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Knowledge about the topology of G protein-coupled receptors (GPCRs) can be very useful in predicting diverse range of properties about these proteins, such as function, three dimensional structure, and ligand binding site. Considering that only few GPCRs have known structures, many computational efforts have been carried out to develop methods for predicting their topology. A novel method to predict the location and the length of transmembrane helices in GPCRs was proposed. This method consists of a “one by one” amino acid feature extraction window which makes it possible for the method to learn the amino acid distribution in helical segments of GPCR proteins. It is based on hidden Markov model (HMM) with a specific architecture that takes advantage of Viterbi decoding algorithm and the observed frequency values for adjusting the transition probabilities. The prediction capability of the method was evaluated for per-protein, per-segment and per-residue accuracies on two datasets consisting of 649 (at least one GPCR from each family) and 2898 (all GPCRs) sequences extracted from UniProt database and compared with other commonly used existing methods. It was found that in all three assessments, the prediction accuracies for the new method on the larger dataset, i.e., 2898 GPCRs, were higher than that obtained by other methods. The results showed that our method was able to predict the topology of GPCR proteins without any sequence length limitation with the accuracies of 88.9 % and 87.4% for the small (i.e., 649 GPCRs) and large (i.e., 2898 GPCRs) datasets, respectively. (Availability status: The source code is available upon request from the authors)

Motivation: The study of biological systems, pathways and processes relies increasingly on analyses of networks. Most often, such analyses focus on network topology, thereby treating all proteins or genes as identical, featureless nodes. Integrating molecular data and insights about the qualities of individual proteins into the analysis may enhance our ability to decipher biological pathways and processes. Results: Here, we introduce a novel platform for data integration that generates networks on the macro system-level, analyzes the molecular characteristics of each protein on the micro level, and then combines the two levels by using the molecular characteristics to assess networks. It also annotates the function and subcellular localization of each protein and displays the process on an image of a cell, rendering each protein in its respective cellular compartment. By thus visualizing the network in a cellular context we are able to analyze pathways and processes in a novel way. As an example, we use the system to analyze proteins implicated with Alzheimers disease and show how the integrated view corroborates previous observations and how it helps in the formulation of new hypotheses regarding the molecular underpinnings of the disease. Availability: http://www.rostlab.org/services/pinat Contact: pinat@rostlab.org; ofran@cubic.bioc.columbia.edu

Summary: The mobility of a residue on the protein surface is closely linked to its function. The identification of extremely rigid or flexible surface residues can therefore contribute information crucial for solving the complex problem of identifying functionally important residues in proteins. Mobility is commonly measured by B-value data from high-resolution three-dimensional X-ray structures. Few methods predict B-values from sequence. Here, we present PROFbval, the first web server to predict normalized B-values from amino acid sequence. The server handles amino acid sequences (or alignments) as input and outputs normalized B-value and two-state (flexible/rigid) predictions. The server also assigns a reliability index for each prediction. For example, PROFbval correctly identifies residues in active sites on the surface of enzymes as particularly rigid. Availability: http://www.rostlab.org/services/profbval Contact: profbval@rostlab.org Supplementary information: Supplementary data are available at Bioinformatics online.