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Hualiang Jiang - One of the best experts on this subject based on the ideXlab platform.

  • pdtd a web accessible Protein Database for drug target identification
    BMC Bioinformatics, 2008
    Co-Authors: Honglin Li, Hailei Zhang, Ling Kang, Kaixian Chen, Xicheng Wang, Hualiang Jiang
    Abstract:

    Background Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding Proteins may be discovered by docking a small molecule to a repository of Proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target Database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Tar get Fis hing Dock ing) http://www.dddc.ac.cn/tarfisdock, which has been used widely by others. Recently, we have constructed a Protein target Database, P otential D rug T arget D atabase (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation.

  • pdtd a web accessible Protein Database for drug target identification
    BMC Bioinformatics, 2008
    Co-Authors: Hailei Zhang, Ling Kang, Kaixian Chen, Xicheng Wang, Zhenting Gao, Xiaofeng Liu, Xiaomin Luo, Weiliang Zhu, Hualiang Jiang
    Abstract:

    Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding Proteins may be discovered by docking a small molecule to a repository of Proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target Database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Tar get Fis hing Dock ing) http://www.dddc.ac.cn/tarfisdock , which has been used widely by others. Recently, we have constructed a Protein target Database, P otential D rug T arget D atabase (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation. PDTD is a web-accessible Protein Database for in silico target identification. It currently contains >1100 Protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online Databases such as TTD, DrugBank and Thomson Pharma. The Database covers diverse information of >830 known or potential drug targets, including Protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding Proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores. PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding Proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/ .

Andrea Detter - One of the best experts on this subject based on the ideXlab platform.

Adam Rauch - One of the best experts on this subject based on the ideXlab platform.

Bertil Schmidt - One of the best experts on this subject based on the ideXlab platform.

  • swaphi smith waterman Protein Database search on xeon phi coprocessors
    Application-Specific Systems Architectures and Processors, 2014
    Co-Authors: Yongchao Liu, Bertil Schmidt
    Abstract:

    The maximal sensitivity of the Smith-Waterman algorithm has enabled its wide use in biological sequence Database search. Unfortunately, the high sensitivity comes at the expense of quadratic time complexity, which makes the algorithm computationally demanding for big Databases. In this paper, we present SWAPHI, the first parallelized algorithm employing the emerging Xeon Phis to accelerate Smith-Waterman Protein Database search. SWAPHI is designed based on the scale-and-vectorize approach, i.e. it boosts alignment speed by effectively utilizing both the coarse-grained parallelism from the many co-processing cores (scale) and the fine-grained parallelism from 512-bit wide single instruction multiple data (SIMD) vectors per core (vectorize). By searching against the large UniProtKB/TrEMBL Protein Database, SWAPHI achieves a performance of up to 58.8 billion cell updates per second (GCUPS) on a single Xeon Phi and up to 228.4 GCUPS on four Xeon Phis. Moreover, SWAPHI using four Xeon Phis is superior to both SWIPE on 16 highend CPU cores and BLAST+ on 8 cores, with the maximum speedup of 1.52 and 1.86, respectively. SWAPHI is freely available at http://swaphi.sourceforge.net.

  • swaphi smith waterman Protein Database search on xeon phi coprocessors
    arXiv: Distributed Parallel and Cluster Computing, 2014
    Co-Authors: Yongchao Liu, Bertil Schmidt
    Abstract:

    The maximal sensitivity of the Smith-Waterman (SW) algorithm has enabled its wide use in biological sequence Database search. Unfortunately, the high sensitivity comes at the expense of quadratic time complexity, which makes the algorithm computationally demanding for big Databases. In this paper, we present SWAPHI, the first parallelized algorithm employing Xeon Phi coprocessors to accelerate SW Protein Database search. SWAPHI is designed based on the scale-and-vectorize approach, i.e. it boosts alignment speed by effectively utilizing both the coarse-grained parallelism from the many co-processing cores (scale) and the fine-grained parallelism from the 512-bit wide single instruction, multiple data (SIMD) vectors within each core (vectorize). By searching against the large UniProtKB/TrEMBL Protein Database, SWAPHI achieves a performance of up to 58.8 billion cell updates per second (GCUPS) on one coprocessor and up to 228.4 GCUPS on four coprocessors. Furthermore, it demonstrates good parallel scalability on varying number of coprocessors, and is also superior to both SWIPE on 16 high-end CPU cores and BLAST+ on 8 cores when using four coprocessors, with the maximum speedup of 1.52 and 1.86, respectively. SWAPHI is written in C++ language (with a set of SIMD intrinsics), and is freely available at this http URL

  • cudasw 3 0 accelerating smith waterman Protein Database search by coupling cpu and gpu simd instructions
    BMC Bioinformatics, 2013
    Co-Authors: Yongchao Liu, Adrianto Wirawan, Bertil Schmidt
    Abstract:

    Background The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for Protein sequence Database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence Databases.

