Molecular dynamics and protein function.

By Martin Karplus, J. Kuriyan. Proceedings of the National Academy of Sciences of the United States of America, 2005

1,077 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/0a010637fbdb69c8ca03d57250a9769e1560dfc9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. N. V. Dokholyan
2. Shuangye Yin
3. Douglas E. V. Pires
4. David B. Ascher
5. Feng Ding
6. Carlos H. M. Rodrigues
7. David W. Borhani
8. Michele Vendruscolo

In Molecular Biology

1. Douglas E. V. Pires
2. David B. Ascher
3. N. V. Dokholyan
4. Carlos H. M. Rodrigues
5. Shuangye Yin
6. Feng Ding
7. Russ B. Altman
8. David W. Borhani

Citations in the corpus

• Rapid flexible docking using a stochastic rotamer library of ligands (2010, methodology): “The receptor conformation ensemble can be obtained experimentally by X-ray crystallography under different conditions and NMR spectroscopy 11 - 14 or computationally by molecular dynamics simulations, 6,15 - 18 comparative modeling, 19 and normal-mode analysis.”
• Improving structure-based function prediction using molecular dynamics (2009, background): “Physics-based molecular dynamics (MD) simulations allow structural investigation of such motions on the femtosecond (fs), nanosecond (ns), and occasionally microsecond scales (Karplus and Kuriyan, 2005; Karplus and McCammon, 2002).”
• The future of molecular dynamics simulations in drug discovery (2011, background): “…step, the forces on each atom—determined from the ‘‘force field,’’ a collection of physics-based parameters that represent both bonded and non-bonded (e.g., van der Waals) inter-atomic forces—are computed and the atomic position and velocity are updated according to Newton’s laws of motion [26].”
• Computational protein design: a review (2017, background): “Advances have been made through the use of atomistic models, which are capable of providing detailed descriptions of protein dynamics [9, 10, 15], and through the development of coarse-grained representations, which enable more comprehensive sampling of the conformational space [3, 16 – 19].”
• The s2D method: simultaneous sequence-based prediction of the statistical populations of ordered and disordered regions in proteins. (2015, background): “Proteins folded in their native states experience conformational fluctuations, which in many cases facilitate their functions [1–6].”
• DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability (2018, methodology): “Proteins are dynamic macromolecules, whose function is intricately linked to their biological motions (1,2).”
• Structure-based drug discovery and protein targets in the CNS. (2011, background): “These molecular simulations can provide insight into protein function and a number of reviews summarise the opportunities and limitations of the methods (e.g. Karplus and McCammon, 2002; Karplus and Kuriyan, 2006).”