Biology:List of Protein subcellular localization prediction tools

From HandWiki

This list of protein subcellular localisation prediction tools includes software, databases, and web services that are used for protein subcellular localization prediction. Some tools are included that are commonly used to infer location through predicted structural properties, such as signal peptide or transmembrane helices, and these tools output predictions of these features rather than specific locations. These software related to protein structure prediction may also appear in lists of protein structure prediction software.

Tools

  • Descriptions sourced from the entry in the https://bio.tools/ registry (used under CC-BY license) are indicated by link
Name Description References URL Year
AAIndexLoc Machine-learning-based algorithm that uses amino acid index to predict protein subcellular localization based on its sequence. (bio.tools entry) [1] http://aaindexloc.bii.a-star.edu.sg/ 2008
APSLAP Prediction of apoptosis protein sub cellular Localization [2] 2013
AtSubP A highly accurate subcellular localization prediction tool for annotating the Arabidopsis thaliana proteome. (bio.tools entry) [3] http://bioinfo3.noble.org/AtSubP/ 2010
BaCelLo BaCelLo is a predictor for the subcellular localization of proteins in eukaryotes. (bio.tools entry) [4] http://gpcr.biocomp.unibo.it/bacello/index.htm 2006
BAR+ BAR+ is a server for the structural and functional annotation of protein sequences (bio.tools entry) [5] http://bar.biocomp.unibo.it/bar2.0/ 2011
BAR BAR 3.0 is a server for the annotation of protein sequences relying on a comparative large-scale analysis on the entire UniProt. With BAR 3.0 and a sequence you can annotate when possible: function (Gene Ontology), structure (Protein Data Bank), protein domains (Pfam). Also if your sequence falls into a cluster with a structural/some structural template/s we provide an alignment towards the template/templates based on the Cluster-HMM (HMM profile) that allows you to directly compute your 3D model. Cluster HMMs are available for downloading. (bio.tools entry) [6][5] https://bar.biocomp.unibo.it/bar3/ 2017
BASys BASys (Bacterial Annotation System) is a tool for automated annotation of bacterial genomic (chromosomal and plasmid) sequences including gene/protein names, GO functions, COG functions, possible paralogues and orthologues, molecular weights, isoelectric points, operon structures, subcellular localization, signal peptides, transmembrane regions, secondary structures, 3-D structures, reactions, and pathways. (bio.tools entry) [7] http://basys.ca 2005
BOMP The beta-barrel Outer Membrane protein Predictor (BOMP) takes one or more fasta-formatted polypeptide sequences from Gram-negative bacteria as input and predicts whether or not they are beta-barrel integral outer membrane proteins. (bio.tools entry) [8] http://www.bioinfo.no/tools/bomp 2004
BPROMPT Bayesian PRediction Of Membrane Protein Topology (BPROMPT) uses a Bayesian Belief Network to combine the results of other membrane protein prediction methods for a protein sequence. (bio.tools entry) [9] http://www.ddg-pharmfac.net/bprompt/BPROMPT/BPROMPT.html 2003
Cell-PLoc A package of web-servers for predicting subcellular localization of proteins in various organisms. [10] 2008
CELLO CELLO uses a two-level Support Vector Machine system to assign localizations to both prokaryotic and eukaryotic proteins. [11][12] 2006
ClubSub-P ClubSub-P is a database of cluster-based subcellular localization (SCL) predictions for Archaea and Gram negative bacteria. [13] 2011
CoBaltDB CoBaltDB is a novel powerful platform that provides easy access to the results of multiple localization tools and support for predicting prokaryotic protein localizations. [14] 2010
ComiR ComiR is a web tool for combinatorial microRNA (miRNA) target prediction. Given an messenger RNA (mRNA) in human, mouse, fly or worm genomes, ComiR predicts whether a given mRNA is targeted by a set of miRNAs. (bio.tools entry) [15] http://www.benoslab.pitt.edu/comir/ 2013
