Free Web Server Research Articles

SMINBR: An Integrated Network and Chemoinformatics Tool Specialized for Prediction of Two-Component Crystal Formation.

Two-component crystals such as pharmaceutical cocrystals and salts have been proven as an effective strategy to improve physicochemical and biopharmaceutical properties of drugs. It is not easy to select proper molecular combinations to form two-component crystals. The network-based models have been successfully utilized to guide cocrystal design. Yet, the traditional social network-derived methods based on molecular-interaction topology information cannot directly predict interaction partners for new chemical entities (NCEs) that have not been observed to form two-component crystals. Herein, we proposed an effective tool, namely substructure-molecular-interaction network-based recommendation (SMINBR), to prioritize potential interaction partners for NCEs. This in silico tool incorporates network and chemoinformatics methods to bridge the gap between NCEs and known molecular-interaction network. The high performance of 10-fold cross validation and external validation shows the high accuracy and good generalization capability of the model. As a case study, top 10 recommended coformers for apatinib were all experimentally confirmed and a new apatinib cocrystal with paradioxybenzene was obtained. The predictive capability of the model attributes to its accordance with complementary patterns driving the formation of intermolecular interactions. SMINBR could automatically recommend new interaction partners for a target molecule, and would be an effective tool to guide cocrystal design. A free web server for SMINBR is available at http://lmmd.ecust.edu.cn/sminbr/.

PIIMS Server: A Web Server for Mutation Hotspot Scanning at the Protein-Protein Interface.

Protein-protein interactions (PPIs) play vital roles in regulating biological processes, such as cellular and signaling pathways. Hotspots are certain residues located at protein-protein interfaces that contribute more in protein-protein binding than other residues. Research on the mutational effects of hotspots is important for understanding basic aspects of protein association. Hence, various computational tools have been developed to explore the impact of mutation hotspots, which will allow a better understanding of the forces that drive PPIs. However, tools that may provide comprehensive substitutions at hotspots are still rare. Hence, there is a strong need for a new free web server to explore mutational effects of hotspots. Herein we introduce a web server named PIIMS that integrates molecular dynamics simulation and one-step free energy perturbation. It contains two main computational functions: (1) computational alanine scanning analysis to identify hotspots and (2) full mutation scanning analysis to evaluate the effects of hotspot mutations. We rigidly validated its ability to predict binding free energy changes by using large and diverse datasets including 1,341 mutations from 50 PPIs with the correlation coefficient R = 0.75. The difference from the existing tools is that PIIMS can perform further evaluation of hotspot residues with regard to their different mutations. The PIIMS web server (accessible at http://chemyang.ccnu.edu.cn/ccb/server/PIIMS/index.php) is free and open to all users without login requirements.

Deform-nu: A DNA Deformation Energy-Based Predictor for Nucleosome Positioning.

The structure and function of chromatin can be regulated through positioning patterns of nucleosomes. DNA-based processes are regulated via nucleosomes. Therefore, it is significant to determine nucleosome positions in DNA-based processes. A deformation energy model was proposed to predict nucleosome positions in our previous study. A free web server based on the model (http://lin-group.cn/server/deform-nu/) was firstly established to estimate the occupancy and rotational positioning of nucleosomes in the study. Then, the performance of the model was verified by several examples. The results indicated that nucleosome positioning relied on the physical properties of DNA, such as deformation energy.

Autonomous molecule generation using reinforcement learning and docking to develop potential novel inhibitors

We developed a computational method named Molecule Optimization by Reinforcement Learning and Docking (MORLD) that automatically generates and optimizes lead compounds by combining reinforcement learning and docking to develop predicted novel inhibitors. This model requires only a target protein structure and directly modifies ligand structures to obtain higher predicted binding affinity for the target protein without any other training data. Using MORLD, we were able to generate potential novel inhibitors against discoidin domain receptor 1 kinase (DDR1) in less than 2 days on a moderate computer. We also demonstrated MORLD’s ability to generate predicted novel agonists for the D4 dopamine receptor (D4DR) from scratch without virtual screening on an ultra large compound library. The free web server is available at http://morld.kaist.ac.kr.

