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137
SIFT: Predicting amino acid changes that affect protein function
- Nucleic Acids Res
, 2003
"... Single nucleotide polymorphism (SNP) studies and random mutagenesis projects identify amino acid substitutions in protein-coding regions. Each sub-stitution has the potential to affect protein function. SIFT (Sorting Intolerant From Tolerant) is a program that predicts whether an amino acid substitu ..."
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Cited by 163 (4 self)
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Single nucleotide polymorphism (SNP) studies and random mutagenesis projects identify amino acid substitutions in protein-coding regions. Each sub-stitution has the potential to affect protein function. SIFT (Sorting Intolerant From Tolerant) is a program that predicts whether an amino acid substitution affects protein function so that users can prioritize substitutions for further study. We have shown that SIFT can distinguish between functionally neutral and deleterious amino acid changes in mutagenesis studies and on human polymorphisms. SIFT is available at
Evaluation of Structural and Evolutionary Contributions to Deleterious Mutation Prediction
- J. Mol. Biol
, 2002
"... Methods for automated prediction of deleterious protein mutations have utilized both structural and evolutionary information but the relative con-tribution of these two factors remains unclear. To address this, we have used a variety of structural and evolutionary features to create simple deleterio ..."
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Cited by 45 (1 self)
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Methods for automated prediction of deleterious protein mutations have utilized both structural and evolutionary information but the relative con-tribution of these two factors remains unclear. To address this, we have used a variety of structural and evolutionary features to create simple deleterious mutation models that have been tested on both experimental mutagenesis and human allele data. We find that the most accurate pre-dictions are obtained using a solvent-accessibility term, the C b density, and a score derived from homologous sequences, SIFT. A classification tree using these two features has a cross-validated prediction error of 20.5% on an experimental mutagenesis test set when the prior probability for deleterious and neutral cases is equal, whereas this prediction error is 28.8% and 22.2% using either the C b density or SIFT alone. The improve-ment imparted by structure increases when fewer homologs are available: when restricted to three homologs the prediction error improves from 26.9% using SIFT alone to 22.4% using SIFT and the C b density, or 24.8% using SIFT and a noisy C b density term approximating the inaccuracy of ab initio structures modeled by the Rosetta method. We conclude that methods for deleterious mutation prediction should include structural information when fewer than five to ten homologs are available, and that ab initio predicted structures may soon be useful in such cases when high-resolution structures are unavailable.
topoSNP: a topographic database of non-synonymous single nucleotide polymorphisms with and without known disease association
, 2004
"... The database of topographic mapping of Single Nucleotide Polymorphism (topoSNP) provides an online resource for analyzing non-synonymous SNPs (nsSNPs) that can be mapped onto known 3D structures of proteins. These include diseaseassociated nsSNPs derived from the Online Mendelian Inheritance in Man ..."
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Cited by 29 (3 self)
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The database of topographic mapping of Single Nucleotide Polymorphism (topoSNP) provides an online resource for analyzing non-synonymous SNPs (nsSNPs) that can be mapped onto known 3D structures of proteins. These include diseaseassociated nsSNPs derived from the Online Mendelian Inheritance in Man (OMIM) database and other nsSNPs derived from dbSNP, a resource at the National Center for Biotechnology Information that catalogs SNPs. TopoSNP further classies each nsSNP site into three categories based on their geometric location: those located in a surface pocket or an interior void of the protein, those on a convex region or a shallow depressed region, and those that are completely buried in the interior of the protein structure. These unique geometric descriptions provide more detailed mapping of nsSNPs to protein structures. The current release also includes relative entropy of SNPs calculated from multiple sequence alignment as obtained from the Pfam database (a database of protein families and conserved protein motifs) as well as manually adjusted multiple alignments obtained from ClustalW. These structural and conservational data can be useful for studying whether nsSNPs in coding regions are likely to lead to phenotypic changes. TopoSNP includes an interactive structural visualization web interface, as well as downloadable batch data. The database will be updated at regular intervals and can be accessed at: http:// gila.bioengr.uic.edu/snp/toposnp.
P and Shatkay H: F-SNP: computationally predicted functional SNPs for disease association studies
- Nucleic Acids Res 2008, ( 36 Database issue):D820–D824
"... for disease association studies ..."
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nsSNPAnalyzer: identifying disease-associated nonsynonymous single nucleotide polymorphisms
- Nucleic Acids Res
, 2005
"... doi:10.1093/nar/gki372 ..."
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EA: ParseSNP: a tool for the analysis of nucleotide polymorphisms
- Nucl Acid Res
, 2003
"... PARSESNP is a tool for the display and analysis of polymorphisms in genes. Using a reference DNA sequence, an exon/intron position model and a list of polymorphisms, it determines the effects of these polymorphisms on the expressed gene product, as well as the changes in restriction enzyme recogniti ..."
