Chapter 15 - Computational strategies and tools for protein tertiary structure prediction

Abstract

Proteins are the basic biological units of life responsible for almost every function within the body. The three-dimensional structure of the protein that represents its native state is critical for the biochemical activity of a protein. The information for proper folding of a protein is hidden in its primary sequence. Hence, several strategies are commonly used for predicting the tertiary structure of a protein from its sequence. A typical protein structure prediction strategy homology modeling is employed for targets which have homologous proteins with high sequence similarity and known structure. It involves the identification of a suitable template structure from which the three-dimensional information for a query sequence can be extrapolated. Some protein targets may share only structure-level homology with proteins with similar folds. Fold recognition method comprises identification of such remote homologs that needs more sensitive search for relevant structural folds. If a structural homolog for the target sequence is unavailable, template-free methods including ab initio modeling can be used. However, template-based methods are preferred as template-free modeling methods are much less reliable and are usually applicable for smaller proteins. More recent automated hybrid strategies include amalgamation of both template based and template-free prediction strategies to obtain protein structure models with high accuracy. Advancement in computational techniques and application of deep learning in protein structure prediction has enabled crystal structure resolution predictions. In this book chapter, we discuss strategies and highlight various tools for protein tertiary structure prediction.