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Disulfide bonding state prediction in proteins by machine learning
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Prediction of disulfide bonding state of cysteine residues in proteins is a sub-problem of a bigger and yet unsolved problem of in-silico protein structure prediction. Improvement in this sub-field will help in putting constraint in three dimensional (3D) space of the respective protein structures, and thus, will eventually help in the prediction of 3D structure of proteins. This book extensively reviews the progress made in this sub-field since its inception in 1990-till date, covering a journey of 22 years, and provides a comprehensive insight of the latest developments. It also explains in ...
Prediction of disulfide bonding state of cysteine residues in proteins is a sub-problem of a bigger and yet unsolved problem of in-silico protein structure prediction. Improvement in this sub-field will help in putting constraint in three dimensional (3D) space of the respective protein structures, and thus, will eventually help in the prediction of 3D structure of proteins. This book extensively reviews the progress made in this sub-field since its inception in 1990-till date, covering a journey of 22 years, and provides a comprehensive insight of the latest developments. It also explains in detail, prediction methods for the sub-problem developed by the author during his research work. It covers all the steps associated with machine learning based method (predictor) development, and manages a proper balance of biological knowledge and computational skills required for a successful bioinformatics research project. This book is intended for all scientists working in this sub-field and in specific for research students who want to do their dissertation in bioinformatics.
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