Publications

*corresponding (**co-corresponding) author

Unified Model of Protein Evolution

• Zhang, Z., Wayment-Steele, H.K., Brixi, G., Wang, H., Dal Peraro, M., Kern, D. and *Ovchinnikov, S., 2024.
  Protein language models learn evolutionary statistics of interacting sequence motifs.
  bioRxiv, pp.2024-01. 🌐Github

• Hettiarachchi, R., Swartz, A. and *Ovchinnikov, S., 2023.
  Differentiable Search of Evolutionary Trees from Leaves.
  bioRxiv, pp.2023-07. 🌐Github

• **Hwang, Y., Cornman, A.L., Kellogg, E.H., **Ovchinnikov, S. and **Girguis, P.R., 2023.
  Genomic language model predicts protein co-regulation and function.
  In press. 🌐Github

• Petti, S., Bhattacharya, N., Rao, R., Dauparas, J., Thomas, N., Zhou, J., Rush, A.M., Koo, P.K. and *Ovchinnikov, S., 2022.
  End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.
  Bioinformatics; Presented at NeurIPS MLSB and MLCB; 🌐GitHub

• Trinquier, J. Petti, S. Feng, S., Soeding, J., Steinegger, M., *Ovchinnikov S., 2022.
  SWAMPNN: End-to-end protein structures alignment. MLSB Workshop at Neurips.
  🌐Github

• Shiau, C., Wang, H., Lee, Y. and *Ovchinnikov, S., 2022.
  Global statistical models of protein coevolution reveal higher-order sectors beyond those obtained from structure alone.
  🌐Github

• Wang, H., Feng, S., Liu, S. and *Ovchinnikov, S., 2022.
  Disentanglement of Entropy and Coevolution using Spectral Regularization.
  🌐Github

• Roney, J.P. and *Ovchinnikov, S., 2022.
  State-of-the-Art estimation of protein model accuracy using AlphaFold.
  Physical Review Letters (PRL Editors' Suggestion) 🌐Github

• Bhattacharya, N., Thomas, N., Rao, R., Dauparas, J., Koo, P.K., Baker, D., Song, Y.S. and *Ovchinnikov, S., 2021.
  Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention.
  In Pacific Symposium on Biocomputing 2022 (pp. 34-45).; 🌐Github

• Rao, R., Meier, J., Sercu, T., Ovchinnikov, S. and Rives, A., 2020.
  Transformer protein language models are unsupervised structure learners.
  ICLR 2021 Conference; 🌐Github

• Marshall, D., Wang, H., Stiffler, M., Dauparas, J., Koo, P. and *Ovchinnikov, S., 2020.
  The structure-fitness landscape of pairwise relations in generative sequence models.
  NeurIPS MLSB Workshop.; 🌐Github

• Agbleke, A.A., Amitai, A., Buenrostro, J.D., Chakrabarti, A., Chu, L., Hansen, A.S., Koenig, K.M., Labade, A.S., Liu, S., Nozaki, T.,
  Ovchinnikov, S., Seeber, A., Shaban H.A, Spille J., Stephens, A.D, Su, J., Wadduwage, D. 2020.
  Advances in chromatin and chromosome research: perspectives from multiple fields.
  Molecular cell, 79(6), pp.881-901.

• Dauparas, J., Wang, H., Swartz, A., Koo, P., Nitzan, M. and *Ovchinnikov, S., 2019.
  Unified framework for modeling multivariate distributions in biological sequences.
  ICML compbio workshop. arXiv preprint arXiv:1906.02598. 🌐Github

• Derkarabetian, S., Castillo, S., Koo, P.K., Ovchinnikov, S. and Hedin, M., 2019.
  A demonstration of unsupervised machine learning in species delimitation.
  Molecular phylogenetics and evolution, 139, p.106562. 🌐Github

