SNAP predicts effect of mutations on protein function.
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ABSTRACT:
SUBMITTER: Bromberg Y
PROVIDER: S-EPMC2562009 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Many genetic variations are single nucleotide polymorphisms (SNPs). Non-synonymous SNPs are 'neutral' if the resulting point-mutated protein is not functionally discernible from the wild type and 'non-neutral' otherwise. The ability to identify non-neutral substitutions could significantly aid targeting disease causing detrimental mutations, as well as SNPs that increase the fitness of particular phenotypes. Here, we introduced comprehensive data sets to assess the performance of methods that predict SNP effects. Along we introduced SNAP (screening for non-acceptable polymorphisms), a neural network-based method for the prediction of the functional effects of non-synonymous SNPs. SNAP needs only sequence information as input, but benefits from functional and structural annotations, if available. In a cross-validation test on over 80,000 mutants, SNAP identified 80% of the non-neutral substitutions at 77% accuracy and 76% of the neutral substitutions at 80% accuracy. This constituted an important improvement over other methods; the improvement rose to over ten percentage points for mutants for which existing methods disagreed. Possibly even more importantly SNAP introduced a well-calibrated measure for the reliability of each prediction. This measure will allow users to focus on the most accurate predictions and/or the most severe effects. Available at http://www.rostlab.org/services/SNAP.
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Project description:SNAP-25 has a cysteine cluster located at its linker domain. In vivo, the cysteine residues in this cluster can be palmitoylated, and the hydrophobic palmitate molecules can target SNAP-25 to the presynaptic membrane. Here, we report that the SNAP-25a expressed in Escherichia coli is also an iron-sulfur protein binding an iron-sulfur cluster using the cysteine residues in its cysteine cluster. Therefore, SNAP-25a uses the same cysteine residues to bind two different prosthetic groups (iron-sulfur cluster and palmitate). Because the binding sites of these two prosthetic groups overlap, we suggest that these two modifications occur at different times, and probably at different places in the cell.
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Project description:Maintaining wild type protein-protein interactions is essential for the normal function of cell and any mutation that alter their characteristics can cause disease. Therefore, the ability to correctly and quickly predict the effect of amino acid mutations is crucial for understanding disease effects and to be able to carry out genome-wide studies. Here, we report a new development of the SAAMBE method, SAAMBE-3D, which is a machine learning-based approach, resulting in accurate predictions and is extremely fast. It achieves the Pearson correlation coefficient ranging from 0.78 to 0.82 depending on the training protocol in benchmarking five-fold validation test against the SKEMPI v2.0 database and outperforms currently existing algorithms on various blind-tests. Furthermore, optimized and tested via five-fold cross-validation on the Cornell University dataset, the SAAMBE-3D achieves AUC of 1.0 and 0.96 on a homo and hereto-dimer test datasets. Another important feature of SAAMBE-3D is that it is very fast, it takes less than a fraction of a second to complete a prediction. SAAMBE-3D is available as a web server and as well as a stand-alone code, the last one being another important feature allowing other researchers to directly download the code and run it on their local computer. Combined all together, SAAMBE-3D is an accurate and fast software applicable for genome-wide studies to assess the effect of amino acid mutations on protein-protein interactions. The webserver and the stand-alone codes (SAAMBE-3D for predicting the change of binding free energy and SAAMBE-3D-DN for predicting if the mutation is disruptive or non-disruptive) are available.
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Project description:BackgroundNon-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study predictions for functional impact of disease-annotated mutations from OMIM, PMD and Swiss-Prot and of variants not linked to disease.ResultsMost disease-causing mutations were predicted to impact protein function. More surprisingly, the raw predictions scores for disease-causing mutations were higher than the scores for the function-altering data set originally used for developing the prediction method (here SNAP). We might expect that diseases are caused by change-of-function mutations. However, it is surprising how well prediction methods developed for different purposes identify this link. Conversely, our predictions suggest that the set of nsSNPs not currently linked to diseases contains very few strong disease associations to be discovered.ConclusionsFirstly, annotations of disease-causing nsSNPs are on average so reliable that they can be used as proxies for functional impact. Secondly, disease-causing nsSNPs can be identified very well by methods that predict the impact of mutations on protein function. This implies that the existing prediction methods provide a very good means of choosing a set of suspect SNPs relevant for disease.
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Project description:Site-specific labeling of cellular proteins with chemical probes is a powerful tool for studying protein function in living cells. A number of small peptide and protein tags have been developed that can be labeled with synthetic probes with high efficiencies and specificities and provide flexibility not available with fluorescent proteins. The SNAP-tag is a modified form of the DNA repair enzyme human O(6)-alkylguanine-DNA-alkyltransferase, and undergoes a self-labeling reaction to form a covalent bond with O(6)-benzylguanine (BG) derivatives. BG can be modified with a wide variety of fluorophores and other reporter compounds, generally without affecting the reaction with the SNAP-tag. In this unit, basic strategies for labeling SNAP-tag fusion proteins, both for live cell imaging and for in vitro analysis, are described. This includes a description of a releasable SNAP-tag probe that allows the user to chemically cleave the fluorophore from the labeled SNAP-tag fusion. In vitro labeling of purified SNAP-tag fusions is briefly described.
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