Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach.

Correct prediction of secondary and tertiary structure of proteins is one of the major challenges in bioinformatics/computational biological research. Predicting the correct secondary structure is the key to predict a good/satisfactory tertiary structure of the protein which not only helps in prediction of protein function but also in prediction of sub-cellular localization. This chapter aims to explain the different algorithms and methodologies, which are used in secondary structure prediction. Similarly, tertiary structure prediction has also emerged as one of developing areas of bioinformatics/computational biological research owing to the large gap between the available number of protein sequences and the known experimentally solved structures. Because of time and cost intensive experimental methods, experimentally determined structures are not available for vast majority of the available protein sequences present in public domain databases. The primary aim of this chapter is to offer a detailed conceptual insight to the algorithms used for protein secondary and tertiary structure prediction. This chapter systematically illustrates flowchart for selecting the most accurate prediction algorithm among different categories for the target sequence against three categories of tertiary structure prediction methods. Out of the three methods, homology modeling which is considered as most reliable method is discussed in detail followed by strengths and limitations for each of these categories. This chapter also explains different practical and conceptual problems, obstructing the high accuracy of the protein structure in each of the steps for all the three methods of tertiary structure prediction. The popular hybrid methodologies which further club together a number of features such as structural alignments, solvent accessibility and secondary structure information are also discussed. Moreover, this chapter elucidates about the Meta-servers that generate consensus result from many servers to build a protein model of high accuracy. Lastly, scope for further research in order to bridge existing gaps and for developing better secondary and tertiary structure prediction algorithms is also highlighted.