Annual Reports in Computational Chemistry
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Annual Reports in Computational Chemistry is a new periodical providing timely and critical reviews of important topics in computational chemistry as applied to all chemical disciplines. Topics covered include quantum chemistry, molecular mechanics, force fields, chemical education, and applications in academic and industrial settings. Each volume is organized into (thematic) sections with contributions written by experts. Focusing on the most recent literature and advances in the field, each article covers a specific topic of importance to computational chemists. Annual Reports in Computational Chemistry is a "must" for researchers and students wishing to stay up-to-date on current developments in computational chemistry.
Key Features
* Broad coverage of computational chemistry and up-to-date information
* Topics covered include bioinformatics, drug discovery, protein NMR, simulation methodologies, and applications in academic and industrial settings
* Each chapter reviews the most recent literature on a specific topic of interest to computational chemists
* Topics covered include bioinformatics, drug discovery, protein NMR, simulation methodologies, and applications in academic and industrial settings
* Each chapter reviews the most recent literature on a specific topic of interest to computational chemists
Readership
Researchers and students interested in computational chemistry
Table of Contents
- Section 1: Bioinformatics (Section Editor: Wei Wang)
1. Structural Perspectives on Protein Evolution
Eric Franzosa and Yu Xia
1. Introduction
2. Determinants of Evolutionary Rate
3. Theoretical Advances
4. Empirical Results: Single Proteins
5. Empirical Results: Higher Order Properties
6. Summation
Acknowledgements
References
2. Predicting Selectivity and Druggability in Drug Discovery
Alan C. Cheng
1. Introduction
2. Selectivity
3. Druggability
4. Conclusion
References
Section 2: Biological Modeling (Section Editor: Nathan Barker)
3. Machine Learning for Protein Structure and Function Prediction
Robert Ezra Langlois and Hui Lu
1. Introduction
2. Machine Learning Problem Formulations
3. Applications in Protein Structure and Function Modeling
4. Discussion and Future Outlook
Acknowledgements
References
4. Modeling Protein-Protein and Protein-Nucleic Acid Interactions: Structure, Thermodynamics, and Kinetics
Huan-Xiang Zhou, Sanbo Qin and Harianto Tjong
1. Introduction
2. Building Structural Models
3. Prediction of Binding Affinities
4. Prediction of Binding Rates
5. Dynamics within Native Complexes and During Complex Formation
6. Summary Points
References
5. Analysing Protein NMR pH-titration Curves
Jens Erik Nielsen
1. Introduction
2. Fitting Protein Titration Curves
3. Conclusion and Outlook
References
6. Implicit Solvent Simulations of Biomolecules in Cellular Environments
Michael Feig, Seiichiro Tanizaki and Maryam Sayadi
1. Introduction
2. Theory
3. Applications and Challenges
4. Summary and Outlook
Acknowledgements
References
Section 3: Simulation Methodologies (Section Editor: Carlos Simmerling)
7. Implicit Solvent Models in Molecular Dynamics Simulations: A Brief Overview
Alexey Onufriev
1. Introduction
2. Implicit Solvent Framework
3. Conclusions and Outlook
Acknowledgments
References
8. Comparing MD Simulations and NMR Relaxation Parameters
Vance Wong and David A. Case
1. Introduction
2. Internal Motions and Flexibility
3. Overall Tumbling and Rotational Diffusion
4. Conclusions
Acknowledgements
References
Product details
- No. of pages: 272
- Language: English
- Copyright: © Elsevier Science 2008
- Published: October 30, 2008
- Imprint: Elsevier Science
- eBook ISBN: 9780080932781
About the Editors
Ralph Wheeler
Affiliations and Expertise
Department of Chemistry & Biochemistry, Duquesne University, Pittsburgh, PA, USA
David Spellmeyer
David Spellmeyer, PhD is an Advisor to startup and early venture companies providing technical and scientific guidance on overcoming technological, scientific and business development challenges. He brings broad business and technical expertise from companies both large (IBM, DuPont) and small (Chiron, CombiChem, Signature BioScience, Nodality). David has been involved in the development of advanced functional assays such as Nodality’s Single Cell Network Profiling (SCNP) and Signature’s label-free molecular and cellular screening systems. He has extensive experience in the management and analysis of high dimensional data (combinatorial chemistry and SCNP). He has worked closely with business development teams in establishing over 20 non-dilutive strategic corporate partnerships, 4 mergers and acquisitions, several rounds of venture financing, and two joint ventures. David received his Ph.D. in theoretical organic chemistry from UCLA. He completed his post-doctoral training in pharmaceutical chemistry at UCSF, where he remains an active Adjunct Associate Professor.
Affiliations and Expertise
Nodality, Inc., CA, USA
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