Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning

We
use a combination of Brownian dynamics (BD) simulation results
and deep learning (DL) strategies for the rapid identification of
large structural changes caused by missense mutations in intrinsically
disordered proteins (IDPs). We used ∼6500 IDP sequences from
MobiDB database of length 20–300 to obtain gyration radii from
BD simulation on a coarse-grained single-bead amino acid model (HPS2
model) used by us and others [Dignon, G. L. PLoS Comput. Biol. 2018, 14, e1005941,Tesei, G. Proc. Natl. Acad. Sci. U.S.A. 2021, 118, e2111696118,Seth, S. J. Chem. Phys. 2024, 160, 014902] to
generate the training sets for the DL algorithm. Using the gyration
radii ⟨Rg⟩ of the simulated
IDPs as the training set, we develop a multilayer perceptron neural
net (NN) architecture that predicts the gyration radii of 33 IDPs
previously studied by using BD simulation with 97% accuracy from the
sequence and the corresponding parameters from the HPS model. We now
utilize this NN to predict gyration radii of every permutation of
missense mutations in IDPs. Our approach successfully identifies mutation-prone
regions that induce significant alterations in the radius of gyration
when compared to the wild-type IDP sequence. We further validate the
prediction by running BD simulations on the subset of identified mutants.
The neural network yields a (104–106)-fold
faster computation in the search space for potentially harmful mutations.
Our findings have substantial implications for rapid identification
and understanding of diseases related to missense mutations in IDPs
and for the development of potential therapeutic interventions. The
method can be extended to accurate predictions of other mutation effects
in disordered proteins.