Sergey Ovchinnikov

Sergey Ovchinnikov

Assistant Professor of Biology

Sergey Ovchinnikov studies protein structure and evolution at environmental, organismal, genomic, structural, and molecular scales.

68-533A

Office

so3@mit.edu

Email

Building 68 - Koch Biology Building

Location

Lab Website
Martha Pham

Assistant

617-258-7851

Assistant Phone

Education

  • Graduate: PhD, 2017, University of Washington
  • Undergraduate: BS, 2010, Micro/Molecular Biology, Portland State University

Research Summary

Sergey Ovchinnikov uses phylogenetic inference, protein structure prediction/determination, protein design, deep learning, energy-based models, and differentiable programming to tackle evolutionary questions at environmental, organismal, genomic, structural, and molecular scales, with the aim of developing a unified model of protein evolution.

Recent Publications

  1. Biomedical data and AI. Xu, H, Zhou, S, Zhu, Z, Vitelli, V, Chen, L, Dai, Z, Yang, N, Lai, L, Yang, S, Ovchinnikov, S et al.. 2025. Sci China Life Sci , .
    doi: 10.1007/s11427-024-2859-1PMID:40100542
  2. actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions. Varga, JK, Ovchinnikov, S, Schueler-Furman, O. 2025. Bioinformatics 41, .
    doi: 10.1093/bioinformatics/btaf107PMID:40080667
  3. Ferromagnetism in LaCoO3: relationship between the crystal structure, morphology and magnetic properties. Orlov, YS, Dudnikov, VA, Vereshchagin, SN, Ustyuzhanin, YN, Nikolaev, SV, Zharkov, SM, Volochaev, MN, Zeer, GM, Gavrilkin, SY, Tsvetkov, AY et al.. 2025. Dalton Trans 54, 4530-4541.
    doi: 10.1039/d4dt03135kPMID:39937131
  4. BindCraft: one-shot design of functional protein binders. Pacesa, M, Nickel, L, Schellhaas, C, Schmidt, J, Pyatova, E, Kissling, L, Barendse, P, Choudhury, J, Kapoor, S, Alcaraz-Serna, A et al.. 2024. bioRxiv , .
    doi: 10.1101/2024.09.30.615802PMID:39677777
  5. Predicting absolute protein folding stability using generative models. Cagiada, M, Ovchinnikov, S, Lindorff-Larsen, K. 2025. Protein Sci 34, e5233.
    doi: 10.1002/pro.5233PMID:39673466
  6. Accurate de novo design of high-affinity protein binding macrocycles using deep learning. Rettie, SA, Juergens, D, Adebomi, V, Bueso, YF, Zhao, Q, Leveille, AN, Liu, A, Bera, AK, Wilms, JA, Üffing, A et al.. 2024. bioRxiv , .
    doi: 10.1101/2024.11.18.622547PMID:39605685
  7. Protein language models learn evolutionary statistics of interacting sequence motifs. Zhang, Z, Wayment-Steele, HK, Brixi, G, Wang, H, Kern, D, Ovchinnikov, S. 2024. Proc Natl Acad Sci U S A 121, e2406285121.
    doi: 10.1073/pnas.2406285121PMID:39467119
  8. Scalable protein design using optimization in a relaxed sequence space. Frank, C, Khoshouei, A, Fuβ, L, Schiwietz, D, Putz, D, Weber, L, Zhao, Z, Hattori, M, Feng, S, de Stigter, Y et al.. 2024. Science 386, 439-445.
    doi: 10.1126/science.adq1741PMID:39446959
  9. Easy and accurate protein structure prediction using ColabFold. Kim, G, Lee, S, Levy Karin, E, Kim, H, Moriwaki, Y, Ovchinnikov, S, Steinegger, M, Mirdita, M. 2025. Nat Protoc 20, 620-642.
    doi: 10.1038/s41596-024-01060-5PMID:39402428
  10. Computational design of soluble and functional membrane protein analogues. Goverde, CA, Pacesa, M, Goldbach, N, Dornfeld, LJ, Balbi, PEM, Georgeon, S, Rosset, S, Kapoor, S, Choudhury, J, Dauparas, J et al.. 2024. Nature 631, 449-458.
    doi: 10.1038/s41586-024-07601-yPMID:38898281
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