Recognition of Ion Ligand Binding Sites Based on Amino Acid Features with the Fusion of Energy, Physicochemical and Structural Features.

In this work, we selected the binding residues of 4 acid radical ion ligands (NO2-, CO32-, SO42- and PO43-) and 10 metal ion ligands (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, K+ and Co2+) as research objects. Based on the protein sequence information, we extracted amino acid features, energy, physicochemical, and structure features. Then, we incorporated the above features and input them into the MultilayerPerceptron (MLP) and support vector machine (SVM) algorithms. In the independent test, the best accuracy was higher than 92.5%, which was better than the previous results on the same dataset. In addition, we found that energy information is an important factor affecting the prediction results. Finally, we set up a free web server for the prediction of protein-ion ligand binding sites (http://39.104.77.103:8081/lsb/HomePage/HomePage.html). This study is helpful for molecular drug design.

AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes.

The accurate and reliable prediction of the 3D structures of proteins and their assemblies remains difficult even though the number of solved structures soars and prediction techniques improve. In this study, a free and open access web server, AWSEM-Suite, whose goal is to predict monomeric protein tertiary structures from sequence is described. The model underlying the server’s predictions is a coarse-grained protein force field which has its roots in neural network ideas that has been optimized using energy landscape theory. Employing physically motivated potentials and knowledge-based local structure biasing terms, the addition of homologous template and co-evolutionary restraints to AWSEM-Suite greatly improves the predictive power of pure AWSEM structure prediction. From the independent evaluation metrics released in the CASP13 experiment, AWSEM-Suite proves to be a reasonably accurate algorithm for free modeling, standing at the eighth position in the free modeling category of CASP13. The AWSEM-Suite server also features a front end with a user-friendly interface. The AWSEM-Suite server is a powerful tool for predicting monomeric protein tertiary structures that is most useful when a suitable structure template is not available. The AWSEM-Suite server is freely available at: https://awsem.rice.edu.

ITTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation

In spite of the repertoire of existing cancer therapies, the ongoing recurrence and new cases of cancer poses a challenging health concern that prompts for novel and effective treatment. Cancer immunotherapy represents a promising venue for treatment by harnessing the body's immune system to combat cancer. Therefore, the identification of tumor T cell antigen represents an exciting area to explore. Computational tools have been instrumental in the identification of tumor T cell antigens and it is highly desirable to attain highly accurate models in a timely fashion from large volumes of peptides generated in the post-genomic era. In this study, we present a reliable, accurate, unbiased and automated sequence-based predictor named iTTCA-Hybrid for identifying tumor T cell antigens. The iTTCA-Hybrid approach proposed herein employs two robust machine learning models (e.g. support vector machine and random forest) constructed using five feature encoding strategies (i.e. amino acid composition, dipeptide composition, pseudo amino acid composition, distribution of amino acid properties in sequences and physicochemical properties derived from the AAindex). Rigorous independent test indicated that the iTTCA-Hybrid approach achieved an accuracy and area under the curve of 73.60% and 0.783, respectively, which corresponds to 4% and 7% performance increase than those of existing methods thereby indicating the superiority of the proposed model. To the best of our knowledge, the iTTCA-Hybrid is the first free web server (Available at http://camt.pythonanywhere.com/iTTCA-Hybrid) for identifying tumor T cell antigens presented by the MHC class I. The proposed web server allows robust predictions to be made without the need to develop in-house prediction models.

LigRMSD: a web server for automatic structure matching and RMSD calculations among identical and similar compounds in protein-ligand docking.

