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Current Protein & Peptide Science


ISSN (Print): 1389-2037
ISSN (Online): 1875-5550

Structural Bioinformatics: Methods, Concepts and Applications to Blood Coagulation Proteins

Author(s): Bruno O. Villoutreix

Volume 3 , Issue 3 , 2002

Page: [341 - 364] Pages: 24

DOI: 10.2174/1389203023380657

Price: $65


Structural and theoretical analyses of proteins are central to the understanding of complex molecular mechanisms and are fundamental to the drug discovery process. Computational techniques yield useful insights into an ever-wider range of biomolecular systems. Protein three-dimensional structures and molecular functions can be predicted in some circumstances, while experimental structures can be analyzed in depth via such computational approaches. Non-covalent binding of biomolecules can be understood by considering structural, thermodynamic and kinetic issues, and theoretical simulations of such events can be attempted. The central role of electrostatic interactions with regard to protein function, structure and stability has been investigated and some electrostatic properties can be modeled theoretically. Computer methods thus help to prioritize, design, analyze and rationalize biochemical experiments. Cardiovascular diseases and associated blood coagulation disorders are leading causes of death worldwide. Blood coagulation involves more than 30 proteins that interact specifically with various degrees of affinity. Many of these molecules can also bind transiently to phospholipid surfaces. Numerous point mutations in the genes of coagulation proteins and regulators have been identified. Understanding the coagulation cascade, its regulation and the impact of mutations is required for the development of new therapies and diagnostic tools. In this review, we describe concepts and methods pertaining to the field of structural bioinformatics. We provide examples of applications of these approaches to blood coagulation proteins and show that such studies can give insights about molecular mechanisms contributing to cardiovascular disease susceptibility.

Keywords: structural bioinformatic

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