Transmembrane Protein Prediction Research Articles

Extending hidden Markov models to allow conditioning on previous observations.

Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both. Here, we implement a simple extension of the standard HMM in which the current observed symbol (amino acid residue) depends both on the current state and on a series of observed previous symbols. The major advantage of the method is the simplicity in the implementation, which is achieved by properly transforming the observation sequence, using an extended alphabet. Thus, it can utilize all the available algorithms for the training and decoding of HMMs. We investigated the use of several encoding schemes and performed tests in a number of important biological problems previously studied by our team (prediction of transmembrane proteins and prediction of signal peptides). The evaluation shows that, when enough data are available, the performance increased by 1.8%-8.2% and the existing prediction methods may improve using this approach. The methods, for which the improvement was significant (PRED-TMBB2, PRED-TAT and HMM-TM), are available as web-servers freely accessible to academic users at www.compgen.org/tools/ .

Identification of Extracellular Segments by Mass Spectrometry Improves Topology Prediction of Transmembrane Proteins

Transmembrane proteins play crucial role in signaling, ion transport, nutrient uptake, as well as in maintaining the dynamic equilibrium between the internal and external environment of cells. Despite their important biological functions and abundance, less than 2% of all determined structures are transmembrane proteins. Given the persisting technical difficulties associated with high resolution structure determination of transmembrane proteins, additional methods, including computational and experimental techniques remain vital in promoting our understanding of their topologies, 3D structures, functions and interactions. Here we report a method for the high-throughput determination of extracellular segments of transmembrane proteins based on the identification of surface labeled and biotin captured peptide fragments by LC/MS/MS. We show that reliable identification of extracellular protein segments increases the accuracy and reliability of existing topology prediction algorithms. Using the experimental topology data as constraints, our improved prediction tool provides accurate and reliable topology models for hundreds of human transmembrane proteins.

COMSAT: Residue contact prediction of transmembrane proteins based on support vector machines and mixed integer linear programming.

In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.

Hidden markov model for the prediction of transmembrane proteins using MATLAB.

Since membranous proteins play a key role in drug targeting therefore transmembrane proteins prediction is active and challenging area of biological sciences. Location based prediction of transmembrane proteins are significant for functional annotation of protein sequences. Hidden markov model based method was widely applied for transmembrane topology prediction. Here we have presented a revised and a better understanding model than an existing one for transmembrane protein prediction. Scripting on MATLAB was built and compiled for parameter estimation of model and applied this model on amino acid sequence to know the transmembrane and its adjacent locations. Estimated model of transmembrane topology was based on TMHMM model architecture. Only 7 super states are defined in the given dataset, which were converted to 96 states on the basis of their length in sequence. Accuracy of the prediction of model was observed about 74 %, is a good enough in the area of transmembrane topology prediction. Therefore we have concluded the hidden markov model plays crucial role in transmembrane helices prediction on MATLAB platform and it could also be useful for drug discovery strategy. The database is available for free at bioinfonavneet@gmail.comvinaysingh@bhu.ac.in.

A Genome Wide Screening Approach for Membrane-targeted Proteins

Membrane-associated proteins are critical for intra- and intercellular communication. Accordingly approaches are needed for rapid and comprehensive identification of all membrane-targeted gene products in a given cell or tissue. Here we describe a modification of the yeast Ras recruitment system to this end and designate the modified approach the Ras membrane trap (RMT). A pilot RMT screen was carried out on the central nervous system of the mollusk Lymnaea stagnalis, a model organism from a phylum that still lacks a representative with a sequenced genome. 112 gene products were identified in the screen of which 79 lack assignable homologs in available data bases. Currently available annotation tools predicted membrane association of only 45% of the 112 proteins, although experimental verification in mammalian cells confirmed membrane association for all clones tested. Thus, genome annotation using currently available tools is likely to underpredict representation of membrane-associated gene products. The 32 proteins with known homologies include many targeted to the endoplasmic reticulum or the nucleus, thus RMT provides a tool that can cover intracellular membrane proteomes. Two sequences were found to represent gene families not found to date in invertebrate genomes, emphasizing the need for whole genome sequences from mollusks and indeed from representatives of all major invertebrate phyla.

An improved hidden Markov model for transmembrane protein detection and topology prediction and its applications to complete genomes

Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane proteins. We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins. Our model uses TMHMM as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD had 84% for topology and 89% for locations. When utilized for identifying transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does. Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for approximately 20-30% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans. http://liao.cis.udel.edu/website/servers/TMMOD/

Prediction of transmembrane proteins based on the continuous wavelet transform.

A novel method based on continuous wavelet transform (CWT) for predicting the number and location of helices in membrane proteins is presented. Two bacteria proteins are chosen as examples to describe the prediction of transmembrane helices (HTM) by using this method. Selections of an appropriate dilation and hydrophobicity data types are discussed in the text. The results indicate that CWT is a promising approach for the prediction of HTM.

