Xingjie Pan

Molecular Biology · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 59 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 369 citations into it; #13,041 of 17,773 by trust here, #11,019 by citations
• Materials Chemistry — 179 citations into it
• Computational Biology & Bioinformatics — 59 citations into it
• Computational Theory and Mathematics — 53 citations into it
• Radiology, Nuclear Medicine and Imaging — 53 citations into it
• Spectroscopy — 36 citations into it
• Computational Chemistry & Drug Discovery — 29 citations into it
• Genetics — 17 citations into it

Whom Xingjie Pan trusts overall

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

1. David Baker
2. Colin A. Smith
3. Noah Ollikainen
4. Rhiju Das
5. Tanja Kortemme
6. Brian Kuhlman
7. Jinbo Xu
8. Philip Bradley
9. Po-Ssu Huang
10. Scott E. Boyken

Who trusts Xingjie Pan overall

1. Daniel Peñas-Utrilla
2. Enrique Marcos
3. Thomas W. Linsky
4. Tanja Kortemme
5. Michael Jendrusch

Papers from Xingjie Pan's perspective

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

1. Native protein sequences are close to optimal for their structures.
2. The Rosetta all-atom energy function for macromolecular modeling and design
3. Modeling T cell receptor recognition of CD1-lipid and MR1-metabolite complexes
4. T-Cell Transfer Therapy Targeting Mutant KRAS in Cancer
5. Systematic Comparison of Amber and Rosetta Energy Functions for Protein Structure Evaluation.
6. Repurposing Citizen Science Games as Software Tools for Professional Scientists
7. A flexible docking approach for prediction of T cell receptor–peptide–MHC complexes
8. Can Self-Inhibitory Peptides be Derived from the Interfaces of Globular Protein-Protein Interactions?
9. Systematic Analysis of Helical Protein Interfaces Reveals Targets for Synthetic Inhibitors
10. Design of a Novel Globular Protein Fold with Atomic-Level Accuracy

Their papers

• Macromolecular modeling and design in Rosetta: recent methods and frameworks (2020) — 783 citations
• Expanding the space of protein geometries by computational design of de novo fold families (2020) — 63 citations
• Recent advances in de novo protein design: Principles, methods, and applications (2021) — 194 citations