Biological impact of mutually exclusive exon switching.

Alternative splicing can expand the diversity of proteomes. Homologous mutually exclusive exons (MXEs) originate from the same ancestral exon and result in polypeptides with similar structural properties but altered sequence. Why would some genes switch homologous exons and what are their biological impact? Here, we analyse the extent of sequence, structural and functional variability in MXEs and report the first large scale, structure-based analysis of the biological impact of MXE events from different genomes. MXE-specific residues tend to map to single domains, are highly enriched in surface exposed residues and cluster at or near protein functional sites. Thus, MXE events are likely to maintain the protein fold, but alter specificity and selectivity of protein function. This comprehensive resource of MXE events and their annotations is available at: http://gene3d.biochem.ucl.ac.uk/mxemod/. These findings highlight how small, but significant changes at critical positions on a protein surface are exploited in evolution to alter function.

CATH: increased structural coverage of functional space.

CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.

A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations.

Tumour sequencing identifies highly recurrent point mutations in cancer driver genes, but rare functional mutations are hard to distinguish from large numbers of passengers. We developed a novel computational platform applying a multi-modal approach to filter out passengers and more robustly identify putative driver genes. The primary filter identifies enrichment of cancer mutations in CATH functional families (CATH-FunFams) - structurally and functionally coherent sets of evolutionary related domains. Using structural representatives from CATH-FunFams, we subsequently seek enrichment of mutations in 3D and show that these mutation clusters have a very significant tendency to lie close to known functional sites or conserved sites predicted using CATH-FunFams. Our third filter identifies enrichment of putative driver genes in functionally coherent protein network modules confirmed by literature analysis to be cancer associated. Our approach is complementary to other domain enrichment approaches exploiting Pfam families, but benefits from more functionally coherent groupings of domains. Using a set of mutations from 22 cancers we detect 151 putative cancer drivers, of which 79 are not listed in cancer resources and include recently validated cancer associated genes EPHA7, DCC netrin-1 receptor and zinc-finger protein ZNF479.

Genome-wide RNA-Sequencing analysis identifies a distinct fibrosis gene signature in the conjunctiva after glaucoma surgery.

Fibrosis-related events play a part in most blinding diseases worldwide. However, little is known about the mechanisms driving this complex multifactorial disease. Here we have carried out the first genome-wide RNA-Sequencing study in human conjunctival fibrosis. We isolated 10 primary fibrotic and 7 non-fibrotic conjunctival fibroblast cell lines from patients with and without previous glaucoma surgery, respectively. The patients were matched for ethnicity and age. We identified 246 genes that were differentially expressed by over two-fold and p < 0.05, of which 46 genes were upregulated and 200 genes were downregulated in the fibrotic cell lines compared to the non-fibrotic cell lines. We also carried out detailed gene ontology, KEGG, disease association, pathway commons, WikiPathways and protein network analyses, and identified distinct pathways linked to smooth muscle contraction, inflammatory cytokines, immune mediators, extracellular matrix proteins and oncogene expression. We further validated 11 genes that were highly upregulated or downregulated using real-time quantitative PCR and found a strong correlation between the RNA-Seq and qPCR results. Our study demonstrates that there is a distinct fibrosis gene signature in the conjunctiva after glaucoma surgery and provides new insights into the mechanistic pathways driving the complex fibrotic process in the eye and other tissues.

Functional innovation from changes in protein domains and their combinations.

Domains are the functional building blocks of proteins. In this work we discuss how domains can contribute to the evolution of new functions. Domains themselves can evolve through various mechanisms, altering their intrinsic function. Domains can also facilitate functional innovations by combining with other domains to make novel proteins. We discuss the mechanisms by which domain and domain combinations support functional innovations. We highlight interesting examples where changes in domain combination promote changes at the domain level.

Residue mutations and their impact on protein structure and function: detecting beneficial and pathogenic changes.

The present review focuses on the evolution of proteins and the impact of amino acid mutations on function from a structural perspective. Proteins evolve under the law of natural selection and undergo alternating periods of conservative evolution and of relatively rapid change. The likelihood of mutations being fixed in the genome depends on various factors, such as the fitness of the phenotype or the position of the residues in the three-dimensional structure. For example, co-evolution of residues located close together in three-dimensional space can occur to preserve global stability. Whereas point mutations can fine-tune the protein function, residue insertions and deletions ('decorations' at the structural level) can sometimes modify functional sites and protein interactions more dramatically. We discuss recent developments and tools to identify such episodic mutations, and examine their applications in medical research. Such tools have been tested on simulated data and applied to real data such as viruses or animal sequences. Traditionally, there has been little if any cross-talk between the fields of protein biophysics, protein structure-function and molecular evolution. However, the last several years have seen some exciting developments in combining these approaches to obtain an in-depth understanding of how proteins evolve. For example, a better understanding of how structural constraints affect protein evolution will greatly help us to optimize our models of sequence evolution. The present review explores this new synthesis of perspectives.

