DeepAssembly is designed to automatically construct multi-domain protein or complex structure through inter-domain interactions from deep learning. Starting from the input sequence of multi-domain protein (or protein complex), DeepAssembly first generates MSAs from genetic databases and searches for templates using our in-house PAthreader. Meanwhile, the input sequence is split into single-domain sequences by a domain boundary predictor, and then the structure for each domain is generated by single-domain structure predictor. Subsequently, features extracted from MSAs, templates and domain boundary information are fed into a deep neural network with self-attention to predict rotations, translations between inter-domain residues, and convert them into inter-domain interactions. Finally, DeepAssembly performs the creation of the initial full-length structure using the single-domain structures, followed by multiple steps of iterative population-based rotation angle optimization and atomic-level structure refinements. The domain assembly simulation is driven by the atomic coordinate deviation potential transformed from predicted inter-domain interactions, where the best conformation by our in-house model quality assessment is selected as the final output structure.
Reference
- Yuhao Xia✝, Kailong Zhao✝, Dong Liu, Xiaogen Zhou, and Guijun Zhang*. Domain-based multi-domain protein and complex structure prediction using inter-domain interactions from deep learning. submitted.
- Kailong Zhao, Yuhao Xia, Fujin Zhang, Xiaogen Zhou, Stan Z. Li*, Guijun Zhang*.Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader. Communications biology, 6, 243 (2023). DOI: https://doi.org/10.1038/s42003-023-04605-8
- Dong Liu✝, Biao Zhang✝, Jun Liu, Hui Li*, Le Song* and Guijun Zhang*. GraphCPLMQA: Assessing protein model quality based on deep graph coupled networks using protein language model. submitted.