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Abstract We describe the machine learning tool that we applied in the CAGI 6 experiment to predict whether single residue mutations in proteins are deleterious or benign. This tool was trained using only single sequences, i.e., without multiple sequence alignments or structural information. Instead, we used global characterizations of the protein sequence. Training and testing data for human gene mutations was obtained from ClinVar (ncbi.nlm.nih.gov/pub/ClinVar/), and for non-human gene mutations from Uniprot (www.uniprot.org). Testing was done on post-training data from ClinVar. This testing yielded high AUC and Matthews correlation coefficient (MCC) for well trained examples but low generalizability. For genes with either sparse or unbalanced training data, the prediction accuracy is poor. The resulting prediction server is available online at http://www.mamiris.com/Shoni.cagi6.

Abstract Mumps is a common childhood infection caused by the mumps virus (MuV). Aseptic meningitis and encephalitis are usual symptoms of mumps together with orchitis and oophoritis that can arise in males and females, respectively. We have used computational tools: RNA22, miRanda and psRNATarget to predict the microRNA-mRNA binding sites to find the putative microRNAs playing role in the host response to mumps virus infection. Our computational studies indicate that hsa-mir-3155a is most likely involved in mumps infection. This was further investigated by the prediction of binding sites of hsa-mir-3155a to the MuV genome. Additionally, structure prediction using MC-Fold and MC-Sym, respectively has been applied to predict the 3D structures of miRNA and mRNA. The miRNA-mRNA interaction profile between has been confirmed through molecular docking simulation studies. Taken together, the putative miRNA (hsa_miR_6794_5p) has been found to be most likely involved in the regulation of transcriptional activity in the MuV infection.

Abstract Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment.

Abstract Background The ability to generate, visualize, and analyze motions of biomolecules has made a significant impact upon modern biology. Molecular Dynamics has gained substantial use, but remains computationally demanding and difficult to setup for many biologists. Elastic network models (ENMs) are an alternative and have been shown to generate the dominant equilibrium motions of biomolecules quickly and efficiently. These dominant motions have been shown to be functionally relevant and also to indicate the likely direction of conformational changes. Most structures have a small number of dominant motions. Comparing computed motions to the structure's conformational ensemble derived from a collection of static structures or frames from an MD trajectory is an important way to understand functional motions as well as evaluate the models. Modes of motion computed from ENMs can be visualized to gain functional and mechanistic understanding and to compute useful quantities such as average positional fluctuations, internal distance changes, collectiveness of motions, and directional correlations within the structure. Results Our new software, MAVEN, aims to bring ENMs and their analysis to a broader audience by integrating methods for their generation and analysis into a user friendly environment that automates many of the steps. Models can be constructed from raw PDB files or density maps, using all available atomic coordinates or by employing various coarse-graining procedures. Visualization can be performed either with our software or exported to molecular viewers. Mixed resolution models allow one to study atomic effects on the system while retaining much of the computational speed of the coarse-grained ENMs. Analysis options are available to further aid the user in understanding the computed motions and their importance for its function. Conclusion MAVEN has been developed to simplify ENM generation, allow for diverse models to be used, and facilitate useful analyses, all on the same platform. This represents an integrated approach that incorporates all four levels of the modeling process - generation, evaluation, analysis, visualization - and also brings to bear multiple ENM types. The intension is to provide a versatile modular suite of programs to a broader audience. MAVEN is available for download at http://maven.sourceforge.net.

Abstract Background Knowledge-based potentials have been widely used in the last 20 years for fold recognition, protein structure prediction from amino acid sequence, ligand binding, protein design, and many other purposes. However generally these are not readily accessible online. Results Our new knowledge-based potential server makes available many of these potentials for easy use to automatically compute the energies of protein structures or models supplied. Our web server for protein energy estimation uses four-body potentials, short-range potentials, and 23 different two-body potentials. Users can select potentials according to their needs and preferences. Files containing the coordinates of protein atoms in the PDB format can be uploaded as input. The results will be returned to the user's email address. Conclusions Our Potentials 'R'Us server is an easily accessible, freely available tool with a web interface that collects all existing and future protein coarse-grained potentials and computes energies of multiple structural models.

Abstract Background By using a standard Support Vector Machine (SVM) with a Sequential Minimal Optimization (SMO) method of training, Naïve Bayes and other machine learning algorithms we are able to distinguish between two classes of protein sequences: those folding to highly-designable conformations, or those folding to poorly- or non-designable conformations. Results First, we generate all possible compact lattice conformations for the specified shape (a hexagon or a triangle) on the 2D triangular lattice. Then we generate all possible binary hydrophobic/polar (H/P) sequences and by using a specified energy function, thread them through all of these compact conformations. If for a given sequence the lowest energy is obtained for a particular lattice conformation we assume that this sequence folds to that conformation. Highly-designable conformations have many H/P sequences folding to them, while poorly-designable conformations have few or no H/P sequences. We classify sequences as folding to either highly – or poorly-designable conformations. We have randomly selected subsets of the sequences belonging to highly-designable and poorly-designable conformations and used them to train several different standard machine learning algorithms. Conclusion By using these machine learning algorithms with ten-fold cross-validation we are able to classify the two classes of sequences with high accuracy – in some cases exceeding 95%.

