34 research outputs found

    Protein translocation: Rehearsing the ABCs

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    AbstractRecent evidence that vacuolar enzymes in yeast can be delivered directly from the cytosol, rather than via the secretory pathway, alerts us to the increasing evidence for ‘non-classical’ forms of protein translocation that may involve ABC transporters

    Mutations in the CDP-Choline Pathway for Phospholipid Biosynthesis Bypass the Requirement for an Essential Phospholipid Transfer Protein

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    SEC14p is the yeast phosphatidylinositol (PI)/phosphatidylcholine (PC) transfer protein, and it effects an essential stimulation of yeast Golgi secretory function. We now report that the SEC14p localizes to the yeast Golgi and that the SEC14p requirement can be specifically and efficiently bypassed by mutations in any one of at least six genes. One of these suppressor genes was the structural gene for yeast choline kinase (CKI), disruption of which rendered the cell independent of the normally essential SEC14p requirement. The antagonistic action of the CKI gene product on SEC14p function revealed a previously unsuspected influence of biosynthetic activities of the CDP-choline pathway for PC biosynthesis on yeast Golgi function and indicated that SEC14p controls the phospholipid content of yeast Golgi membranes in vivo

    Knowledge-guided docking: accurate prospective prediction of bound configurations of novel ligands using Surflex-Dock

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    Prediction of the bound configuration of small-molecule ligands that differ substantially from the cognate ligand of a protein co-crystal structure is much more challenging than re-docking the cognate ligand. Success rates for cross-docking in the range of 20–30 % are common. We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands. The benchmark data set, called PINC (“PINC Is Not Cognate”), is publicly available. Protein pocket similarity was used to choose representative structures for ensemble-docking. The docking protocol made use of known ligand poses prior to the cutoff-date, both to help guide the configurational search and to adjust the rank of predicted poses. Overall, the top-scoring pose family was correct over 60 % of the time, with the top-two pose families approaching a 75 % success rate. Correct poses among all those predicted were identified nearly 90 % of the time. The largest improvements came from the use of molecular similarity to improve ligand pose rankings and the strategy for identifying representative protein structures. With the exception of a single outlier target, the knowledge-guided docking protocol produced results matching the quality of cognate-ligand re-docking, but it did so on a very challenging temporally-segregated cross-docking benchmark

    A structure-guided approach for protein pocket modeling and affinity prediction

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    Binding affinity prediction is frequently addressed using computational models constructed solely with molecular structure and activity data. We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships. The Surflex-QMOD approach has been shown to produce accurate predictions of binding affinity by constructing an interpretable physical model of a binding site with no experimental binding site structural information. We introduce a method to integrate protein structure information into the model induction process in order to construct more robust physical models. The structure-guided models accurately predict binding affinities over a broad range of compounds while producing more accurate representations of the protein pockets and ligand binding modes. Structure-guidance for the QMOD method yielded significant performance improvements, both for affinity and pose prediction, especially in cases where predictions were made on ligands very different from those used for model induction

    Chemical and protein structural basis for biological crosstalk between PPARα and COX enzymes

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    We have previously validated a probabilistic framework that combined computational approaches for predicting the biological activities of small molecule drugs. Molecule comparison methods included molecular structural similarity metrics and similarity computed from lexical analysis of text in drug package inserts. Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk. Considering those cases where the predicted target was an enzyme with known 3D structure allowed incorporation of information from molecular docking and protein binding pocket similarity in addition to ligand-based comparisons. Taken together, the combination of orthogonal information sources led to investigation of a surprising predicted relationship between a transcription factor and an enzyme, specifically, PPARα and the cyclooxygenase enzymes. These predictions were confirmed by direct biochemical experiments which validate the approach and show for the first time that PPARα agonists are cyclooxygenase inhibitors
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