End-to-End Optimized Pipeline for Prediction of Protein Folding Kinetics

TL;DR

This paper introduces an end-to-end machine learning pipeline that accurately predicts protein folding kinetics with significantly reduced memory usage and faster performance, aiding early detection of folding-related diseases.

Contribution

It presents a novel, efficient ML pipeline that outperforms existing models in accuracy, memory efficiency, and speed for predicting protein folding kinetics.

Findings

Achieved 4.8% higher accuracy than state-of-the-art models.

Reduced memory consumption by 327 times.

Improved prediction speed by 7.3%.

Abstract

Protein folding is the intricate process by which a linear sequence of amino acids self-assembles into a unique three-dimensional structure. Protein folding kinetics is the study of pathways and time-dependent mechanisms a protein undergoes when it folds. Understanding protein kinetics is essential as a protein needs to fold correctly for it to perform its biological functions optimally, and a misfolded protein can sometimes be contorted into shapes that are not ideal for a cellular environment giving rise to many degenerative, neuro-degenerative disorders and amyloid diseases. Monitoring at-risk individuals and detecting protein discrepancies in a protein's folding kinetics at the early stages could majorly result in public health benefits, as preventive measures can be taken. This research proposes an efficient pipeline for predicting protein folding kinetics with high accuracy and low memory footprint. The deployed machine learning (ML) model outperformed the state-of-the-art ML models by 4.8% in terms of accuracy while consuming 327x lesser memory and being 7.3% faster.