1,721,005 research outputs found

    Protein dynamic properties: essential dynamics method vs. NMR backbone dynamics

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    Proteins are dynamic systems whose internal motions and resulting conformational changes are essential for their functional skills. While the rigidity is required to maintain the structure, flexibility is needed to perform the function. The study of protein flexibility can be handled by both experimental, such as NMR backbone dynamics in solutions, and computational methods, such as molecular dynamics simulations. The major problem with molecular dynamics simulations is due to the conformational sampling efficiency that requires long times of calculation. In recent decades a new computational approach, based on the essential dynamics sampling (EDS) has been applied to the study of protein flexibility, folding etc. In essential dynamics sampling, an usual molecular dynamics simulation is performed, but only those steps, not increasing the distance from a target structure, are accepted. This method offers the possibility of representing protein dynamics in the essential subspace only, so reducing the complex protein dynamics to its essential degrees of freedom. In this work we apply ED simulations to identify flexible regions in two protein systems previously studied by NMR backbone dynamics

    PASTA sequence composition is a predictive tool for protein class identification

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    Abstract PASTA domains are small modules expressed in bacteria and found in one or multiple copies at the C-terminal end of several penicillin binding proteins (PBPs) and Ser/Thr protein kinases (STPKs) and represent potential targets for a new class of antibiotics. PASTA domains are currently annotated as sensor domains, as they are thought to activate their cognate proteins in response to binding to opportune ligands. However, recent studies have shown that PASTA domains linked to proteins of different classes, STPKs or PBPs, do not share the same binding abilities. Despite this, there is currently no way to distinguish between PASTA domains from the two classes, since all of them share the same fold, independent of the class they belong to. To identify a predictive tool of class identification, we here analyse a pool of parameters, including amino acid compositions and total charges of PASTA domains either linked to PBPs or to STPKs. We screened sequences from Actinobacteria, Firmicutes and Bacteroidetes. The first two phyla include some of the most dangerous micro-organisms for human health such as Mycobacterium tuberculosis and Staphylococcus aureus. Based on this analysis, our study proposes a predictive method to assign PASTA domains with unknown origin to their corresponding enzyme class, based solely on sequence information
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