  • cudasw 2 0 enhanced smith waterman Protein Database search on cuda enabled gpus based on simt and virtualized simd abstractions
    BMC Research Notes, 2010
    Co-Authors: Yongchao Liu, Bertil Schmidt, Douglas L Maskell
    Abstract:

    Due to its high sensitivity, the Smith-Waterman algorithm is widely used for biological Database searches. Unfortunately, the quadratic time complexity of this algorithm makes it highly time-consuming. The exponential growth of biological Databases further deteriorates the situation. To accelerate this algorithm, many efforts have been made to develop techniques in high performance architectures, especially the recently emerging many-core architectures and their associated programming models. This paper describes the latest release of the CUDASW++ software, CUDASW++ 2.0, which makes new contributions to Smith-Waterman Protein Database searches using compute unified device architecture (CUDA). A parallel Smith-Waterman algorithm is proposed to further optimize the performance of CUDASW++ 1.0 based on the single instruction, multiple thread (SIMT) abstraction. For the first time, we have investigated a partitioned vectorized Smith-Waterman algorithm using CUDA based on the virtualized single instruction, multiple data (SIMD) abstraction. The optimized SIMT and the partitioned vectorized algorithms were benchmarked, and remarkably, have similar performance characteristics. CUDASW++ 2.0 achieves performance improvement over CUDASW++ 1.0 as much as 1.74 (1.72) times using the optimized SIMT algorithm and up to 1.77 (1.66) times using the partitioned vectorized algorithm, with a performance of up to 17 (30) billion cells update per second (GCUPS) on a single-GPU GeForce GTX 280 (dual-GPU GeForce GTX 295) graphics card. CUDASW++ 2.0 is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant performance improvement over CUDASW++ 1.0 using either the optimized SIMT algorithm or the partitioned vectorized algorithm for Smith-Waterman Protein Database searches by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.

Geert Baggerman - One of the best experts on this subject based on the ideXlab platform.

  • a combined strategy of neuropeptide prediction and tandem mass spectrometry identifies evolutionarily conserved ancient neuropeptides in the sea anemone nematostella vectensis
    PLOS ONE, 2019
    Co-Authors: Eisuke Hayakawa, Thomas W Holstein, Hiroshi Watanabe, Gerben Menschaert, Geert Baggerman
    Abstract:

    Neuropeptides are a class of bioactive peptides shown to be involved in various physiological processes, including metabolism, development, and reproduction. Although neuropeptide candidates have been predicted from genomic and transcriptomic data, comprehensive characterization of neuropeptide repertoires remains a challenge owing to their small size and variable sequences. De novo prediction of neuropeptides from genome or transcriptome data is difficult and usually only efficient for those peptides that have identified orthologs in other animal species. Recent peptidomics technology has enabled systematic structural identification of neuropeptides by using the combination of liquid chromatography and tandem mass spectrometry. However, reliable identification of naturally occurring peptides using a conventional tandem mass spectrometry approach, scanning spectra against a Protein Database, remains difficult because a large search space must be scanned due to the absence of a cleavage enzyme specification. We developed a pipeline consisting of in silico prediction of candidate neuropeptides followed by peptide-spectrum matching. This approach enables highly sensitive and reliable neuropeptide identification, as the search space for peptide-spectrum matching is highly reduced. Nematostella vectensis is a basal eumetazoan with one of the most ancient nervous systems. We scanned the Nematostella Protein Database for sequences displaying structural hallmarks typical of eumetazoan neuropeptide precursors, including amino- and carboxyterminal motifs and associated modifications. Peptide-spectrum matching was performed against a dataset of peptides that are cleaved in silico from these putative peptide precursors. The dozens of newly identified neuropeptides display structural similarities to bilaterian neuropeptides including tachykinin, myoinhibitory peptide, and neuromedin-U/pyrokinin, suggesting these neuropeptides occurred in the eumetazoan ancestor of all animal species.

  • a combined strategy of neuropeptide predictions and tandem mass spectrometry identifies evolutionarily conserved ancient neuropeptides in the sea anemone nematostella vectensis
    bioRxiv, 2019
    Co-Authors: Eisuke Hayakawa, Thomas W Holstein, Hiroshi Watanabe, Gerben Menschaert, Geert Baggerman
    Abstract:

    Abstract Neuropeptides are a class of bioactive peptides and are responsible for various physiological processes including metabolism, development and reproduction. Although accumulated genome and transcriptome data have reported a number of neuropeptide candidates, it still remains difficult to obtain a comprehensive view of neuropeptide repertoires due to their small and variable nature. Neuropeptide prediction tools usually work only for those peptides for which sequentially related homologs have previously been identified. Recent peptidomics technology has enabled systematic structural identification of neuropeptides by using the combination of liquid chromatography and tandem mass spectrometry. However, obtaining reliable identifications of endogenous peptides is still difficult using a conventional tandem mass spectrometry-based peptide identification approach using Protein Database because a large search space has to be scanned due to the absence of a cleavage enzyme specification. We developed a pipeline consisting of the prediction of in silico cleaved endogenous neuropeptides followed by peptide-spectrum matching enabling highly sensitive and reliable neuropeptide identification. This approach effectively reduces the search space of peptide-spectrum matching, and thus increases search sensitivity. To identify neuropeptides in Nematostella vectensis, a basal eumetazoan having one of the most primitive nervous systems, we scanned the Nematostella Protein Database for sequences displaying structural hallmarks of metazoan neuropeptides, including C/N-terminal structures and modifications. Peptide-spectrum matching was performed against the in silico cleaved peptides and successfully identified dozens of neuropeptides at high confidence. The identification of Nematostella neuropeptides structurally related the tachykinin, GnRH/AKH, neuromedin-U/pyrokinin peptide families indicate that these peptides already originated in the eumetazoan ancestor of all animal species, most likely concomitantly with the development of a nervous system.

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