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.

Input: Consider Input: 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.

Output: AMS 3.0: prediction of post-translational modifications

Input: Consider Input: Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

Output: In silico pharmacology for drug discovery: applications to targets and beyond

Input: Consider Input: Avian infectious bronchitis virus (IBV) encodes 15 nonstructural proteins (nsps) which play crucial roles in RNA transcription and genome replication. One of them, nsp3, contains an ADRP (adenosine diphosphate-ribose-1 0 -phosphatase) domain which was revealed in recent studies to have ADP-ribose-1 0 -monophosphatase (Appr-1 0 -pase) activity. Appr-1 0 -pase catalyzes the conversion of ADPribose-1 0 -monophosphate (Appr-1 0 -p) to ADP-ribose in the tRNA-splicing pathway. The gene segment encoding the IBV nsp3 ADRP domain has been cloned and expressed in Escherichia coli. The protein has been crystallized and the crystals diffracted to 1.8 A resolution. They belonged to space group P1, with unit-cell parameters a = 41.1, b = 43.2, c = 48.9 A , = 78.0, = 80.0, = 73.6 . Each asymmetric unit contains two molecules.
Output: Structural Biology and Crystallization Communications Purification, crystallization and preliminary crystallographic analysis of avian infectious bronchitis virus nsp3 ADRP domain