Author

Claudia Millán

University of CambridgeORCID 0000-0002-9283-2220Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:benchmark study· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Assay:protein structure· 1 Research Focus:protein structure prediction· 1 Approach:statistical model· 1

Frequent Co-Authors

Randy J. ReadiD· 2 shared Adam J. SimpkiniD· 1 shared Airlie J. McCoyiD· 1 shared Alberdina Aike van DijkiD· 1 shared Ana Cristina EbrechtiD· 1 shared Andrei N. LupasiD· 1 shared Andria V. RodriguesiD· 1 shared Andy M. DeGiovanni· 1 shared

Activity by Year

2021-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioinformatics

Randy J. ReadORCID

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2 papers

Recent·Most cited

Assessing the utility of CASP14 models for molecular replacement

Claudia Millán, Ronan M. Keegan, Joana Pereira, Massimo Domenico Sammito, Adam J. Simpkin, Airlie J. McCoy, Andrei N. Lupas, Marcus D. Hartmann, Daniel John Rigden, Randy J. Read

This paper evaluates CASP14 models for molecular replacement using a new metric, reLLG, which is independent of diffraction data and shows AlphaFold2 models outperform others.

Proteins Structure Function and Bioinformatics · Aug 12, 2021Peer-reviewedBioinformatics

Accurate prediction of protein structures and interactions using a 3-track network

Minkyung Baek, Frank DiMaio, Ivan V. Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, Richard Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy M. DeGiovanni, , David A. Baker

The paper introduces a 3-track network for accurate protein structure and interaction prediction, achieving performance close to DeepMind's CASP14 results and enabling rapid modeling of protein complexes from sequence data.

Science · Jul 15, 2021Peer-reviewedBioinformatics