Rapid prediction of protein natural frequencies using graph neural networks.

ABSTRACT: Natural vibrational frequencies of proteins help to correlate functional shifts with sequence or geometric variations that lead to negligible changes in protein structures, such as point mutations related to disease lethality or medication effectiveness. Normal mode analysis is a well-known approach to accurately obtain protein natural frequencies. However, it is not feasible when high-resolution protein structures are not available or time consuming to obtain. Here we provide a machine learning model to directly predict protein frequencies from primary amino acid sequences and low-resolution structural features such as contact or distance maps. We utilize a graph neural network called principal neighborhood aggregation, trained with the structural graphs and normal mode frequencies of mor

SUBMITTER: Guo K 

PROVIDER: S-EPMC9189858 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature