Maxim V. Shapovalov

Molecular Biology · 2009–2021

A researcher in the Science corpus: 7 papers; trusts 25 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 139 citations into it; #1,038 of 17,773 by trust here, #2,892 by citations
• Materials Chemistry — 92 citations into it; #648 of 11,513 by trust here, #2,915 by citations
• Computational Biology & Bioinformatics — 35 citations into it
• Computational Theory and Mathematics — 19 citations into it; #3,655 of 12,182 by trust here, #5,046 by citations
• Computational Chemistry & Drug Discovery — 17 citations into it
• Spectroscopy — 15 citations into it
• Organic Chemistry — 14 citations into it
• Radiology, Nuclear Medicine and Imaging — 12 citations into it

Whom Maxim V. Shapovalov trusts overall

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

1. Roland L. Dunbrack
2. G. Sheldrick
3. Carol A. Rohl
4. S. Parasuraman
5. David Baker
6. Gerard J. Kleywegt
7. Philip Bradley
8. Adrian A. Canutescu
9. Charles E.M. Strauss
10. Georgii G. Krivov

Who trusts Maxim V. Shapovalov overall

1. Santrupti Nerli
2. Yang Shen
3. Noah Ollikainen
4. Andrew M. Waterhouse
5. Samuel L. DeLuca
6. Jared Adolf-Bryfogle
7. Jack B. Maguire
8. Yilun Du
9. Mark G. F. Sun
10. Andrew C. McShan

Papers from Maxim V. Shapovalov's perspective

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

1. The Protein Data Bank.
2. The penultimate rotamer library
3. Accurate Bond and Angle Parameters for X-ray Protein Structure Refinement
4. Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and Bayesian scoring functions.
5. Alternate states of proteins revealed by detailed energy landscape mapping
6. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins.
7. Protein data bank
8. The Rosetta all-atom energy function for macromolecular modeling and design
9. Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. (Journal of Molecular Biology)
10. Generalized born models of macromolecular solvation effects. (Annual review of physical chemistry (Print))

Their papers

• The Rosetta all-atom energy function for macromolecular modeling and design (2017) — 1,486 citations
• A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions (2011) — 970 citations
• Improved prediction of protein side-chain conformations with SCWRL4 (2009) — 384 citations
• Biological function derived from predicted structures in CASP11 (2016) — 10 citations
• Conformation Dependence of Backbone Geometry in Proteins (2009) — 108 citations
• Growing Glycans in Rosetta: Accurate de novo glycan modeling, density fitting, and rational sequon design (2021) — 29 citations
• Improved prediction of protein side-chain conformations with SCWRL 4 (2009)