Protein Design

• Frank, C.J., Khoshouei, A., de Stigter, Y., Schiewitz, D., Feng, S., **Ovchinnikov, S. and **Dietz, H., 2023.
  Efficient and scalable de novo protein design using a relaxed sequence space.
  bioRxiv. 🌐Colab Notebook

• Watson JL, Juergens D, Bennett NR, Trippe BL, Yim J, Eisenach HE, Ahern W, Borst AJ, Ragotte RJ, Milles LF, Wicky BIM, Hanikel N, Pellock SJ, Courbet A, Sheffler W, Wang J, Venkatesh P, Sappington I, Torres SV, Lauko A, Bortoli VD, Mathieu E, Ovchinnikov S, Barzilay R, Jaakkola TS, DiMaio F, Baek M, Baker D, 2023.
  De novo design of protein structure and function with RFdiffusion.
  Nature, 620(7976), pp.1089-1100. 🌐Github, 🌐Colab Notebook

• Tsuboyama, K., Dauparas, J., Chen, J., Laine, E., Mohseni Behbahani, Y., Weinstein, J.J., Mangan, N.M., Ovchinnikov, S. and Rocklin, G.J., 2023.
  Mega-scale experimental analysis of protein folding stability in biology and design.
  Nature, 620(7973), pp.434-444.

• Hermosilla, A.M., Berner, C., Ovchinnikov, S. and Vorobieva, A.A., 2023.
  Validation of de novo designed water-soluble and transmembrane proteins by in silico folding and melting.
  bioRxiv, pp.2023-06. 🌐Colab Notebook

• Rettie, S.A., Campbell, K.V., Bera, A.K., Kang, A., Kozlov, S., De La Cruz, J., Adebomi, V., Zhou, G., DiMaio, F., **Ovchinnikov, S. and **Bhardwaj, G., 2023.
  Cyclic peptide structure prediction and design using AlphaFold.
  bioRxiv. 🌐Colab Notebook

• Verkuil, R., Kabeli, O., Du, Y., Wicky, B.I., Milles, L.F., Dauparas, J., Baker, D., Ovchinnikov, S., Sercu, T. and Rives, A., 2022.
  Language models generalize beyond natural proteins.

• Dowling, Q., Volkman, H.E., Gray, E.E., Ovchinnikov, S., Cambier, S., Bera, A.K., Bick, M., Kang, A., Stetson, D.B., King, N.P. 2022.
  Computational design of constitutively active cGAS.
  Nature Structural & Molecular Biology.

• Wang, J., Lisanza, S., Juergens, D., Tischer, D., Watson, J.L., Castro, K.M., Ragotte, R., Saragovi, A., Milles, L.F., Baek, M., Anishchenko, I., Yang, W., Hicks, D.R., Expòsit, M., Schlichthaerle, T., Chun, J., Dauparas, J., Bennett, N., Wicky B.I.M., Muenks, A., DiMaio, F. Correia, B., **Ovchinnikov, S. and **Baker, D., 2022.
  Scaffolding protein functional sites using deep learning.
  Science, 377(6604), pp.387-394. 🌐Github 🌐ColabDesign 

• Anishchenko, I., Pellock, S.J., Chidyausiku, T.M., Ramelot, T.A., Ovchinnikov, S., Hao, J., Bafna, K., Norn, C., Kang, A., Bera, A.K., DiMaio, F., Carter, L., Chow, C.M., Montelione, G.T., and Baker, D. 2021.
  De novo protein design by deep network hallucination.
  Nature, 600(7889), pp.547-552.

• Ovchinnikov, S. and Huang, P.S., 2021.
  Structure-based protein design with deep learning.
  Current opinion in chemical biology, 65, pp.136-144.

• Norn, C., Wicky, B.I., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., **Baker, D. and **Ovchinnikov, S., 2021.
  Protein sequence design by conformational landscape optimization.
  Proceedings of the National Academy of Sciences, 118(11). 🌐Github

• Tischer, D., Lisanza, S., Wang, J., Dong, R., Anishchenko, I., Milles, L.F., **Ovchinnikov, S. and **Baker, D., 2020.
  Design of proteins presenting discontinuous functional sites using deep learning.

