John Karanicolas
Molecular Biology · 2004–2020
A researcher in the Science corpus: 12 papers; trusts 42 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 450 citations into it; #1,659 of 17,773 by trust here, #1,594 by citations
- Materials Chemistry — 197 citations into it; #3,071 of 11,513 by trust here, #6,608 by citations
- Computational Theory and Mathematics — 117 citations into it; #3,999 of 12,182 by trust here, #3,562 by citations
- Radiology, Nuclear Medicine and Imaging — 64 citations into it
- Computational Chemistry & Drug Discovery — 50 citations into it
- Computational Biology & Bioinformatics — 41 citations into it
- Spectroscopy — 39 citations into it
- Organic Chemistry — 29 citations into it; #689 of 1,495 by trust here, #1,410 by citations
Whom John Karanicolas trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David K. Johnson
- Bingding Huang
- Yang Zhang
- Jeffrey Skolnick
- John J. Irwin
- Konrad Hinsen
- Roland L. Dunbrack
- Garrett M. Morris
- Claudio N. Cavasotto
- Shengyou Huang
Who trusts John Karanicolas overall
- Andrew M. Watkins
- Ragul Gowthaman
- Fang-Chieh Chou
- I. Samish
- David K. Johnson
- Rhiju Das
- Sagar D. Khare
- Sergey Lyskov
- Xingjie Pan
- Karen R. Khar
Papers from John Karanicolas's perspective
Ordered by how strongly the researchers John Karanicolas trusts endorse each paper. Same corpus, a different order for every researcher.
- The Protein Data Bank.
- A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes
- Can Self-Inhibitory Peptides be Derived from the Interfaces of Globular Protein-Protein Interactions?
- Exploring protein native states and large‐scale conformational changes with a modified generalized born model (Proteins: Structure, Function, and Bioinformatics)
- Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
- T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer
- Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation.
- Repurposing Citizen Science Games as Software Tools for Professional Scientists
- Systematic Analysis of Helical Protein Interfaces Reveals Targets for Synthetic Inhibitors
- Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes.
Their papers
- Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules (2011) — 1,780 citations
- Atomic accuracy in predicting and designing non-canonical RNA structure (2010) — 363 citations
- Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020) — 783 citations
- A de novo protein binding pair by computational design and directed evolution (2011) — 169 citations
- MMTSB Tool Set: enhanced sampling and multiscale modeling methods for applications in structural biology. (2004) — 839 citations
- Druggable Protein Interaction Sites Are More Predisposed to Surface Pocket Formation than the Rest of the Protein Surface (2013) — 88 citations
- Fast Docking on Graphics Processing Units via Ray-Casting (2013) — 12 citations
- DARC: Mapping Surface Topography by Ray-Casting for Effective Virtual Screening at Protein Interaction Sites. (2016) — 22 citations
- DARC 2.0: Improved Docking and Virtual Screening at Protein Interaction Sites (2015) — 21 citations
- Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions (2016) — 52 citations
- Allostery in Its Many Disguises: From Theory to Applications (2019) — 322 citations
- Selectivity by Small-Molecule Inhibitors of Protein Interactions Can Be Driven by Protein Surface Fluctuations (2015) — 26 citations