Abstract

MutSα is a key component in the mismatch repair (MMR) pathway. This protein is responsible for initiating the signaling pathways for DNA repair or cell death. Herein we investigate this heterodimer’s post-recognition, post-binding response to three types of DNA damage involving cytotoxic, anti-cancer agents—carboplatin, cisplatin, and FdU. Through a combination of supervised and unsupervised machine learning techniques along with more traditional structural and kinetic analysis applied to all-atom molecular dynamics (MD) calculations, we predict that MutSα has a distinct response to each of the three damage types. Via a binary classification tree (a supervised machine learning technique), we identify key hydrogen bond motifs unique to each type of damage and suggest residues for experimental mutation studies. Through a combination of a recently developed clustering (unsupervised learning) algorithm, RMSF calculations, PCA, and correlated motions we predict that each type of damage causes MutSα to explore a specific region of conformation space. Detailed analysis suggests a short range effect for carboplatin—primarily altering the structures and kinetics of residues within 10 angstroms of the damaged DNA—and distinct longer-range effects for cisplatin and FdU. In our simulations, we also observe that a key phenylalanine residue—known to stack with a mismatched or unmatched bases in MMR—stacks with the base complementary to the damaged base in 88.61% of MD frames containing carboplatinated DNA. Similarly, this Phe71 stacks with the base complementary to damage in 91.73% of frames with cisplatinated DNA. This residue, however, stacks with the damaged base itself in 62.18% of trajectory frames with FdU-substituted DNA and has no stacking interaction at all in 30.72% of these frames. Each drug investigated here induces a unique perturbation in the MutSα complex, indicating the possibility of a distinct signaling event and specific repair or death pathway (or set of pathways) for a given type of damage.

Highlights

  • In human cells, the most prevalent binding factor is the heterodimer MutSα, formed by two MutS homologs (MSH)—MSH2 and MSH6

  • Due to the relative novelty of the cisplatinated, carboplatinated and fluorinated uracil-containing DNA, additional parameters based on pre-existing carboplatin, cisplatin, and FdU parameters were used in topology generation and simulation [8, 17, 23, 30, 45]

  • Fitting a decision tree to the binary hydrogen bond trajectory of interactions between protein residues and nucleic bases from the concatenated data of all systems yielded a decision tree with 37 levels of depth that correctly labels the type of damage in 99.82% (i.e., 0.18% loss) of molecular dynamics (MD) frames (Supplementary Figure 1)

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Summary

Introduction

The most prevalent binding factor is the heterodimer MutSα, formed by two MutS homologs (MSH)—MSH2 and MSH6. Even though binding of MSH26 Damage Response damaged DNA occurs in one region, we predict using all-atom microsecond timescale molecular dynamics (MD) simulations and machine learning techniques, that conformational changes and shifts in hydrogen bonding motifs across the entire complex differentiate the heterodimer’s response to carboplatinated, cisplatinated, and flouridated nucleic bases. These results are consistent with several previous studies [8,9,10,11,12,13,14,15,16,17,18] that have shown conformational shifts across interfaces in response to cisplatinated and carboplatinated DNA damage, using short-time (≤10 ns) molecular dynamics with more conventional analysis.

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