cropPAL A data portal to access the compendium of data on crop protein subcellular locations. (bio.tools entry) [16] http://crop-pal.org/ 2016
DAS-TMfilter DAS (Dense Alignment Surface) is based on low-stringency dot-plots of the query sequence against a set of library sequences - non-homologous membrane proteins - using a previously derived, special scoring matrix. The method provides a high precision hyrdophobicity profile for the query from which the location of the potential transmembrane segments can be obtained. The novelty of the DAS-TMfilter algorithm is a second prediction cycle to predict TM segments in the sequences of the TM-library. (bio.tools entry) [17] http://mendel.imp.ac.at/sat/DAS/DAS.html 2002
DeepLoc Prediction of eukaryotic protein subcellular localization using deep learning (bio.tools entry) [18] http://www.cbs.dtu.dk/services/DeepLoc/ 2017
Light Attention Deep learning architecture for predicting eukaryotic subcellular localization and web server which predicts 10 locations for arbitrary amounts of sequences that can be uploaded as .fasta or copy-pasted (bio.tools entry) [19] https://github.com/HannesStark/protein-localization 2021
DIANA-microT v5.0 Web server which predicts targets for miRNAs and provides functional information on the predicted miRNA:target gene interaction from various online biological resources. Updates enable the association of miRNAs to diseases through bibliographic analysis and connection to the UCSC genome browser. Updates include sophisticated workflows. (bio.tools entry) [20][21] http://diana.imis.athena-innovation.gr/DianaTools/index.php?r=MicroT_CDS/index 2013
2006
E.Coli Index Comprehensive guide of information relating to E. coli; home of Echobase: a database of E. coli genes characterized since the completion of the genome. (bio.tools entry) [22] http://www.york.ac.uk/res/thomas/ 2009
ePlant A suite of open-source world wide web-based tools for the visualization of large-scale data sets from the model organism Arabidopsis thaliana. It can be applied to any model organism. Currently has 3 modules: a sequence conservation explorer that includes homology relationships and single nucleotide polymorphism data, a protein structure model explorer, a molecular interaction network explorer, a gene product subcellular localization explorer, and a gene expression pattern explorer. (bio.tools entry) [23] http://bar.utoronto.ca/eplant/ 2011
ESLpred ESLpred is a tool for predicting subcellular localization of proteins using support vector machines. The predictions are based on dipeptide and amino acid composition, and physico-chemical properties. (bio.tools entry) [24] http://www.imtech.res.in/raghava/eslpred/ 2004
Euk-mPLoc 2.0 Predicting the subcellular localization of eukaryotic proteins with both single and multiple sites. [25] 2010
2011
HMMTOP Prediction of transmembranes helices and topology of proteins. (bio.tools entry) [26][27] http://www.enzim.hu/hmmtop/ 2001
HSLpred Allows predicting the subcellular localization of human proteins. This is based on various type of residue composition of proteins using SVM technique. (bio.tools entry) [28] http://www.imtech.res.in/raghava/hslpred/ 2005
idTarget idTarget is a web server for identifying biomolecular targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. idTarget screens against protein structures in PDB. (bio.tools entry) [29] http://idtarget.rcas.sinica.edu.tw 2012
iLoc-Cell Predictor for subcellular locations of human proteins with multiple sites. (bio.tools entry) [30] http://www.jci-bioinfo.cn/iLoc-Hum 2012
KnowPredsite A knowledge-based approach to predict the localization site(s) of both single-localized and multi-localized proteins for all eukaryotes. [31] 2009
2011
Loc3D LOC3D is a database of predicted subcellular localization for eukaryotic proteins of known three-dimensional (3D) structure and includes tools to predict the subcellular localization for submitted protein sequences. (bio.tools entry) [32][33][34] http://cubic.bioc.columbia.edu/db/LOC3d/ 2005
