The discovery of new pharmaceuticals via computer modeling is one of the key challenges in modern medicine. The advent of global networks of genomic, proteomic and metabolomic endeavors is ushering in an increasing number of novel and clinically important targets for screening. Computational methods are anticipated to play a pivotal role in exploiting the structural and functional information to understand specific molecular recognition events of the target macromolecule with candidate hits leading ultimately to the design of improved leads for the target. In this review, we sketch a system independent, comprehensive physicochemical pathway for lead molecule design focusing on the emerging in silico trends and techniques. We survey strategies for the generation of candidate molecules, docking them with the target and ranking them based on binding affinities. We present a molecular level treatment for distinguishing affinity from specificity of a ligand for a given target. We also discuss the significant aspects of drug absorption, distribution, metabolism, excretion and toxicity (ADMET) and highlight improved protocols required for higher quality and throughput of in silico methods employed at early stages of discovery. We present a realization of the various stages in the pathway proposed with select examples from the literature and from our own research to demonstrate the way in which an iterative process of computer design and validation can aid in developing potent leads. The review thus summarizes recent advances and presents a viewpoint on improvements envisioned in the years to come for automated computer aided lead molecule discovery.
Automation of lead compound design in silico given the structure of the protein target and a definition of its active site vies for the top of the wish list in any drug discovery programme. We present here an enumeration of steps starting from chemical templates and propose a solution at the state of the art, in the form of a system independent comprehensive computational pathway. This methodology is illustrated with cyclooxygenase-2 (COX-2) as a target. We built candidate molecules including a few Non Steroidal Anti-inflammatory Drugs (NSAIDs) from chemical templates, passed them through empirical filters to assess drug-like properties, optimized their geometries, derived partial atomic charges via quantum calculations, performed Monte Carlo docking, carried out molecular mechanics and developed free energy estimates with Molecular Mechanics Generalized Born Solvent Accessibility (MMGBSA) methodology for each of the candidate molecules. For the case of aspirin, we also conducted molecular dynamics on the enzyme, the drug and the complex with explicit solvent followed by binding free energy analysis. Collectively, the results obtained from the above studies viz. sorting of drugs from non-drugs, semi-quantitative estimates of binding free energies, amply demonstrate the viability of the strategy proposed for lead selection/design for biomolecular targets.
Drug discovery in the 21 st century is expected to be different in at least two distinct ways: development of individualized medicine utilizing genomic information and emergence of an integrated in silico protocol for facilitating target identification, structure prediction and lead discovery. The expectations from computational methods for developing suggestions on potential leads reliably and expeditiously, are continuously on the increase. Several conceptual and methodological concerns remain before an automation of lead design in silico could be contemplated. The novelty of the candidates generated, their geometries, the partial atomic charges and other force field parameters for enabling energy evaluations is one concern. A proper account of the flexibility of the candidate molecule and the target, a consideration of solvent and salt effects in binding and a reliable methodology for developing quantitative estimates of binding affinities is another. Finally the drug-likeness of the candidates generated is yet another concern. Each of these issues warrants a careful consideration. In this review, we sketch a system independent, binding free energy based, comprehensive computational pathway from chemical templates to lead-like molecules, given the three dimensional structure of the target protein and a definition of its active site, focusing on some emerging in silico trends and techniques. We survey current methods for generation of candidate molecules and some popular protocols for docking candidates in the protein active site. We discuss the theory of protein-ligand binding in the rigorous framework of statistical mechanics and assess the current strategies for affinity based filtering of candidates. We address concerns related to flexibility of the target and the candidate, solvent and salt effects in lead design. We present a realization of the pathway proposed in a high performance computing environment for cyclooxygenase-2 target wherein the computational protocols could sort drugs from nondrugs, assuring the viability of the overall strategy. We highlight a few case studies indicating the current level of agreement between theory and experiment in eliciting binding affinities. Finally, we present a critical assessment of the computational steps involved in binding affinity based active site directed lead molecule design and further improvements envisioned for potential automation.
Sigma 1 Receptor is a subtype of opioid receptor that participates in membrane remodeling and cellular differentiation in the nervous system. Sigma1 Receptor protein with amino acid length ranging from 229 is widely distributed in the liver and moderately in the intestine, kidney, white pulp of the spleen, adrenal gland, brain, placenta and the lung. In this study, the three dimensional structure for sigma 1 receptor protein has been developed by in- silico analysis based on evolutionary trace analysis of 37 sigma proteins from different sources. The present work focus on identification of functionally important residues and its interaction with antipsychotic drugs reported in literature.
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