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Backbone Structure-Conditioned Sequence Design Using Machine Learning-Based Approaches
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DOI：10.1134/s1068162026601497.png)
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Abstract
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Rational modification and enhancement of protein function are extremely important both for understanding the fundamental principles of operation of natural macromolecules and for practical applications. Recent flourishing of machine learning-based methods in structural biology led to noteworthy advances in protein structure prediction tasks, which in turn enabled development of versatile protein engineering approaches. Among them, the methods aimed at predicting the sequence that would fold into a conditioned structure proved particularly useful. Here, we review the diversity of these methods, their applications in basic and applied research, and discuss the challenges that remain to be overcome in the future.
Keywords:
protein engineering
de novo protein design
protein stability
machine learning
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