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TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information

TranCEP: التنبؤ بفئة الركيزة لبروتينات النقل عبر الغشاء باستخدام المعلومات التركيبية والتطورية والموضعية
Authors: Munira Alballa; Faizah Aplop; Gregory Butler;

TranCEP: Predicting the substrate class of transmembrane transport proteins using compositional, evolutionary, and positional information

Abstract

Les transporteurs médient le mouvement des composés à travers les membranes qui séparent la cellule de son environnement et à travers les membranes internes entourant les compartiments cellulaires. On estime qu'un tiers d'un protéome est constitué de protéines membranaires, dont beaucoup sont des protéines de transport. Compte tenu de l'augmentation du nombre de génomes séquencés, il est nécessaire de disposer d'outils informatiques qui prédisent les substrats transportés par les protéines de transport transmembranaire. Dans cet article, nous présentons TranCEP, un prédicteur du type de substrat transporté par une protéine de transport transmembranaire. TranCEP combine l'utilisation traditionnelle de la composition en acides aminés de la protéine, avec des informations évolutives capturées dans un alignement de séquences multiples (MSA), et la restriction à des positions importantes de l'alignement qui jouent un rôle dans la détermination de la spécificité de la protéine. Nos résultats expérimentaux montrent que TranCEP surpasse de manière significative les prédicteurs de pointe. Les résultats quantifient la contribution apportée par chaque type d'information utilisée.

Los transportadores median el movimiento de compuestos a través de las membranas que separan la célula de su entorno y a través de las membranas internas que rodean los compartimentos celulares. Se estima que un tercio de un proteoma consiste en proteínas de membrana, y muchas de ellas son proteínas de transporte. Dado el aumento en el número de genomas que se están secuenciando, existe la necesidad de herramientas computacionales que predigan los sustratos que son transportados por las proteínas de transporte transmembrana. En este artículo, presentamos TranCEP, un predictor del tipo de sustrato transportado por una proteína de transporte transmembrana. TranCEP combina el uso tradicional de la composición de aminoácidos de la proteína, con información evolutiva capturada en un alineamiento de secuencias múltiples (MSA) y la restricción a posiciones importantes del alineamiento que desempeñan un papel en la determinación de la especificidad de la proteína. Nuestros resultados experimentales muestran que TranCEP supera significativamente a los predictores de última generación. Los resultados cuantifican la contribución realizada por cada tipo de información utilizada.

Transporters mediate the movement of compounds across the membranes that separate the cell from its environment and across the inner membranes surrounding cellular compartments. It is estimated that one third of a proteome consists of membrane proteins, and many of these are transport proteins. Given the increase in the number of genomes being sequenced, there is a need for computational tools that predict the substrates that are transported by the transmembrane transport proteins. In this paper, we present TranCEP, a predictor of the type of substrate transported by a transmembrane transport protein. TranCEP combines the traditional use of the amino acid composition of the protein, with evolutionary information captured in a multiple sequence alignment (MSA), and restriction to important positions of the alignment that play a role in determining the specificity of the protein. Our experimental results show that TranCEP significantly outperforms the state-of-the-art predictors. The results quantify the contribution made by each type of information used.

تتوسط الناقلات حركة المركبات عبر الأغشية التي تفصل الخلية عن بيئتها وعبر الأغشية الداخلية المحيطة بالأقسام الخلوية. تشير التقديرات إلى أن ثلث البروتين يتكون من بروتينات غشائية، والعديد منها بروتينات نقل. بالنظر إلى الزيادة في عدد الجينومات التي يتم تسلسلها، هناك حاجة إلى أدوات حسابية تتنبأ بالركائز التي تنقلها بروتينات النقل عبر الغشاء. في هذه الورقة، نقدم TranCEP، وهو مؤشر لنوع الركيزة التي ينقلها بروتين نقل عبر الغشاء. يجمع TranCEP بين الاستخدام التقليدي لتكوين الأحماض الأمينية للبروتين، مع المعلومات التطورية التي تم التقاطها في محاذاة تسلسل متعددة (MSA)، والتقييد إلى مواضع مهمة للمحاذاة التي تلعب دورًا في تحديد خصوصية البروتين. تظهر نتائجنا التجريبية أن TranCEP يتفوق بشكل كبير على أحدث المؤشرات. تحدد النتائج المساهمة التي يقدمها كل نوع من المعلومات المستخدمة.

Keywords

Proteome, Substrate specificity, Biochemistry, Gene, Substrate Specificity, Transmembrane domain, Computational biology, Secondary Structure Prediction, Amino Acids, Databases, Protein, Ribosome Structure and Translation Mechanisms, Membrane transport, Genome, Q, R, Membrane, Life Sciences, Amino acid, Subcellular Localization, Medicine, Algorithms, Research Article, Receptor, Science, Biophysics, Membrane transport protein, Transmembrane Topology, Transmembrane protein, Biochemistry, Genetics and Molecular Biology, Peptide sequence, Protein sequencing, Amino Acid Sequence, RNA Sequencing Data Analysis, Molecular Biology, Biology, Prediction of Protein Subcellular Localization, Base Sequence, Computational Biology, Membrane Proteins, Membrane Transport Proteins, Biological Transport, Enzyme, Membrane protein, Carrier Proteins, Transport protein, Sequence Alignment, Software

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
22
Top 10%
Average
Top 10%
Green
gold