TASK DEFINITION: In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
PROBLEM: Background: We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs. The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods. The standalone version of AMS 3.0 presents an efficient way to indentify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at Additional file 1 Supplementary materials for the publication. Supplementary.xls contains detailed results of training and testing of AMS 3.0 tool in MS Excel file format. Additional file 2 PTM predictor software with training data. PTM_Archive.tar.gz includes the predictor software binaries together with all trained ANNs in tar.gz compressed form.

SOLUTION: AMS 3.0: prediction of post-translational modifications

PROBLEM: A newly identified betacoronavirus, human coronavirus EMC (HCoV-EMC), has been isolated from several patients with respiratory and renal disease in the Middle East. While only a few infected patients have been identified, the mortality of the infection is greater than 50%. Like its better-known cousin severe acute respiratory syndrome coronavirus (SARS-CoV), HCoV-EMC appears to have originated from bats. In a recent article in mBio, Müller et al. described several important differences between the two viruses [M. A. Müller et al., mBio 3(6):e00515-12, 2012, doi:10.1128/mBio.00515-12]. Unlike SARS-CoV, HCoV-EMC can directly infect bat cells. As important, HCoV-EMC does not enter cells using the SARS-CoV receptor, human angiotensin-converting receptor -2 (hACE2) . These results provide a strong incentive for identifying the host cell receptor used by HCoV-EMC. Identification of the receptor will provide insight into the pathogenesis of pulmonary and renal disease and may also suggest novel therapeutic interventions. Citation Perlman S, Zhao J. 2013. Human coronavirus EMC is not the same as severe acute respiratory syndrome coronavirus. mBio 4(1):e00002-13.

SOLUTION: Human Coronavirus EMC Is Not the Same as Severe Acute Respiratory Syndrome Coronavirus

PROBLEM: The protein sequence-structure gap results from the contrast between rapid, low-cost deep sequencing, and slow, expensive experimental structure determination techniques. Comparative homology modelling may have the potential to close this gap by predicting protein structure in target sequences using existing experimentally solved structures as templates. This paper presents the first use of force-directed graphs for the visualization of sequence space in two dimensions, and applies them to the choice of suitable RNA-dependent RNA polymerase (RdRP) target-template pairs within human-infective RNA virus genera. Measures of centrality in protein sequence space for each genus were also derived and used to identify centroid nearest-neighbour sequences (CNNs) potentially useful for production of homology models most representative of their genera. Homology modelling was then carried out for targettemplate pairs in different species, different genera and different families, and model quality assessed using several metrics. Reconstructed ancestral RdRP sequences for individual genera were also used as templates for the production of ancestral RdRP homology models. High quality ancestral RdRP models were consistently produced, as were good quality models for target-template pairs in the same genus. Homology modelling between genera in the same family produced mixed results and inter-family modelling was unreliable. We present a protocol for the production of optimal RdRP homology models for use in further experiments, e.g. docking to discover novel anti-viral compounds. (219 words)

SOLUTION:
Visualization of protein sequence space with force-directed graphs, and their application to the choice of target-template pairs for homology modelling