LOCATE LOCATE is a curated database that houses data describing the membrane organization and subcellular localization of mouse proteins. (bio.tools entry) [35] https://web.archive.org/web/20171231015119/http://locate.imb.uq.edu.au/ 2006
LocDB LocDB is a manually curated database with experimental annotations for the subcellular localizations of proteins in Homo sapiens (HS, human) and Arabidopsis thaliana (AT, thale cress). Each database entry contains the experimentally derived localization in Gene Ontology (GO) terminology, the experimental annotation of localization, localization predictions by state-of-the-art methods and, where available, the type of experimental information. LocDB is searchable by keyword, protein name and subcellular compartment, as well as by identifiers from UniProt, Ensembl and TAIR resources. (bio.tools entry) [36] http://www.rostlab.org/services/locDB/ 2011
LOCtarget LOCtarget is a tool for predicting, and a database of pre-computed predictions for, sub-cellular localization of eukaryotic and prokaryotic proteins. Several methods are employed to make the predictions, including text analysis of SWISS-PROT keywords, nuclear localization signals, and the use of neural networks. (bio.tools entry) [37] http://www.rostlab.org/services/LOCtarget/ 2004
LOCtree Prediction based on mimicking the cellular sorting mechanism using a hierarchical implementation of support vector machines. LOCtree is a comprehensive predictor incorporating predictions based on PROSITE/PFAM signatures as well as SwissProt keywords. [33] 2005
LocTree2 Framework to predict localization in life's three domains, including globular and membrane proteins (3 classes for archaea; 6 for bacteria and 18 for eukaryota). The resulting method, LocTree2, works well even for protein fragments. It uses a hierarchical system of support vector machines that imitates the cascading mechanism of cellular sorting. The method reaches high levels of sustained performance (eukaryota: Q18=65%, bacteria: Q6=84%). LocTree2 also accurately distinguishes membrane and non-membrane proteins. In our hands, it compared favorably with top methods when tested on new data (bio.tools entry) [38] https://rostlab.org/owiki/index.php/Loctree2 2012
LocTree3 Prediction of protein subcellular localization in 18 classes for eukaryota, 6 for bacteria and 3 for archaea (bio.tools entry) [38][39] https://rostlab.org/services/loctree3/ 2014
MARSpred Prediction method  for discrimination between Mitochondrial-AARSs and Cytosolic-AARSs. (bio.tools entry) [40] http://www.imtech.res.in/raghava/marspred/ 2012
MDLoc Dependency-Based Protein Subcellular Location Predictor. (bio.tools entry) [41] http://128.4.31.235/ 2015
MemLoci Predictor for the subcellular localization of proteins associated or inserted in eukaryotes membranes. (bio.tools entry) [42] https://mu2py.biocomp.unibo.it/memloci 2011
MemPype Prediction of topology and subcellular localization of Eukaryotic membrane proteins. (bio.tools entry) [43] https://mu2py.biocomp.unibo.it/mempype 2011
MetaLocGramN Meta subcellular localization predictor of Gram-negative protein. MetaLocGramN is a gateway to a number of primary prediction methods (various types: signal peptide, beta-barrel, transmembrane helices and subcellular localization predictors). In author's benchmark, MetaLocGramN performed better in comparison to other SCL predictive methods, since the average Matthews correlation coefficient reached 0.806 that enhanced the predictive capability by 12% (compared to PSORTb3). MetaLocGramN can be run via SOAP. [44] 2012
MirZ MirZ is a web server that for evaluation and analysis of miRNA. It integrates two miRNA resources: the smiRNAdb miRNA expression atlas and the E1MMo miRNA target prediction algorithm. (bio.tools entry) [45] http://www.mirz.unibas.ch 2009
MitPred Web-server specifically trained to predict the proteins which are destined to localized in mitochondria in yeast and animals particularly. (bio.tools entry) [46] http://www.imtech.res.in/raghava/mitpred/ 2006