Computational differentiation of N-terminal signal peptides and transmembrane helices

Signal peptides and transmembrane helices both contain a stretch of hydrophobic amino acids. This common feature makes it difficult for signal peptide and transmembrane helix predictors to correctly assign identity to stretches of hydrophobic residues near the N-terminal methionine of a protein sequence. The inability to reliably distinguish between N-terminal transmembrane helix and signal peptide is an error with serious consequences for the prediction of protein secretory status or transmembrane topology. In this study, we report a new method for differentiating protein N-terminal signal peptides and transmembrane helices. Based on the sequence features extracted from hydrophobic regions (amino acid frequency, hydrophobicity, and the start position), we set up discriminant functions and examined them on non-redundant datasets with jackknife tests. This method can incorporate other signal peptide prediction methods and achieve higher prediction accuracy. For Gram-negative bacterial proteins, 95.7% of N-terminal signal peptides and transmembrane helices can be correctly predicted (coefficient 0.90). Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 99% (coefficient 0.92). For eukaryotic proteins, 94.2% of N-terminal signal peptides and transmembrane helices can be correctly predicted with coefficient 0.83. Given a sensitivity of 90%, transmembrane helices can be identified from signal peptides with a precision of 87% (coefficient 0.85). The method can be used to complement current transmembrane protein prediction and signal peptide prediction methods to improve their prediction accuracies.

Wavpred: A Wavelet-Based Algorithm for the Prediction of Transmembrane Proteins

Mathematical using wavelet transform was applied to analyse hydropathy signals of membrane proteins. The accuracy of our localization of transmembrane approaches that of well-established methods. The analysis of hydrophobicity plots using wavelets presents advantages with respect to other filtering methods based on fixed windows or Fourier transforms and compared to training (neutral networks) techniques. Although the method embodies principles that have long been appreciated, its simplicity makes it a very useful tool for the evaluation of protein membrane-spanning segments. Generalization of use of wavelets should be encouraged in other aspects of bioinformatics.

Major histocompatibility complex class II DR-restricted memory CD4(+) T lymphocytes recognize conserved immunodominant epitopes of Anaplasma marginale major surface protein 1a.

Native major surface protein 1 (MSP1) of Anaplasma marginale, composed of covalently associated MSP1a and MSP1b proteins, stimulates protective immunity in cattle against homologous and heterologous strain challenge. Protective immunity against pathogens in the family Anaplasmataceae involves both CD4(+) T cells and neutralizing immunoglobulin G. Thus, an effective vaccine should contain both CD4(+) T- and B-lymphocyte epitopes that will elicit strong memory responses upon infection with homologous and heterologous strains. Previous studies demonstrated that the predominant CD4(+) T-cell response in MSP1 vaccinates is directed against the MSP1a subunit. The present study was designed to identify conserved CD4(+) T-cell epitopes in MSP1a presented by a broadly represented subset of major histocompatibility complex (MHC) class II molecules that would be suitable for inclusion in a recombinant vaccine. Transmembrane protein prediction analysis of MSP1a from the Virginia strain revealed a large hydrophilic domain (HD), extending from amino acids (aa) 1 to 366, and a hydrophobic region extending from aa 367 to 593. The N terminus (aa 1 to 67) includes one 28-aa form A repeat and one 29-aa form B repeat, which each contain an antibody neutralization-sensitive epitope [Q(E)ASTSS]. In MSP1 vaccinates, recombinant MSP1a HD (aa 1 to 366) stimulated recall proliferative responses that were comparable to those against whole MSP1a excluding the repeat region (aa 68 to 593). Peptide mapping determined a minimum of five conserved epitopes in aa 151 to 359 that stimulated CD4(+) T cells from cattle expressing DR-DQ haplotypes common in Holstein-Friesian breeds. Peptides representing three epitopes (aa 231 to 266, aa 270 to 279, and aa 290 to 319) were stimulatory for CD4(+) T-cell clones and restricted by DR. A DQ-restricted CD4(+) T-cell epitope, present in the N-terminal form B repeat (VSSQSDQASTSSQLG), was also mapped using T-cell clones from one vaccinate. Although form B repeat-specific T cells did not recognize the form A repeat peptide (VSSQS_EASTSSQLG), induction of T-cell anergy by this peptide was ruled out. The presence of multiple CD4(+) T-cell epitopes in the MSP1a HD, in addition to the neutralization-sensitive epitope, supports the testing of this immunogen for induction of protective immunity against A. marginale challenge.

A new family of putative insulin receptor-like proteins in C. elegans

I thank Anders Krogh and Gabor Tusnady for providing computer programs for topology prediction of transmembrane proteins. I am grateful to an anonymous referee for useful suggestions. This work was funded by a Special Fellowship from the Leukemia & Lymphoma Society.

Wavelet change-point prediction of transmembrane proteins

A non-parametric method, based on a wavelet data-dependent threshold technique for change-point analysis, is applied to predict location and topology of helices in transmembrane proteins. A new propensity scale generated from a transmembrane helix database is proposed. We show that wavelet change-point performs well for smoothing hydropathy and transmembrane profiles generated using different scales. We investigate which wavelet bases and threshold functions are overall most appropriate to detect transmembrane segments. Prediction accuracy is based on the analysis of two data sets used as standard benchmarks for transmembrane prediction algorithms. The analysis of a test set of 83 proteins results in accuracy per segment equal to 98.2%; the analysis of a 48 proteins blind-test set, i.e. containing proteins not used to generate the propensity scales, results in accuracy per segment equal to 97.4%. We believe that this method can also be applied to the detection of boundaries of other patterns such as G + Cisochores and dot-plots. The transmembrane database, TMALN and source code are available upon request from the authors.