Optimization in Computational Chemistry and Molecular Biology

Optimization in Computational Chemistry and Molecular Biology: Local and Global Approaches covers recent developments in optimization techniques for addressing several computational chemistry and biology problems. A tantalizing problem that cuts across the fields of computational chemistry, biology, medicine, engineering and applied mathematics is how proteins fold. Global and local optimization provide a systematic framework of conformational searches for the prediction of three-dimensional protein structures that represent the global minimum free energy, as well as low-energy biomolecular conformations.
Each contribution in the book is essentially expository in nature, but of scholarly treatment. The topics covered include advances in local and global optimization approaches for molecular dynamics and modeling, distance geometry, protein folding, molecular structure refinement, protein and drug design, and molecular and peptide docking.
Audience: The book is addressed not only to researchers in mathematical programming, but to all scientists in various disciplines who use optimization methods in solving problems in computational chemistry and biology.

Klappentext

Optimization in Computational Chemistry and Molecular Biology: Local and Global Approaches covers recent developments in optimization techniques for addressing several computational chemistry and biology problems. A tantalizing problem that cuts across the fields of computational chemistry, biology, medicine, engineering and applied mathematics is how proteins fold. Global and local optimization provide a systematic framework of conformational searches for the prediction of three-dimensional protein structures that represent the global minimum free energy, as well as low-energy biomolecular conformations. Each contribution in the book is essentially expository in nature, but of scholarly treatment. The topics covered include advances in local and global optimization approaches for molecular dynamics and modeling, distance geometry, protein folding, molecular structure refinement, protein and drug design, and molecular and peptide docking. Audience: The book is addressed not only to researchers in mathematical programming, but to all scientists in various disciplines who use optimization methods in solving problems in computational chemistry and biology.

Inhalt
Predicting Protein Tertiary Structure using a Global Optimization Algorithm with Smoothing.- Methodology for Elucidating the Folding Dynamics of Peptides: Met-enkephalin Case Study.- Energy Landscape Projections of Molecular Potential Functions.- Global Optimization and Sampling in the Context of Tertiary Structure Prediction: A Comparison of Two Algorithms.- Protein Folding Simulations by Monte Carlo Simulated Annealing and Multicanonical Algorithm.- Thermodynamics of Protein Folding The Generalized-Ensemble Approach.- An approach to detect the dominant folds of proteinlike heteropolymers from the statistics of a homopolymeric chain.- Gene Sequences are Locally Optimized for Global mRNA Folding.- Structure Calculations of Symmetric Dimers using Molecular Dynamics/Simulated Annealing and NMR Restraints: The Case of the RII? Subunit of Protein Kinase A.- Structure Prediction of Binding Sites of MHC Class II Molecules based on the Crystal of HLA-DRB1 and Global Optimization.- A Coupled Scanning and Optimization Scheme for Analyzing Molecular Interactions.- Improved Evolutionary Hybrids for Flexible Ligand Docking in AutoDock.- Electrostatic Optimization in Ligand Complementarity and Design.- Exploring potential solvation sites of proteins by multistart local minimization.- On relative position of two biopolymer molecules minimizing the weighted sum of interatomic distances squared.- Visualization of Chemical Databases Using the Singular Value Decomposition and Truncated-Newton Minimization.- Optimization of Carbon and Silicon Cluster Geometry for Tersoff Potential using Differential Evolution.- D.C. Programming Approach for Large-Scale Molecular Optimization via the General Distance Geometry Problem.