Neil P. King

Molecular Biology · 2012–2022

A researcher in the Science corpus: 18 papers; trusts 9 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 306 citations into it; #9,404 of 17,773 by trust here, #2,739 by citations
• Radiology, Nuclear Medicine and Imaging — 89 citations into it
• Ecology — 88 citations into it; #5,414 of 10,717 by trust here, #1,251 by citations
• Computational Biology & Bioinformatics — 60 citations into it
• Biomaterials — 28 citations into it

Whom Neil P. King trusts overall

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

1. Y. Lai
2. Paul Wilhelm Karl Rothemund
3. Nadrian C. Seeman
4. Po-Ssu Huang
5. Shuguang Zhang
6. S. Howorka
7. J. Sinclair
8. David Baker
9. David S. Goodsell
10. Brian Kuhlman

Who trusts Neil P. King overall

1. Torsten Schwede
2. I. Korendovych
3. Xingjie Pan
4. A. Walls
5. Po-Ssu Huang
6. D. Listov
7. Joost Snijder
8. S. Gonen
9. William F. DeGrado
10. Christoffer H Norn

Papers from Neil P. King's perspective

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

1. The Rosetta all-atom energy function for macromolecular modeling and design
2. Overview of the CCP4 suite and current developments
3. Phaser crystallographic software
4. Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking
5. Highly accurate protein structure prediction with AlphaFold
6. Rosetta3: An Object-Oriented Software Suite for the Simulation and Design of Macromolecules
7. Structure of RSV Fusion Glycoprotein Trimer Bound to a Prefusion-Specific Neutralizing Antibody
8. Accurate design of megadalton-scale two-component icosahedral protein complexes
9. Generative Models for Graph-Based Protein Design
10. RosettaRemodel: A Generalized Framework for Flexible Backbone Protein Design

Their papers

• Computational design of self-assembling protein nanomaterials with atomic level accuracy (2012) — 628 citations
• Accurate design of coassembling multi-component protein nanomaterials (2014) — 518 citations
• Robust deep learning based protein sequence design using ProteinMPNN (2022) — 1,912 citations
• Corrigendum: Design of a hyperstable 60-subunit protein icosahedron (2016) — 45 citations
• Accurate design of megadalton-scale two-component icosahedral protein complexes (2016) — 524 citations
• Design of a hyperstable 60-subunit protein icosahedron (2016) — 410 citations
• Tailored design of protein nanoparticle scaffolds for multivalent presentation of viral glycoprotein antigens (2020) — 147 citations
• Deep Mutational Scanning of SARS-CoV-2 Receptor Binding Domain Reveals Constraints on Folding and ACE2 Binding (2020) — 968 citations
• Quadrivalent influenza nanoparticle vaccines induce broad protection (2021) — 258 citations
• Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design (2021) — 29 citations
• Protocol and Reagents for Pseudotyping Lentiviral Particles with SARS-CoV-2 Spike Protein for Neutralization Assays (2020) — 780 citations
• Elicitation of Potent Neutralizing Antibody Responses by Designed Protein Nanoparticle Vaccines for SARS-CoV-2 (2020) — 428 citations
• Elicitation of broadly protective immunity to influenza by multivalent hemagglutinin nanoparticle vaccines (2020) — 30 citations
• Enhancing and shaping the immunogenicity of native-like HIV-1 envelope trimers with a two-component protein nanoparticle (2019) — 177 citations
• Induction of Potent Neutralizing Antibody Responses by a Designed Protein Nanoparticle Vaccine for Respiratory Syncytial Virus (2019) — 417 citations
• New Vaccine Design and Delivery Technologies (2019) — 60 citations
• Practical approaches to designing novel protein assemblies. (2013) — 74 citations
• Principles for designing ordered protein assemblies. (2012) — 134 citations