Performance and enhancement of the LZerD protein assembly pipeline in CAPRI 38‐46. Issue 8 (25th November 2019)

• Record Type: Journal Article
• Authors: Christoffer, Charles; Terashi, Genki; Shin, Woong‐Hee; Aderinwale, Tunde; Maddhuri Venkata Subramaniya, Sai Raghavendra; Peterson, Lenna; Verburgt, Jacob; Kihara, Daisuke
• Is Part Of: Proteins. Volume 88:Issue 8(2020)
• Page Start: 948
• Page End: 961
• Publication Date: 2019-11-25
• Subjects: CAPRI -- LZerD -- protein assembly -- protein complex modeling -- protein docking -- protein structure prediction -- protein‐protein interaction; Proteins -- Periodicals; Proteins -- Periodicals; 572.6
• DOI: 10.1002/prot.25850
• Languages: English
• ISSNs: 0887-3585

Abstract

Abstract: We report the performance of the protein docking prediction pipeline of our group and the results for Critical Assessment of Prediction of Interactions (CAPRI) rounds 38‐46. The pipeline integrates programs developed in our group as well as other existing scoring functions. The core of the pipeline is the LZerD protein‐protein docking algorithm. If templates of the target complex are not found in PDB, the first step of our docking prediction pipeline is to run LZerD for a query protein pair. Meanwhile, in the case of human group prediction, we survey the literature to find information that can guide the modeling, such as protein‐protein interface information. In addition to any literature information and binding residue prediction, generated docking decoys were selected by a rank aggregation of statistical scoring functions. The top 10 decoys were relaxed by a short molecular dynamics simulation before submission to remove atom clashes and improve side‐chain conformations. In these CAPRI rounds, our group, particularly the LZerD server, showed robust performance. On the other hand, there are failed cases where some other groups were successful. To understand weaknesses of our pipeline, we analyzed sources of errors for failed targets. Since we noted that structure refinement is a step that needs improvement, we newly performed a comparative study of several refinement approaches. Finally, we show several examples that illustrate successful and unsuccessful cases by our group.