Author

R. F. Alford

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Biophysics· 4

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 3 Assay:protein structure· 3 Research Focus:protein design· 2 Approach:statistical model· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:protein folding & dynamics· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

J. J. Gray· 4 shared A. Leaver-Fay· 1 shared B. Kuhlman· 1 shared D. Baker· 1 shared F. P. DiMaio· 1 shared H. Park· 1 shared H. S. Young· 1 shared J. R. Jeliazkov· 1 shared

Activity by Year

2017-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

4 shared papersBiophysics

J. J. Gray

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Diverse scientific benchmarks for implicit membrane energy functions

R. F. Alford, J. J. Gray

The paper introduces 12 benchmarks to evaluate implicit membrane energy functions using diverse datasets and demonstrates their application with the franklin2019 energy function.

bioRxiv · Jun 23, 2020Biophysics

Protein Docking and Steered Molecular Dynamics Reveal Alternative Regulatory Sites on the SERCA Calcium Transporter

R. F. Alford, N. Smolin, H. S. Young, J. J. Gray, S. L. Robia

This study uses SMD and docking to test if PLB interacts with alternative sites on SERCA beyond the canonical site, finding that the canonical site is preferred but alternative sites are favorable under certain conditions.

bioRxiv · Dec 19, 2019Biophysics

Protein structure prediction and design in a biologically-realistic implicit membrane

R. F. Alford, P. J. Fleming, K. G. Fleming, J. J. Gray

This paper develops an implicit membrane model for protein structure prediction and design, improving accuracy in membrane protein properties and sequence recovery.

bioRxiv · May 8, 2019Biophysics

The Rosetta all-atom energy function for macromolecular modeling and design

R. F. Alford, A. Leaver-Fay, J. R. Jeliazkov, M. J. O'Meara, F. P. DiMaio, H. Park, M. V. Shapovalov, P. D. Renfrew, V. K. Mulligan, K. Kappel, J. W. Labonte, M. S. Pacella, R. Bonneau, P. Bradley, R. L. Dunbrack, , J. J. Gray

The paper explains the Rosetta all-atom energy function, its mathematical models, and its application to biomolecular modeling and design, including new capabilities for membrane proteins and other macromolecules.

bioRxiv · Feb 7, 2017Biophysics