Abstract

Proteins are large molecules indispensable for the existence and proper functioning of biological organisms. They perform a wide array of functions including catalysis, structure formation, transport, body defense, etc. Understanding the functions of proteins is a fundamental problem in the discovery of drugs to treat various diseases. The structure of a protein can be determined by physical methods which are slow and expensive but owing to the dramatic increase in the numbers of proteins sent to the public data bank during the last few years, it is highly desirable to develop some rapid and effective computational methods to predict the structure of new proteins so as to expedite the process of deducing their function. All the structure prediction methods basically rely on the idea that there is a correlation between residue sequence and structure. The primary structure is unique for each protein and it is generally accepted that a protein’s primary structure is enough to determine its folding process to secondary, tertiary and quaternary structure. Despite recent efforts to develop automated protein structure determination protocols, structural genomic projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. Protein structure determination and prediction has been a focal research subject in life sciences due to the importance of protein structure in understanding the biological and chemical activities of organisms/cell. This review comprehends the various recent advanced methods for protein structure predictions such as a two-stage method for assigning residues one of the three secondary structure states, prediction of homo-oligomeric proteins based on nearest neighbour algorithm, sequence–based hidden markov model, practical ab initio methods aimed at finding the native structure of the protein by simulating the biological process of protein folding, and metapredictors based on consensus form multiple methods.

Key words: Structure prediction, data classification, Hidden Markov model, homo-oligomeric proteins, nearest neighbour algorithm, twilight zone sequences.