Protein structure prediction using deep learning distance and hydrogen‐bonding restraints in CASP14 is a research paper published in Proteins Structure Function and Bioinformatics (2021). On theSindex it has a DataRank of 0. It has been cited 63 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
NCI NIH HHS
Grant: T32 CA140044
NIGMS NIH HHS
Grant: T32 GM070449
National Institute of Allergy and Infectious Diseases
Grant: AI134678
National Science Foundation
Grant: DBI2030790
National Science Foundation
Grant: IIS1901191
National Science Foundation
Grant: MTM2025426
National Institute of General Medical Sciences
Grant: S10OD026825
National Institute of General Medical Sciences
Grant: GM136422
NIGMS NIH HHS
Grant: R01 GM083107
NIAID NIH HHS
Grant: R01 AI134678
NIGMS NIH HHS
Grant: R35 GM136422
National Science Foundation
Grant: ACI‐1548562
National Science Foundation
Grant: 2025426
MTM 2: Combining structural informatics and crosslinking mass spectrometry to predict the key protein-protein interactions shaping symbiotic microbial communities
National Institutes of Health
Grant: 5R35GM136422-02
Advanced approaches to protein structure prediction
National Science Foundation
Grant: 1901191
III: Medium: Collaborative Research: Multi-level computational approaches to protein function prediction
National Institutes of Health
Grant: 5R01AI134678-03
Structure-based functional annotation of microbial genomes
National Science Foundation
Grant: 2030790
IIBR: Informatics: RAPID: Genome-wide Structure and Function Modeling of the SARS-CoV-2 Virus
Fields of Study
MeSH Terms
Keywords