3].…”

Insight into Nek2A activity regulation and its pharmacological prospects

“…Shape complementarity and the physicochemical complementarity are the two governing factors of enthalpy contributions in protein ligand interactions. Shape complementarity permits the protein and small molecules to achieve sufficient proximity and contact surface area to form stabilizing interactions, while physicochemical complementarity determines the nature of these interactions [35]. The most complementary conformation from an ensemble of equilibrium structures is selected by the ligand molecules.…”

“…Understanding the structural orientations of the corresponding protein, and their respective properties, will help in determining the corresponding drug response cascades and will eventually facilitate in the drug discovery process. When we talk about drug discovery, the most leading factor that governs the efficiency level of the drug molecule is the fine-tuning of the molecular properties of drugs to the corresponding structures of their protein targets, which promotes the binding interactions of high affinity and specificity [35]. The protein-drug interaction events are governed by the enthalpic and entropic contributions.…”

<em>Sarcocystis neurona</em> Protein Kinases: Computational Identification, Evolutionary Analysis and Putative Inhibitor Docking

“…Analysis of putative binding pockets using DoGSiteScorer [59] Here, it should be noted that the presence of a binding pocket does not necessarily imply that a target protein is druggable. However, the binding volume is one of the important cavity properties that influence the druggability of a particular target protein, of which most of druggable proteins have volumes of between 500-1000 Å3 [60].…”