Details

Autor(en) / Beteiligte

• Liu, Zi
• Zhu, Yi-Heng
• Shen, Long-Chen
• Xiao, Xuan
• Qiu, Wang-Ren
• Yu, Dong-Jun

Titel

Integrating unsupervised language model with multi-view multiple sequence alignments for high-accuracy inter-chain contact prediction

Ist Teil von

• Computers in biology and medicine, 2023-11, Vol.166, p.107529, Article 107529

Ort / Verlag

United States: Elsevier Ltd

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• Accurate identification of inter-chain contacts in the protein complex is critical to determine the corresponding 3D structures and understand the biological functions. We proposed a new deep learning method, ICCPred, to deduce the inter-chain contacts from the amino acid sequences of the protein complex. This pipeline was built on the designed deep residual network architecture, integrating the pre-trained language model with three multiple sequence alignments (MSAs) from different biological views. Experimental results on 709 non-redundant benchmarking protein complexes showed that the proposed ICCPred significantly increased inter-chain contact prediction accuracy compared to the state-of-the-art approaches. Detailed data analyses showed that the significant advantage of ICCPred lies in the utilization of pre-trained transformer language models which can effectively extract the complementary co-evolution diversity from three MSAs. Meanwhile, the designed deep residual network enhances the correlation between the co-evolution diversity and the patterns of inter-chain contacts. These results demonstrated a new avenue for high-accuracy deep-learning inter-chain contact prediction that is applicable to large-scale protein-protein interaction annotations from sequence alone. •Integrate multi-view MSA generation methodology with unsupervised protein language transformers to capture co-evolutionary patterns.