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 bench-marks 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 tasks, 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 tasks, all data are presented in a standard format. FLIP scripts and data are freely accessible at https://benchmark.protein.properties.

中文翻译：

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FLIP：蛋白质适应性景观推断的基准任务

机器学习可以在用于治疗和工业应用的蛋白质工程中实现前所未有的控制水平。对于其在设计具有所需特性的蛋白质中的应用至关重要，机器学习模型必须捕捉蛋白质序列-功能关系，通常称为适应度景观. CASP 或 CAFA 等现有基准分别评估蛋白质的结构和功能预测，但它们并不针对与蛋白质工程相关的指标。在这项工作中，我们介绍了蛋白质的适应度景观推断 (FLIP)，这是一种功能预测基准，以鼓励对蛋白质工程的表征学习进行快速评分。我们策划的任务、基线和指标在与蛋白质工程相关的设置中探索模型泛化，例如低资源和外推。目前，FLIP 涵盖了用于基因治疗的腺相关病毒稳定性、蛋白质结构域 B1 稳定性和免疫球蛋白结合以及来自多个蛋白质家族的热稳定性的实验数据。为了便于使用和将来扩展到新任务，所有数据都以标准格式呈现。