COLOR: A compositional linear operation-based representation of protein sequences for identification of monomer contributions to properties

TL;DR

This paper introduces a novel interpretable deep learning model for protein sequences that accurately identifies key motifs influencing properties, surpassing existing explainability methods and aiding biomaterial design.

Contribution

The paper presents a new DL model with interpretable steps and a quantitative metric for analyzing monomer contributions in protein sequences, improving motif identification accuracy.

Findings

Model achieves 22% higher explainability.

Identifies motifs that destabilize anti-cancer peptides.

Finds motifs that enhance antimicrobial activity by 50%.

Abstract

The properties of biological materials like proteins and nucleic acids are largely determined by their primary sequence. While certain segments in the sequence strongly influence specific functions, identifying these segments, or so-called motifs, is challenging due to the complexity of sequential data. While deep learning (DL) models can accurately capture sequence-property relationships, the degree of nonlinearity in these models limits the assessment of monomer contributions to a property - a critical step in identifying key motifs. Recent advances in explainable AI (XAI) offer attention and gradient-based methods for estimating monomeric contributions. However, these methods are primarily applied to classification tasks, such as binding site identification, where they achieve limited accuracy (40-45%) and rely on qualitative evaluations. To address these limitations, we introduce a DL model with interpretable steps, enabling direct tracing of monomeric contributions. We also propose a metric ($\mathcal{I}$), inspired by the masking technique in the field of image analysis and natural language processing, for quantitative analysis on datasets mainly containing distinct properties of anti-cancer peptides (ACP), antimicrobial peptides (AMP), and collagen. Our model exhibits 22% higher explainability, pinpoints critical motifs (RRR, RRI, and RSS) that significantly destabilize ACPs, and identifies motifs in AMPs that are 50% more effective in converting non-AMPs to AMPs. These findings highlight the potential of our model in guiding mutation strategies for designing protein-based biomaterials.