Profiling patterns of interhelical associations in membrane proteins.

A novel set of methods has been developed to characterize polytopic membrane proteins at the topological, organellar and functional level, in order to reduce the existing functional gap in the membrane proteome. Firstly, a novel clustering tool was implemented, named PROCLASS, to facilitate the manual curation of large sets of proteins, in readiness for feature extraction. TMLOOP and TMLOOP writer were implemented to refine current topological models by predicting membrane dipping loops. TMLOOP applies weighted predictive rules in a collective motif method, to overcome the inherent limitations of single motif methods. The approach achieved 92.4% accuracy in sensitivity and 100% reliability in specificity and 1,392 topological models described in the Swiss-Prot database were refined. The subcellular location (TMLOCATE) and molecular function (TMFUN) prediction methods rely on the TMDEPTH feature extraction method along data mining techniques. TMDEPTH uses refined topological models and amino acid sequences to calculate pairs of residues located at a similar depth in the membrane. Evaluation of TMLOCATE showed a normalized accuracy of 75% in discriminating between proteins belonging to the main organelles. At a sequence similarity threshold of 40%, TMFLTN predicted main functional classes with a sensitivity of 64.1-71.4%) and 70% of the olfactory GPCRs were correctly predicted. At a sequence similarity threshold of 90%, main functional classes were predicted with a sensitivity of 75.6-92.8%) and class A GPCRs were sub-classified with a sensitivity of 84.5%>-92.9%. These results reflect a direct association between the spatial arrangement of residues in the transmembrane regions and the capacity for polytopic membrane proteins to carry out their functions. The developed methods have for the first time categorically shown that the transmembrane regions hold essential information associated with a wide range of functional properties such as filtering and gating processes, subcellular location and molecular function

The RAST Server: Rapid Annotations using Subsystems Technology

<p>Abstract</p> <p>Background</p> <p>The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them.</p> <p>Description</p> <p>We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment.</p> <p>The service normally makes the annotated genome available within 12–24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service.</p> <p>Conclusion</p> <p>By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.</p

JACOP: A simple and robust method for the automated classification of protein sequences with modular architecture

BACKGROUND: Whole-genome sequencing projects are rapidly producing an enormous number of new sequences. Consequently almost every family of proteins now contains hundreds of members. It has thus become necessary to develop tools, which classify protein sequences automatically and also quickly and reliably. The difficulty of this task is intimately linked to the mechanism by which protein sequences diverge, i.e. by simultaneous residue substitutions, insertions and/or deletions and whole domain reorganisations (duplications/swapping/fusion). RESULTS: Here we present a novel approach, which is based on random sampling of sub-sequences (probes) out of a set of input sequences. The probes are compared to the input sequences, after a normalisation step; the results are used to partition the input sequences into homogeneous groups of proteins. In addition, this method provides information on diagnostic parts of the proteins. The performance of this method is challenged by two data sets. The first one contains the sequences of prokaryotic lyases that could be arranged as a multiple sequence alignment. The second one contains all proteins from Swiss-Prot Release 36 with at least one Src homology 2 (SH2) domain – a classical example for proteins with modular architecture. CONCLUSION: The outcome of our method is robust, highly reproducible as shown using bootstrap and resampling validation procedures. The results are essentially coherent with the biology. This method depends solely on well-established publicly available software and algorithms

Protein family classification using multiple-class neural networks.

The objective of genomic sequence analysis is to retrieve important information from the vast amount of genomic sequence data, such as DNA, RNA and protein sequences. The main task includes the interpretation of the function of DNA sequence on a genomic scale, the comparisons among genomes to gain insight into the universality of biological mechanisms and into the details of gene structure and function, the determination of the structure of all proteins and protein family classification. With its many features and capabilities for recognition, generalization and classification, artificial neural network technology is well suited for sequence analysis. At the state of the art, many methods have been devised to determine if a given protein sequence is member of a given protein superfamily. This is a binary classification problem, and efficient neural network techniques are mentioned in literature for solving such problem. In this Master\u27s thesis, we consider the problem of classifying given protein sequences into one among at least three protein families using neural networks, and, propose two methods: Pair-wise Multiple Classification Approach and Single Network Approach for this problem. In Pair-wise Multiple Classification Approach , several sub-networks are employed to perform the task whereas a compact network system is used in Single Network Approach . We performed experiments, using SNNS and UOWNNS neural network simulator on our NNs with different input/output representation, and reported accuracies as high as 95%. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Z54. Source: Masters Abstracts International, Volume: 43-01, page: 0248. Adviser: Alioune Ngom. Thesis (M.Sc.)--University of Windsor (Canada), 2004

DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding

The study of comorbidity can provide new insights into the pathogenesis of the disease and has important economic significance in the clinical evaluation of treatment difficulty, medical expenses, length of stay, and prognosis of the disease. In this paper, we propose a disease association prediction model DapBCH, which constructs a cross-species biological network and applies heterogeneous graph embedding to predict disease association. First, we combine the human disease–gene network, mouse gene–phenotype network, human–mouse homologous gene network, and human protein–protein interaction network to reconstruct a heterogeneous biological network. Second, we apply heterogeneous graph embedding based on meta-path aggregation to generate the feature vector of disease nodes. Finally, we employ link prediction to obtain the similarity of disease pairs. The experimental results indicate that our model is highly competitive in predicting the disease association and is promising for finding potential disease associations