Results of the Protein Engineering Tournament: An Open Science Benchmark for Protein Modeling and Design
Chase ArmeriD, Hassan Kané, Dana L. CortadeiD, Henning RedestigiD, David A. EstelliD, Adil YusufiD, Nathan J. RollinsiD, Aviv SpinneriD, Debora S. MarksiD, TJ BrunetteiD, Peter J. KellyiD, Erika Alden DeBenedictisiD
Abstract
The grand challenge of protein engineering is the development of computational models to characterize and generate protein sequences for arbitrary functions. Progress is limited by lack of 1) benchmarking opportunities, 2) large protein function datasets, and 3) access to experimental protein characterization. We introduce the Protein Engineering Tournament--a fully-remote competition designed to foster the development and evaluation of computational approaches in protein engineering. The tournament consists of an in silico round, predicting biophysical properties from protein sequences, followed by an in vitro round where novel protein sequences are designed, expressed and characterized using automated methods. Upon completion, all datasets, experimental protocols, and methods are made publicly available. We detail the structure and outcomes of a pilot Tournament involving seven protein design teams, powered by six multi-objective datasets, with experimental characterization by our partner, International Flavors and Fragrances. Forthcoming Protein Engineering Tournaments aim to mobilize the scientific community towards transparent evaluation of progress in the field. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/606135v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8f9769org.highwire.dtl.DTLVardef@11da734org.highwire.dtl.DTLVardef@1cc51c0org.highwire.dtl.DTLVardef@10b3c27_HPS_FORMAT_FIGEXP M_FIG C_FIG
02 /Specification & Classification
Click any tag to browse matching papers- Citations
- 26
- FWCI
- 5.05
- Field percentile
- top 3%
- Assay
- Research Focus
- Approach
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering
Jonathan Funk, Laura Machado, … Carlos G. Acevedo‐Rocha·bioRxiv·Oct 1, 2024
- FLOP: Tasks for Fitness Landscapes Of Protein wildtypes
Peter Mørch Groth, Michaël Richard, … Wouter Boomsma·bioRxiv·Jun 21, 2023
- Efficient Protein Engineering via Integrated Language Models and Bayesian Optimization
Joshua Meehl, Prasad Siddavatam·bioRxiv·Sep 30, 2025
- Protein Language Models: Learning From Evolution, Designing Beyond It
Maurice Brenner, Julius Schlensok, … Michael Heinzinger·bioRxiv·Sep 24, 2026
- Unified sampling framework and experimental benchmarking of sequence- and structure-based protein models
Aviv Spinner, Pascal Notin, … Debora S. Marks·bioRxiv·May 12, 2026
- FLIP: Benchmark tasks in fitness landscape inference for proteins
C. Dallago, J. Mou, … K. K. Yang·bioRxiv·Nov 9, 2021