Root mean square deviation (RMSD) is one of the most useful and straightforward features for structural comparison between different conformations of the same molecule. Commonly, protein-ligand docking programs have included some utilities that allow the calculation of this value; however, they only work efficiently when exists a complete atom label equivalence between the evaluated conformations. We present LigRMSD, a free web-server for the automatic matching and RMSD calculations among identical or similar chemical compounds. This server allows the user to submit only a pair of identical or similar molecules or dataset of similar compounds to compare their three-dimensional conformations. LigRMSD can be freely accessed at https://ligrmsd.appsbio.utalca.cl. Supplementary data are available at Bioinformatics online.

3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns.

Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification. The web server can be freely utilized at https://appsbio.utalca.cl/3d-pp/.

MATHT: A web server for comprehensive transcriptome data analysis

The current software/algorithms for high-throughput sequence data analysis are not user-friendly. We developed MATHT, the Multifaceted Analysis Tool for Human Transcriptome, which is a free web server available at www.biocloudservice.com, to provide more comprehensive and reliable analysis of transcriptome data. The web server provides modules for data preprocessing, differential expression analysis, dataset integration, functional analysis, and network analysis. The sequence and structure analysis module is specially designed for RNA-seq data. MATHT is a user-friendly web server that provides comprehensive analysis of transcriptome data, especially integration analysis using special standardization across different platforms.

IGlu-Lys: A Predictor for Lysine Glutarylation Through Amino Acid Pair Order Features.

As one of the new posttranslational modification, lysine glutarylation has been identified in both prokaryotic and eukaryotic cells. These glutarylated proteins are involved in various cellular functions, such as translation, metabolism, and exhibited diverse subcellular localizations. Experimental identification of lysine glutarylation sites was founded in 2014 and also identified its deglutarylase sirturn 5(SIRT 5). Computational prediction of lysine glutarylation could be a complementary way to the experimental technique. In this work, the lysine glutarylation predictor iGlu-Lys has been developed based on the machine learning scheme. We have selected the best feature scheme which took the amino acid pair order and special-position information into account from four constructions. The machine learning algorithm support vector machine has been adopted and its performance has been measured for different window length of peptides. In the 10-fold cross-validation with window length 19, the AUC and MCC were 0.8944 and 0.5098, respectively. Different ROC curves in 6-, 8-, and 10-fold cross-validations were very close which illustrated the robustness of our predictor. The results of iGLu-Lys were better than the existing method GlutPred. Meanwhile, a free webserver for iGlu-Lys is accessible at http://app.aporc.org/iGlu-Lys/.

LigQ: A Webserver to Select and Prepare Ligands for Virtual Screening.

Virtual screening is a powerful methodology to search for new small molecule inhibitors against a desired molecular target. Usually, it involves evaluating thousands of compounds (derived from large databases) in order to select a set of potential binders that will be tested in the wet-lab. The number of tested compounds is directly proportional to the cost, and thus, the best possible set of ligands is the one with the highest number of true binders, for the smallest possible compound set size. Therefore, methods that are able to trim down large universal data sets enriching them in potential binders are highly appreciated. Here we present LigQ, a free webserver that is able to (i) determine best structure and ligand binding pocket for a desired protein, (ii) find known binders, as well as potential ligands known to bind to similar protein domains, (iii) most importantly, select a small set of commercial compounds enriched in potential binders, and (iv) prepare them for virtual screening. LigQ was tested with several proteins, showing an impressive capacity to retrieve true ligands from large data sets, achieving enrichment factors of over 10%. LigQ is available at http://ligq.qb.fcen.uba.ar/ .

Обеспечение систем автоматического управления современными информационными средствами удаленного доступа и мобильного контроля

The work is devoted to the equipment of technological processes under the control of automation systems of modern telecommunication functions that enable remote access and mobile control. Shows the mechanisms of realization of such functions, and issues to ensure their information security. The paper presents the features of building a Web application, automation system, made by technology based on free ASPX Web server running MS IIS 7.5 Express. Given the capabilities of the developed on the basis of the FTP server from a mobile browser, providing remote access and control of the manufacturing process using mobile devices. The practice of using developed means of telecommunication gave a positive result and showed the possibility of using them on other automation objects.

