Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach - Complex System Modelling and Control Through Intelligent Soft Computations - page 531

Secondary and Tertiary Structure
Prediction of Proteins: A Bioinformatic
Approach

Minu Kesheri, Swarna Kanchan, Shibasish Chowdhury
and Rajeshwar Prasad Sinha

Abstract 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