Rosetta Refinement

Rosetta Refinement is a computational method in structural biology that belongs to the class of protein structure prediction and modeling techniques, distinguished by its integration of fragment-based sampling, all-atom energy minimization, and knowledge-based scoring functions within the Rosetta software suite to iteratively improve macromolecular structural models toward atomic-level accuracy.

136

Publications

20K

Citations

504

Authors

206

Institutions

Publications per year

2017–2026

50

Authors

504

  1. DB
    David Baker

    University of Washington, United States

    41 publications · 9.9K citations

    35

    h-index

  2. FD
    Frank DiMaio

    University of Washington, United States

    14 publications · 4K citations

    14

    h-index

  3. JM
    Jens Meiler

    Vanderbilt University, United States

    13 publications · 736 citations

    13

    h-index

  4. JJ
    Jeffrey J. Gray

    Johns Hopkins University, United States

    12 publications · 2.2K citations

    12

    h-index

  5. SL
    Steffen Lindert

    The Ohio State University, United States

    11 publications · 407 citations

    11

    h-index

1–5 of 504

Venues

  1. Proteins Structure Function and Bioinformatics

    27 publications · 4.1K citations

    22

    h-index

  2. Journal of Chemical Theory and Computation

    JCTC · 10 publications · 2.7K citations

    10

    h-index

  3. PLoS ONE

    9 publications · 1.1K citations

    9

    h-index

  4. Journal of the American Chemical Society

    JACS · 7 publications · 574 citations

    7

    h-index

  5. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 6 publications · 177 citations

    6

    h-index

1–5