MultiLoc An SVM-based prediction engine for a wide range of subcellular locations. [47] 2006
Mycosub This web-server was used to predict the subcellular localizations of mycobacterial proteins based on optimal tripeptide compositions. (bio.tools entry) [48] http://lin.uestc.edu.cn/server/Mycosub 2015
NetNES Prediction of the leucine-rich nuclear export signals (NES) in eukaryotic proteins (bio.tools entry) [49] http://cbs.dtu.dk/services/NetNES/ 2004
ngLOC ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 85.3% to 91.4% across species. (bio.tools entry) [50] http://genome.unmc.edu/ngLOC/index.html 2007
OBCOL Software we designed to perform organelle-based colocalisation analysis from multi-fluorophore microscopy 2D, 3D and 4D cell imaging. (bio.tools entry) [51] http://obcol.imb.uq.edu.au/ 2009
PA-SUB PA-SUB (Proteome Analyst Specialized Subcellular Localization Server) can be used to predict the subcellular localization of proteins using established machine learning techniques. (bio.tools entry) [52][53] http://www.cs.ualberta.ca/~bioinfo/PA/Sub/ 2004
PharmMapper PharmMapper is a web server that identifies potential drug targets from its PharmTargetDB for a given input molecule. Potential targets are identified from a prediction of the spatial arrangement of features essential for a given molecule to interact with a target. (bio.tools entry) [54] http://59.78.96.61/pharmmapper 2010
PlantLoc PlantLoc is a web server for predicting plant protein subcellular localization by substantiality motif. (bio.tools entry) [55] http://cal.tongji.edu.cn/PlantLoc/ 2013
PRED-TMBB PRED-TMBB is a tool that takes a Gram-negative bacteria protein sequence as input and predicts the transmembrane strands and the probability of it being an outer membrane beta-barrel protein. The user has a choice of three different decoding methods. (bio.tools entry) [56][57] http://bioinformatics.biol.uoa.gr/PRED-TMBB/ 2004
PredictNLS Prediction and analysis of nuclear localization signals (bio.tools entry) [58] https://www.rostlab.org/owiki/index.php/PredictNLS 2000
PredictProtein Open Prediction of various aspects of protein structure and function. A user may submit a query to the server without registration. (bio.tools entry) [59][60][61][62] http://ppopen.informatik.tu-muenchen.de/ 2014
PREP Suite The PREP (Predictive RNA Editors for Plants) suite predicts sites of RNA editing based on the principle that editing in plant organelles increases the conservation of proteins across species. Predictors for mitochondrial genes, chloroplast genes, and alignments input by the user are included. (bio.tools entry) [63][64] http://prep.unl.edu/ 2009
ProLoc-GO ProLoc-GO is an efficient sequence-based method by mining informative Gene Ontology terms for predicting protein subcellular localization. (bio.tools entry) [65] http://140.113.239.45/prolocgo/ 2008
ProLoc Evolutionary support vector machine (ESVM) based classifier with automatic selection from a large set of physicochemical composition (PCC) features to design an accurate system for predicting protein subnuclear localization. (bio.tools entry) [66] http://140.113.239.45/proloc/ 2007
Protegen Protegen is a web-based database and analysis system that curates, stores and analyzes protective antigens. Protegen includes basic antigen information and experimental evidence curated from peer-reviewed articles. It also includes detailed gene/protein information (e.g. DNA and protein sequences, and COG classification). Different antigen features, such as protein weight and pI, and subcellular localizations of bacterial proteins are precomputed. (bio.tools entry) [67] http://www.violinet.org/protegen 2011
Proteome Analyst Proteome Analyst is a high-throughput tool for predicting properties for each protein in a proteome. The user provides a proteome in fasta format, and the system employs Psi-blast, Psipred and Modeller to predict protein function and subcellular localization. Proteome Analyst uses machine-learned classifiers to predict things such as GO molecular function. User-supplied training data can also be used to create custom classifiers. (bio.tools entry) [53] http://www.cs.ualberta.ca/~bioinfo/PA/ 2004
