Integration of Diverse Research Methods to Analyze and Engineer Ca2+- Binding Proteins: From Prediction to Production

Michael      Kirberger; Xue      Wang; Kun      Zhao; Shen      Tang; Guantao      Chen; Jenny   J.   Yang

doi:10.2174/157489310790596358

Abstract

In recent years, increasingly sophisticated computational and bioinformatics tools have evolved for the analyses of protein structure, function, ligand interactions, modeling and energetics. This includes the development of algorithms to recursively evaluate side-chain rotamer permutations, identify regions in a 3D structure that meet some set of search parameters, calculate and minimize energy values, and provide high-resolution visual tools for theoretical modeling. Here we discuss the interdependency between different areas of bioinformatics, the evolution of different algorithm design approaches, and finally the transition from theoretical models to real-world design and application as they relate to Ca2+- binding proteins. Within this context, it has become evident that significant pre-experimental design and calculations can be modeled through computational methods, thus eliminating potentially unproductive research and increasing our confidence in the correlation between real and theoretical models. Moving from prediction to production, it is anticipated that bioinformatics tools will play an increasingly significant role in research and development, improving our ability to both understand the physiological roles of Ca2+ and other metals and to extend that knowledge to the design of functionspecific synthetic proteins capable of fulfilling different roles in medical diagnostics and therapeutics.

Keywords: Calcium-binding, EF-hand, prediction, algorithm, design, statistics, graph theory, machine learning