Sergey Ovchinnikov

About

Sergey Ovchinnikov is a Helen and Irwin Sizer Career Development Professor at MIT Department of Biology. He studies protein structure and evolution at environmental, organismal, genomic, structural, and molecular scales. His research employs phylogenetic inference, protein structure prediction and determination, protein design, deep learning, energy-based models, and differentiable programming to address evolutionary questions. His goal is to develop a unified model of protein evolution.

Research topics

• Computer Science
• Artificial Intelligence
• Biology
• Biochemistry
• Machine Learning
• Computational biology
• Chemistry
• Engineering
• Biological system
• Algorithm

Selected publications

• ColabFold: making protein folding accessible to all

  Nature Methods · 2022-05-30 · 9616 citations

  articleOpen accessCorresponding

  ColabFold offers accelerated prediction of protein structures and complexes by combining the fast homology search of MMseqs2 with AlphaFold2 or RoseTTAFold. ColabFold's 40-60-fold faster search and optimized model utilization enables prediction of close to 1,000 structures per day on a server with one graphics processing unit. Coupled with Google Colaboratory, ColabFold becomes a free and accessible platform for protein folding. ColabFold is open-source software available at https://github.com/s…

  PublisherOA PDFDOI

• Unraveling the functional dark matter through global metagenomics

  Nature · 2023 · 193 citations

  . Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overa…

  PublisherOA PDFDOI

• One-shot design of functional protein binders with BindCraft

  Nature · 2025-08-27 · 128 citations

  articleOpen access

  Protein–protein interactions are at the core of all key biological processes. However, the complexity of the structural features that determine protein–protein interactions makes their design challenging. Here we present BindCraft, an open-source and automated pipeline for de novo protein binder design with experimental success rates of 10–100%. BindCraft leverages the weights of AlphaFold2 (ref. 1) to generate binders with nanomolar affinity without the need for high-throughput screening or exp…

  PublisherOA PDFDOI

• Cyclic peptide structure prediction and design using AlphaFold2

  Nature Communications · 2025-05-21 · 50 citations

  articleOpen access

  Small cyclic peptides have gained significant traction as a therapeutic modality; however, the development of deep learning methods for accurately designing such peptides has been slow, mostly due to the lack of sufficiently large training sets. Here, we introduce AfCycDesign, a deep learning approach for accurate structure prediction, sequence redesign, and de novo hallucination of cyclic peptides. Using AfCycDesign, we identified over 10,000 structurally-diverse designs predicted to fold into…

  PublisherOA PDFDOI

• Accurate de novo design of high-affinity protein-binding macrocycles using deep learning

  Nature Chemical Biology · 2025-06-20 · 43 citations

  articleOpen access

  Abstract Developing macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource intensive and provide little control over binding mode. Despite progress in protein design, there are currently no robust approaches for de novo design of protein-binding macrocycles. Here we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic binders against protein targets of interest. We tested 20 or fewer designed macrocycles aga…

  PublisherOA PDFDOI

Recent grants

• Exploring the unknown protein universe using evolutionary information

  NIH · $2.0M · 2018–2024

Frequent coauthors

• David Baker

  University of Washington

  108 shared

• Milot Mirdita

  Seoul National University

  33 shared

• Martin Steinegger

  Seoul National University

  32 shared

• Yoshitaka Moriwaki

  The University of Tokyo

  30 shared

• Lim Heo

  Michigan State University

  29 shared

• Justas Dauparas

  University of Washington

  28 shared

• Konstantin Schütze

  Seoul National University

  28 shared

• Ivan Anishchenko

  27 shared

Labs

• Sergey Ovchinnikov LabPI

Education

• PhD, Molecular and Cellular Biology / Biochemistry

  University of Washington

  2017

• BS, Micro/Molecular Biology

  Portland State University

  2010

Similar researchers at Massachusetts Institute of Technology

• Rudolf Jaenischh-295
• Robert A. Weinbergh-291
• Susumu Tonegawah-237