PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps

Abstract

The structures of protein assemblies are important for elucidating cellular processes at the molecular level. Three-dimensional electron microscopy (3DEM) is a powerful method to identify the structures of assemblies, especially those that are challenging to study by crystallography. Here, a new approach, PRISM-EM, is reported to computationally generate plausible structural models using a procedure that combines crystallographic structures and density maps obtained from 3DEM. The predictions are validated against seven available structurally different crystallographic complexes. The models display mean deviations in the backbone of <5 Å . PRISM-EM was further tested on different benchmark sets; the accuracy was evaluated with respect to the structure of the complex, and the correlation with EM density maps and interface predictions were evaluated and compared with those obtained using other methods. PRISM-EM was then used to predict the structure of the ternary complex of the HIV-1 envelope glycoprotein trimer, the ligand CD4 and the neutralizing protein m36.

Emerging roles of allosteric modulators in the regulation of protein‐protein interactions (PPIs): A new paradigm for PPI drug discovery

“…As exemplified by Protein Interactions by Structural Matching (PRISM), structural features, rather than gene sequences, are used for analysis and prediction of PPIs. [86][87][88]103,104 In addition, the application of machine learning methods such as the random forest model and deep learning algorithms has significantly improved the performance of bioinformatics software, 93 such as IntPred. 90 Importantly, prediction of PPIs through bioinformatics methods provides a starting point for drug discovery, for which high-throughput screening and other pharmacological research can be carried out.…”

“…Advances in structural biology also provide an avenue for the computational development of PPI prediction. As exemplified by Protein Interactions by Structural Matching (PRISM), structural features, rather than gene sequences, are used for analysis and prediction of PPIs . In addition, the application of machine learning methods such as the random forest model and deep learning algorithms has significantly improved the performance of bioinformatics software, such as IntPred .…”

Integrative modelling of cellular assemblies

“…Recent advances in the detection of correlated mutations in sequences across species enable prediction of residue pairs that interact not only within but also between proteins [14]. Experimental interaction data can be combined with evolutionary information, by mapping known interactions [15–17] and the 3D structure of the related complex if available [18,19]. …”

PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps. Corrigendum

“…*Correspondence e-mail: okeskin@ku.edu.tr, agursoy@ku.edu.tr A revised Table 6 and Supporting Information are provided for the article by Kuzu et al [(2016), Acta Cryst. D72, 1137-1148.…”

“…After the online publication of the article by Kuzu et al (2016) Table 6 were computed using a definition which is different from that used in CAPRI. In the paper, the superposition was performed for the entire protein, and the r.m.s.d.…”