Scott E. Boyken
Molecular Biology · 2016–2021
A researcher in the Science corpus: 9 papers; trusts 6 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Molecular Biology — 378 citations into it; #3,910 of 17,773 by trust here, #3,184 by citations
- Materials Chemistry — 169 citations into it; #8,056 of 11,513 by trust here, #7,794 by citations
- Computational Biology & Bioinformatics — 55 citations into it; #345 of 660 by trust here, #321 by citations
- Spectroscopy — 41 citations into it
- Ecology — 13 citations into it
Whom Scott E. Boyken trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Guoli Wang
- Roland L. Dunbrack
- David Baker
- Andrew Leaver-Fay
- Maxim V. Shapovalov
- Po-Ssu Huang
- Carol A. Rohl
- N. Koga
- Brian Kuhlman
- S. Gonen
Who trusts Scott E. Boyken overall
- Xingjie Pan
- I. Korendovych
- Robin Pearce
- Enrique Marcos
- Tanja Kortemme
- John Ingraham
- William F. DeGrado
- Yang Zhang
- Jack B. Maguire
- M. Kanekiyo
Papers from Scott E. Boyken's perspective
Ordered by how strongly the researchers Scott E. Boyken trusts endorse each paper. Same corpus, a different order for every researcher.
- Native protein sequences are close to optimal for their structures.
- The Rosetta all-atom energy function for macromolecular modeling and design
- Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
- Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.
- Tertiary templates for proteins. Use of packing criteria in the enumeration of allowed sequences for different structural classes.
- Protein Structure Prediction Using Rosetta
- 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
Their papers
- The coming of age of de novo protein design (2016) — 1,345 citations
- Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020) — 783 citations
- De novo design of protein homo-oligomers with modular hydrogen-bond network–mediated specificity (2016) — 295 citations
- Programmable design of orthogonal protein heterodimers (2018) — 167 citations
- Accurate computational design of multipass transmembrane proteins (2018) — 159 citations
- Rapid Sampling of Hydrogen Bond Networks for Computational Protein Design (2018) — 51 citations
- Computational Design of Transmembrane Pores (2020) — 141 citations
- De Novo Design of Bioactive Protein Switches (2019) — 242 citations
- De novo design of modular and tunable protein biosensors (2021) — 228 citations