Binary classification of proteins by a Machine Learning approach

TL;DR

This paper introduces a deep learning system using CNNs to classify proteins based on their chemical, physical, and geometric properties from XML data, aiming to handle large datasets and generalize to biomolecular classification tasks.

Contribution

It presents a novel CNN-based framework for protein classification utilizing detailed property data, demonstrating its potential for large-scale biomolecular analysis.

Findings

Effective classification of proteins using CNNs.

Validation on protein data from the Protein Data Bank.

Potential for broader biomolecular classification applications.

Abstract

In this work we present a system based on a Deep Learning approach, by using a Convolutional Neural Network, capable of classifying protein chains of amino acids based on the protein description contained in the Protein Data Bank. Each protein is fully described in its chemical-physical-geometric properties in a file in XML format. The aim of the work is to design a prototypical Deep Learning machinery for the collection and management of a huge amount of data and to validate it through its application to the classification of a sequences of amino acids. We envisage applying the described approach to more general classification problems in biomolecules, related to structural properties and similarities.