Retinitis pigmentosa (RP) refers to a heterogeneous group of inherited diseases that result in progressive retinal degeneration, characterized by visual field constriction and night blindness. A total of 103 mutations in rhodopsin are linked to RP to date, and the phenotypes range from severe to asymptomatic. To study the relation between phenotype and rhodopsin stability in disease mutants, we used a structure-based approach. For 12 of the mutants located at the protein-lipid interphase, we used the von Heijne water-membrane transfer scale, and we find that 9 of the mutations could affect membrane insertion. For 91 mutants, we used the protein design algorithm FoldX. The 3 asymptomatic mutations had no significant reduced stability, 2 were unsuitable for FoldX analysis since the structure was incorrect in this region, 63 mutations had a significant change in protein stability (>1.6 kcal/mol), and 23 mutations had energy change values under the prediction error threshold (<1.6 kcal/mol). Out of these 23, the disease-causing effect could be explained by the involvement in other functions (e.g., glycosylation motifs, the interface with arrestin and transducin, and the cilia-binding motif) for 19 mutants. The remaining 4 mutants were probably incorrectly associated with RP or have functionalities not discovered yet. For destabilizing mutations where clinical data were available, we found a highly significant correlation between FoldX energy changes and the average age of night blindness and between FoldX energy changes and daytime vision loss onset. Our detailed structural, functional, and energetic analysis provides a complete picture of the rhodopsin mutations and can guide mutation-specific therapies.

This study addresses the relation between structural and functional similarity in proteins. We introduce a novel method named tree based on root mean square deviation (T-RMSD), which uses distance RMSD (dRMSD) variations to build fine-grained structure-based classifications of proteins. The main improvement of the T-RMSD over similar methods, such as Dali, is its capacity to produce the equivalent of a bootstrap value for each cluster node. We validated our approach on two domain families studied extensively for their role in many biological and pathological pathways: the small GTPase RAS superfamily and the cysteine-rich domains (CRDs) associated with the tumor necrosis factor receptors (TNFRs) family. Our analysis showed that T-RMSD is able to automatically recover and refine existing classifications. In the case of the small GTPase ARF subfamily, T-RMSD can distinguish GTP- from GDP-bound states, while in the case of CRDs it can identify two new subgroups associated with well defined functional features (ligand binding and formation of ligand pre-assembly complex). We show how hidden Markov models (HMMs) can be built on these new groups and propose a methodology to use these models simultaneously in order to do fine-grained functional genomic annotation without known 3D structures. T-RMSD, an open source freeware incorporated in the T-Coffee package, is available online.

A great challenge in the proteomics and structural genomics era is to discover protein structure and function, including the identification of biological partners. Experimental investigation is costly and time-consuming, making computational methods very attractive for predicting protein function. In this work, we used the existing structural information in the SH3 family to first extract all SH3 structural features important for binding and then used this information to select the right templates to homology model most of the Saccharomyces cerevisiae SH3 domains. Second, we classified, based on ligand orientation with respect to the SH3 domain, all SH3 peptide ligands into 29 conformations, of which 18 correspond to variants of canonical type I and type II conformations and 11 correspond to non-canonical conformations. Available SH3 templates were expanded by chimera construction to cover some sequence variability and loop conformations. Using the 29 ligand conformations and the homology models, we modelled all possible complexes. Using these complexes and in silico mutagenesis scanning, we constructed position-specific ligand binding matrices. Using these matrices, we determined which sequences will be favorable for every SH3 domain and then validated them with available experimental data. Our work also allowed us to identify key residues that determine loop conformation in SH3 domains, which could be used to model human SH3 domains and do target prediction. The success of this methodology opens the way for sequence-based, genome-wide prediction of protein-protein interactions given enough structural coverage.