Publications

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Unified Model of Protein Evolution

2025
CIRPIN: Learning Circular Permutation-Invariant Representations to Uncover Putative Protein Homologs.
Kolodziej A, Abulnaga M and Ovchinnikov S. bioRxiv. GitHub
2025
Hit or Miss: Understanding Emergence and Absence of Homo-oligomeric Contacts in Protein Language Models.
Zhang Z, Akiyama Y, Cho Y, Jajoo S and Ovchinnikov S. bioRxiv.
2025
Democratizing protein language model training, sharing and collaboration.
Su J, Li Z, Tao T et al. Nature Biotechnology, pp.1-7.
2024
The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling.
Cornman A, West-Roberts J, Camargo AP, Roux S, Beracochea M, Mirdita M, Ovchinnikov S and Hwang Y. bioRxiv.
2024
Disentanglement of Evolutionary Constraints in Statistical Models of Proteins.
Wang H, Feng S, Tsuboyama K, Liu S, Rocklin GJ and Ovchinnikov S. PRX Life, 2 (2), 023005.
2024
Protein language models learn evolutionary statistics of interacting sequence motifs.
Zhang Z, Wayment-Steele HK, Brixi G, Wang H, Dal Peraro M, Kern D and Ovchinnikov S. bioRxiv, pp.2024-01. GitHub
2023
Differentiable Search of Evolutionary Trees from Leaves.
Hettiarachchi R, Swartz A and Ovchinnikov S. bioRxiv, pp.2023-07. GitHub
2023
Genomic language model predicts protein co-regulation and function.
Hwang Y, Cornman AL, Kellogg EH, Ovchinnikov S and Girguis PR. In press. GitHub
2022
End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman.
Petti S, Bhattacharya N, Rao R, Dauparas J, Thomas N, Zhou J, Rush AM, Koo PK and Ovchinnikov S. Bioinformatics ; Presented at NeurIPS MLSB and MLCB GitHub
2022
SWAMPNN: End-to-end protein structures alignment.
Trinquier J, Petti S, Feng S, Soeding J, Steinegger M, Ovchinnikov S. MLSB Workshop at NeurIPS. GitHub
2022
Global statistical models of protein coevolution reveal higher-order sectors beyond those obtained from structure alone.
Shiau C, Wang H, Lee Y and Ovchinnikov S. GitHub
2022
Disentanglement of Entropy and Coevolution using Spectral Regularization.
Wang H, Feng S, Liu S and Ovchinnikov S. GitHub
2022
State-of-the-Art estimation of protein model accuracy using AlphaFold.
Roney JP and Ovchinnikov S. Physical Review Letters (PRL Editors' Suggestion) GitHub
2021
Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention.
Bhattacharya N, Thomas N, Rao R, Dauparas J, Koo PK, Baker D, Song YS and Ovchinnikov S. Pacific Symposium on Biocomputing 2022. GitHub
2020
Transformer protein language models are unsupervised structure learners.
Rao R, Meier J, Sercu T, Ovchinnikov S and Rives A. ICLR 2021. GitHub
2020
The structure-fitness landscape of pairwise relations in generative sequence models.
Marshall D, Wang H, Stiffler M, Dauparas J, Koo P and Ovchinnikov S. NeurIPS MLSB Workshop. GitHub
2020
Advances in chromatin and chromosome research: perspectives from multiple fields.
Agbleke AA, Amitai A, Buenrostro JD et al. Molecular Cell, 79 (6), pp.881-901.
2019
Unified framework for modeling multivariate distributions in biological sequences.
Dauparas J, Wang H, Swartz A, Koo P, Nitzan M and Ovchinnikov S. ICML CompBio Workshop. GitHub
2019
A demonstration of unsupervised machine learning in species delimitation.
Derkarabetian S, Castillo S, Koo PK, Ovchinnikov S and Hedin M. Molecular Phylogenetics and Evolution, 139, 106562. GitHub

