Lecture Notes On Computational Structural Biology
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Lecture Notes on Computational Structural Biology
1. Introduction. 1.1. Protein structure. 1.2. Structure determination. 1.3. Dynamics simulation. 1.4. The myth of protein folding -- 2. X-ray crystallography computing. 2.1. The phase problem. 2.2. Least squares solutions. 2.3. Entropy maximization. 2.4. Indirect methods -- 3. NMR structure determination. 3.1. Nuclear magnetic resonance. 3.2. Distance geometry. 3.3. Distance-based modeling. 3.4. Structural analysis -- 4. Potential energy minimization. 4.1. Potential energy function. 4.2. Local optimization. 4.3. Global optimization. 4.4. Energy transformation -- 5. Molecular dynamics simulation. 5.1. Equations of motion. 5.2. Initial-value problem. 5.3. Boundary-value problem. 5.4. Normal mode analysis -- 6. Knowledge-based protein modeling. 6.1. Sequence/structural alignment. 6.2. Fold recognition/inverse folding. 6.3. Knowledge-based structural refinement. 6.4. Structural computing and beyond
Computational Structural Biology
This work covers the impact of computational structural biology on protein structure prediction methods, macromolecular function and protein design, and key methods in drug discovery. It also addresses the computational challenges of experimental approaches in structural biology.
Algorithms in Structural Molecular Biology
An overview of algorithms important to computational structural biology that addresses such topics as NMR and design and analysis of proteins.Using the tools of information technology to understand the molecular machinery of the cell offers both challenges and opportunities to computational scientists. Over the past decade, novel algorithms have been developed both for analyzing biological data and for synthetic biology problems such as protein engineering. This book explains the algorithmic foundations and computational approaches underlying areas of structural biology including NMR (nuclear magnetic resonance); X-ray crystallography; and the design and analysis of proteins, peptides, and small molecules. Each chapter offers a concise overview of important concepts, focusing on a key topic in the field. Four chapters offer a short course in algorithmic and computational issues related to NMR structural biology, giving the reader a useful toolkit with which to approach the fascinating yet thorny computational problems in this area. A recurrent theme is understanding the interplay between biophysical experiments and computational algorithms. The text emphasizes the mathematical foundations of structural biology while maintaining a balance between algorithms and a nuanced understanding of experimental data. Three emerging areas, particularly fertile ground for research students, are highlighted: NMR methodology, design of proteins and other molecules, and the modeling of protein flexibility. The next generation of computational structural biologists will need training in geometric algorithms, provably good approximation algorithms, scientific computation, and an array of techniques for handling noise and uncertainty in combinatorial geometry and computational biophysics. This book is an essential guide for young scientists on their way to research success in this exciting field.