Alzheimer’s disease (AD) is distinguished by the gradual loss of cognitive function, which is associated with neuronal loss and death. Accumulating evidence supports that protein phosphatases (PPs; PP1, PP2A, PP2B, PP4, PP5, PP6, and PP7) are directly linked with amyloid beta (Aβ) as well as the formation of the neurofibrillary tangles (NFTs) causing AD. Published data reported lower PP1 and PP2A activity in both gray and white matters in AD brains than in the controls, which clearly shows that dysfunctional phosphatases play a significant role in AD. Moreover, PP2A is also a major causing factor of AD through the deregulation of the tau protein. Here, we review recent advances on the role of protein phosphatases in the pathology of AD and other neurodegenerative diseases. A better understanding of this problem may lead to the development of phosphatase-targeted therapies for neurodegenerative disorders in the near future.

Abstract Background The abundant data available for protein interaction networks have not yet been fully understood. New types of analyses are needed to reveal organizational principles of these networks to investigate the details of functional and regulatory clusters of proteins. Results In the present work, individual clusters identified by an eigenmode analysis of the connectivity matrix of the protein-protein interaction network in yeast are investigated for possible functional relationships among the members of the cluster. With our functional clustering we have successfully predicted several new protein-protein interactions that indeed have been reported recently. Conclusion Eigenmode analysis of the entire connectivity matrix yields both a global and a detailed view of the network. We have shown that the eigenmode clustering not only is guided by the number of proteins with which each protein interacts, but also leads to functional clustering that can be applied to predict new protein interactions.

Abstract Background Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Results In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. Conclusions We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.

In this work, a series of pyrazole‐linked 1,2,4‐triazole‐3‐thiol derivatives (3a–i) were prepared and identified by 13C NMR, 1H NMR, and mass spectrometry (ESI‐MS) data. The newly synthesized molecules were also evaluated in vitro for their α‐amylase and α‐glucosidase inhibitory potential. All newly synthesized compounds exhibited potent α‐glucosidase inhibition activity with IC50 in the range of 1.016 ± 0.70 to 24.40 ± 0.02 µM and good α‐amylase inhibitory with IC50 in the range of 49.91 ± 0.32 to 500 µM, as compared to acarbose. The most potent compound among this series is derivative 3e, with IC50 value of 1.016 ± 0.70 µM, which is many folds more than that of acarbose. In addition, in docking studies, both compounds exhibited good interactions at the active region of target proteins. Therefore, this study may lead via structural modifications to the discovery of new potent α‐amylase and α‐glucosidase inhibitors useful in the diabetes treatment. [ABSTRACT FROM AUTHOR]

Mushrooms are new potential sources of valuable medicines, long neglected because of difficulties experienced in their cultivation. There is a large variety of medicinal mushrooms which possess significant therapeutic properties and are used as medications for various diseases because they contain several novel highly bioactive components. Medicinal mushrooms can be identified based on their morphology, size, mass, and the color of the stalk, cap and spore, and attachment to the stalk. Medicinal mushrooms possess a variety of important biological activities and are used as antioxidants, hepatoprotectors, anticancer, antidiabetic, anti-inflammatory, antiaging, antiviral, antiparasitic, and antimicrobial agents, among others. This review provides a basic overview of the chemical scaffolds present in mushrooms and their therapeutic implications in the human body.

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research.

Dosage-sensitive sex reversal, adrenal hypoplasia critical region, on chromosome X, gene 1 (DAX1) is an orphan nuclear receptor encoded by the NR0B1 gene. The functional study showed that DAX1 is a physiologically significant target for EWS/FLI1-mediated oncogenesis, particularly Ewing Sarcoma (ES). In this study, a three-dimensional DAX1 structure was modeled by employing a homology modeling approach. Furthermore, the network analysis of genes involved in Ewing Sarcoma was also carried out to evaluate the association of DAX1 and other genes with ES. Moreover, a molecular docking study was carried out to check the binding profile of screened flavonoid compounds against DAX1. Therefore, 132 flavonoids were docked in the predicted active binding pocket of DAX1. Moreover, the pharmacogenomics analysis was performed for the top ten docked compounds to evaluate the ES-related gene clusters. As a result, the five best flavonoid-docked complexes were selected and further evaluated by Molecular Dynamics (MD) simulation studies at 100 ns. The MD simulation trajectories were evaluated by generating RMSD, hydrogen bond plot analysis, and interaction energy graphs. Our results demonstrate that flavonoids showed interactive profiles in the active region of DAX1 and can be used as potential therapeutic agents against DAX1-mediated augmentation of ES after in-vitro and in-vivo evaluations.

Chronic pain is frequently associated with neuropathy, inflammation, or the malfunctioning of nerves. Chronic pain is associated with a significant burden of morbidity due to opioid use, associated with addiction and tolerance, and disability. MicroRNAs (miRs) are emerging therapeutic targets to treat chronic pain through the regulation of genes associated with inflammation, neuronal excitability, survival, or de-differentiation. In this review, we discuss the possible involvement of miRs in pain-related molecular pathways. miRs are known to regulate high-conviction pain genes, supporting their potential as therapeutic targets. [ABSTRACT FROM AUTHOR]