A highly accurate statistical approach for the prediction of transmembrane beta-barrels.

Department of Biochemistry, Tulane University Health Sciences Center, New Orleans, LA 70112, USA.
Bioinformatics (Impact Factor: 4.62). 08/2010; 26(16):1965-74. DOI: 10.1093/bioinformatics/btq308
Source: PubMed

ABSTRACT Transmembrane beta-barrels (TMBBs) belong to a special structural class of proteins predominately found in the outer membranes of Gram-negative bacteria, mitochondria and chloroplasts. TMBBs are surface-exposed proteins that perform a variety of functions ranging from nutrient acquisition to osmotic regulation. These properties suggest that TMBBs have great potential for use in vaccine or drug therapy development. However, membrane proteins, such as TMBBs, are notoriously difficult to identify and characterize using traditional experimental approaches and current prediction methods are still unreliable.
A prediction method based on the physicochemical properties of experimentally characterized TMBB structures was developed to predict TMBB-encoding genes from genomic databases. The Freeman-Wimley prediction algorithm developed in this study has an accuracy of 99% and MCC of 0.748 when using the most efficient prediction criteria, which is better than any previously published algorithm.
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    ABSTRACT: Outer membrane proteins (OMPs) play important roles in bacterial cellular processes. Discriminating OMPs from different fold types of proteins is helpful for successful prediction of their structures and for exact designs of OMP-targeted drugs. In this paper, we developed a novel prediction method based on primary sequence features and support vector machine (SVM) algorithms. For protein sequences, discriminative features were extracted by the combination of sequence encoding based on grouped weights (EBGW), amino acid compositions and biochemical properties. Feature subsets were screened using F-score algorithm for training a SVM-based classifier, namely EBGW_OMP. The performance of EBGW_OMP was examined on a benchmark dataset of 1087 proteins. The results show that EBGW_OMP can discriminate OMPs from globular proteins, α-helical membrane proteins or non-OMPs with cross-validated accuracy of 98.0%, 97.6% or 97.9%, respectively, which outperformed existing sequence-based methods. EBGW_OMP also successfully distinguished 681 out of 722 OMPs with 97.0% accuracy in another benchmark dataset of 2657 proteins. Genome-wide tests show that EBGW_OMP has excellent capability of correctly detecting OMPs and is considerable for genomic OMPs prediction. The web server implements EBGW_OMP is freely accessible at OMP.
    2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB); 05/2014
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    ABSTRACT: Transmembrane β-barrel (TMB) proteins constitute a special class of proteins, which are located in outer membranes of Gram-negative bacteria, mitochondria, and chloroplasts. These proteins play diverse important roles in biological organisms. However, only a small number of TMB protein structures are currently known due to difficulties with experimental techniques. Hence, computational structure prediction methods based on learning are poorly tractable for these proteins. We introduce here a graph-theoretic ab initio model for predicting structures of TMB proteins by free energy minimization. TMB super-secondary structures with permuted arrangements are taken into consideration in this model. We show that finding a permuted structure is an NP-hard problem and then analyze the complexity of a tree decomposition-based algorithm to search for the optimal structure of a TMB protein corresponding to a given permutation. The robust performance of the model has been proven in our previously published results.
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    ABSTRACT: Current understanding of the forces directing the folding of integral membrane proteins is very limited compared to the detailed picture available for water-soluble proteins. While mechanistic studies of the folding process in vitro have been conducted for only a small number of membrane proteins, the available evidence indicates that their folding process is thermodynamically driven like that of soluble proteins. In vivo, however, the majority of integral membrane proteins are installed in membranes by dedicated machinery, suggesting that the cellular systems may act to facilitate and regulate the spontaneous physical process of folding. Both the in vitro folding process and the in vivo pathway must navigate an energy landscape dominated by the energetically favorable burial of hydrophobic segments in the membrane interior and the opposition to folding due to the need for passage of polar segments across the membrane. This manuscript describes a simple, exactly solvable model which incorporates these essential features of membrane protein folding. The model is used to compare the folding time under conditions which depict both the in vitro and in vivo pathways. It is proposed that the cellular complexes responsible for insertion of membrane proteins act by lowering the energy barrier for passage of polar regions through the membrane, thereby allowing the chain to more rapidly achieve the folded state.
    Physical Review E 08/2014; 90(2-1):022707. · 2.33 Impact Factor


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