Author

Michael Allen Stiffler

Dana-Farber Cancer Institute

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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Bioinformatics· 1 Synthetic Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:protein sequence· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:gene regulatory network inference· 1 Research Focus:protein design· 1 Research Focus:protein structure prediction· 1

Frequent Co-Authors

Chris SanderiD· 2 shared Debora S. MarksiD· 2 shared Nicholas Paul GauthieriD· 2 shared Adam J. RiesselmaniD· 1 shared Alessandro Passera· 1 shared Amir Rafiq KhaniD· 1 shared Benjamin J. FramiD· 1 shared Christopher D. BahliD· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersSynthetic Biology

Chris SanderORCID

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Author profile →

3 papers

Recent·Most cited

Simultaneous enhancement of multiple functional properties using evolution-informed protein design

Benjamin J. Fram, Yang Su, Ian Truebridge, Adam J. Riesselman, John Ingraham, Alessandro Passera, Eve Napier, Nicole N. Thadani, Samuel Lim, Kristen Roberts, Gurleen Kaur, Michael Allen Stiffler, Debora S. Marks, Christopher D. Bahl, Amir Rafiq Khan, , Nicholas Paul Gauthier

This study uses evolutionary models to design highly divergent protein variants with enhanced functional properties while maintaining structure.

Nature Communications · Jun 20, 2024Peer-reviewedSynthetic Biology

The structure-fitness landscape of pairwise relations in generative sequence models

Dylan Marshall, Haobo Wang, Michael Allen Stiffler, Justas Dauparas, Peter K. Koo, Sergey Ovchinnikov

This study evaluates how well generative sequence models capture structural and fitness patterns from protein families, finding that structural and fitness information are not necessarily correlated.

bioRxiv · Nov 29, 2020 · PreprintBioinformatics

3D protein structure from genetic epistasis experiments

Nathan J. Rollins, Kelly P. Brock, Frank J. Poelwijk, Michael Allen Stiffler, Nicholas Paul Gauthier, Chris Sander, Debora S. Marks

The paper uses genetic epistasis experiments to predict 3D protein structures with 86% accuracy for residue contacts and generates models within 2.4 Å of known structures for small proteins.

bioRxiv · May 11, 2018 · PreprintSystems Biology