Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction

The rapid development
of deep learning-based methods has considerably
advanced the field of protein structure prediction. The accuracy of
predicting the 3D structures of simple proteins is comparable to that
of experimentally determined structures, providing broad possibilities
for structure-based biological studies. Another critical question
is whether and how multistate structures can be predicted from a given
protein sequence. In this study, analysis of tens of two-state proteins
demonstrated that deep learning-based contact map predictions contain
structural information on both states, which suggests that it is probably
appropriate to change the target of deep learning-based protein structure
prediction from one specific structure to multiple likely structures.
Furthermore, by combining deep learning- and physics-based computational
methods, we developed a protocol for exploring alternative conformations
from a known structure of a given protein, by which we successfully
approached the holo-state conformations of multiple representative
proteins from their apo-state structures.