Evaluation of Transmembrane Protein Structural Models Using HPMScore

Abstract

Transmembrane proteins (TMPs) are a class of essential proteins for biological and therapeutic purposes. Despite an increasing number of structures, the gap with the number of available sequences remains impressive. The choice of a dedicated function to select the most probable/relevant model among hundreds is a specific problem of TMPs. Indeed, the majority of approaches are mostly focused on globular proteins. We developed an alternative methodology to evaluate the quality of TMP structural models. HPMScore took into account sequence and local structural information using the unsupervised learning approach called hybrid protein model. The methodology was extensively evaluated on very different TMP all-α proteins. Structural models with different qualities were generated, from good to bad quality. HPMScore performed better than DOPE in recognizing good comparative models over more degenerated models, with a Top 1 of 46.9% against DOPE 40.1%, both giving the same result in 13.0%. When the alignments used are higher than 35%, HPM is the best for 52%, against 36% for DOPE (12% for both). These encouraging results need further improvement particularly when the sequence identity falls below 35%. An area of enhancement would be to train on a larger training set. A dedicated web server has been implemented and provided to the scientific community. It can be used with structural models generated from comparative modeling to deep learning approaches.

Molecular dynamics of the human RhD and RhAG blood group proteins

“…Indeed, in our previous study ( de Brevern et al, 2018 ), the best monomer model for RhD was obtained with a multi-template approach based on the structural templates of RhCG ( Gruswitz et al, 2010 ) and NeRH50 ( Li et al, 2007 ; Lupo et al, 2007 ). However, among the models of RhAG 3 trimer built using as a template NeRH50 or RhCG alone, or together in the multi-template approach, the best structural models were obtained using RhCG alone as a template according to both DOPE ( Shen and Sali, 2006 ) and HPM ( de Brevern and Hazout, 2003 ; Esque et al, 2015 ; Téletchéa et al, 2023 ) scores ( Supplementary Figure S5 ). The high sequence identity of RhCG with both RhAG (52.32%) and RhD (31.65%) made this template appropriate to build all trimer models.…”

“…Hundreds of structural models were generated with Modeller 9.12 ( Sali and Blundell, 1993 ; Martí-Renom et al, 2000 ). These models were analysed and compared with TM-Align ( Zhang and Skolnick, 2005 ), the Discrete Optimized Protein Energy (DOPE) ( Shen and Sali, 2006 ) potential implemented in Modeller, the Hybrid Protein Model (HPM, dedicated to transmembrane proteins) ( de Brevern and Hazout, 2003 ; Esque et al, 2015 ; Téletchéa et al, 2023 ) webserver and the MAIDEN approach ( Postic et al, 2016a ). The positioning of the models within the lipid bilayer were performed by OPM ( Lomize et al, 2006 ; Lomize et al, 2011 ) and OREMPRO ( Postic et al, 2016b ).…”

BioMedInformatics, the Link between Biomedical Informatics, Biology and Computational Medicine

“…However, few approaches are specifically dedicated to them and in particular to the evaluation of the quality of TMP models [21]. Télétchéa and collaborators have therefore developed a specific method called HPMScore [22] (https: //www.dsimb.inserm.fr/dsimb_tools/hpmscore, accessed on 17 December 2023). It takes into account sequence and local structural information using the unsupervised learning approach called the hybrid protein model.…”