Current Computer - Aided Drug Design - Volume 6, Issue 3, 2010

Proteins are currently the major drug targets and thus play a critical role in the process of modern drug design. This typically involves construction of drug compounds based on the structure of a drug target, validation for therapeutic efficacy of the drug compounds, evaluation of drug toxicity, and finally, clinical trial. Proteomics, defined as the comprehensive analysis of the proteins that are expressed in cells or tissues, can be employed at different stages of this process. Comparative proteomics can distinguish subtle changes in protein abundance at a depth of several thousand proteins at different conditions i.e. normal vs disease, to facilitate drug target identification. Also, chemical proteomics can be used to determine drug-target interactions and systematically analyze drug specificity and selectivity. Moreover, phosphoproteomics can be employed to monitor changes in phosphorylation events to characterize drug actions on cell signaling pathways. Similarly, functional proteomics can be utilized to investigate protein-protein and protein-ligand interactions for the clarification of the mechanism of drug action, identification of disease-related sub-networks and novel drug targets. Furthermore, quantitative proteomics can be used to characterize long-term drug effects on protein expression. In addition, computational approaches have emerged to convert complex proteomic data into sophisticated computer models of cellular protein networks. In this review, we will provide an overview of these state-of-the-art proteomics techniques, describe their underlying experimental concepts and compare them to each other, and discuss existing and future applications in the art of drug design and development.

As crucial members of the G-protein coupled receptor (GPCR) superfamily, α1-adrenergic receptors (α1-ARs) are recognized to intervene the actions of endogenous catecholamines such as norepinephrine and epinephrine. So far three distinct α1-AR subtypes, α1A, α1B and α1D, have been characterized by functional analysis, radio-ligand binding and molecular biology studies. The α1-ARs are of therapeutic interest because of their distinct and critical roles in many physiological processes, containing hypertension, benign prostatic hyperplasia, smooth muscle contraction, myocardial inotropy and chronotropy, and hepatic glucose metabolism. Accordingly, designing subtype-selective antagonists for each of the three α1-AR subtypes has been an enthusiastic region of medicinal research. Even though a large number of studies on GPCRs have been conducted, understanding of how known antagonists bind to α1-ARs still remains sketchy and has been a serious impediment to search for potent and subtype-selective α1-AR antagonists because of the lack of detailed experimental structural knowledge. This review deliberates the simulation of α1-ARs and their interactions with antagonists by using ligand-based (pharmacophore identification and QSAR modeling) and structure-based (comparative modeling and molecular docking) approaches. Combined with experimental data, these computational attempts could improve our understanding of the structural basis of antagonist binding and the molecular basis of receptor activation, thus offering a more reasonable approach in the design of drugs targeting α1-ARs.

Metabolomics, the comprehensive and global analysis of diverse metabolites produced in cells and organisms, has greatly expanded metabolite fingerprinting and profiling as well as the selection and identification of marker metabolites. The methodology typically employs multivariate analysis to statistically process the massive amount of analytical chemistry data resulting from high-throughput and simultaneous metabolite analysis. Although the technology of plant metabolomics has mainly developed with other post-genomics in systems biology and functional genomics, it is independently applied to the evaluation of the qualities of medicinal plants, based on the diversity of metabolite fingerprints resulting from multivariate analysis of non-targeted or widely targeted metabolite analysis. One advantage of applying metabolomics is that medicinal plants are evaluated based not only on the limited number of metabolites that are pharmacologically important chemicals, but also on the fingerprints of minor metabolites and bioactive chemicals. In particular, score plot and loading plot analyses e.g. principal component analysis (PCA), partial-least-squares discriminant analysis (PLS-DA), and discrimination map analysis such as batch-learning self-organizing map (BL-SOM) analysis, are often employed for the reduction of a metabolite fingerprint and the classification of analyzed samples. Based on recent studies, we now understand that metabolomics can be an effective approach for comprehensive evaluation of the qualities of medicinal plants. In this review, we describe practical cases in which metabolomic study was performed on medicinal plants, and discuss the utility of metabolomics for this research field, with focus on multivariate analysis.

The interaction between DNA and proteins comprises a pivotal role in almost every cellular process, including gene regulation and DNA replication. Given a protein, it is very important to know whether it is a DNA-binding protein or not and where the binding sites are. Over the last three decades, since the discovery that lac operon was regulated by a protein, knowledge of the DNA-protein interactions has soared. However, it is very difficult to use experimental techniques to identify the DNA-binding proteins because these experiments can be prohibitively labor-intensive in studying all the possible mutations of the residues on the molecular surface. Hence, it has been generally recognized that the ability to automatically identify the DNA binding proteins and their binding sites can significantly speed up our understanding of cellular activities and contribute to advances in drug discovery. The main goal of present paper is to review the recent progress in the development of computational approaches to predict DNA-protein bindings. We will show a historical roadmap of the amelioration, and how the modifications promote better performance.

Computational modeling techniques and computer simulations have become a routine in biological sciences and have gained great attention from researchers. Molecular dynamics simulation is a valuable tool towards an understanding of the complex structure of biological systems, especially in the study of the flexibility of the biological molecules such as peptides or proteins. Peptides play a very important role in human physiology and control many of the processes involved in the immune system response. Designing new and optimal peptide vaccines is one of the hottest challenges of the 21st century science and it brings together researchers from different fields. Molecular dynamics simulations have proven to be a helpful tool assisting laboratory work, saving financial sources and opening possibilities for exploring properties of the molecular systems that are hardly accessible by conventional experimental methods. Present review is dedicated to the recent contributions in applications of molecular dynamics simulations in peptide design for immunological purposes, such as B or T cell epitopes.