Predicting Binding Affinity of Protein-DNA Interactions Using Machine Learning-based Scoring Functions

Proteins and DNA play important roles to maintaining life in living cells. The binding of protein to specific DNA sequences is the beginning of lots of bio-activities. For instance, the binding of regulatory sites of DNA by transcription factors, which are a kind of proteins that trigger transcription of a particular gene, initiates the transcription process. Research on this issue could facilitate the studies of gene regulation and regulatory networks. For these reasons, the study of interactions between protein and DNA has attracted much attention for a long time. Recently, with the advances of computer technology and algorithm development, developing computational methods to predict binding affinity of protein-protein, protein-ligand and even protein-DNA interactions has been largely considered recently. Some of the scoring functions for predicting protein-ligand are shown to perform well on this challenge.
In this thesis, a machine learning-based scoring function was developed to predict the binding affinity of protein-DNA interactions. For this purpose, a high-quality dataset containing the information of binding affinity associated with a protein-DNA complex was collected from PDBbind. The performance of the proposed method was compared with existing scoring functions, and it is concluded that the proposed machine learning-based scoring function perfrom well in predicting the binding affinities of protein-DNA complexes and can benefit future studies on this problem.