APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction

2015 ◽  
Vol 59 ◽  
pp. 142-157 ◽  
Author(s):  
Bruno Borguesan ◽  
Mariel Barbachan e Silva ◽  
Bruno Grisci ◽  
Mario Inostroza-Ponta ◽  
Márcio Dorn
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Knowledge Based

Prediction of Structural and Functional Aspects of Protein

2014 ◽  
pp. 317-333
Author(s):  
Arun G. Ingale
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Protein Structures ◽  
Three Dimensional ◽  
Dimensional Structure ◽  
Sequence Information ◽  
Predict Protein Structure ◽  
Basic Ideas

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

Protein Structure Prediction

Biotechnology ◽  
2019 ◽  
pp. 156-184
Author(s):  
Hirak Jyoti Chakraborty ◽  
Aditi Gangopadhyay ◽  
Sayak Ganguli ◽  
Abhijit Datta
Keyword(s):  
Protein Structure ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Future Research ◽  
Computational Techniques ◽  
Target Sequence ◽  
Knowledge Based ◽  
Research Areas ◽  
Three Phases

The great disagreement between the number of known protein sequences and the number of experimentally determined protein structures indicate an enormous necessity of rapid and accurate protein structure prediction methods. Computational techniques such as comparative modeling, threading and ab initio modelling allow swift protein structure prediction with sufficient accuracy. The three phases of computational protein structure prediction comprise: the pre-modelling analysis phase, model construction and post-modelling refinement. Protein modelling is primarily comparative or ab initio. Comparative or template-based methods such as homology and threading-based modelling require structural templates for constructing the structure of a target sequence. The ab initio is a template-free modelling approach which proceeds by satisfying various physics-based and knowledge-based parameters. The chapter will elaborate on the three phases of modelling, the programs available for performing each, issues, possible solutions and future research areas.

Protein Structure Prediction

2018 ◽  
pp. 48-79 ◽  
Author(s):  
Hirak Jyoti Chakraborty ◽  
Aditi Gangopadhyay ◽  
Sayak Ganguli ◽  
Abhijit Datta
Keyword(s):  
Protein Structure ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Future Research ◽  
Computational Techniques ◽  
Target Sequence ◽  
Knowledge Based ◽  
Research Areas ◽  
Three Phases

The great disagreement between the number of known protein sequences and the number of experimentally determined protein structures indicate an enormous necessity of rapid and accurate protein structure prediction methods. Computational techniques such as comparative modeling, threading and ab initio modelling allow swift protein structure prediction with sufficient accuracy. The three phases of computational protein structure prediction comprise: the pre-modelling analysis phase, model construction and post-modelling refinement. Protein modelling is primarily comparative or ab initio. Comparative or template-based methods such as homology and threading-based modelling require structural templates for constructing the structure of a target sequence. The ab initio is a template-free modelling approach which proceeds by satisfying various physics-based and knowledge-based parameters. The chapter will elaborate on the three phases of modelling, the programs available for performing each, issues, possible solutions and future research areas.

Three-dimensional protein structure prediction: Methods and computational strategies

2014 ◽  
Vol 53 ◽  
pp. 251-276 ◽  
Author(s):  
Márcio Dorn ◽  
Mariel Barbachan e Silva ◽  
Luciana S. Buriol ◽  
Luis C. Lamb
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Prediction Methods

Algorithmic Aspects of Protein Threading

2011 ◽  
pp. 605-619
Author(s):  
Tatsuya Akutsu
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Structure Alignment ◽  
Protein Structure Alignment ◽  
Amino Acid Residues ◽  
Optimal Solutions ◽  
Score Functions ◽  
Protein Threading

This chapter provides an overview of computational problems and techniques for protein threading. Protein threading is one of the most powerful approaches to protein structure prediction, where protein structure prediction is to infer three-dimensional (3-D) protein structure for a given protein sequence. Protein threading can be modeled as an optimization problem. Optimal solutions can be obtained in polynomial time using simple dynamic programming algorithms if profile type score functions are employed. However, this problem is computationally hard (NP-hard) if score functions include pairwise interaction preferences between amino acid residues. Therefore, various algorithms have been developed for finding optimal or near-optimal solutions. This chapter explains the ideas employed in these algorithms. This chapter also gives brief explanations of related problems: protein threading with constraints, comparison of RNA secondary structures and protein structure alignment.

scholarly journals Three-dimensional protein structure prediction based on memetic algorithms

2018 ◽  
Vol 91 ◽  
pp. 160-177 ◽  
Author(s):  
Leonardo de Lima Corrêa ◽  
Bruno Borguesan ◽  
Mathias J. Krause ◽  
Márcio Dorn
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Memetic Algorithms

Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling?

2015 ◽  
Vol 11 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Michal Brylinski
Keyword(s):  
Protein Structure ◽  
Protein Data Bank ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Structural Information ◽  
Three Dimensional ◽  
Data Bank ◽  
Prediction Problem ◽  
Three Dimensional Models ◽  
Protein Structure Prediction Problem

AbstractThe Protein Data Bank (PDB) undergoes an exponential expansion in terms of the number of macromolecular structures deposited every year. A pivotal question is how this rapid growth of structural information improves the quality of three-dimensional models constructed by contemporary bioinformatics approaches. To address this problem, we performed a retrospective analysis of the structural coverage of a representative set of proteins using remote homology detected by COMPASS and HHpred. We show that the number of proteins whose structures can be confidently predicted increased during a 9-year period between 2005 and 2014 on account of the PDB growth alone. Nevertheless, this encouraging trend slowed down noticeably around the year 2008 and has yielded insignificant improvements ever since. At the current pace, it is unlikely that the protein structure prediction problem will be solved in the near future using existing template-based modeling techniques. Therefore, further advances in experimental structure determination, qualitatively better approaches in fold recognition, and more accurate template-free structure prediction methods are desperately needed.

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