Tokyo Institute of Technology · 컴퓨터과학
마사히토 오우에 교수의 연구실은 단백질 상호작용, 특히 단백질-단백질 상호작용(PPI)과 단백질-RNA 상호작용(PRIs)을 정밀하게 예측하는 데 초점을 맞추고 있습니다. 구조 기반 예측 기법, 특히 AlphaFold Multimer의 모델을 활용한 상호작용 가능성 분석과 함께, 공통된 솔루션 기반의 리스크코어링(consensus rescoring) 기법을 도입하여 예측 정확도를 향상시키는 데 기여하고 있습니다. 이는 실험적 검증 비용을 줄이고, 인터액톰(Interactome) 분석의 효율성을 높이는 데 기여합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Supplementary data are available at Bioinformatics online.
Our consensus method successfully predicted a PPI network with greater precision than conventional template/non-template methods, which may thus reduce the cost of validation by laboratory experiments for confirming novel PPIs from predicted PPIs. Therefore, our method may serve as an aid for promoting interactome analysis.
Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy in predicting the structure of protein complexes. However, it cannot distinguish whether th
Elucidating protein-RNA interactions (PRIs) is important for understanding many cellular systems. We developed a PRI prediction method by using a rigid-body protein-RNA docking calculation with tertiary structure data. We evaluated this method by using 78 protein-RNA complex structures from the Protein Data Bank. We predicted the interactions for pairs in 78×78 combinations. Of these, 78 original complexes were defined as positive pairs, and the other 6,006 complexes were defined as negative pai
Scoring is a challenging step in protein-protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by Oliva et al. (2013) with the CONSRANK method, where the set of solutions is used to build statistics in order to identify recurrent solutions. We explore several ways to perform consensus-based rescoring on the ZDOCK decoy set for Benchmark 4. We show that the information of the interface s
