Vol. 7, Issue 1 (2019)

Proteins form the very basis of life. Proteins regulate a range of activities in all known organisms, from replication of the genetic code to carrying oxygen, and are in general responsible for regulating the cellular machinery and subsequently, the phenotype of an organism. Proteins complete their task by three-dimensional tertiary and quaternary interactions between different substrates such as DNA and RNA, and other proteins. Thus knowing the structure of a protein is a prerequisite to gain a thorough understanding of the protein's function.

A major problem in structural bioinformatics is to conclude the three-dimensional (3-D) structure of a protein when only the sequence of amino acid residues is known. Predicting the three-dimensional structure of a protein that has no templates in the Protein Data Bank is a very hard and sometimes virtually intractable task. Over the last years, many computational methods, systems and algorithms have been developed with the purpose of solving this difficult problem. Nevertheless, the problem still challenges biologists, computer scientists, bioinformaticians, chemists and mathematicians since the complexity and high dimensionality of the protein conformational exploration space. Many computational methodologies and algorithms have been recommended as a solution to the 3-D Protein Structure Prediction (3-D-PSP) problem.

These approaches can be classified as following:

(a) First principle methods without any database information

(b) First principle methods with database information

(c) Fold recognition and threading methods

(d) Comparative modelling methods and sequence alignment strategies.

Deterministic optimization techniques, computational techniques, data mining and machine learning approaches are typically used in the construction of computational solutions for the PSP problem. This paper reviews the various recent advances in the science of protein structure prediction.