ProTox ProTox is a web server for the in silico prediction of oral toxicities of small molecules in rodents. (bio.tools entry) [68][69] http://tox.charite.de/tox 2018
PSLpred Method for subcellular localization proteins belongs to prokaryotic genomes. (bio.tools entry) [70] http://www.imtech.res.in/raghava/pslpred/ 2005
PSORTb PSORTb (for “bacterial” PSORT) is a high-precision localization prediction method for bacterial proteins.PSORTb has remained the most precise bacterial protein subcellular localization (SCL) predictor since it was first made available in 2003. PSORTb version improved recall, higher proteome-scale prediction coverage, and new refined localization subcategories. It is the first SCL predictor specifically geared for all prokaryotes, including archaea and bacteria with atypical membrane/cell wall topologies. (bio.tools entry) [71] http://www.psort.org/psortb/ 2010
PSORTdb PSORTdb (part of the PSORT family) is a database of protein subcellular localizations for bacteria and archaea that contains both information determined through laboratory experimentation (ePSORTdb dataset) and computational predictions (cPSORTdb dataset). (bio.tools entry) [72][73] http://db.psort.org 2010
psRobot psRobot is a web-based tool for plant small RNA meta-analysis. psRobot computes stem-loop small RNA prediction, which aligns user uploaded sequences to the selected genome, extracts their predicted precursors, and predicts whether the precursors can fold into stem-loop shaped secondary structure. psRobot also computes small RNA target prediction, which predict the possible targets of user provided small RNA sequences from the selected transcript library. (bio.tools entry) [74] http://omicslab.genetics.ac.cn/psRobot/ 2012
pTARGET pTARGET predicts the subcellular localization of eukaryotic proteins based on the occurrence patterns of location-specific protein functional domains and the amino acid compositional differences in proteins from nine distinct subcellular locations. (bio.tools entry) [75][76] http://bioinformatics.albany.edu/~ptarget 2006
RegPhos RegPhos is a database for exploration of the phosphorylation network associated with an input of genes/proteins. Subcellular localization information is also included. (bio.tools entry) [77] http://regphos.mbc.nctu.edu.tw/ 2011
RepTar RepTar is a database of miRNA target predictions, based on the RepTar algorithm that is independent of evolutionary conservation considerations and is not limited to seed pairing sites. (bio.tools entry) [78] http://reptar.ekmd.huji.ac.il 2011
RNApredator RNApredator is a web server for the prediction of bacterial sRNA targets. The user can choose from a large selection of genomes. Accessibility of the target to the sRNA is considered. (bio.tools entry) [79] http://rna.tbi.univie.ac.at/RNApredator 2011
S-PSorter A novel cell structure-driven classifier construction approach for predicting image-based protein subcellular location by employing the prior biological structural information. (bio.tools entry) [80] https://github.com/shaoweinuaa/S-PSorter 2016
SChloro Prediction of protein sub-chloroplastinc localization. (bio.tools entry) [81] http://schloro.biocomp.unibo.it 2017
SCLAP An Adaptive Boosting Method for Predicting Subchloroplast Localization of Plant Proteins. [82] 2013
SCLPred SCLpred protein subcellular localization prediction by N-to-1 neural networks. [83] 2011
SCLpred-EMS Subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks [84] http://distilldeep.ucd.ie/SCLpred2/ 2020
SCLpred-MEM Subcellular localization prediction of membrane proteins by deep N-to-1 convolutional neural networks [85] http://distilldeep.ucd.ie/SCLpred-MEM/ 2021
SecretomeP Predictions of non-classical (i.e. not signal peptide triggered) protein secretion (bio.tools entry) [86][87] http://cbs.dtu.dk/services/SecretomeP/ 2005
