Protein Structure Prediction and Analysis, Volume 146 offers a comprehensive exploration of the methodologies, tools, and techniques utilized in the prediction and analysis of protein structures. The book delves into the fundamental principles of protein folding, modeling, and computational simulations, providing insights into the intricate process of predicting the three-dimensional structures of proteins. Sections in this new release include coverage of Current approaches for protein structure prediction and design, Protein Modelling, Prediction servers and applications in molecular modelling, Toward accurate in silico prediction of antigen binding affinities in antibody engineering, Molecular Blueprints: Guiding Drug Discovery through Protein Structure Analysis, and much more.

Additional sections focus on Protein Structure Prediction Using Dynamic Speciation Evolutionary Algorithm with Problem Information Aggregation, Tau protein structure and dynamics, Tau interacts with G-protein coupled receptors (GPCRs), How to accurately predict nanobody structure: Classical physics-based simulations or deep learning approaches, Medicinal Chemistry-based Novel Therapeutics for Tauopathies in Alzheimer’s Disease, Mapping the Pathogenic Nexus: Gene Overlap and Protein Interaction Networks in Alzheimer's and Breast Cancer as a Precursor to Protein Structure Prediction and Analysis, and much more.

• Provides comprehensive coverage on the principles and fundamentals underlying protein structure, including amino acid sequence, secondary structure, tertiary structure, and quaternary structure
• Explores computational methods, algorithms, and tools used for predicting protein structure, including homology modeling, ab initio methods, and machine learning approaches
• Includes detailed discussions on techniques such as molecular modeling, molecular dynamics simulations, and structural bioinformatics for predicting and analyzing protein structures