Split and Merge Proxy: pre-training protein-protein contact prediction by mining rich information from monomer data

Hao Du, Yuchen Ren, Yan Lu, He Huang, Yating Liu, Zhendong Mao, Xinqi Gong, Wanli Ouyang

Keywords: Protein Bioinformatics, Protein-Protein Contact Prediction, Pre-training

Abstract: Protein-protein contact prediction is a key intelligent biology computation technology for complex multimer protein function analysis but still sufferers from low accuracy. An important problem is that the number of training data cannot meet the requirements of deep-learning-based methods due to the expensive cost of capturing structure information of multimer data. In this paper, we solve this data volume bottleneck in a cheap way, borrowing rich information from monomer data. To utilize monomer (single chain) data in this multimer (multiple chains) problem, we propose a simple but effective pre-training method called Split and Merger Proxy (SMP), which utilizes monomer data to construct a proxy task for model pre-training. This proxy task cuts monomer data into two sub-parts, called pseudo multimer, and pre-trains the model to merge them back together by predicting their pseudo contacts. The pre-trained model is then used to initialize for our target – protein-protein contact prediction. Because of the consistency between this proxy task and the final target, the whole method brings a stronger pre-trained model for subsequent fine-tuning, leading to significant performance gains. Extensive experiments validate the effectiveness of our method and show the model performs better than the state of the art by 11.40% and 2.97% on the P@ L/10 metric for bounded benchmarks DIPS-Plus and CASP-CAPRI, respectively. Further, the model also achieves almost 1.5 times performance superiority to the state of the art on the harder unbounded benchmark DB5. The code, model, and pre-training data will be released after this paper is accepted.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: Machine Learning for Sciences (eg biology, physics, health sciences, social sciences, climate/sustainability )