SemiBiomarker New semi-supervised protocol that can use unlabeled cancer protein data in model construction by an iterative and incremental training strategy.It can result in improved accuracy and sensitivity of subcellular location difference detection. (bio.tools entry) [88] http://www.csbio.sjtu.edu.cn/bioinf/SemiBiomarker/ 2015
SherLoc An SVM-based predictor combining MultiLoc with text-based features derived from PubMed abstracts. [89] 2007
SUBA3 A subcellular localisation database for Arabidopsis proteins, with online search interface. (bio.tools entry) [90][91] http://suba3.plantenergy.uwa.edu.au/ 2014
SubChlo Computational system for predicting protein subchloroplast locations from its primary sequence. It can locate the protein whose subcellular location is chloroplast in one of the four parts: envelope (which consists of outer membrane and inner membrane), thylakoid lumen, stroma and thylakoid membrane. (bio.tools entry) [92] http://bioinfo.au.tsinghua.edu.cn/software/subchlo/ 2009
SuperPred The SuperPred web server compares the structural fingerprint of an input molecule to a database of drugs connected to their drug targets and affected pathways. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. Such information can be useful in drug classification and target prediction. (bio.tools entry) [93] http://bioinformatics.charite.de/superpred 2008
SuperTarget Web resource for analyzing drug-target interactions. Integrates drug-related info associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. (bio.tools entry) [94] http://bioinformatics.charite.de/supertarget/ 2012
SwissTargetPrediction SwissTargetPrediction is a web server for target prediction of bioactive small molecules. This website allows you to predict the targets of a small molecule. Using a combination of 2D and 3D similarity measures, it compares the query molecule to a library of 280 000 compounds active on more than 2000 targets of 5 different organisms. (bio.tools entry) [95][96] http://www.swisstargetprediction.ch 2014
T3DB The Toxin and Toxin-Target Database (T3DB) is a unique bioinformatics resource that compiles comprehensive information about common or ubiquitous toxins and their toxin-targets. Each T3DB record (ToxCard) contains over 80 data fields providing detailed information on chemical properties and descriptors, toxicity values, protein and gene sequences (for both targets and toxins), molecular and cellular interaction data, toxicological data, mechanistic information and references. This information has been manually extracted and manually verified from numerous sources, including other electronic databases, government documents, textbooks and scientific journals. A key focus of the T3DB is on providing ??depth?? over ??breadth?? with detailed descriptions, mechanisms of action, and information on toxins and toxin-targets. Potential applications of the T3DB include clinical metabolomics, toxin target prediction, toxicity prediction and toxicology education. (bio.tools entry) [97] http://www.t3db.org 2010
TALE-NT Transcription activator-like (TAL) Effector-Nucleotide Targeter 2.0 (TALE-NT) is a suite of web-based tools that allows for custom design of TAL effector repeat arrays for desired targets and prediction of TAL effector binding sites. (bio.tools entry) [98] https://boglab.plp.iastate.edu/ 2012
TarFisDock Target Fishing Dock (TarFisDock) is a web server that docks small molecules with protein structures in the Potential Drug Target Database (PDTD) in an effort to discover new drug targets. (bio.tools entry) [99] http://www.dddc.ac.cn/tarfisdock/ 2006
TargetRNA TargetRNA is a web based tool for identifying mRNA targets of small non-coding RNAs in bacterial species. (bio.tools entry) [100] http://cs.wellesley.edu/~btjaden/TargetRNA2/ 2008
TargetP Prediction of N-terminal sorting signals. [101] 2000
TDR Targets Tropical Disease Research (TDR) Database: Designed and developed to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data for genes collected from various sources, including literature curation. Information can be browsed and queried. (bio.tools entry) [102] http://tdrtargets.org/ 2012
