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Complex System Modelling and Control Through Intelligent Soft Computations pp 541–569Cite as

Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 319))

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 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.

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Abbreviations

PSS:

Protein secondary structure

SSE:

Secondary structure elements

UniProtKB:

UNIversal PROTein resource KnowledgeBase

TrEMBL:

Translated European molecular biology laboratory

PDB:

Protein data bank

NMR:

Nuclear magnetic resonance

FM:

Free modelling

TBM:

Template based modelling

GOR:

Garnier-Osguthorpe-Robson

NNSSP:

Nearest-neighbor secondary structure prediction

ANN:

Artificial neural networks

SVM:

Support vector machines

SOV:

Segment overlap

CASP:

Critical assessment of protein structure prediction

EVA:

EValuation of automatic protein structure prediction

FR:

Fold recognition

BLAST:

Basic local alignment search tool

PSI-BLAST:

Position specific iterative basic local alignment search tool

MEGA:

Molecular evolutionary genetics analysis

PHYLIP:

PHYLogeny inference package

GROMACS:

GROningen machine for chemical simulations

AMBER:

Assisted model building and energy refinement

CHARMM:

Chemistry at HARvard molecular mechanics

GDT:

Global displacement test

PROCHECK:

PROtein structure CHECK

PROSA:

PROtein structure analysis

MAT:

MonoAmine transporters

HMM:

Hidden Markov model

CPU:

Central processing unit

RPS-BLAST:

Reversed position specific BLAST

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Acknowledgments

Minu Kesheri is thankful to University Grant Commission, Govt. of India, New Delhi, for providing financial assistance in the form of research fellowship. Swarna Kanchan is thankful to University Grant Commission, Govt. of India, New Delhi for providing the financial support in the form of the Basic Science Research Fellowship under University Grant Commission (New Delhi) Special Assistance Programme to Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, India.

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Authors and Affiliations

  1. Laboratory of Photobiology and Molecular Microbiology, Centre of Advanced Study in Botany, Banaras Hindu University, Varanasi, 221005, India

    Minu Kesheri & Rajeshwar Prasad Sinha

  2. Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Rajasthan, 333031, India

    Swarna Kanchan & Shibasish Chowdhury

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  1. Minu Kesheri
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  2. Swarna Kanchan
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  3. Shibasish Chowdhury
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  4. Rajeshwar Prasad Sinha
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Correspondence to Swarna Kanchan .

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  1. Department of Engineering Design and Mathematics, University of the West of England, Bristol, United Kingdom

    Quanmin Zhu

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

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Kesheri, M., Kanchan, S., Chowdhury, S., Sinha, R.P. (2015). Secondary and Tertiary Structure Prediction of Proteins: A Bioinformatic Approach. In: Zhu, Q., Azar, A. (eds) Complex System Modelling and Control Through Intelligent Soft Computations. Studies in Fuzziness and Soft Computing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-12883-2_19

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  • DOI: https://doi.org/10.1007/978-3-319-12883-2_19

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