02859nas a2200301   4500008004100000245009400041210006900135300001300204490000800217520172300225653007401948653002202022653001902044653002402063653002802087653002002115653015502135653002502290100001502315700001402330700001702344700001602361700002202377700002202399700001502421700001502436856010602451       2006                        eng d00aSelective pressures at a codon-level predict deleterious mutations in human disease genes0 aSelective pressures at a codonlevel predict deleterious mutation  a1390-4040 v3583 aDeleterious mutations affecting biological function of proteins are constantly being rejected by purifying selection from the gene pool. The non-synonymous/synonymous substitution rate ratio (omega) is a measure of selective pressure on amino acid replacement mutations for protein-coding genes. Different methods have been developed in order to predict non-synonymous changes affecting gene function. However, none has considered the estimation of selective constraints acting on protein residues. Here, we have used codon-based maximum likelihood models in order to estimate the selective pressures on the individual amino acid residues of a well-known model protein: p53. We demonstrate that the number of residues under strong purifying selection in p53 is much higher than those that are strictly conserved during the evolution of the species. In agreement with theoretical expectations, residues that have been noted to be of structural relevance, or in direct association with DNA, were among those showing the highest signals of purifying selection. Conversely, those changing according to a neutral, or nearly neutral mode of evolution, were observed to be irrelevant for protein function. Finally, using more than 40 human disease genes, we demonstrate that residues evolving under strong selective pressures (omega<0.1) are significantly associated (p<0.01) with human disease. We hypothesize that non-synonymous change on amino acids showing omega<0.1 will most likely affect protein function. The application of this evolutionary prediction at a genomic scale will provide an a priori hypothesis of the phenotypic effect of non-synonymous coding single nucleotide polymorphisms (SNPs) in the human genome.10aAmino Acid Sequence Amino Acid Substitution Codon/*genetics Databases10aGenetic Evolution10aGenetic Models10aHuman Humans Models10aInborn/*genetics Genome10aMolecular Genes10aMolecular Molecular Sequence Data *Mutation Neoplasms/genetics Proteins/genetics *Selection (Genetics) Tumor Suppressor Protein p53/chemistry/genetics10ap53 Genetic Diseases1 aArbiza, L.1 aDuchi, S.1 aMontaner, D.1 aBurguet, J.1 aPantoja-Uceda, D.1 aPineda-Lucena, A.1 aDopazo, J.1 aDopazo, H.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=1658474602406nas a2200205   4500008004100000245005300041210005100094300001000145490000700155520166900162653001601831653008601847653001901933653007101952100001502023700001902038700001602057700002102073856010602094       2003                        eng d00aA model for the emergence of adaptive subsystems0 amodel for the emergence of adaptive subsystems  a27-560 v653 aWe investigate the interaction of learning and evolution in a changing environment. A stable learning capability is regarded as an emergent adaptive system evolved by natural selection of genetic variants. We consider the evolution of an asexual population. Each genotype can have ’fixed’ and ’flexible’ alleles. The former express themselves as synaptic connections that remain unchanged during ontogeny and the latter as synapses that can be adjusted through a learning algorithm. Evolution is modelled using genetic algorithms and the changing environment is represented by two optimal synaptic patterns that alternate a fixed number of times during the ’life’ of the individuals. The amplitude of the change is related to the Hamming distance between the two optimal patterns and the rate of change to the frequency with which both exchange roles. This model is an extension of that of Hinton and Nowlan in which the fitness is given by a probabilistic measure of the Hamming distance to the optimum. We find that two types of evolutionary pathways are possible depending upon how difficult (costly) it is to cope with the changes of the environment. In one case the population loses the learning ability, and the individuals inherit fixed synapses that are optimal in only one of the environmental states. In the other case a flexible subsystem emerges that allows the individuals to adapt to the changes of the environment. The model helps us to understand how an adaptive subsystem can emerge as the result of the tradeoff between the exploitation of a congenital structure and the exploration of the adaptive capabilities practised by learning.10a*Adaptation10aBiological Algorithms Alleles Animals Evolution Genotype Humans *Learning *Models10aGenetic Models10aStatistical Neural Networks (Computer) Phenotype Synapses/genetics1 aDopazo, H.1 aGordon, M., B.1 aPerazzo, R.1 aRisau-Gusman, S.  uhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12597115