@article {494, title = {The role of the interactome in the maintenance of deleterious variability in human populations.}, journal = {Mol Syst Biol}, volume = {10}, year = {2014}, month = {2014 Sep 26}, pages = {752}, abstract = {

Recent genomic projects have revealed the existence of an unexpectedly large amount of deleterious variability in the human genome. Several hypotheses have been proposed to explain such an apparently high mutational load. However, the mechanisms by which deleterious mutations in some genes cause a pathological effect but are apparently innocuous in other genes remain largely unknown. This study searched for deleterious variants in the 1,000 genomes populations, as well as in a newly sequenced population of 252 healthy Spanish individuals. In addition, variants causative of monogenic diseases and somatic variants from 41 chronic lymphocytic leukaemia patients were analysed. The deleterious variants found were analysed in the context of the interactome to understand the role of network topology in the maintenance of the observed mutational load. Our results suggest that one of the mechanisms whereby the effect of these deleterious variants on the phenotype is suppressed could be related to the configuration of the protein interaction network. Most of the deleterious variants observed in healthy individuals are concentrated in peripheral regions of the interactome, in combinations that preserve their connectivity, and have a marginal effect on interactome integrity. On the contrary, likely pathogenic cancer somatic deleterious variants tend to occur in internal regions of the interactome, often with associated structural consequences. Finally, variants causative of monogenic diseases seem to occupy an intermediate position. Our observations suggest that the real pathological potential of a variant might be more a systems property rather than an intrinsic property of individual proteins.

}, keywords = {Alleles, Exome, Gene Library, Genetic Variation, Genetics, Population, Genome, Human, Genomics, Humans, Models, Genetic, mutation, Phenotype, Protein Conformation, Protein Interaction Maps, Sequence Analysis, DNA, Whites}, issn = {1744-4292}, doi = {10.15252/msb.20145222}, author = {Garc{\'\i}a-Alonso, Luz and Jim{\'e}nez-Almaz{\'a}n, Jorge and Carbonell-Caballero, Jos{\'e} and Vela-Boza, Alicia and Santoyo-L{\'o}pez, Javier and Anti{\v n}olo, Guillermo and Dopazo, Joaquin} } @article {519, title = {SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Jan}, pages = {D935-9}, abstract = {

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants.

}, keywords = {Databases, Protein, Humans, Internet, Meta-Analysis as Topic, Phenotype, Polymorphism, Single Nucleotide, Protein Conformation, Proteins}, issn = {1362-4962}, doi = {10.1093/nar/gkr996}, author = {De Baets, Greet and Van Durme, Joost and Reumers, Joke and Maurer-Stroh, Sebastian and Vanhee, Peter and Dopazo, Joaquin and Schymkowitz, Joost and Rousseau, Frederic} } @article {603, title = {DBAli tools: mining the protein structure space.}, journal = {Nucleic Acids Res}, volume = {35}, year = {2007}, month = {2007 Jul}, pages = {W393-7}, abstract = {

The DBAli tools use a comprehensive set of structural alignments in the DBAli database to leverage the structural information deposited in the Protein Data Bank (PDB). These tools include (i) the DBAlit program that allows users to input the 3D coordinates of a protein structure for comparison by MAMMOTH against all chains in the PDB; (ii) the AnnoLite and AnnoLyze programs that annotate a target structure based on its stored relationships to other structures; (iii) the ModClus program that clusters structures by sequence and structure similarities; (iv) the ModDom program that identifies domains as recurrent structural fragments and (v) an implementation of the COMPARER method in the SALIGN command in MODELLER that creates a multiple structure alignment for a set of related protein structures. Thus, the DBAli tools, which are freely accessible via the World Wide Web at http://salilab.org/DBAli/, allow users to mine the protein structure space by establishing relationships between protein structures and their functions.

}, keywords = {Algorithms, Amino Acid Sequence, Computational Biology, Data Interpretation, Statistical, Databases, Protein, Internet, Molecular Sequence Data, Protein Conformation, Proteins, Pseudomonas aeruginosa, Sequence Alignment, Sequence Analysis, Protein, Sequence Homology, Amino Acid, Software, Structure-Activity Relationship}, issn = {1362-4962}, doi = {10.1093/nar/gkm236}, author = {Marti-Renom, Marc A and Pieper, Ursula and Madhusudhan, M S and Rossi, Andrea and Eswar, Narayanan and Davis, Fred P and Al-Shahrour, F{\'a}tima and Dopazo, Joaquin and Sali, Andrej} }