TetraMito Sequence-based predictor for identifying submitochondria location of proteins. (bio.tools entry) [103] http://lin.uestc.edu.cn/server/TetraMito 2013
TMBETA-NET Tool that predicts transmembrane beta strands in an outer membrane protein from its amino acid sequence. (bio.tools entry) [104][105] http://psfs.cbrc.jp/tmbeta-net/ 2005
TMHMM Prediction of transmembrane helices to identify transmembrane proteins. 2001
TMPred The TMpred program makes a prediction of membrane-spanning regions and their orientation. The algorithm is based on the statistical analysis of TMbase, a database of naturally occurring transmembrane proteins (bio.tools entry) [106] http://embnet.vital-it.ch/software/TMPRED_form.html 1993
TPpred 1.0 Organelle targeting peptide prediction (bio.tools entry) [107] http://tppred.biocomp.unibo.it/tppred/default/index 2013
TPpred 2.0 Mitochondrial targeting peptide prediction (bio.tools entry) [108][107] https://tppred3.biocomp.unibo.it 2015
TPpred 3.0 Organelle-targeting peptide detection and cleavage-site prediction (bio.tools entry) [108] http://tppred3.biocomp.unibo.it/tppred3 2015
2010
UM-PPS The University of Minnesota Pathway Prediction System (UM-PPS) is a web tool that recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions and predicts transformations of these groups based on biotransformation rules. Multi-level predictions are made. (bio.tools entry) [109] http://eawag-bbd.ethz.ch/predict/aboutPPS.html 2008
WoLF PSORT WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. (bio.tools entry) [110] https://wolfpsort.hgc.jp/ 2007
YLoc YLoc is a web server for the prediction of subcellular localization. Predictions are explained and biological properties used for the prediction highlighted. In addition, a confidence estimates rates the reliability of individual predictions. (bio.tools entry) [111] http://www.multiloc.org/YLoc 2010
Zinc Finger Tools Zinc Finger Tools provides several tools for selecting zinc finger protein target sites and for designing the proteins that will target them. (bio.tools entry) [112][113][114][115][116][117] http://www.scripps.edu/mb/barbas/zfdesign/zfdesignhome.php 2006

References

  1. ↑ "AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices". Amino Acids 35 (2): 345–53. August 2008. doi:10.1007/s00726-007-0616-y. PMID 18163182. 
  2. ↑ "APSLAP: an adaptive boosting technique for predicting subcellular localization of apoptosis protein". Acta Biotheoretica 61 (4): 481–97. December 2013. doi:10.1007/s10441-013-9197-1. PMID 23982307. 
  3. ↑ "Combining machine learning and homology-based approaches to accurately predict subcellular localization in Arabidopsis". Plant Physiology 154 (1): 36–54. September 2010. doi:10.1104/pp.110.156851. PMID 20647376. 
  4. ↑ "BaCelLo: a balanced subcellular localization predictor". Bioinformatics 22 (14): e408–16. July 2006. doi:10.1093/bioinformatics/btl222. PMID 16873501. 
  5. ↑ 5.0 5.1 "BAR-PLUS: the Bologna Annotation Resource Plus for functional and structural annotation of protein sequences". Nucleic Acids Research 39 (Web Server issue): W197–202. July 2011. doi:10.1093/nar/gkr292. PMID 21622657. 
  6. ↑ "The Bologna Annotation Resource (BAR 3.0): improving protein functional annotation". Nucleic Acids Research 45 (W1): W285–W290. July 2017. doi:10.1093/nar/gkx330. PMID 28453653. 
  7. ↑ "BASys: a web server for automated bacterial genome annotation". Nucleic Acids Research 33 (Web Server issue): W455–9. July 2005. doi:10.1093/nar/gki593. PMID 15980511. 
  8. ↑ "BOMP: a program to predict integral beta-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria". Nucleic Acids Research 32 (Web Server issue): W394–9. July 2004. doi:10.1093/nar/gkh351. PMID 15215418. 
  9. ↑ "BPROMPT: A consensus server for membrane protein prediction". Nucleic Acids Research 31 (13): 3698–700. July 2003. doi:10.1093/nar/gkg554. PMID 12824397. 
  10. ↑ "Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms". Nature Protocols 3 (2): 153–62. 2008-01-01. doi:10.1038/nprot.2007.494. PMID 18274516. 
