RESUMO
The amino acid sequences of transmembrane regions of helical membrane proteins are highly constrained, diverging at slower rates than their extramembrane regions and than water-soluble proteins. Moreover, helical membrane proteins seem to fall into fewer families than water-soluble proteins. The reason for the differential restrictions on sequence remains unexplained. Here, we show that the evolution of transmembrane regions is slowed by a previously unrecognized structural constraint: Transmembrane regions bury more residues than extramembrane regions and soluble proteins. This fundamental feature of membrane protein structure is an important contributor to the differences in evolutionary rate and to an increased susceptibility of the transmembrane regions to disease-causing single-nucleotide polymorphisms.
Assuntos
Evolução Molecular , Proteínas de Membrana/química , Proteínas de Membrana/genética , Fenômenos Biofísicos , Bases de Dados de Proteínas , Humanos , Interações Hidrofóbicas e Hidrofílicas , Modelos Moleculares , Mutação , Polimorfismo de Nucleotídeo Único , Dobramento de Proteína , Estrutura Secundária de Proteína , SolubilidadeRESUMO
We describe a fully automated algorithm for finding functional sites on protein structures. Our method finds surface patches of unusual physicochemical properties on protein structures, and estimates the patches' probability of overlapping functional sites. Other methods for predicting the locations of specific types of functional sites exist, but in previous analyses, it has been difficult to compare methods when they are applied to different types of sites. Thus, we introduce a new statistical framework that enables rigorous comparisons of the usefulness of different physicochemical properties for predicting virtually any kind of functional site. The program's statistical models were trained for 11 individual properties (electrostatics, concavity, hydrophobicity, etc.) and for 15 neural network combination properties, all optimized and tested on 15 diverse protein functions. To simulate what to expect if the program were run on proteins of unknown function, as might arise from structural genomics, we tested it on 618 proteins of diverse mixed functions. In the higher-scoring top half of all predictions, a functional residue could typically be found within the first 1.7 residues chosen at random. The program may or may not use partial information about the protein's function type as an input, depending on which statistical model the user chooses to employ. If function type is used as an additional constraint, prediction accuracy usually increases, and is particularly good for enzymes, DNA-interacting sites, and oligomeric interfaces. The program can be accessed online (at http://hotpatch.mbi.ucla.edu).
Assuntos
Caspase 7/química , Biologia Computacional/métodos , Proteínas/química , Algoritmos , Motivos de Aminoácidos , Animais , Sítios de Ligação , Bioquímica/métodos , Humanos , Modelos Estatísticos , Redes Neurais de Computação , Conformação Proteica , Estrutura Terciária de Proteína , SoftwareRESUMO
The Database of Interacting Proteins (http://dip.doe-mbi.ucla.edu) aims to integrate the diverse body of experimental evidence on protein-protein interactions into a single, easily accessible online database. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks.
