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FLIP: Benchmark tasks in fitness landscape inference for proteins
Christian Dallago · Jody Mou · Kadina Johnston · Bruce Wittmann · Nicholas Bhattacharya · Sam Goldman · Ali Madani · Kevin Yang

Machine learning could enable an unprecedented level of control in protein engineering for therapeutic and industrial applications. Critical to its use in designing proteins with desired properties, machine learning models must capture the protein sequence-function relationship, often termed fitness landscape. Existing benchmarks like CASP or CAFA assess structure and function predictions of proteins, respectively, yet they do not target metrics relevant for protein engineering. In this work, we introduce Fitness Landscape Inference for Proteins (FLIP), a benchmark for function prediction to encourage rapid scoring of representation learning for protein engineering. Our curated splits, baselines, and metrics probe model generalization in settings relevant for protein engineering, e.g. low-resource and extrapolative. Currently, FLIP encompasses experimental data across adeno-associated virus stability for gene therapy, protein domain B1 stability and immunoglobulin binding, and thermostability from multiple protein families. In order to enable ease of use and future expansion to new splits, all data are presented in a standard format. FLIP scripts and data are freely accessible at https://benchmark.protein.properties .

Author Information

Christian Dallago (Technical University of Munich)
Jody Mou (Massachusetts Institute of Technology)
Kadina Johnston (California Institute of Technology)
Bruce Wittmann
Nicholas Bhattacharya (UC Berkeley)
Sam Goldman (MIT)

MIT PhD Student in Computational and Systems Biology

Ali Madani (Salesforce Research)
Kevin Yang (Microsoft)

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