Protein Design

2026
Toward life with a 19-amino acid alphabet through generative artificial intelligence design.
Liu L, Rochereau C, Kozlov S et al. Science, 392 (6797), eaeb5171.
2026
Accurate protein stability prediction for small domains using mega-scale experiments.
Cho Y, Tsuboyama K, Litberg TJ, Jung MD, Obisesan A, Wang Q, Phoumyvong CM, Thibeault J, Ovchinnikov S and Rocklin GJ. bioRxiv.
2025
Protein Hunter: exploiting structure hallucination within diffusion for protein design.
Cho Y, Rangel G, Bhardwaj G and Ovchinnikov S. bioRxiv.
2025
BoltzDesign1: Inverting all-atom structure prediction model for generalized biomolecular binder design.
Cho Y, Pacesa M, Zhang Z, Correia BE and Ovchinnikov S. bioRxiv. GitHub
2025
Stable de novo protein design via joint conformational landscape and sequence optimization.
Cho Y, Dauparas J, Tsuboyama K, Rocklin GJ and Ovchinnikov S. Nature Communications.
2025
Designing novel solenoid proteins with in silico evolution.
Pretorius D, Nikov GI, Washio K, Florent SW, Taunt HN, Ovchinnikov S and Murray JW. Communications Chemistry.
2025
Artificial intelligence methods for protein folding and design.
Zhang Z, Ou C, Cho Y, Akiyama Y and Ovchinnikov S. Current Opinion in Structural Biology, 93, 103066.
2025
Accurate de novo design of high-affinity protein-binding macrocycles using deep learning.
Rettie SA, Juergens D, Adebomi V et al. Nature Chemical Biology, pp.1-9.
2025
De novo design of a peptide modulator to reverse sodium channel dysfunction linked to cardiac arrhythmias and epilepsy.
Mahling R, Hegyi B, Cullen ER et al. Cell, 188 (22), pp.6170-6185.
2024
Computational design of soluble and functional membrane protein analogues.
Goverde CA, Pacesa M, Goldbach N et al. Nature, 631 (8020), pp.449-458.
2023
De novo design of small beta barrel proteins.
Kim DE, Jensen DR, Feldman D et al. PNAS, 120 (11), e2207974120.
2024
Scalable protein design using optimization in a relaxed sequence space.
Frank C, Khoshouei A, Fuß L et al. Science, 386 (6720), pp.439-445. Colab Notebook
2023
De novo design of protein structure and function with RFdiffusion.
Watson JL, Juergens D, Bennett NR et al. Nature, 620 (7976), pp.1089-1100. GitHub Colab Notebook
2023
Mega-scale experimental analysis of protein folding stability in biology and design.
Tsuboyama K, Dauparas J, Chen J et al. Nature, 620 (7973), pp.434-444.
2024
Validation of de novo designed water-soluble and transmembrane β-barrels by in silico folding and melting.
Hermosilla AM, Berner C, Ovchinnikov S and Vorobieva AA. Protein Science, 33 (7), e5033. Colab Notebook
2025
Cyclic peptide structure prediction and design using AlphaFold2.
Rettie SA, Campbell KV, Bera AK et al. Nature Communications, 16 (1), 4730. Colab Notebook
2022
Language models generalize beyond natural proteins.
Verkuil R, Kabeli O, Du Y et al. bioRxiv.
2022
Computational design of constitutively active cGAS.
Dowling Q, Volkman HE, Gray EE, Ovchinnikov S et al. Nature Structural & Molecular Biology.
2022
Scaffolding protein functional sites using deep learning.
Wang J, Lisanza S, Juergens D et al. Science, 377 (6604), pp.387-394. GitHub ColabDesign
2021
De novo protein design by deep network hallucination.
Anishchenko I, Pellock SJ, Chidyausiku TM, Ramelot TA, Ovchinnikov S et al. and Baker D. Nature, 600 (7889), pp.547-552.
2021
Structure-based protein design with deep learning.
Ovchinnikov S and Huang PS. Current Opinion in Chemical Biology, 65, pp.136-144.
2021
Protein sequence design by conformational landscape optimization.
Norn C, Wicky BI, Juergens D et al. PNAS, 118 (11). GitHub
2020
Design of proteins presenting discontinuous functional sites using deep learning.
Tischer D, Lisanza S, Wang J, Dong R, Anishchenko I, Milles LF, Ovchinnikov S and Baker D. bioRxiv.
2019
A structural and data-driven approach to engineering a plant cytochrome P450 enzyme.
Li D, Ma Y, Zhou Y, Gou J, Zhong Y, Zhao L, Han L, Ovchinnikov S et al. Science China Life Sciences, pp.1-10.
2019
Development of a dual-functional conjugate of antigenic peptide and Fc-III mimetics (DCAF) for targeted antibody blocking.
Zhang L, Shen H, Gong Y et al. Chemical Science, 10 (11), pp.3271-3280.
2018
De novo design of a fluorescence-activating β-barrel.
Dou J, Vorobieva A, Sheffler W et al. Nature, 561 (7724), p.485.