  11. ↑ "Predicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositions". Protein Science 13 (5): 1402–6. May 2004. doi:10.1110/ps.03479604. PMID 15096640. 
  12. ↑ "Prediction of protein subcellular localization". Proteins 64 (3): 643–51. August 2006. doi:10.1002/prot.21018. PMID 16752418. 
  13. ↑ "ClubSub-P: Cluster-Based Subcellular Localization Prediction for Gram-Negative Bacteria and Archaea". Frontiers in Microbiology 2: 218. 2011. doi:10.3389/fmicb.2011.00218. PMID 22073040. 
  14. ↑ "CoBaltDB: Complete bacterial and archaeal orfeomes subcellular localization database and associated resources". BMC Microbiology 10: 88. March 2010. doi:10.1186/1471-2180-10-88. PMID 20331850. 
  15. ↑ "ComiR: Combinatorial microRNA target prediction tool". Nucleic Acids Research 41 (Web Server issue): W159–64. July 2013. doi:10.1093/nar/gkt379. PMID 23703208. 
  16. ↑ "Finding the Subcellular Location of Barley, Wheat, Rice and Maize Proteins: The Compendium of Crop Proteins with Annotated Locations (cropPAL)". Plant & Cell Physiology 57 (1): e9. January 2016. doi:10.1093/pcp/pcv170. PMID 26556651. 
  17. ↑ Cserzö, Miklos; Eisenhaber, Frank; Eisenhaber, Birgit; Simon, Istvan (Sep 2002). "On filtering false positive transmembrane protein predictions". Protein Engineering, Design and Selection 15 (9): 745–752. doi:10.1093/protein/15.9.745. ISSN 1741-0134. PMID 12456873. 
  18. ↑ "DeepLoc: prediction of protein subcellular localization using deep learning". Bioinformatics 33 (21): 3387–3395. November 2017. doi:10.1093/bioinformatics/btx431. PMID 29036616. 
  19. ↑ "Light Attention Predicts Protein Location from the Language of Life". bioRxiv. April 2021. doi:10.1101/2021.04.25.441334. http://biorxiv.org/content/early/2021/04/26/2021.04.25.441334.abstract. 
  20. ↑ "DIANA-microT web server: elucidating microRNA functions through target prediction". Nucleic Acids Research 37 (Web Server issue): W273–6. July 2009. doi:10.1093/nar/gkp292. PMID 19406924. 
  21. ↑ "DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows". Nucleic Acids Research 41 (Web Server issue): W169–73. July 2013. doi:10.1093/nar/gkt393. PMID 23680784. 
  22. ↑ "EchoLOCATION: an in silico analysis of the subcellular locations of Escherichia coli proteins and comparison with experimentally derived locations". Bioinformatics 25 (2): 163–6. January 2009. doi:10.1093/bioinformatics/btn596. PMID 19015139. 
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  26. ↑ Tusnády, Gábor E.; Simon, István (Oct 1998). "Principles governing amino acid composition of integral membrane proteins: application to topology prediction 1 1Edited by J. Thornton". Journal of Molecular Biology 283 (2): 489–506. doi:10.1006/jmbi.1998.2107. ISSN 0022-2836. PMID 9769220. 
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  29. ↑ "idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach". Nucleic Acids Research 40 (Web Server issue): W393–9. July 2012. doi:10.1093/nar/gks496. PMID 22649057. 
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  33. ↑ 33.0 33.1 "Mimicking cellular sorting improves prediction of subcellular localization". Journal of Molecular Biology 348 (1): 85–100. April 2005. doi:10.1016/j.jmb.2005.02.025. PMID 15808855. 
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  47. ↑ "MultiLoc: prediction of protein subcellular localization using N-terminal targeting sequences, sequence motifs and amino acid composition". Bioinformatics 22 (10): 1158–65. May 2006. doi:10.1093/bioinformatics/btl002. PMID 16428265. 
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