Biological processes such as transcription, repair, and regulation require the interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental methods for this purpose are costly and technically demanding. Consequently, there is an urgent need for computational techniques to identify the structures of DNA-protein complexes. Despite technological advancements, accurately identifying DNA-protein complexes through computational methods still poses a challenge.

Our team has developed a cutting-edge deep-learning approach called DDPScore that assesses DNA-protein complex structures. DDPScore utilizes a 4D convolutional neural network to overcome limited training data. This approach effectively captures local and global features while comprehensively considering the conformational changes arising from the flexibility during the DNA-protein docking process. DDPScore consistently outperformed the available methods in comprehensive DNA-protein complex docking evaluations, even for the flexible docking challenges. DDPScore has a wide range of applications in predicting and designing structures of DNA-protein complexes.

Highlights:

(1) Our cutting-edge DNA-protein complex structures evaluation algorithm employs a deep-learning approach seamlessly integrating local and global properties. The precision of the results has been significantly improved compared to the current DNA-protein structure evaluation methods.

(2) The DNA-protein structure evaluation algorithm utilizes a 4D convolutional neural network that accurately models the complex interactions between DNA and proteins by considering space and sequential dimensions.

(3) Our algorithm tackles the issue of limited training data by utilizing a substantial number of decoy structures generated through physics-based simulations. This effectively considers the conformational changes that occur during the DNA-protein docking process.

(4) Our algorithm is a valuable and interpretable bioinformatics tool that can effectively tackle DNA-protein complex structure prediction challenges that will be of general interest to a broad range of users. This groundbreaking technology unlocks new insights into critical biological processes and helps to solve unanswered challenging questions relevant to health and disease.