Author

R. C. Kretsch

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

Biology Disciplines

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Biophysics· 4 Biochemistry· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Approach:experimental assay· 2 Organism:human· 2 Research Focus:protein folding & dynamics· 2 Research Focus:protein structure prediction· 2 Approach:statistical model· 2 Assay:structural biology· 2 Research Focus:benchmarking· 1

Frequent Co-Authors

R. Das· 3 shared R. Huang· 2 shared A. Bakulin· 1 shared A. J. Simpkin· 1 shared A. Manglik· 1 shared A. S. Powers· 1 shared A. Zinkevich· 1 shared C. Azumaya· 1 shared

Activity by Year

2020-2024 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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3 shared papersBiophysics

R. Das

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Author profile →

5 papers

Recent·Most cited

Tertiary folds of the SL5 RNA from the 5' proximal region of SARS-CoV-2 and related coronaviruses

R. C. Kretsch, L. Xu, I. N. Zheludev, X. Zhou, R. Huang, G. Nye, S. Li, K. Zhang, W. Chiu, R. Das

This paper determines the 3D structures of SL5 RNA domains from six coronaviruses, revealing conserved tertiary features with implications for protein-binding and antiviral targets.

Proceedings of the National Academy of Sciences · Mar 1, 2024Peer-reviewedBiophysics

Conformational Ensembles Reveal the Origins of Serine Protease Catalysis

S. Du, R. C. Kretsch, J. Parres-Gold, E. Pieri, V. W. D. Cruzeiro, M. Zhu, M. M. Pinney, F. Yabukarski, J. P. Schwans, T. J. Martinez, D. Herschlag

Ensemble-function analyses provide a quantitative model for serine protease catalysis, reveal previously unknown conformational features that contribute to their catalysis, and identify a structural motif that underlie these features and has evolved in >100 different enzymes from 32 protein folds.

bioRxiv · Feb 28, 2024Biophysics

Ribonanza: deep learning of RNA structure through dual crowdsourcing

S. He, R. Huang, J. Townley, R. C. Kretsch, T. G. Karagianes, D. B. T. Cox, H. Blair, D. Penzar, V. Vyaltsev, E. Aristova, A. Zinkevich, A. Bakulin, H. Sohn, D. Krstevski, T. Fukui, , R. Das

The paper introduces RibonanzaNet, a deep learning model trained on crowdsourced RNA chemical mapping data to predict RNA structure and related properties.

bioRxiv · Feb 24, 2024 · PreprintBiophysics

Assessment of three-dimensional RNA structure prediction in CASP15

R. Das, R. C. Kretsch, A. J. Simpkin, T. Mulvaney, P. Pham, R. Rangan, F. Bu, R. M. Keegan, M. Topf, D. J. Rigden, Z. Miao, E. Westhof

This paper evaluates RNA 3D structure prediction methods in CASP15, finding that non-deep learning groups outperformed deep learning models, with challenges in fine details and model ranking.

bioRxiv · Apr 25, 2023Biophysics

Structure of human ferroportin bound to hepcidin reveals mechanisms of iron homeostasis

C. Billesboelle, C. Azumaya, R. C. Kretsch, A. S. Powers, S. Gonen, S. Schneider, T. Arvedson, R. Dror, Y. Cheng, A. Manglik

This study determines the cryo-EM structures of human ferroportin bound to hepcidin and identifies mechanisms of iron homeostasis regulation.

bioRxiv · Mar 16, 2020 · PreprintBiochemistry