David K. Johnson

Molecular Biology · 2013–2020

A researcher in the Science corpus: 5 papers; trusts 25 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 354 citations into it; #5,574 of 17,773 by trust here, #9,851 by citations
• Materials Chemistry — 144 citations into it; #3,483 of 11,513 by trust here, #8,635 by citations
• Computational Theory and Mathematics — 93 citations into it; #3,907 of 12,182 by trust here, #4,531 by citations
• Radiology, Nuclear Medicine and Imaging — 56 citations into it
• Computational Chemistry & Drug Discovery — 35 citations into it
• Organic Chemistry — 25 citations into it; #691 of 1,495 by trust here, #1,410 by citations
• Oncology — 15 citations into it
• Cell Biology — 12 citations into it

Whom David K. Johnson trusts overall

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

1. Claudio N. Cavasotto
2. Shengyou Huang
3. Bingding Huang
4. David D. Boehr
5. Andrew Leaver-Fay
6. John J. Irwin
7. Garrett M. Morris
8. John Karanicolas
9. D. Altieri
10. Alan C. Cheng

Who trusts David K. Johnson overall

1. John Karanicolas
2. Ragul Gowthaman
3. Zhenze Yang
4. Karen R. Khar
5. Sergey Lyskov
6. Chi-Hua Yu
7. Lukasz Goldschmidt
8. Markus J. Buehler
9. Michelle Arkin

Papers from David K. Johnson's perspective

Ordered by how strongly the researchers David K. Johnson trusts endorse each paper. Same corpus, a different order for every researcher.

1. A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes
2. Can Self-Inhibitory Peptides be Derived from the Interfaces of Globular Protein-Protein Interactions?
3. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
4. Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
5. T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer
6. Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation.
7. Repurposing Citizen Science Games as Software Tools for Professional Scientists
8. Systematic Analysis of Helical Protein Interfaces Reveals Targets for Synthetic Inhibitors
9. Comprehensive Analysis of Loops at Protein-Protein Interfaces for Macrocycle Design
10. Efficient Sampling of Protein Conformational Space Using Fast Loop Building and Batch Minimization on Highly Parallel Computers

Their papers

• Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020) — 783 citations
• Druggable Protein Interaction Sites Are More Predisposed to Surface Pocket Formation than the Rest of the Protein Surface (2013) — 88 citations
• DARC: Mapping Surface Topography by Ray-Casting for Effective Virtual Screening at Protein Interaction Sites. (2016) — 22 citations
• Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions (2016) — 52 citations
• Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations (2015) — 26 citations