Extending the New Generation of Structure Predictors to Account for Dynamics and Allostery

Research output: Contribution to journalArticlepeer-review


Recent progress in structure-prediction methods that rely on deep learning suggests that the atomic structure of almost any protein may soon be predictable directly from its amino acid sequence. This much-awaited revolution was driven by substantial improvements in the reliability of methods for inferring the spatial distances between amino acid pairs from an analysis of homologous sequences. Improved reliability has been accompanied, however, by a reduced ability to detect amino acid relationships that are not due to direct spatial contacts, such as those that arise from protein dynamics or allostery. Given the central importance of dynamics and allostery to protein activity, we argue that an important future advance would extend modeling beyond predicting a single static structure. Here, we briefly review some of the developments that have led to the remarkable recent achievement in structure prediction and speculate what methods and sources of information may be leveraged in the future to develop a modeling framework that addresses protein dynamics and allostery.
Original languageEnglish
Article number167007
Number of pages6
JournalJournal of Molecular Biology
Issue number20
Early online date24 Apr 2021
StatePublished - 1 Oct 2021


Dive into the research topics of 'Extending the New Generation of Structure Predictors to Account for Dynamics and Allostery'. Together they form a unique fingerprint.

Cite this