FastTrans

Membrane transport proteins and their substrate specificities play crucial roles in various cellular functions. Identifying the substrate specificities of membrane transport proteins is closely related to protein-target interaction prediction, drug design, membrane recruitment, and dysregulation analysis. This study defines a protein sequence by the word embeddings including sub-word information of its biological words which then serve as features to be fed in binary classifiers. Different ways to generate word embeddings were tried by changing the lengths of protein sequence’s constituent biological words.

Fig.1. The model architecture to identity substrate specificities of transporters

Result

Compared to 4 different feature types generated from protein sequences, our proposed features can help prediction models yield superior performance. FastTran, our best model, can reach the average area under the curve of 0.96 on 5-fold cross validation data and 0.99 on independent test data. Accordingly, this study provides a basis for further research that can enrich a field of applying natural language processing techniques in bioinformatics.

Dataset

The dataset used in this server were retrieved from UniProt. The detail of the dataset is listed in the table below.

If you would like to build a model and evaluate our model, we provide the dataset as the below link.

Submission

In order to avoid the errors, please submit the sequence in fasta format (we also give you the fasta file examples). The user can choose two options to submit, including paste the sequence into text area and upload sequence file. The user can submit one single fasta file or multiple fasta file. In the result page, we show the results for the sequences which belong to electron transport proteins or not.

Sample fasta Sequence(s)

>O68460
MAGIYLFVVAAALAALGYGALTIKTIMAADAGTARMQEISGAVQEGASAFLNRQYKTIAV
VGAVVFVILTALLGISVGFGFLIGAVCSGIAGYVGMYISVRANVRVAAGAQQGLARGLEL
AFQSGAVTGMLVAGLALLSVAFYYILLVGIGATGRALIDPLVALGFGASLISIFARLGGG
IFTKGADVGADLVGKVEAGIPEDDPRNPAVIADNVGDNVGDCAGMAADLFETYAVTVVAT
MVLASIFFAGVPAMTSMMAYPLAIGGVCILASILGTKFVKLGPKNNIMGALYRGFLVSAG
ASFVGIILATAIVPGFGDIQGANGVLYSGFDLFLCAVIGLLVTGLLIWVTEYYTGTNFRP
VRSVAKASTTGHGTNVIQGLAISMEATALPALIICAAIITTYQLSGLFGIAITVTSMLAL
AGMVVALDAYGPVTDNAGGIAEMANLPEDVRKTTDALDAVGNTTKAVTKGYAIGSAGLGA
LVLFAAYTEDLAFFKANVDAYPAFAGVDVNFSLSSPYVVVGLFIGGLLPYLFGSMGMTAV
GRAAGSVVEEVRRQFREIPGIMEGTAKPEYGRCVDMLTKAAIKEMIIPSLLPVLAPIVLY
FVILGIADKSAAFSALGAMLLGVIVTGLFVAISMTAGGGAWDNAKKYIEDGHYGGKGSEA
HKAAVTGDTVGDPYKDTAGPAVNPMIKITNIVALLLLAVLAH
>O06342
MFPAAVGVLWQSGLRDPTPPGGPHGIEGLSLAFEKPSPVTALTQELRFATTMTGGVSLAI
WMAGVTREINLLAQASQWRRLGGTFPTNSQLTNESAASLRLYAQLIDLLDMVVDVDILSG
TSAGGINAALLASSRVTGSDLGGIRDLWLDLGALTELLRDPRDKKTPSLLYGDERIFAAL
AKRLPKLATGPFPPTTFPEAARTPSTTLYITTTLLAGETSRFTDSFGTLVQDVDLRGLFT
FTETDLARPDTAPALALAARSSASFPLAFEPSFLPFTKGTAKKGEVPARPAMAPFTSLTR
PHWVSDGGLLDNRPIGVLFKRIFDRPARRPVRRVLLFVVPSSGPAPDPMHEPPPDNVDEP
LGLIDGLLKGLAAVTTQSIAADLRAIRAHQDCMEARTDAKLRLAELAATLRNGTRLLTPS
LLTDYRTREATKQAQTLTSALLRRLSTCPPESGPATESLPKSWSAELTVGGDADKVCRQQ
ITATILLSWSQPTAQPLPQSPAELARFGQPAYDLAKGCALTVIRAAFQLARSDADIAALA
EVTEAIHRAWRPTASSDLSVLVRTMCSRPAIRQGSLENAADQLAADYLQQSTVPGDAWER
LGAALVNAYPTLTQLAASASADSGAPTDSLLARDHVAAGQLETYLSYLGTYPGRADDSRD
APTMAWKLFDLATTQRAMLPADAEIEQGLELVQVSADTRSLLAPDWQTAQQKLTGMRLHH
FGAFYKRSWRANDWMWGRLDGAGWLVHVLLDPRRVRWIVGERADTNGPQSGAQWFLGKLK
ELGAPDFPSPGYPLPAVGGGPAQHLTEDMLLDELGFLDDPAKPLPASIPWTALWLSQAWQ
QRVLEEELDGLANTVLDPQPGKLPDWSPTSSRTWATKVLAAHPGDAKYALLNENPIAGET
FASDKGSPLMAHTVAKAAATAAGAAGSVRQLPSVLKPPLITLRTLTLSGYRVVSLTKGIA
RSTIIAGALLLVLGVAAAIQSVTVFGVTGLIAAGTGGLLVVLGTWQVSGRLLFALLSFSV
VGAVLALATPVVREWLFGTQQQPGWVGTHAYWLGAQWWHPLVVVGLIALVAIMIAAATPG
RR
>P11166
MEPSSKKLTGRLMLAVGGAVLGSLQFGYNTGVINAPQKVIEEFYNQTWVHRYGESILPTT
LTTLWSLSVAIFSVGGMIGSFSVGLFVNRFGRRNSMLMMNLLAFVSAVLMGFSKLGKSFE
MLILGRFIIGVYCGLTTGFVPMYVGEVSPTALRGALGTLHQLGIVVGILIAQVFGLDSIM
GNKDLWPLLLSIIFIPALLQCIVLPFCPESPRFLLINRNEENRAKSVLKKLRGTADVTHD
LQEMKEESRQMMREKKVTILELFRSPAYRQPILIAVVLQLSQQLSGINAVFYYSTSIFEK
AGVQQPVYATIGSGIVNTAFTVVSLFVVERAGRRTLHLIGLAGMAGCAILMTIALALLEQ
LPWMSYLSIVAIFGFVAFFEVGPGPIPWFIVAELFSQGPRPAAIAVAGFSNWTSNFIVGM
CFQYVEQLCGPYVFIIFTVLLVLFFIFTYFKVPETKGRTFDEIASGFRQGGASQSDKTPE
ELFHPLGADSQV

Class name	Number of proteins
Class name	Original	After 20% similarity check	Train dataset	Test dataset
Amino acid transporter	189	73	61	12
Electron transporter	596	221	184	37
Cation transporter	294	88	73	15
Lipid transporter	144	78	66	12
Protein/mRNA transporter	1056	455	380	75
Sugar transporter	205	84	71	13
Other transporter	471	198	165	33
Membrane	3853	1050	875	175

FastTrans

Introduction

Result

Dataset

Submission

Members

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