The Diamond STING server

Diamond STING is a new version of the STING suite of programs for a comprehensive analysis of a relationship between protein sequence, structure, function and stability. We have added a number of new functionalities by both providing more structure parameters to the STING Database and by improving/expanding the interface for enhanced data handling. The integration among the STING components has also been improved. A new key feature is the ability of the STING server to handle local files containing protein structures (either modeled or not yet deposited to the Protein Data Bank) so that they can be used by the principal STING components: (Java)Protein Dossier ((J)PD) and STING Report. The current capabilities of the new STING version and a couple of biologically relevant applications are described here. We have provided an example where Diamond STING identifies the active site amino acids and folding essential amino acids (both previously determined by experiments) by filtering out all but those residues by selecting the numerical values/ranges for a set of corresponding parameters. This is the fundamental step toward a more interesting endeavor—the prediction of such residues. Diamond STING is freely accessible at and

Role of Resultant Dipole Moment in Mechanical Dissociation of Biological Complexes

Protein-peptide interactions play essential roles in many cellular processes and their structural characterization is the major focus of current experimental and theoretical research. Two decades ago, it was proposed to employ the steered molecular dynamics to assess the strength of protein-peptide interactions. The idea behind using steered molecular dynamics simulations is that the mechanical stability can be used as a promising and an efficient alternative to computationally highly demanding estimation of binding affinity. However, mechanical stability defined as a peak in force-extension profile depends on the choice of the pulling direction. Here we propose an uncommon choice of the pulling direction along resultant dipole moment vector, which has not been explored in simulations so far. Using explicit solvent all-atom MD simulations, we apply steered molecular dynamics technique to probe mechanical resistance of protein-peptide system pulled along two different vectors. A novel pulling direction, along the resultant dipole moment vector, results in stronger forces compared to commonly used peptide unbinding along center of masses vector. Our results demonstrate that resultant dipole moment is one of the factors influencing the mechanical stability of protein-peptide complex.Comment: 11 pages, 4 figures, 2 table

To automate or not to automate: this is the question

New protocols and instrumentation significantly boost the outcome of structural biology, which has resulted in significant growth in the number of deposited Protein Data Bank structures. However, even an enormous increase of the productivity of a single step of the structure determination process may not significantly shorten the time between clone and deposition or publication. For example, in a medium size laboratory equipped with the LabDB and HKL-3000 systems, we show that automation of some (and integration of all) steps of the X-ray structure determination pathway is critical for laboratory productivity. Moreover, we show that the lag period after which the impact of a technology change is observed is longer than expected

Molecular dynamics force probe simulations of antibody/antigen unbinding: Entropic control and non-additivity of unbinding forces.

Unbinding of a spin-labeled dinitrophenyl (DNP) hapten from the monoclonal antibody AN02 Fab fragment has been studied by force probe molecular dynamics (FPMD) simulations. In our nanosecond simulations, unbinding was enforced by pulling the hapten molecule out of the binding pocket. Detailed inspection of the FPMD trajectories revealed a large heterogeneity of enforced unbinding pathways and a correspondingly large flexibility of the binding pocket region, which exhibited induced fit motions. Principal component analyses were used to estimate the resulting entropic contribution of ∼6 kcal/mol to the AN02/DNP-hapten bond. This large contribution may explain the surprisingly large effect on binding kinetics found for mutation sites that are not directly involved in binding. We propose that such “entropic control” optimizes the binding kinetics of antibodies. Additional FPMD simulations of two point mutants in the light chain, Y33F and I96K, provided further support for a large flexibility of the binding pocket. Unbinding forces were found to be unchanged for these two mutants. Structural analysis of the FPMD simulations suggests that, in contrast to free energies of unbinding, the effect of mutations on unbinding forces is generally nonadditive

