Integrating sequence and chemical insights: a co-modeling AI prediction framework for peptides

Understanding the impact of the primary structure of peptides on a range of physicochemical properties is crucial for the development of various applications. Peptides can be conceptualized as sequences of amino acids in their biological representation and as molecular architectures composed of atoms and chemical bonds in their chemical representation. This study examines the influence of different biological and chemical representations of peptides on the local interpretability and accuracy of their respective prediction models and has developed “feature attribution” methodologies based on these representations. The effectiveness of these methodologies is validated through physicochemical analyses, specifically within the context of peptide aggregation propensity (AP) prediction, with training datasets derived from high-throughput molecular dynamics (MD) simulations. Our findings reveal significant discrepancies in the attribution extracted from sequence-based and chemical structure-based representations, which has led to the proposal of a co-modeling framework that integrates insights from both perspectives. Empirical comparisons have demonstrated that the contrastive learning-based co-modeling framework excels in terms of effectiveness and efficiency. This research not only extends the applicability of the attribution method but also lays the groundwork for elucidating the intrinsic mechanisms governing peptide activities and functions with the aid of domain-specific knowledge. Moreover, the co-modeling strategy is poised to enhance the precision of downstream applications and facilitate future endeavors in drug discovery and protein engineering.