Protein Property Prediction: Regression-Based Approaches for Structure and Function Prediction

Context: Proteins are important molecules that are found in all living organisms. They are made up of long chains of amino acids that are linked through peptide bonds. Protein function prediction is one of the most challenging problems in the post-genomic era. The number of newly identified proteins has been exponentially increasing with the advances of high-throughput techniques.

Objectives: The main objective of this research study is to predict protein structure and function using regression-based approaches. The second aim is to discuss recent advancements in machine learning techniques that have significantly contributed to improving regression-based approaches for protein property prediction.

Method and materials: In this research study, we use a machine learning algorithm to predict details about protein structure, function, shape, and mechanism of action. We implement regression algorithms such as linear regression, decision tree regression, random forest regression, and gradient boosting regression using advanced frameworks such as Tensor Flow and sci-kit-learn.

Result: Our analysis reveals promising outcomes, with the random forest regression model achieving an accuracy of 96%, precision of 95.1%, and recall of 95%. These results underscore our ability to predict various protein properties and structures with enhanced precision, leveraging the complicated relationships within protein data.