Low-density lipoprotein receptor gene familial hypercholesterolemia variant database: update and pathological assessment.

Familial hypercholesterolemia (FH) is caused predominately by variants in the low-density lipoprotein receptor gene (LDLR). We report here an update of the UCL LDLR variant database to include variants reported in the literature and in-house between 2008 and 2010, transfer of the database to LOVDv.2.0 platform (https://grenada.lumc.nl/LOVD2/UCL-Heart/home.php?select_db=LDLR) and pathogenicity analysis. The database now contains over 1288 different variants reported in FH patients: 55% exonic substitutions, 22% exonic small rearrangements (<100 bp), 11% large rearrangements (>100 bp), 2% promoter variants, 10% intronic variants and 1 variant in the 3' untranslated sequence. The distribution and type of newly reported variants closely matches that of the 2008 database, and we have used these variants (n= 223) as a representative sample to assess the utility of standard open access software (PolyPhen, SIFT, refined SIFT, Neural Network Splice Site Prediction Tool, SplicePort and NetGene2) and additional analyses (Single Amino Acid Polymorphism database, analysis of conservation and structure and Mutation Taster) for pathogenicity prediction. In combination, these techniques have enabled us to assign with confidence pathogenic predictions to 8/8 in-frame small rearrangements and 8/9 missense substitutions with previously discordant results from PolyPhen and SIFT analysis. Overall, we conclude that 79% of the reported variants are likely to be disease causing.

Characterization of pathogenic germline mutations in human protein kinases.

BACKGROUND: Protein Kinases are a superfamily of proteins involved in crucial cellular processes such as cell cycle regulation and signal transduction. Accordingly, they play an important role in cancer biology. To contribute to the study of the relation between kinases and disease we compared pathogenic mutations to neutral mutations as an extension to our previous analysis of cancer somatic mutations. First, we analyzed native and mutant proteins in terms of amino acid composition. Secondly, mutations were characterized according to their potential structural effects and finally, we assessed the location of the different classes of polymorphisms with respect to kinase-relevant positions in terms of subfamily specificity, conservation, accessibility and functional sites. RESULTS: Pathogenic Protein Kinase mutations perturb essential aspects of protein function, including disruption of substrate binding and/or effector recognition at family-specific positions. Interestingly these mutations in Protein Kinases display a tendency to avoid structurally relevant positions, what represents a significant difference with respect to the average distribution of pathogenic mutations in other protein families. CONCLUSIONS: Disease-associated mutations display sound differences with respect to neutral mutations: several amino acids are specific of each mutation type, different structural properties characterize each class and the distribution of pathogenic mutations within the consensus structure of the Protein Kinase domain is substantially different to that for non-pathogenic mutations. This preferential distribution confirms previous observations about the functional and structural distribution of the controversial cancer driver and passenger somatic mutations and their use as a proxy for the study of the involvement of somatic mutations in cancer development.

Cancer-associated mutations are preferentially distributed in protein kinase functional sites.

Protein kinases are a superfamily involved in many crucial cellular processes, including signal transmission and regulation of cell cycle. As a consequence of this role, kinases have been reported to be associated with many types of cancer and are considered as potential therapeutic targets. We analyzed the distribution of pathogenic somatic point mutations (drivers) in the protein kinase superfamily with respect to their location in the protein, such as in structural, evolutionary, and functionally relevant regions. We find these driver mutations are more clearly associated with key protein features than other somatic mutations (passengers) that have not been directly linked to tumor progression. This observation fits well with the expected implication of the alterations in protein kinase function in cancer pathogenicity. To explain the relevance of the detected association of cancer driver mutations at the molecular level in the human kinome, we compare these with genetically inherited mutations (SNPs). We find that the subset of nonsynonymous SNPs that are associated to disease, but sufficiently mild to the point of being widespread in the population, tend to avoid those key protein regions, where they could be more detrimental for protein function. This tendency contrasts with the one detected for cancer associated-driver-mutations, which seems to be more directly implicated in the alteration of protein function. The detailed analysis of protein kinase groups and a number of relevant examples, confirm the relation between cancer associated-driver-mutations and key regions for protein kinase structure and function.

Sequence and structural differences between enzyme and nonenzyme homologs.

To improve our understanding of the evolution of novel functions, we performed a sequence, structural, and functional analysis of homologous enzymes and nonenzymes of known three-dimensional structure. In most examples identified, the nonenzyme is derived from an ancestral catalytic precursor (as opposed to the reverse evolutionary scenario, nonenzyme to enzyme), and the active site pocket has been disrupted in some way, owing to the substitution of critical catalytic residues and/or steric interactions that impede substrate binding and catalysis. Pairwise sequence identity is typically insignificant, and almost one-half of the enzyme and nonenzyme pairs do not share any similarity in function. Heterooligomeric enzymes comprising homologous subunits in which one chain is catalytically inactive and enzyme polypeptides that contain internal catalytic and noncatalytic duplications of an ancient enzyme domain are also discussed.