Ken A. Dill

Molecular Biology · 1985–2020

A researcher in the Science corpus: 23 papers; trusts 24 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 190 citations into it; #2,097 of 17,773 by trust here, #2,516 by citations
• Materials Chemistry — 102 citations into it; #2,070 of 11,513 by trust here, #4,427 by citations
• Computational Biology & Bioinformatics — 51 citations into it; #377 of 660 by trust here, #454 by citations
• Atomic and Molecular Physics, and Optics — 32 citations into it
• Statistical and Nonlinear Physics — 23 citations into it
• Genetics — 12 citations into it
• Biophysics — 7 citations into it
• 3D Vision & Pose — 5 citations into it

Whom Ken A. Dill trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Michael R. Shirts
2. Joël Janin
3. John Moult
4. David Baker
5. David C. Jones
6. Manfred J. Sippl
7. K. Henzler-Wildman
8. John L. Klepeis
9. Andrej Sali
10. Adam T. Zemla

Who trusts Ken A. Dill overall

1. Natalio Krasnogor
2. Sheng Wang
3. Zsuzsanna Dosztányi
4. James Smith
5. Steve Pressé
6. Shengyou Huang
7. Min-Yi Shen
8. Mohammed Alquraishi
9. Hahnbeom Park
10. Jinbo Xu

Papers from Ken A. Dill's perspective

Ordered by how strongly the researchers Ken A. Dill trusts endorse each paper. Same corpus, a different order for every researcher.

1. Protein data bank
2. The Protein Data Bank: a computer-based archival file for macromolecular structures.
3. Protein Structure Prediction and Structural Genomics
4. A method to identify protein sequences that fold into a known three-dimensional structure.
5. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins.
6. MODBASE: a database of annotated comparative protein structure models and associated resources
7. Comparative protein modelling by satisfaction of spatial restraints.
8. High resolution protein structure prediction and the crystallographic phase problem
9. Improved protein structure prediction using potentials from deep learning
10. The Protein Data Bank.

Their papers

• Assessment of protein structure refinement in CASP9 (2011) — 97 citations
• The Protein-Folding Problem, 50 Years On (2012) — 1,526 citations
• Assessment of the protein-structure refinement category in CASP8 (2009) — 74 citations
• NMR-assisted protein structure prediction with MELDxMD (2019) — 20 citations
• FlexE: Using elastic network models to compare models of protein structure (2012) — 24 citations
• The protein folding problem. (2008) — 1,379 citations
• Automatic discovery of metastable states for the construction of Markov models of macromolecular conformational dynamics. (2007) — 597 citations
• Resultants and Loop Closure (2006) — 69 citations
• Principles of maximum entropy and maximum caliber in statistical physics (2013) — 383 citations
• Binding of Small-Molecule Ligands to Proteins: “What You See” Is Not Always “What You Get” (2009) — 560 citations
• Use of the Weighted Histogram Analysis Method for the Analysis of Simulated and Parallel Tempering Simulations. (2007) — 451 citations
• Inaugural Article: Physical limits of cells and proteomes (2011) — 254 citations
• Using quaternions to calculate RMSD (2004) — 369 citations
• Protein storytelling through physics (2020) — 67 citations
• Molecular driving forces : statistical thermodynamics in biology, chemistry, physics, and nanoscience (2012) — 284 citations
• Annual review of biophysics (2008) — 87 citations
• A kinematic view of loop closure (2004) — 306 citations
• Molecular driving forces (2002) — 278 citations
• Molecular driving forces : statistical thermodynamics in chemistry and biology (2002) — 338 citations
• How ions affect the structure of water. (2002) — 734 citations