# Mechanism-Driven Features Enable Asn Deamidation Reactivity Prediction via Machine Learning Methods

## TL;DR

This paper uses machine learning and molecular dynamics to predict which asparagine residues in proteins are likely to undergo deamidation based on structural and environmental factors.

## Contribution

The study introduces mechanism-driven features derived from molecular dynamics simulations to improve Asn deamidation reactivity prediction.

## Key findings

- Random Forest achieved the best predictive performance among the tested machine learning models.
- Mechanism-tailored features effectively capture key physicochemical factors influencing deamidation rates.
- The descriptors encompass solvation, hydrogen bonds, conformational free energy, and electrostatic effects.

## Abstract

The spontaneous deamidation of Asparagine (Asn) residues
is a common
post-translational modification of proteins that can occur on disparate
time scales, ranging from hours to thousands of years. This variability
in the reaction rate reflects the influence of structural and environmental
factors on the multistep mechanism of the deamidation reaction. Understanding
the fine connection between reactivity and these modulating factors
is essential to advance our knowledge of the deamidation kinetics
in proteins and improve the prediction of deamidation-prone residues.
In this work, we assessed the step-specific structural-dynamics parameters
underlying the chemical basis of the first two reaction stages (the
deprotonation and ring-closure steps) and developed novel descriptors
derived from molecular dynamics (MD) simulations, which encompass
solvation, hydrogen bonds, conformational free energy, and an environment
electrostatic effect. These descriptors were evaluated across 63 Asn
residues from six distinct proteins and used as input features for
three machine learning models, Random Forest, Naive Bayes, and Logistic
Regression, to classify Asn residue reactivity. Among these, the Random
Forest classifier achieved the best predictive metrics, underscoring
the significance of mechanism-tailored features in discriminating
Asn reactivity and unveiling the key physicochemical factors that
govern deamidation rates in proteins.