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Cited by 23 (2 self)
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PARSESNP is a tool for the display and analysis of polymorphisms in genes. Using a reference DNA sequence, an exon/intron position model and a list of polymorphisms, it determines the effects of these polymorphisms on the expressed gene product, as well as the changes in restriction enzyme recognition sites. It shows the locations and effects of the polymorphisms in summary on a stylized graphic and in detail on a display of the protein sequence aligned with the DNA sequence. The addition of a homology model, in the form of an alignment of related protein sequences, allows for prediction of the severity of missense changes. PARSESNP is available on the World Wide Web at
Deleterious mutation prediction in the secondary structure of RNAs. Nucleic Acids Res 31:6578–6584
"... Methods for computationally predicting deleterious mutations have recently been investigated for proteins, mainly by probabilistic estimations in the context of genomic research for identifying single nucleotide polymorphisms that can potentially affect protein function. It has been demonstrated tha ..."
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Cited by 22 (10 self)
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Methods for computationally predicting deleterious mutations have recently been investigated for proteins, mainly by probabilistic estimations in the context of genomic research for identifying single nucleotide polymorphisms that can potentially affect protein function. It has been demonstrated that in cases where a few homologs are available, ab initio predicted structures modeled by the Rosetta method can become useful for including structural information to improve the deleterious mutation prediction methods for proteins. In the ®eld of RNAs where very few homologs are avail-able at present, this analogy can serve as a precur-sor to investigate a deleterious mutation prediction approach that is based on RNA secondary struc-ture. When attempting to develop models for the prediction of deleterious mutations in RNAs, useful structural information is available from folding algo-rithms that predict the secondary structure of RNAs, based on energy minimization. Detecting mutations with desired structural effects among all possible point mutations may then be valuable for the prediction of deleterious mutations that can be tested experimentally. Here, a method is introduced for the prediction of deleterious mutations in the secondary structure of RNAs. The mutation predic-tion method, based on subdivision of the initial structure into smaller substructures and construc-tion of eigenvalue tables, is independent of the fold-ing algorithms but relies on their success to predict the folding of small RNA structures. Application of this method to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs, is given for prokaryotic transcription termination in the thiamin pyrophosphate and S-adenosyl-methionine induced riboswitches. Ribo-switches are mRNA structures that have recently been found to regulate transcription termination or translation initiation in bacteria by conformation rearrangement in response to direct metabolite binding. Predicting deleterious mutations on riboswitches may succeed to systematically intervene in bacterial genetic control.
PupaSNP Finder: a web tool for finding SNPs with putative effect at transcriptional level
- Nucleic Acids Res
, 2004
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MutDB services: interactive structural analysis of mutation data. Nucleic Acids Res 33:W311–W314
, 2005
"... Non-synonymous single nucleotide polymorphisms (SNPs) and mutations have been associated with human phenotypes and disease. As more and more SNPs are mapped to phenotypes, understanding how these variations affect the function and expression of genes and gene products becomes an important endeavor. ..."
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Cited by 11 (1 self)
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Non-synonymous single nucleotide polymorphisms (SNPs) and mutations have been associated with human phenotypes and disease. As more and more SNPs are mapped to phenotypes, understanding how these variations affect the function and expression of genes and gene products becomes an important endeavor. We have developed a set of tools to aid in the understanding of how amino acid substitutions affect protein structures. To do this, we have annot-ated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, if available. We then developed a novel web interface to this data that allows for visualization of the location of these substitutions. We have also developed a web service interface to the dataset and developed interactive plugins for UCSF’s Chimera structural modeling tool and PyMOL that integrate our annota-tions with these sophisticated structural visualiza-tion and modeling tools. The web services portal and plugins can be downloaded from
SIFT web server: predicting effects of amino acid substitutions on proteins
, 2012
"... The Sorting Intolerant from Tolerant (SIFT) algorithm predicts the effect of coding variants on protein function. It was first introduced in 2001, with a corresponding website that provides users with predictions on their variants. Since its release, SIFT has become one of the standard tools for cha ..."
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Cited by 10 (0 self)
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The Sorting Intolerant from Tolerant (SIFT) algorithm predicts the effect of coding variants on protein function. It was first introduced in 2001, with a corresponding website that provides users with predictions on their variants. Since its release, SIFT has become one of the standard tools for characterizing missense variation. We have updated SIFT’s genome-wide prediction tool since our last publication in 2009, and added new features to the insertion/deletion (indel) tool. We also show accuracy metrics on independent data sets. The original developers have hosted the SIFT web server at FHCRC, JCVI and the web server is cur-rently located at BII. The
