MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm

The prediction of ligand binding sites is an essential part of the drug discovery process. Knowing the location of binding sites greatly facilitates the search for hits, the lead optimization process, the design of site-directed mutagenesis experiments and the hunt for structural features that influence the selectivity of binding in order to minimize the drug's adverse effects. However, docking is still the rate-limiting step for such predictions; consequently, much more efficient algorithms are required. In this article, the design of the MEDock web server is described. The goal of this sever is to provide an efficient utility for predicting ligand binding sites. The MEDock web server incorporates a global search strategy that exploits the maximum entropy property of the Gaussian probability distribution in the context of information theory. As a result of the global search strategy, the optimization algorithm incorporated in MEDock is significantly superior when dealing with very rugged energy landscapes, which usually have insurmountable barriers. This article describes four different benchmark cases that span a diverse set of different types of ligand binding interactions. These benchmarks were compared with the use of the Lamarckian genetic algorithm (LGA), which is the major workhorse of the well-known AutoDock program. These results demonstrate that MEDock consistently converged to the correct binding modes with significantly smaller numbers of energy evaluations than the LGA required. When judged by a threshold of the number of energy evaluations consumed in the docking simulation, MEDock also greatly elevates the rate of accurate predictions for all benchmark cases. MEDock is available at and

Experimental study on critical heat flux characteristics of R134a flow boiling in horizontal helically-coiled tubes

Critical heat flux (CHF) experiments were performed to study the R134a CHF characteristics in horizontal helically-coiled tubes. The stainless steel test sections were heated uniformly, with tube inner diameters of 3.8e11 mm, coil diameters of 135e370 mm, helical pitches of 40e105 mm and heated lengths of 0.85e7.54 m. The experimental conditions are pressures of 0.30e1.10 MPa, mass fluxes of 60e480 kg m 2 s 1, inlet qualities of 0.32e0.36 and heat fluxes of 6.0 103e9.0 104Wm 2. It was found that the wall temperatures jumped abruptly once the CHF occurred. The CHF values decrease with increasing heated lengths, coil diameters and inner diameters, but the DNB (departure from nucleate boiling) CHF seems independent when length-to-diameter L/di> 200. The coil-to-diameter ratios are more important than length-to-diameter ratios for CHF in helically-coiled tubes, while the helical pitches have little effect on CHF. The CHF value increases greatly with increasing mass flux and decreases smoothly with increasing pressure. It decreases nearly linearly with increasing inlet and critical qualities, but it varies more acutely at xcr< 0.5 than higher critical qualities. New correlations for R134a flow boiling CHF in horizontal helically-coiled tubes were developed for applications

Protemot: prediction of protein binding sites with automatically extracted geometrical templates

Geometrical analysis of protein tertiary substructures has been an effective approach employed to predict protein binding sites. This article presents the Protemot web server that carries out prediction of protein binding sites based on the structural templates automatically extracted from the crystal structures of protein–ligand complexes in the PDB (Protein Data Bank). The automatic extraction mechanism is essential for creating and maintaining a comprehensive template library that timely accommodates to the new release of PDB as the number of entries continues to grow rapidly. The design of Protemot is also distinctive by the mechanism employed to expedite the analysis process that matches the tertiary substructures on the contour of the query protein with the templates in the library. This expediting mechanism is essential for providing reasonable response time to the user as the number of entries in the template library continues to grow rapidly due to rapid growth of the number of entries in PDB. This article also reports the experiments conducted to evaluate the prediction power delivered by the Protemot web server. Experimental results show that Protemot can deliver a superior prediction power than a web server based on a manually curated template library with insufficient quantity of entries. Availability:

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these <it>ab initio </it>approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.</p> <p>Results</p> <p>This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G²DE) based classifier. The G²DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.</p> <p>Conclusion</p> <p>Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G²DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G²DE based predictor.</p

IHTC14-23415 DRY-OUT CHF CHARACTERISTICS OF R134a FLOW BOILING IN A HORIZONTAL HELICALLY-COILED TUBE

ABSTRACT An experimental study was carried out to investigate the dry-out critical heat flux (CHF) characteristics of R134a flow boiling in a horizontal helically-coiled tube. The test section was heated uniformly by DC high-power sources and the geometrical parameters are the outer diameter of 10 mm, inner diameter of 8. In total of sixty-eight 0.2 mm T-type thermocouples were set along the tube to measure wall temperatures exactly. A method based on the event-driven Agilent BenchLink Data Logger Pro software was developed to determine the occurrence of CHF. It was found that the wall temperatures jumped abruptly once the CHF occurred. The CHF usually starts to form at the front and offside (270°and 90°) of the sections near outlet. The CHF value increases largely with increasing mass flux and decreases slightly with increasing pressure. It decreases nearly linearly with increasing inlet qualities, while it decreases acutely with increasing critical qualities under larger mass flux conditions. An experimental correlation was developed to estimate dry-out CHF of R134a flow boiling in horizontal helically-coiled tubes under current conditions compared with the calculated results of Bowring and Shah correlations

Design and performance of an ultrahigh vacuum spectroscopic-imaging scanning tunneling microscope with a hybrid vibration isolation system

A spectroscopic imaging-scanning tunneling microscope (SI-STM) allows the atomic scale visualization of surface electronic and magnetic structure of novel quantum materials with high energy resolution. To achieve the optimal performance, low vibration facility is required. Here, we describe the design and the performance of an ultrahigh vacuum STM system supported by a hybrid vibration isolation system that consists of a pneumatic passive and a piezoelectric active vibration isolation stages. The STM system is equipped with a 1K pot cryogenic insert and a 9 Tesla superconducting magnet, capable of continuous SI-STM measurements for 7 days. A field ion microscopy system is installed for in situ STM tip treatment. We present the detailed vibrational noise analysis of the hybrid vibration isolation system and demonstrate the performance of our STM system by taking high resolution spectroscopic maps and topographic images on several quantum materials. Our results establish a new strategy to achieve an effective vibration isolation system for high-resolution STM and other scanning probe microscopy to investigate the nanoscale quantum phenomena