Prediction of cell-penetrating peptides with feature selection techniques

Cell-penetrating peptides are a group of peptides which can transport different types of cargo molecules such as drugs across plasma membrane and have been applied in the treatment of various diseases. Thus, the accurate prediction of cell-penetrating peptides with bioinformatics methods will accelerate the development of drug delivery systems. The study aims to develop a powerful model to accurately identify cell-penetrating peptides. At first, the peptides were translated into a set of vectors with the same dimension by using dipeptide compositions. Secondly, the Analysis of Variance-based technique was used to reduce the dimension of the vector and explore the optimized features. Finally, the support vector machine was utilized to discriminate cell-penetrating peptides from non-cell-penetrating peptides. The five-fold cross-validated results showed that our proposed method could achieve an overall prediction accuracy of 83.6%. Based on the proposed model, we constructed a free webserver called C2Pred (http://lin.uestc.edu.cn/server/C2Pred).

ORFanFinder: automated identification of taxonomically restricted orphan genes.

Orphan genes, also known as ORFans, are newly evolved genes in a genome that enable the organism to adapt to specific living environment. The gene content of every sequenced genome can be classified into different age groups, based on how widely/narrowly a gene's homologs are distributed in the context of species taxonomy. Those having homologs restricted to organisms of particular taxonomic ranks are classified as taxonomically restricted ORFans. Implementing this idea, we have developed an open source program named ORFanFinder and a free web server to allow automated classification of a genome's gene content and identification of ORFans at different taxonomic ranks. ORFanFinder and its web server will contribute to the comparative genomics field by facilitating the study of the origin of new genes and the emergence of lineage-specific traits in both prokaryotes and eukaryotes. http://cys.bios.niu.edu/orfanfinder yyin@niu.edu Supplementary data are available at Bioinformatics online.

Large scale rigidity-based flexibility analysis of biomolecules.

KINematics And RIgidity (KINARI) is an on-going project for in silico flexibility analysis of proteins. The new version of the software, Kinari-2, extends the functionality of our free web server KinariWeb, incorporates advanced web technologies, emphasizes the reproducibility of its experiments, and makes substantially improved tools available to the user. It is designed specifically for large scale experiments, in particular, for (a) very large molecules, including bioassemblies with high degree of symmetry such as viruses and crystals, (b) large collections of related biomolecules, such as those obtained through simulated dilutions, mutations, or conformational changes from various types of dynamics simulations, and (c) is intended to work as seemlessly as possible on the large, idiosyncratic, publicly available repository of biomolecules, the Protein Data Bank. We describe the system design, along with the main data processing, computational, mathematical, and validation challenges underlying this phase of the KINARI project.

ProteinVolume: calculating molecular van der Waals and void volumes in proteins.

BackgroundVoids and cavities in the native protein structure determine the pressure unfolding of proteins. In addition, the volume changes due to the interaction of newly exposed atoms with solvent upon protein unfolding also contribute to the pressure unfolding of proteins. Quantitative understanding of these effects is important for predicting and designing proteins with predefined response to changes in hydrostatic pressure using computational approaches. The molecular surface volume is a useful metric that describes contribution of geometrical volume, which includes van der Waals volume and volume of the voids, to the total volume of a protein in solution, thus isolating the effects of hydration for separate calculations.ResultsWe developed ProteinVolume, a highly robust and easy-to-use tool to compute geometric volumes of proteins. ProteinVolume generates the molecular surface of a protein and uses an innovative flood-fill algorithm to calculate the individual components of the molecular surface volume, van der Waals and intramolecular void volumes. ProteinVolume is user friendly and is available as a web-server or a platform-independent command-line version.ConclusionsProteinVolume is a highly accurate and fast application to interrogate geometric volumes of proteins. ProteinVolume is a free web server available on http://gmlab.bio.rpi.edu. Free-standing platform-independent Java-based ProteinVolume executable is also freely available at this web site.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0531-2) contains supplementary material, which is available to authorized users.