• Li, D., Ma, Y., Zhou, Y., Gou, J., Zhong, Y., Zhao, L., Han, L., Ovchinnikov, S., Ma, L., Huang, S. and Greisen, P., 2019.
  A structural and data-driven approach to engineering a plant cytochrome P450 enzyme.
  Science China Life Sciences, pp.1-10.

• Zhang, L., Shen, H., Gong, Y., Pang, X., Yi, M., Guo, L., Li, J., Arroyo, S., Lu, X., Ovchinnikov, S., Cheng, G., Liu, X., Jiang, X., Feng, S., and Deng, H. 2019.
  Development of a dual-functional conjugate of antigenic peptide and Fc-III mimetics (DCAF) for targeted antibody blocking.
  Chemical science, 10(11), pp.3271-3280.

• Dou, J., Vorobieva, A., Sheffler, W., Doyle, L., Park, H. Bick, M., Mao, B, Foight, G., Lee, M., Gagnon, L., Carter, L., Sankaran, B., Ovchinnikov, S., Marcos, E., Huang, P., Vaughan, J., Stoddard, B., Baker D, 2018.
  De novo design of a fluorescence-activating β-barrel.
  Nature, 561(7724), p.485.

Protein Structure Prediction

• Gazizov, A., Lian, A., Goverde, C.A., **Ovchinnikov, S. and **Polizzi, N.F., 2023.
  AF2BIND: Predicting ligand-binding sites using the pair representation of AlphaFold2.
  bioRxiv, pp.2023-10. 🌐Colab Notebook

• Feng, S., Chen, Z., Zhang, C., Xie, Y., Ovchinnikov, S., Gao, Y.Q. and Liu, S., 2023.
  ColabDock: inverting AlphaFold structure prediction model for protein-protein docking with experimental restraints.
  bioRxiv, pp.2023-07. 🌐Github

• Pavlopoulos GA, Baltoumas FA, Liu S, Selvitopi O, Camargo AP, Nayfach S, Azad A, Roux S, Call L, Ivanova NN, Chen IM, Paez-Espino D, Karatzas E, Consortium NMPF, Iliopoulos I, Konstantinidis K, Tiedje JM, Pett-Ridge J, Baker D, Visel A, Ouzounis CA, Ovchinnikov S, Buluç A, Kyrpides NC, 2023.
  Unraveling the functional dark matter through global metagenomics.
  Nature, pp.1-9. 🌐Database

• Wayment-Steele, H.K., Ojoawo, A., Otten, R., Apitz, J.M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L. and Kern, D., 2023.
  Predicting multiple conformations via sequence clustering and AlphaFold2.
  Nature, pp.1-3. 🌐Github

• Schweke H, Levin T, Pacesa M, Goverde CA, Kumar P, Duhoo Y, Dornfeld LJ, Dubreuil B, Georgeon S, Ovchinnikov S, Woolfson DN, Correia BE, Dey S, Levy ED, 2023.
  An atlas of protein homo-oligomerization across domains of life.
  bioRxiv, pp.2023-06. 🌐Colab Notebook

• Akdel, M., Pires, D.E.V., Pardo, E.P., Jänes, J., Zalevsky, A.O., Mészáros, B., Bryant, P., Good, L.L., Laskowski, R.A., Pozzati, G., Shenoy, A., Zhu, W., Kundrotas, P., Serra, V.R., Rodrigues, C.H.M., Dunham, A.S., Burke, D., Borkakoti, N., Velankar, S., Frost, A., Basquin, J., Lindorff-Larsen, K., Bateman, A., Kajava, A. V., **Valencia, A., **Ovchinnikov, S., **Durairaj, J., **Ascher, D.B., **Thornton, J.M., **Davey, N.E., **Stein, A., **Elofsson, A., **Croll, T.I., **Beltrao, P. 2022.
  A structural biology community assessment of AlphaFold2 applications.
  Nature Structural & Molecular Biology, 1545-9985.

• Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., **Ovchinnikov, S. and **Steinegger, M., 2022.
  ColabFold: making protein folding accessible to all.
  Nature Methods, pp.1-4. 🌐Github

• Humphreys, I.R., Pei, J., Baek, M., Krishnakumar, A., Anishchenko, I., Ovchinnikov, S., Zhang, J., Ness, T.J., Banjade, S., Bagde, S.R. and Stancheva, V.G., Li, X.  Liu, K., Zheng, Z., Barrero, D.J., Roy, U., Kuper, J., Fernández, I.S., Szakal, B., Branzei, D., Rizo, J., Kisker, C., Greene, E. C., Biggins, S., Keeney S., Miller, E.A., Fromme, J.C., Hendrickson, T.L., Cong, Q., Baker, D. 2021.
  Computed structures of core eukaryotic protein complexes.
  Science, 374(6573), p.eabm4805.

• Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G.R., Wang, J., Cong, Q., Kinch, L.N., Schaeffer, R.D., Millán, C., Park, H., Adams, C., Glassman, C.R., DeGiovanni, A., Pereira, J.H., Rodrigues, A.V., van Dijk, A.A., Ebrecht, A.C., …, Baker, D. 2021.
  Accurate prediction of protein structures and interactions using a three-track neural network.
  Science, 373(6557), pp.871-876. 🌐Github

• Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S. and Baker, D., 2020.
  Improved protein structure prediction using predicted interresidue orientations.
  Proceedings of the National Academy of Sciences, 117(3), pp.1496-1503.

• Cong, Q., Anishchenko, I., Ovchinnikov, S., Baker, D., 2019.
  Protein interaction networks revealed by proteome coevolution.
  Science, 365(6449), pp.185-189.

• Ovchinnikov, S., Park, H., Varghese, N., Huang, P.S., Pavlopoulos, G.A., Kim, D.E., Kamisetty, H., Kyrpides, N.C. and Baker, D., 2017.
  Protein structure determination using metagenome sequence data.
  Science, 355(6322), pp.294-298. 🌐Github

Protein Structure Determination

• Zhang, N., Chang, Y.G., Tseng, R., Ovchinnikov, S., Schwarz, R. and LiWang, A., 2020.
  Solution NMR structure of Se0862, a highly conserved cyanobacterial protein involved in biofilm formation.
  Protein Science, 29(11), pp.2274-2280.

• Cooper, C.J., Zheng, K., Rush, K.W., Johs, A., Sanders, B.C., Pavlopoulos, G.A., Kyrpides, N.C., Podar, M., Ovchinnikov, S., Ragsdale, S.W. and Parks, J.M., 2020.
  Structure determination of the HgcAB complex using metagenome sequence data: insights into microbial mercury methylation.
  Communications biology, 3(1), pp.1-9.

• Wu, X., Siggel, M., Ovchinnikov, S., Mi, W., Svetlov, V., Nudler, E., Liao, M., Hummer, G. and Rapoport, T.A., 2020.
  Structural basis of ER-associated protein degradation mediated by the Hrd1 ubiquitin ligase complex.
  Science, 368(6489), p.eaaz2449.

• Sutherland, M.C., Jarodsky, J.M., Ovchinnikov, S., Baker, D. and Kranz, R.G., 2018.
  Structurally Mapping Endogenous Heme in the CcmCDE Membrane Complex for Cytochrome c Biogenesis.
  Journal of molecular biology.

• Ekiert, D.C., Bhabha, G., Isom, G.L., Greenan, G., Ovchinnikov, S., Henderson, I.R., Cox, J.S. and Vale, R.D., 2017.
  Architectures of lipid transport systems for the bacterial outer membrane.
  Cell, 169(2), pp.273-285.

For work prior to 2017 see Google Scholar