human next - generation sequencing projects currently generate millions of previously unknown single nucleotide variants ( snvs ) ( 1 ) . on average , every newly sequenced genome generates about 300  000 novel snvs ( 2 ) . although it is quite straightforward to annotate these snvs according to their genomic location ( coding , non - coding and regulatory regions ) , and for coding snvs to denote their effect on the translated protein ( synonymous or non - synonymous ) , predicting the detailed effect of a coding mutation on the structure and function of a protein is a largely unsolved problem . as these variants can influence drug selection , dosing and adverse effects ( 3 ) , it is recognized that this genetic information is of great importance for drug development in general ( 4 ) and crucial for personalized medicine ( 5 ) . most current approaches classify snvs into neutral or deleterious variants by using either conservation based measures ( 6 ) or by using a combination of conservation scores and structural features ( 79 ) . tools for predicting stability changes upon mutation have also been developed ( 10,11 ) , however these do not use explicit stability predictions based on a high - resolution structure but rather depend on black - box predictions using intelligent machine - learning approaches such as support - vector machines or neural networks . coding non - synonymous snvs can affect protein structure and function to various degrees ( 12,13 ) . although predicting neutral or fully disruptive variants is relatively easy , a large portion of variants will result in more subtle intermediate phenotypic effects that are much more challenging to predict . to tackle this challenge web servers such as polyphen ( 9 ) and hope ( 8) , for example , base their predictions on a statistical analysis of protein structures extrapolated to the protein under study and do currently not provide quantitative free energy changes of point mutations . snpeffect on the other hand uses the foldx ( 14 ) force field and aims at calculating realistic free - energy changes upon mutation ( g ) , thereby providing high - accuracy protein stability information . as structure quality is crucial for the accuracy of g predictions using foldx we currently do not model structures with < 90% sequence identity to the modeling template structure . as a result however , by integrating several in - house developed structural bioinformatics tools designed to quantify protein misfolding ( foldx ) , protein aggregation [ tango ( 15 ) and waltz ( 16 ) ] and chaperone interaction [ limbo ( 17 ) ] , snpeffect was developed with the specific aim of mapping the effect of snvs on the protein homeostasis landscape . i.e. the ability of a cell to maintain appropriate concentrations of properly folded proteins in the correct cellular compartment ( 18 ) . currently snpeffect provides pre - calculated mutant analyses for more than 60  000 human coding protein variants , benefiting the speed of information retrieval , but it also allows calculation of custom mutant sets . finally snpeffect provides features for meta - analysis of selected data sets allowing to analyze the proteostatic landscape of a given protein or protein family for example . the raw data source of the snpeffect database consists of the uniprot human variation database ( http://www.uniprot.org/docs/humsavar ) , containing single amino acid polymorphisms , classified either as disease mutations , polymorphisms or yet unclassified mutations . snpeffect predicts the impact of these variants on ( i ) protein aggregation and amyloid formation ( tango and waltz , respectively ) , ( ii ) chaperone binding ( limbo ) and ( iii ) structural stability ( foldx ) . the availability of a crystal structure with a minimal resolution of 4   is required to accurately analyze the effect on protein stability with foldx . if an exact structural match is not found , homologous structures with no < 90% sequence identity are considered as template structures to build a homology model of the original sequence with foldx . the stability analysis furthermore , snpeffect holds annotations on functional sites , structural features , domain information , cellular processing and post - translational modifications for each variant . the effect on functional sites and structural features data from the catalytic site atlas is parsed to analyze whether the residue is part of the active site ( 19 ) . secondary structure information is generated by foldx and transmembrane topology ( extracellular , intracellular , transmembrane ) is predicted by tmhmm ( 20 ) . snpeffect also maps changes in post - translational lipid anchor attachment and the peroxisomal targeting signal pts1 ( 24 ) . lipid attachment predictions include myristoylation ( 25,26 ) , farnesylation ( 26 ) , gpi - anchor attachment ( 27 ) and type-1 and type-2 geranylgeranylation ( 26 ) . all entries are additionally linked to the omim genetic disorder database ( online mendelian inheritance in man , omim . mckusick - nathans institute of genetic medicine , johns hopkins university ( baltimore , md ) , 2011 . automatic updates from the uniprot human variation database are scheduled every 6 months . the database interface ( figure 1 , left ) allows users to search snvs by filtering on molecular phenotypic effects , mutation type , disease , uniprot identifier , dbsnp identifier and gene name . molecular phenotypic effects include changes in aggregation tendency ( dtango ) , amyloid formation ( dwaltz ) , chaperone binding ( dlimbo ) and structural stability change upon mutation ( ddg ) . applying the filter settings results in a set of variants that can be analyzed in a protein - centered or variant - centered view ( figure 1 , right ) . the webpage on the right shows a variant that meets the filter criteria and displays summarized information on the effect on aggregation tendency , amyloid propensity , chaperone binding and structural stability , as well as domain annotation from the smart and pfam databases . below the