Title:Enhancing Drug-Target Binding Affinity Prediction through Deep Learning and Protein Secondary Structure Integration
Volume: 19
Issue: 10
Author(s): Runhua Zhang, Baozhong Zhu, Tengsheng Jiang, Zhiming Cui and Hongjie Wu*
Affiliation:
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009,
China
Keywords:
Drug-target binding affinity, deep learning, convolutional neural network, graph neural network, protein secondary structure, protein primary sequence.
Abstract:
Background: Conventional approaches to drug discovery are often characterized by
lengthy and costly processes. To expedite the discovery of new drugs, the integration of artificial
intelligence (AI) in predicting drug-target binding affinity (DTA) has emerged as a crucial approach.
Despite the proliferation of deep learning methods for DTA prediction, many of these
methods primarily concentrate on the amino acid sequence of proteins. Yet, the interactions between
drug compounds and targets occur within distinct segments within the protein structures,
whereas the primary sequence primarily captures global protein features. Consequently, it falls short
of fully elucidating the intricate relationship between drugs and their respective targets.
Objective: This study aims to employ advanced deep-learning techniques to forecast DTA while incorporating
information about the secondary structure of proteins.
Methods: In our research, both the primary sequence of protein and the secondary structure of protein
were leveraged for protein representation. While the primary sequence played the role of the
overarching feature, the secondary structure was employed as the localized feature. Convolutional
neural networks and graph neural networks were utilized to independently model the intricate features
of target proteins and drug compounds. This approach enhanced our ability to capture drugtarget
interactions more effectively.
Results: We have introduced a novel method for predicting DTA. In comparison to DeepDTA, our
approach demonstrates significant enhancements, achieving a 3.9% increase in the Concordance Index
(CI) and a remarkable 34% reduction in Mean Squared Error (MSE) when evaluated on the KIBA
dataset.
Conclusion: In conclusion, our results unequivocally demonstrate that augmenting DTA prediction
with the inclusion of the protein's secondary structure as a localized feature yields significantly improved
accuracy compared to relying solely on the primary structure.
