PeptimAIze: Revolutionazing Peptide Design through Multi-Property Optimization and Explainable AI

Descripción del proyecto

Modelo innovador de aprendizaje profundo para el diseño eficiente de péptidos

Las interacciones proteína-proteína (IPP) son fundamentales para muchos procesos fisiológicos y patológicos del cuerpo humano, lo que las convierte en dianas prometedoras para el tratamiento de enfermedades como las infecciones bacterianas y el cáncer. Sin embargo, el uso terapéutico y el descubrimiento de péptidos se ven obstaculizados por propiedades fisicoquímicas desfavorables y por métodos ineficaces que consumen muchos recursos. Con el apoyo de las acciones Marie Skłodowska-Curie, el proyecto PeptimAIze desarrollará un modelo de aprendizaje profundo especializado en la generación de péptidos terapéuticos para inhibir las IPP. Para lograrlo, el proyecto abordará las carencias de los enfoques actuales de aprendizaje profundo aplicados al diseño de péptidos mediante el desarrollo de un modelo con una mejor interpretabilidad. El modelo superará las limitaciones existentes y, al mismo tiempo, será más eficiente y accesible.

Objetivo

PeptimAIze aims to develop a deep learning model capable of generating therapeutic peptides for inhibiting protein-protein interactions (PPIs). Many PPIs are essential to key physiological and pathological processes, making these interactions ideal targets for selective intervention in several human diseases, including cancer and bacterial infections. However, the therapeutic potential of peptides is often limited by unfavorable physicochemical properties, such as low solubility and poor bioavailability. Current peptide discovery approaches are time- and resource-intensive, generating significant waste through iterative and labour-intensive screening of compound batches across multiple optimisation rounds to experimentally achieve the desired physicochemical properties. While deep learning and generative models have recently emerged for on-demand peptide design, most focus on a single aspect, typically binding affinity, disregarding physicochemical properties and experimental validation. PeptimAIze addresses these gaps by developing a multi-objective deep learning model based on protein language processing that designs peptides by co-optimising binding to the target and the physicochemical properties necessary for optimal administrability and bioavailability. The model will incorporate explainable AI techniques to enhance interpretability and reveal which amino acids in the peptide sequence are associated with the tuning of each considered property. A key step will be the wet-lab validation of the model on targets responsible for vital bacterial functions (Doc/Phd) and cancer development (p53/MDM2 and Bcl-xL/Bak), demonstrating real-world lab applicability. To maximise impact and usability, the model will be deployed through an intuitive, user-friendly app, enabling researchers in academia and industry to design peptides with tailored properties.