M-DeepAssembly: enhanced DeepAssembly based on multi-objective multi-domain protein conformation sampling

• Published in: BMC bioinformatics Vol. 26; no. 1; pp. 120 - 13
• Main Authors: Cui, Xinyue, Xia, Yuhao, Hou, Minghua, Zhao, Xuanfeng, Wang, Suhui, Zhang, Guijun
• Format: Journal Article
• Language: English
• Published: London BioMed Central 05.05.2025; BioMed Central Ltd; BMC
• Subjects: Ab initio modelling of protein structure; Algorithms; Bioinformatics; Biology; Biomedical and Life Sciences; Computational Biology - methods; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Conformation; Conformation sampling; Databases, Protein; Deep Learning; Drug development; Energy; Feature extraction; Forecasts and trends; Life Sciences; Methods; Microarrays; Models, Molecular; Multi-domain protein assembly; Multi-objective energy model; Optimization; Pareto optimum; Predictions; Protein Conformation; Protein Domains; Protein structure; Protein structure prediction; Proteins; Proteins - chemistry; Quality assessment; Quality control; Sampling; China; Protein structure prediction; Conformation sampling; Multi-objective energy model; Multi-domain protein assembly
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-025-06131-2

Background Association and cooperation among structural domains play an important role in protein function and drug design. Despite remarkable advancements in highly accurate single-domain protein structure prediction through the collaborative efforts of the community using deep learning, challenges still exist in predicting multi-domain protein structures when the evolutionary signal for a given domain pair is weak or the protein structure is large. Results To alleviate the above challenges, we proposed M-DeepAssembly, a protocol based on multi-objective protein conformation sampling algorithm for multi-domain protein structure prediction. Firstly, the inter-domain interactions and full-length sequence distance features are extracted through DeepAssembly and AlphaFold2, respectively. Secondly, subject to these features, we constructed a multi-objective energy model and designed a sampling algorithm for exploring and exploiting conformational space to generate ensembles. Finally, the output protein structure was selected from the ensembles using our in-house developed model quality assessment algorithm. On the test set of 164 multi-domain proteins, the results show that the average TM-score of M-DeepAssembly is 15.4% and 2.0% higher than AlphaFold2 and DeepAssembly, respectively. It is worth noting that there are models with higher accuracy in ensembles, achieving an improvement of 20.3% and 6.4% relative to the two baseline methods, although these models were not selected. Furthermore, when compared to the prediction results of AlphaFold2 for CASP15 multi-domain targets, M-DeepAssembly demonstrates certain performance advantages. Conclusions M-DeepAssembly provides a distinctive multi-domain protein assembly algorithm, which can alleviate the current challenges of weak evolutionary signals and large structures to some extent by forming diverse ensembles using multi-objective protein conformation sampling algorithm. The proposed method contributes to exploring the functions of multi-domain proteins, especially providing new insights into targets with multiple conformational states.