GENERATING PROTEIN SEQUENCES USING MACHINE LEARNING MODELS

Abstract: the present disclosure describes techniques for generating protein sequences using machine learning models. a machine learning model is configured by implanting a structural adapter into a sequence decoder. the machine learning model is configured to generate a protein sequence from a specified structure. the machine learning model is endowed with protein structural awareness by the structural adapter. the machine learning model is equipped with protein sequential evolutionary knowledge by the sequence decoder. the machine learning model comprises the structural adapter, the sequence decoder, and a structure encoder. an initial sequence is generated based on the specified structure by the structure encoder. the protein sequence is optimized through an iterative process. the iterative process comprises progressively refining the protein sequence by iterative decoding. the structural adapter non-linearly imposes representations of the specified structure on a sequence predicted in the iterative process.

Inventor(s): Zaixiang Zheng, Yifan Deng, Dongyu Xue, Yi Zhou, Fei Ye, Quanquan Gu

CPC Classification: G06N20/00 (Machine learning)

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