Current Computer - Aided Drug Design - Volume 6, Issue 1, 2010

Cysteine proteases are implicated in a variety of human physiological processes and also form an essential component of the life cycle of a number of pathogenic protozoa and viruses. The present review highlights the drug design approaches utilized to understand the mechanism of inhibition and discovery of inhibitors against protozoal cysteine protease, falcipain (a cysteine protease of P. falciparum which causes malaria), and viral cysteine protease, SARS-CoV Mpro (a cysteine protease of severe acute respiratory syndrome corona virus). The article describes rational approaches for the design of inhibitors and focuses on a variety of structure as well as ligand-based modeling strategies adopted for the discovery of the inhibitors. Also, the key features of ligand recognition against these targets are accentuated. Although no apparent similarities exist between viral and protozoal cysteine proteases discussed here, the goal is to provide examples of rational drug design approaches adopted to design inhibitors against these proteases. The current review would be of interest to scientists engaged in the development of drug design strategies to target the cysteine proteases present in mammals and other lower order organisms.

In quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR), there is a considerable interest in support vector machine (SVM) and support vector regression (SVR) for data modeling. SVM and SVR have a high performance for classification and regression rates, but their chemical interpretations are not feasible. In this review, we present some promising approaches to visualize and interpret the SVM and SVR models. This type analysis would be useful for molecular design. Representative examples derived from chemoinformatics and bioinformatics are highlighted in detail. We also refer to a structure generator based on SVR score in the framework of de novo design. Furthermore, we provide readers the theoretical description of SVM and SVR.

This review summarizes the background and updated progress of pharmacophore based drug design and provides the fundamental approach strategies on both structure based and ligand based pharmacophore approaches. The different programs and methodologies enable the implementation of more accurate and sophisticated pharmacophore model generation and application in drug discovery. This review will discuss and illustrate their advantages in pharmacophore based virtual screening and exemplify the detailed application workflow, which can be easily utilized by pharmaceutical bench work medicinal chemists. Pharmacophore based drug design process includes pharmacophore modeling and validation; pharmacophore based virtual screening, virtual hits profiling and lead identification. Strategies and proven methodologies for pharmacophore modeling are described including common feature and 3D QSAR based pharmacophore generation as well as structure based pharmacophore development. Different virtual screening strategies will be described in this review with detailed case studies for supporting practical applications. Representative success examples of pharmacophore based virtual screening for lead generation will be collected to demonstrate capabilities.

In recent years, there has been an explosion in the availability of publicly accessible chemical information, including chemical structures of small molecules, structure-derived properties and associated biological activities in a variety of assays. These data sources present us with a significant opportunity to develop and apply computational tools to extract and understand the underlying structureactivity relationships. Furthermore, by integrating chemical data sources with biological information (protein structure, gene expression and so on), we can attempt to build up a holistic view of the effects of small molecules in biological systems. Equally important is the ability for non-experts to access and utilize state of the art cheminformatics method and models. In this review we present recent developments in cheminformatics methodologies and infrastructure that provide a robust, distributed approach to mining large and complex chemical datasets. In the area of methodology development, we highlight recent work on characterizing structure-activity landscapes, Quantitative Structure Activity Relationship (QSAR) model domain applicability and the use of chemical similarity in text mining. In the area of infrastructure, we discuss a distributed web services framework that allows easy deployment and uniform access to computational (statistics, cheminformatics and computational chemistry) methods, data and models. We also discuss the development of PubChem derived databases and highlight techniques that allow us to scale the infrastructure to extremely large compound collections, by use of distributed processing on Grids. Given that the above work is applicable to arbitrary types of cheminformatics problems, we also present some case studies related to virtual screening for anti-malarials and predictions of anti-cancer activity.

In addition to being responsible for the majority of absorption of dietary nitrogen, the mammalian proton-coupled di- and tripeptide transporter PepT1 is also recognised as a major route of drug delivery for several important classes of compound, including β- lactam antibiotics and angiotensin-converting enzyme inhibitors. Thus there is considerable interest in the PepT1 protein and especially its substrate binding site. In the absence of a crystal structure, computer modelling has been used to try to understand the relationship between PepT1 3D structure and function. Two basic approaches have been taken: modelling the transporter protein, and modelling the substrate. For the former, computer modelling has evolved from early interpretations of the twelve transmembrane domain structure to more recent homology modelling based on recently crystallised bacterial members of the major facilitator superfamily (MFS). Substrate modelling has involved the proposal of a substrate binding template, to which all substrates must conform and from which the affinity of a substrate can be estimated relatively accurately, and identification of points of potential interaction of the substrate with the protein by developing a pharmacophore model of the substrates. Most recently, these two approaches have moved closer together, with the attempted docking of a substrate library onto a homology model of the human PepT1 protein. This article will review these two approaches in which computers have been applied to peptide transport and suggest how such computer modelling could affect drug design and delivery through PepT1.