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...

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Bibliographic Details
Main Author:	Swarnadeep Seth (20868664) (author)
Other Authors:	Aniket Bhattacharya (5234780) (author)
Published:	2025
Subjects:	Biophysics Biochemistry Medicine Genetics Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified potential therapeutic interventions neural network yields induce significant alterations potentially harmful mutations running bd simulations predict gyration radii obtain gyration radii gyration radii ⟨< type idp sequence intrinsically disordered proteins using bd simulation gyration radii disordered proteins bd simulation missense mutations simulation results training sets training set substantial implications search space rapid identification r </ prone regions plos comput mutation effects mobidb database identified mutants hps2 model hps model grained single every permutation diseases related deep learning corresponding parameters brownian dynamics accurate predictions >< sub
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Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning

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