Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties

Chen, Shu-An; Ou, Yu‐Yen; Lee, Tzong-Yi; Gromiha, M. Michael

doi:10.1093/bioinformatics/btr340

Cited by 78 publications

(66 citation statements)

References 32 publications

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“…[13] developed models for four substrate-specific transporter classes: electron transporters, protein/mRNA transporters, ion transporters, and other transporters. The use of only four transporter classes makes difficult a direct comparison to our models, which were developed on seven transporter classes.…”

Section: Resultsmentioning

confidence: 99%

“…* Four substrate-specific transporter classes described in Chen et al [13]; $ Non-transporter, % other class; ∧ ion transporter.…”

Section: Resultsmentioning

confidence: 99%

“…Chen et al . [13] have developed models to predict the substrate specificity for electron transporters, protein/mRNA transporters, ion transporters, and other transporters, and more recently improved this method to differentiate transporters from non-transporters using a probability distribution function for each query protein. This improved method, which is essentially a combination of the original Chen et al model and the Ou et al model, is limited in that the proposed threshold of 0.65 is not reliable for the prediction of transporters.…”

Section: Discussionmentioning

confidence: 99%

“…We used a set of 49 selected physical, chemical, energetic, and conformational properties to define the biochemical composition of each protein sequence [13]. These values are subsets of the AAIndex database [21], which has been successfully used to study protein folding and stability [22]–[24] and transporter classification [25].…”

Section: Methodsmentioning

confidence: 99%

“…This score is then used to assign a substrate to the query sequence. More recently, Chen et al [13] developed neural network-based models to predict substrate specificity for electron transporters, protein/mRNA transporters, ion transporters, and other transporters using a combination of amino acid composition, position-specific scoring matrices (PSSM), and biochemical properties such as the amino acid index (AAindex). Recently Barghash et al .…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Membrane Transport Proteins and Their Substrate Specificities Using Primary Sequence Information

2014

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BackgroundMembrane transport proteins (transporters) move hydrophilic substrates across hydrophobic membranes and play vital roles in most cellular functions. Transporters represent a diverse group of proteins that differ in topology, energy coupling mechanism, and substrate specificity as well as sequence similarity. Among the functional annotations of transporters, information about their transporting substrates is especially important. The experimental identification and characterization of transporters is currently costly and time-consuming. The development of robust bioinformatics-based methods for the prediction of membrane transport proteins and their substrate specificities is therefore an important and urgent task.ResultsSupport vector machine (SVM)-based computational models, which comprehensively utilize integrative protein sequence features such as amino acid composition, dipeptide composition, physico-chemical composition, biochemical composition, and position-specific scoring matrices (PSSM), were developed to predict the substrate specificity of seven transporter classes: amino acid, anion, cation, electron, protein/mRNA, sugar, and other transporters. An additional model to differentiate transporters from non-transporters was also developed. Among the developed models, the biochemical composition and PSSM hybrid model outperformed other models and achieved an overall average prediction accuracy of 76.69% with a Mathews correlation coefficient (MCC) of 0.49 and a receiver operating characteristic area under the curve (AUC) of 0.833 on our main dataset. This model also achieved an overall average prediction accuracy of 78.88% and MCC of 0.41 on an independent dataset.ConclusionsOur analyses suggest that evolutionary information (i.e., the PSSM) and the AAIndex are key features for the substrate specificity prediction of transport proteins. In comparison, similarity-based methods such as BLAST, PSI-BLAST, and hidden Markov models do not provide accurate predictions for the substrate specificity of membrane transport proteins. TrSSP: The Transporter Substrate Specificity Prediction Server, a web server that implements the SVM models developed in this paper, is freely available at http://bioinfo.noble.org/TrSSP.

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Section: Resultsmentioning

confidence: 99%

“…* Four substrate-specific transporter classes described in Chen et al [13]; $ Non-transporter, % other class; ∧ ion transporter.…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Membrane Transport Proteins and Their Substrate Specificities Using Primary Sequence Information

2014

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Functional classification of membrane transporters and channels based on filtered TM/non‐TM amino acid composition

Schaadt

Helms

2012

Biopolymers

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Membrane transporters catalyze the transport of small solute molecules across biological barriers such as lipid bilayer membranes. As the experimental annotation of which proteins transport which substrates is incomplete it is highly desirable to develop computational methods that can assist in the classification and substrate annotation of putative membrane transport proteins. Here, we determined the similarity of membrane transporter sequences annotated in the Transport Classification Database (Saier et al., Nucleic Acids Res 2006, 34, D181‐D186) and Arabidopsis thaliana membrane transporters annotated in the database Aramemnon (Schwacke et al., Plant Physiol 2003, 131, 16‐26). The similarity measure was based on the amino acid composition either considering the full sequences or separately in the transmembrane (TM) and external parts of the sequences. We considered four different substrate sets and three different subfamilies and tried to classify the given proteins into these classes. Family or substrate prediction based on the simple amino acid frequency had an average accuracy of 76%. The differentiation between TM and non‐TM regions led to an improved accuracy of 80% on average. © 2012 Wiley Periodicals, Inc. Biopolymers 97:558–567, 2012.

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PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates

Anteghini,

Santos,

Saccenti

2023

J of Cellular Biochemistry

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The physiology of every living cell is regulated at some level by transporter proteins which constitute a relevant portion of membrane‐bound proteins and are involved in the movement of ions, small and macromolecules across bio‐membranes. The importance of transporter proteins is unquestionable. The prediction and study of previously unknown transporters can lead to the discovery of new biological pathways, drugs and treatments. Here we present PortPred, a tool to accurately identify transporter proteins and their substrate starting from the protein amino acid sequence. PortPred successfully combines pre‐trained deep learning‐based protein embeddings and machine learning classification approaches and outperforms other state‐of‐the‐art methods. In addition, we present a comparison of the most promising protein sequence embeddings (Unirep, SeqVec, ProteinBERT, ESM‐1b) and their performances for this specific task.

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Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties

Abstract: http://rbf.bioinfo.tw/~sachen/ttrbf.html

Cited by 78 publications

References 32 publications

Prediction of Membrane Transport Proteins and Their Substrate Specificities Using Primary Sequence Information

Prediction of Membrane Transport Proteins and Their Substrate Specificities Using Primary Sequence Information

Functional classification of membrane transporters and channels based on filtered TM/non‐TM amino acid composition

PortPred: Exploiting deep learning embeddings of amino acid sequences for the identification of transporter proteins and their substrates

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