A Metaheuristic Approach to Protein Structure Prediction
Algorithms and Insights from Fitness Landscape Analysis
Authors: Jana, Nanda Dulal, Das, Swagatam, Sil, Jaya
- Presents structural features of the protein-structure prediction (PSP) problem and well-known metaheuristic techniques
- Introduces algorithms and insights from fitness landscape analysis
- Demonstrates how to generate the protein landscape structure based on the sampling technique for determining the structural properties of the protein landscape
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- About this book
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This book introduces characteristic features of the protein structure prediction (PSP) problem. It focuses on systematic selection and improvement of the most appropriate metaheuristic algorithm to solve the problem based on a fitness landscape analysis, rather than on the nature of the problem, which was the focus of methodologies in the past.
Protein structure prediction is concerned with the question of how to determine the three-dimensional structure of a protein from its primary sequence. Recently a number of successful metaheuristic algorithms have been developed to determine the native structure, which plays an important role in medicine, drug design, and disease prediction.
This interdisciplinary book consolidates the concepts most relevant to protein structure prediction (PSP) through global non-convex optimization. It is intended for graduate students from fields such as computer science, engineering, bioinformatics and as a reference for researchers and practitioners.
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Bibliographic Information
- Bibliographic Information
-
- Book Title
- A Metaheuristic Approach to Protein Structure Prediction
- Book Subtitle
- Algorithms and Insights from Fitness Landscape Analysis
- Authors
-
- Nanda Dulal Jana
- Swagatam Das
- Jaya Sil
- Series Title
- Emergence, Complexity and Computation
- Series Volume
- 31
- Copyright
- 2018
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing AG
- eBook ISBN
- 978-3-319-74775-0
- DOI
- 10.1007/978-3-319-74775-0
- Hardcover ISBN
- 978-3-319-74774-3
- Series ISSN
- 2194-7287
- Edition Number
- 1
- Number of Pages
- XXIX, 220
- Topics
