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ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering
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ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering
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Paper
ProteusAI: An Open-Source and User-Friendly Platform for Machine Learning-Guided Protein Design and Engineering
2024
Overview
Protein design and engineering are crucial for advancements in biotechnology, medicine, and sustainability. Machine learning (ML) models are used to design or enhance protein properties such as stability, catalytic activity, and selectivity. However, many existing ML tools require specialized expertise or lack open-source availability, limiting broader use and further development. To address this, we developed ProteusAI, a user-friendly and open-source ML platform to streamline protein engineering and design tasks. ProteusAI offers modules to support researchers in various stages of the design-build-test-learn (DBTL) cycle, including protein discovery, structure-based design, zero-shot predictions, and ML-guided directed evolution (MLDE). Our benchmarking results demonstrate ProteusAI’s efficiency in improving proteins and enyzmes within a few DBTL-cycle iterations. ProteusAI democratizes access to ML-guided protein engineering and is freely available for academic and commercial use. Future work aims to expand and integrate novel methods in computational protein and enzyme design to further develop ProteusAI.
Publisher
Cold Spring Harbor Laboratory
Subject