SEABED: Small molEcule activity scanner weB servicE baseD.

Motivation: The SEABED web server integrates a variety of docking and QSAR techniques in a user-friendly environment. SEABED goes beyond the basic docking and QSAR web tools and implements extended functionalities like receptor preparation, library editing, flexible ensemble docking, hybrid docking/QSAR experiments or virtual screening on protein mutants. SEABED is not a monolithic workflow tool but Software as a Service platform. Availability and implementation: SEABED is a free web server available athttp://www.bsc.es/SEABED. No registration is required. Contact: ramon.goni@bsc.es Supplementary information: Supplementary data are available atBioinformatics online.

CDSbank: taxonomy-aware extraction, selection, renaming and formatting of protein-coding DNA or amino acid sequences

BackgroundProtein-coding DNA sequences and their corresponding amino acid sequences are routinely used to study relationships between sequence, structure, function, and evolution. The rapidly growing size of sequence databases increases the power of such comparative analyses but it makes it more challenging to prepare high quality sequence data sets with control over redundancy, quality, completeness, formatting, and labeling. Software tools for some individual steps in this process exist but manual intervention remains a common and time consuming necessity.DescriptionCDSbank is a database that stores both the protein-coding DNA sequence (CDS) and amino acid sequence for each protein annotated in Genbank. CDSbank also stores Genbank feature annotation, a flag to indicate incomplete 5′ and 3′ ends, full taxonomic data, and a heuristic to rank the scientific interest of each species. This rich information allows fully automated data set preparation with a level of sophistication that aims to meet or exceed manual processing. Defaults ensure ease of use for typical scenarios while allowing great flexibility when needed. Access is via a free web server at http://hazeslab.med.ualberta.ca/CDSbank/.ConclusionsCDSbank presents a user-friendly web server to download, filter, format, and name large sequence data sets. Common usage scenarios can be accessed via pre-programmed default choices, while optional sections give full control over the processing pipeline. Particular strengths are: extract protein-coding DNA sequences just as easily as amino acid sequences, full access to taxonomy for labeling and filtering, awareness of incomplete sequences, and the ability to take one protein sequence and extract all synonymous CDS or identical protein sequences in other species. Finally, CDSbank can also create labeled property files to, for instance, annotate or re-label phylogenetic trees.

BioSuper: A web tool for the superimposition of biomolecules and assemblies with rotational symmetry

BackgroundMost of the proteins in the Protein Data Bank (PDB) are oligomeric complexes consisting of two or more subunits that associate by rotational or helical symmetries. Despite the myriad of superimposition tools in the literature, we could not find any able to account for rotational symmetry and display the graphical results in the web browser.ResultsBioSuper is a free web server that superimposes and calculates the root mean square deviation (RMSD) of protein complexes displaying rotational symmetry. To the best of our knowledge, BioSuper is the first tool of its kind that provides immediate interactive visualization of the graphical results in the browser, biomolecule generator capabilities, different levels of atom selection, sequence-dependent and structure-based superimposition types, and is the only web tool that takes into account the equivalence of atoms in side chains displaying symmetry ambiguity. BioSuper uses ICM program functionality as a core for the superimpositions and displays the results as text, HTML tables and 3D interactive molecular objects that can be visualized in the browser or in Android and iOS platforms with a free plugin.ConclusionsBioSuper is a fast and functional tool that allows for pairwise superimposition of proteins and assemblies displaying rotational symmetry. The web server was created after our own frustration when attempting to superimpose flexible oligomers. We strongly believe that its user-friendly and functional design will be of great interest for structural and computational biologists who need to superimpose oligomeric proteins (or any protein). BioSuper web server is freely available to all users at http://ablab.ucsd.edu/BioSuper.