Sergey Ovchinnikov

Sergey Ovchinnikov

Helen and Irwin Sizer Career Development Professor

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. One-shot design of functional protein binders with BindCraft. Pacesa, M, Nickel, L, Schellhaas, C, Schmidt, J, Pyatova, E, Kissling, L, Barendse, P, Choudhury, J, Kapoor, S, Alcaraz-Serna, A et al.. 2025. Nature 646, 483-492.
    doi: 10.1038/s41586-025-09429-6PMID:40866699
  2. De novo design of a peptide modulator to reverse sodium channel dysfunction linked to cardiac arrhythmias and epilepsy. Mahling, R, Hegyi, B, Cullen, ER, Cho, TM, Rodriques, AR, Fossier, L, Yehya, M, Yang, L, Chen, BX, Katchman, AN et al.. 2025. Cell , .
    doi: 10.1016/j.cell.2025.07.038PMID:40829586
  3. Magnetic order in disordered NiCr(BO3)O. Gokhfeld, YS, Kazak, NV, Tarasova, AS, Sukhikh, AS, Velikanov, DA, Eremin, EV, Kondratev, OA, Belyaeva, AO, Gavrilkin, SY, Vasiliev, AD et al.. 2025. Dalton Trans 54, 13271-13281.
    doi: 10.1039/d5dt01230aPMID:40827123
  4. Does Sequence Clustering Confound AlphaFold2? Wayment-Steele, HK, Ovchinnikov, S, Colwell, L, Kern, D. 2025. J Mol Biol 437, 169376.
    doi: 10.1016/j.jmb.2025.169376PMID:40780395
  5. 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.. 2025. Nat Chem Biol , .
    doi: 10.1038/s41589-025-01929-wPMID:40542165
  6. Artificial intelligencemethods for protein folding and design. Zhang, Z, Ou, C, Cho, Y, Akiyama, Y, Ovchinnikov, S. 2025. Curr Opin Struct Biol 93, 103066.
    doi: 10.1016/j.sbi.2025.103066PMID:40505455
  7. Cyclic peptide structure prediction and design using AlphaFold2. Rettie, SA, Campbell, KV, Bera, AK, Kang, A, Kozlov, S, Bueso, YF, De La Cruz, J, Ahlrichs, M, Cheng, S, Gerben, SR et al.. 2025. Nat Commun 16, 4730.
    doi: 10.1038/s41467-025-59940-7PMID:40399308
  8. Patterns and drivers of diatom diversity and abundance in the global ocean. Pierella Karlusich, JJ, Cosnier, K, Zinger, L, Henry, N, Nef, C, Bernard, G, Scalco, E, Dvorak, E, Tara Oceans Coordinators, Rocha Jimenez Vieira, F et al.. 2025. Nat Commun 16, 3452.
    doi: 10.1038/s41467-025-58027-7PMID:40216740
  9. 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 68, 1536-1540.
    doi: 10.1007/s11427-024-2859-1PMID:40100542
  10. 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
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