RESUMO
Due to the importance of protein phosphorylation in cellular control, many researches are undertaken to predict the kinase-specific phosphorylation sites. Referred to our previous work, KinasePhos 1.0, incorporated profile hidden Markov model (HMM) with flanking residues of the kinase-specific phosphorylation sites. Herein, a new web server, KinasePhos 2.0, incorporates support vector machines (SVM) with the protein sequence profile and protein coupling pattern, which is a novel feature used for identifying phosphorylation sites. The coupling pattern [XdZ] denotes the amino acid coupling-pattern of amino acid types X and Z that are separated by d amino acids. The differences or quotients of coupling strength C(XdZ) between the positive set of phosphorylation sites and the background set of whole protein sequences from Swiss-Prot are computed to determine the number of coupling patterns for training SVM models. After the evaluation based on k-fold cross-validation and Jackknife cross-validation, the average predictive accuracy of phosphorylated serine, threonine, tyrosine and histidine are 90, 93, 88 and 93%, respectively. KinasePhos 2.0 performs better than other tools previously developed. The proposed web server is freely available at http://KinasePhos2.mbc.nctu.edu.tw/.
Assuntos
Biologia Computacional/métodos , Fosfoproteínas/química , Proteínas Quinases/metabolismo , Análise de Sequência de Proteína/métodos , Software , Domínio Catalítico , Simulação por Computador , Internet , Cadeias de Markov , Redes Neurais de Computação , Fosfoproteínas/metabolismo , Fosforilação , Probabilidade , Sensibilidade e Especificidade , Interface Usuário-ComputadorRESUMO
Structural analysis is useful in elucidating structural features responsible for enhanced thermal stability of proteins. However, due to the rapid increase of sequenced genomic data, there are far more protein sequences than the corresponding three-dimensional (3D) structures. The usual sequence-based amino acid composition analysis provides useful but simplified clues about the amino acid types related to thermal stability of proteins. In this work, we developed a statistical approach to identify the significant amino acid coupling sequence patterns in thermophilic proteins. The amino acid coupling sequence pattern is defined as any 2 types of amino acids separated by 1 or more amino acids. Using this approach, we construct the rho profiles for the coupling patterns. The rho value gives a measure of the relative occurrence of a coupling pattern in thermophiles compared with mesophiles. We found that thermophiles and mesophiles exhibit significant bias in their amino acid coupling patterns. We showed that such bias is mainly due to temperature adaptation instead of species or GC content variations. Though no single outstanding coupling pattern can adequately account for protein thermostability, we can use a group of amino acid coupling patterns having strong statistical significance (p values < 10(-7)) to distinguish between thermophilic and mesophilic proteins. We found a good correlation between the optimal growth temperatures of the genomes and the occurrences of the coupling patterns (the correlation coefficient is 0.89). Furthermore, we can separate the thermophilic proteins from their mesophilic orthologs using the amino acid coupling patterns. These results may be useful in the study of the enhanced stability of proteins from thermophiles-especially when structural information is scarce. Proteins 2005. (c) 2005 Wiley-Liss, Inc.
Assuntos
Sequência de Aminoácidos , Aminoácidos/química , Proteínas/química , Aminoácidos/análise , Proteínas Arqueais/química , Proteínas Arqueais/genética , Proteínas de Bactérias/química , Proteínas de Bactérias/genética , Bases de Dados de Proteínas , Genoma , Temperatura Alta , Reconhecimento Automatizado de Padrão , Probabilidade , Proteínas/genética , TermodinâmicaRESUMO
We developed a technique to compute structural entropy directly from protein sequences. We explored the possibility of using structural entropy to identify residues involved in thermal stabilization of various protein families. Examples include methanococcal adenylate kinase, Ribonuclease HI and holocytochrome c(551). Our results show that the positions of the largest structural entropy differences between wild type and mutant usually coincide with the residues relevant to thermostability. We also observed a good linear relationship between the average structural entropy and the melting temperatures for adenylate kinase and its chimeric constructs. To validate this linear relationship, we compiled a large dataset comprised of 1153 sequences and found that most protein families still display similar linear relationships. Our results suggest that the multitude of interactions involved in thermal stabilization may be generalized into the tendency of proteins to maintain local structural conservation. The linear relationship between structural entropy and protein thermostability should be useful in the study of protein thermal stabilization.
Assuntos
Entropia , Proteínas/química , Adenilato Quinase/química , Sequência de Aminoácidos , Proteínas de Bactérias/química , Grupo dos Citocromos c/química , Estabilidade Enzimática , Modelos Moleculares , Dados de Sequência Molecular , Mutação/genética , Estrutura Terciária de Proteína , Proteínas/análise , Ribonuclease H/química , Temperatura de TransiçãoRESUMO
Recent developments in research on the stability of proteins - specifically, comparisons of the ion pairs of homologous structures - show that ion pairs potentially contribute to the thermostability of proteins. This study proposes a probabilistic Bayesian statistical method to efficiently predict the thermostability of proteins based on the properties of ion pairs. The experimental results suggest that the numbers, types and bond lengths of ion pairs can be used to predict with high accuracy (up to 80%) the thermostability of functionally similar proteins. The predictions have high precision (99%), especially for hyperthermophilic proteins. Results for proteins with differing functions also indicate that the number of ion pairs is related to the thermostability of proteins, and that predictions of thermostability can also be made for proteins with different functions.
Assuntos
Algoritmos , Modelos Químicos , Modelos Estatísticos , Desnaturação Proteica , Proteínas/análise , Proteínas/química , Teorema de Bayes , Sítios de Ligação , Íons/análise , Íons/química , Ligação Proteica , Estatística como AssuntoRESUMO
UNLABELLED: Included in Prokaryotic Growth Temperature database (PGTdb) are a total of 1334 temperature data from 1072 prokaryotic organisms, Bacteria and Archaea: PGTdb integrates microbial growth temperature data from literature survey with their nucleotide/protein sequence and protein structure data from related databases. A direct correlation is observed between the average growth temperature of an organism and the melting temperature of proteins from the organism. Therefore, this database is useful not only for microbiologists to obtain cultivation condition, but also for biochemists and structure biologists to study the correlation between protein sequences/structures and their thermostability. In addition, the taxonomy and ribosomal RNA sequence(s) of an organism are linked through NCBI Taxonomy and the Ribosomal RNA Operon Copy Number Database umdb, respectively. PGTdb is the only integrated database on the Internet to provide the growth temperature data of the prokaryotes and the combined information of their nucleotide/protein sequences, protein structures, taxonomy and phylogeny. AVAILABILITY: http://pgtdb.csie.ncu.edu.tw