Protein Structure Prediction

2025
Protein Diffusion Models as Statistical Potentials.
Roney JP, Ou C and Ovchinnikov S. bioRxiv.
2025
Does sequence clustering confound AlphaFold2?
Wayment-Steele HK, Ovchinnikov S, Colwell L and Kern D. Journal of Molecular Biology, 169376.
2025
Learning millisecond protein dynamics from what is missing in NMR spectra.
Wayment-Steele HK, El Nesr G, Hettiarachchi R, Ojoawo AM, Kariyawasam H, Ovchinnikov S and Kern D. bioRxiv.
2025
actifpTM: a refined confidence metric of AlphaFold2 predictions involving flexible regions.
Varga JK, Ovchinnikov S and Schueler-Furman O. Bioinformatics, 41 (3), btaf107.
2024
NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes.
Baltoumas FA, Karatzas E, Liu S, Ovchinnikov S, Sofianatos Y, Chen IM, Kyrpides NC and Pavlopoulos GA. Nucleic Acids Research, 52 (D1), pp.D502-D512.
2026
AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2.
Gazizov A, Lian A, Goverde CA, Mou J, Ovchinnikov S and Polizzi NF. Nature Methods, 23 (3), pp.626-635. Colab Notebook
2024
Integrated structure prediction of protein-protein docking with experimental restraints using ColabDock.
Feng S, Chen Z, Zhang C, Xie Y, Ovchinnikov S, Gao YQ and Liu S. Nature Machine Intelligence, 6 (8), pp.924-935. GitHub
2023
Co-evolution-based prediction of metal-binding sites in proteomes by machine learning.
Cheng Y, Wang H, Xu H et al. Nature Chemical Biology, 19 (5), pp.548-555.
2023
Exploring microbial functional biodiversity at the protein family level—From metagenomic sequence reads to annotated protein clusters.
Baltoumas FA, Karatzas E, Paez-Espino D et al. Frontiers in Bioinformatics, 3, 1157956.
2023
Unraveling the functional dark matter through global metagenomics.
Pavlopoulos GA, Baltoumas FA, Liu S et al. Nature, pp.1-9. Database
2023
Predicting multiple conformations via sequence clustering and AlphaFold2.
Wayment-Steele HK, Ojoawo A, Otten R et al. Nature, pp.1-3. GitHub
2023
An atlas of protein homo-oligomerization across domains of life.
Schweke H, Levin T, Pacesa M et al. bioRxiv. Colab Notebook
2022
A structural biology community assessment of AlphaFold2 applications.
Akdel M, Pires DEV, Pardo EP et al. Nature Structural & Molecular Biology, 1545-9985.
2022
ColabFold: making protein folding accessible to all.
Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S and Steinegger M. Nature Methods, pp.1-4. GitHub
2021
Computed structures of core eukaryotic protein complexes.
Humphreys IR, Pei J, Baek M et al. Science, 374 (6573), p.eabm4805.
2021
Accurate prediction of protein structures and interactions using a three-track neural network.
Baek M, DiMaio F, Anishchenko I et al. Science, 373 (6557), pp.871-876. GitHub
2020
Improved protein structure prediction using predicted interresidue orientations.
Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S and Baker D. PNAS, 117 (3), pp.1496-1503.
2019
Protein interaction networks revealed by proteome coevolution.
Cong Q, Anishchenko I, Ovchinnikov S, Baker D. Science, 365 (6449), pp.185-189.
2017
Protein structure determination using metagenome sequence data.
Ovchinnikov S, Park H, Varghese N, Huang PS, Pavlopoulos GA, Kim DE, Kamisetty H, Kyrpides NC and Baker D. Science, 355 (6322), pp.294-298. GitHub

Protein Structure Determination

2020
Solution NMR structure of Se0862, a highly conserved cyanobacterial protein involved in biofilm formation.
Zhang N, Chang YG, Tseng R, Ovchinnikov S, Schwarz R and LiWang A. Protein Science, 29 (11), pp.2274-2280.
2020
Structure determination of the HgcAB complex using metagenome sequence data: insights into microbial mercury methylation.
Cooper CJ, Zheng K, Rush KW et al. Communications Biology, 3 (1), pp.1-9.
2020
Structural basis of ER-associated protein degradation mediated by the Hrd1 ubiquitin ligase complex.
Wu X, Siggel M, Ovchinnikov S, Mi W, Svetlov V, Nudler E, Liao M, Hummer G and Rapoport TA. Science, 368 (6489), eaaz2449.
2018
Structurally Mapping Endogenous Heme in the CcmCDE Membrane Complex for Cytochrome c Biogenesis.
Sutherland MC, Jarodsky JM, Ovchinnikov S, Baker D and Kranz RG. Journal of Molecular Biology.
2017
Architectures of lipid transport systems for the bacterial outer membrane.
Ekiert DC, Bhabha G, Isom GL, Greenan G, Ovchinnikov S, Henderson IR, Cox JS and Vale RD. Cell, 169 (2), pp.273-285.