phenotypic summary section , the webpage on the right shows a variant that meets the filter criteria and displays summarized information on the effect on aggregation tendency , amyloid propensity , chaperone binding and structural stability , as well as domain annotation from the smart and pfam databases . below the phenotypic summary section , this snpeffect update focuses primarily on the scientist user 's ability to quickly retrieve and rapidly analyze the effect of protein variants . moreover , the wild - type protein of each variant is also fully analyzed and directly linked from the variant webpage . structural data is retrieved from the protein data bank ( pdb ) ( 29 ) . when an exact match to the wild - type sequence is not found , a homology model is built from a template structure that has at least 90% sequence identity to the original sequence . if structural information is retrieved , we offer visualization of both the wild - type and mutant residue environment in the protein structure . additionally , every phenotypic analysis is accompanied by a graphical and textual comparison to the wild - type protein . figure 1 illustrates the summary of a variant that meets the criteria set in the filter . a new feature in snpeffect 4.0 is the ability to analyze and plot phenotypic features of a specific subset ( or all ) of the snpeffect database . the meta - analysis tool enables scientists to carry out large - scale data mining of the specified data and visualize the results in a graphical plot . the data set of variants is primarily chosen on disease associations and the mutation type . an additional filter can be applied to limit the results by one or more disease terms that are selected from a list or specified by keywords . snpeffect will then search for all variants of the selected type and retrieve those that are linked to the selected disease(s ) . for the disease type , these are solely the mutations annotated with that disease . for the polymorphisms and unclassifieds , snpeffect retrieves all of these variants from proteins associated with the selected disease(s ) . next , the two phenotypic effects that will be analyzed and plotted can be specified ( dtango , dwaltz , dlimbo or ddg ) ( figure 2 ) . for example , one can create an aggregation / stability feature plot of a set of variants to correlate aggregation changes with stability changes . if the number of hits for one of the mutation types exceeds 500 , the average y is plotted for each x bin , to keep the plots clear and readable . the meta - analysis tool converts phenotypic features of a selected set of variants to comprehensible scatter plots , boxplots and frequency plots ( figure 2 ) . the bottom images show ( from left to right ) a scatter plot , a histogram and a boxplot for the selected data set . the bottom images show ( from left to right ) a scatter plot , a histogram and a boxplot for the selected data set . novel to previous versions of snpeffect ( 3032 ) , the current version includes a data submission framework that allows submitting ( human or non - human ) custom single protein variants for a detailed snpeffect analysis including tango , waltz , limbo and foldx . possible input types are uniprot i d , fasta sequence , pdb i d , or an uploaded pdb file . if only sequence information but no structural information is provided , snpeffect will search the pdb for a matching structure to complete the stability analysis with foldx . when an exact match is not found , a homology filter allows setting the minimum percent sequence identity a structural homolog template should have to build a homology model . the effect on structural stability users receive an e - mail notification when the analysis has finished and can download the results from their snpeffect account . this file contains figures and extensive life scientist - friendly text reports with comparison to the wild - type protein . snpeffect 4.0 offers a detailed and comprehensible molecular and structural phenotypic analysis of all known human protein variants . major phenotypic features such as aggregation propensity prediction , stability analysis , structural features , post - translational modification and cellular localization are intelligibly visualized and explained for each variant . the meta - analysis tool allows plotting correlations between phenotypic effects concerning a specified set of variants . custom protein variants can now be submitted for a detailed snpeffect analysis , including automated structure modeling . interuniversity attraction poles ( iap network 6/43 ) of the belgian federal science policy office ( belspo ) ( vib switch laboratory ) ; flanders institute for science and technology ( iwt ) ( to g.d.b . ) ; fund for scientific research ( fwo ) , flanders ( to j.v.d . ) ; micinn projects bio2008 - 04212 , and rd06/0020/1019 ( rticc , isciii ) ( dopazo lab , partial ) gva - feder ( prometeo/2010/001 , partial ) .
single nucleotide variants ( snvs ) are , together with copy number variation , the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease . linking structural effects of non - synonymous snvs to functional outcomes 
 is a major issue in structural bioinformatics . 
 the snpeffect database ( http://snpeffect.switchlab.org ) uses sequence- and structure - based bioinformatics tools to predict the effect of protein - coding snvs on the structural phenotype of proteins . 
 it integrates aggregation prediction ( tango ) , amyloid prediction ( waltz ) , chaperone - binding prediction ( limbo ) and protein stability analysis ( foldx ) for structural phenotyping . additionally , snpeffect holds information on affected catalytic sites and a number of post - translational modifications . 
 the database contains all known human protein variants from uniprot , but users can now also submit custom protein variants for a snpeffect analysis , including automated structure modeling . 
 the new meta - analysis application allows plotting correlations between phenotypic features for a user - selected set of variants .