Integrative Protein Assembly With <scp>LZerD</scp> and Deep Learning in <scp>CAPRI</scp> 47–55

We report the performance of the protein complex prediction approaches of our group and their results in CAPRI Rounds 47-55, excluding the joint CASP Rounds 50 and 54, as well as the special COVID-19 Round 51. Our approaches integrated classical pipelines developed in our group as well as more recently developed deep learning pipelines. In the cases of human group prediction, we surveyed the literature to find information to integrate into the modeling, such as assayed interface residues. In addition to any literature information, generated complex models were selected by a rank aggregation of statistical scoring functions, by generative model confidence, or by expert inspection. In these CAPRI rounds, our human group successfully modeled eight interfaces and achieved the top quality level among the submissions for all of them, including two where no other group did. We note that components of our modeling pipelines have become increasingly unified within deep learning approaches. Finally, we discuss several case studies that illustrate successful and unsuccessful modeling using our approaches.

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Publication Details

Journal
Proteins Structure Function and Bioinformatics
Published
2025-03-17
DOI
https://doi.org/10.1002/prot.26818
Citations
3
Primary Topic
Protein Structure and Dynamics
Type
article
Field-Weighted Citation Impact
2.42

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article

Integrative Protein Assembly With LZerD and Deep Learning in CAPRI 47–55

Yuki Kagaya, Tunde Aderinwale, Woong‐Hee Shin, Sai Raghavendra Maddhuri Venkata Subramaniya et al.
3 citations
Proteins Structure Function and Bioinformatics
Protein Structure and Dynamics
2.42
article

Integrative Protein Assembly With LZerD and Deep Learning in CAPRI 47–55

Yuki Kagaya, Tunde Aderinwale, Woong‐Hee Shin, Sai Raghavendra Maddhuri Venkata Subramaniya, Anika Jain, Yuanyuan Zhang, Daisuke Kihara, Jacob Verburgt, Genki Terashi, Charles Christoffer, Daipayan Sarkar, Xiao Wang, Zicong Zhang
article en
3 citations

Abstract

We report the performance of the protein complex prediction approaches of our group and their results in CAPRI Rounds 47-55, excluding the joint CASP Rounds 50 and 54, as well as the special COVID-19 Round 51. Our approaches integrated classical pipelines developed in our group as well as more recently developed deep learning pipelines. In the cases of human group prediction, we surveyed the literature to find information to integrate into the modeling, such as assayed interface residues. In addition to any literature information, generated complex models were selected by a rank aggregation of statistical scoring functions, by generative model confidence, or by expert inspection. In these CAPRI rounds, our human group successfully modeled eight interfaces and achieved the top quality level among the submissions for all of them, including two where no other group did. We note that components of our modeling pipelines have become increasingly unified within deep learning approaches. Finally, we discuss several case studies that illustrate successful and unsuccessful modeling using our approaches.

Proteins Structure Function and Bioinformatics
Korea University (KR), Purdue University West Lafayette (US), Purdue University Institute for Cancer Research
National Institutes of Health, National Science Foundation of Sri Lanka, National Institute of General Medical Sciences
Industry, innovation and infrastructure
Openalex Percentile: Top 13%
Protein Structure and Dynamics
2.42
DOIOpen Access PDFOpenAlex
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