BioSom

Resumo: Genes e proteínas são de grande importância biológica para a compreensão de processos bioquímicos e requerem nomes consistentes. Existem diversas diretrizes para nomenclatura de genes, mas elas não são rigorosamente aplicadas à atribuição de nomes aos genes recém-identificados, gerando assim, inúmeras maneiras de nomear um mesmo gene. Este trabalho tem o objetivo de detectar e minimizar a redundância e a inconsistência de dados para colaborar com a identificação correta de genes. Para isso foram utilizadas técnicas de Inteligência Artificial para identificar os sinônimos realizando um estudo dirigido a dez experimentos distintos. Para selecionar os dados dos experimentos foi construído um banco de dados relacional para armazenar as informações constantes na base NR do NCBI e as informações identificadas neste estudo. Os dados do experimento foram minerados através das técnicas de mapas auto-organizáveis de Kohonen. A Rede SOM de Kohonen foi aplicada para exprimir as relações de similaridade entre os dados. Para identificação dos agrupamentos gerados pela rede SOM foi utilizada a técnica denominada Matriz-U. As informações resultantes deste trabalho permitem inferir os sinônimos dos genes, identificar prováveis nomes para genes nomeados como hipotéticos e apontar possíveis erros de anotação

Data Enrichment for Data Mining Applied to Bioinformatics and Cheminformatics Domains

Problemas cada vez mais complexos estão a ser tratados na àrea das ciências da vida. A aquisição de todos os dados que possam estar relacionados com o problema em questão é primordial. Igualmente importante é saber como os dados estão relacionados uns com os outros e com o próprio problema. Por outro lado, existem grandes quantidades de dados e informações disponíveis na Web. Os investigadores já estão a utilizar Data Mining e Machine Learning como ferramentas valiosas nas suas investigações, embora o procedimento habitual seja procurar a informação baseada nos modelos indutivos. Até agora, apesar dos grandes sucessos já alcançados com a utilização de Data Mining e Machine Learning, não é fácil integrar esta vasta quantidade de informação disponível no processo indutivo, com algoritmos proposicionais. A nossa principal motivação é abordar o problema da integração de informação de domínio no processo indutivo de técnicas proposicionais de Data Mining e Machine Learning, enriquecendo os dados de treino a serem utilizados em sistemas de programação de lógica indutiva. Os algoritmos proposicionais de Machine Learning são muito dependentes dos atributos dos dados. Ainda é difícil identificar quais os atributos mais adequados para uma determinada tarefa na investigação. É também difícil extrair informação relevante da enorme quantidade de dados disponíveis. Vamos concentrar os dados disponíveis, derivar características que os algoritmos de ILP podem utilizar para induzir descrições, resolvendo os problemas. Estamos a criar uma plataforma web para obter informação relevante para problemas de Bioinformática (particularmente Genómica) e Quimioinformática. Esta vai buscar os dados a repositórios públicos de dados genómicos, proteicos e químicos. Após o enriquecimento dos dados, sistemas Prolog utilizam programação lógica indutiva para induzir regras e resolver casos específicos de Bioinformática e Cheminformática. Para avaliar o impacto do enriquecimento dos dados com ILP, comparamos com os resultados obtidos na resolução dos mesmos casos utilizando algoritmos proposicionais.Increasingly more complex problems are being addressed in life sciences. Acquiring all the data that may be related to the problem in question is paramount. Equally important is to know how the data is related to each other and to the problem itself. On the other hand, there are large amounts of data and information available on the Web. Researchers are already using Data Mining and Machine Learning as a valuable tool in their researches, albeit the usual procedure is to look for the information based on induction models. So far, despite the great successes already achieved using Data Mining and Machine Learning, it is not easy to integrate this vast amount of available information in the inductive process with propositional algorithms. Our main motivation is to address the problem of integrating domain information into the inductive process of propositional Data Mining and Machine Learning techniques by enriching the training data to be used in inductive logic programming systems. The algorithms of propositional machine learning are very dependent on data attributes. It still is hard to identify which attributes are more suitable for a particular task in the research. It is also hard to extract relevant information from the enormous quantity of data available. We will concentrate the available data, derive features that ILP algorithms can use to induce descriptions, solving the problems. We are creating a web platform to obtain relevant bioinformatics (particularly Genomics) and Cheminformatics problems. It fetches the data from public repositories with genomics, protein and chemical data. After the data enrichment, Prolog systems use inductive logic programming to induce rules and solve specific Bioinformatics and Cheminformatics case studies. To assess the impact of the data enrichment with ILP, we compare with the results obtained solving the same cases using propositional algorithms