What question did this study set out to answer?

This work aims to explore how machine learning can enhance protein function by predicting sequences that fold into specific structures.

May 7, 2026

Backbone Structure-Conditioned Sequence Design Using Machine Learning-Based Approaches

Key Points

This work aims to explore how machine learning can enhance protein function by predicting sequences that fold into specific structures.
Reviewed various machine learning-based approaches in structural biology.
Discussed applications of these methods in both basic and applied protein engineering.
Identified ongoing challenges in protein structure prediction and sequence design.
Machine learning methods have significantly advanced protein structure prediction tasks.
Diverse techniques have been developed for predicting sequences that fold into conditioned protein structures.
Identification of remaining challenges points towards future directions in the field.

Abstract

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.

Backbone Structure-Conditioned Sequence Design Using Machine Learning-Based Approaches

Key Points

Abstract

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