MOLE: a Voronoi diagram-based explorer of molecular channels, pores, and tunnels

We have developed an algorithm, "MOLE," for the rapid, fully automated location and characterization of molecular channels, tunnels, and pores. This algorithm has been made freely available on the Internet (http://mole.chemi.muni.cz/) and overcomes many of the shortcomings and limitations of the recently developed CAVER software. The core of our MOLE algorithm is a Dijkstra's path search algorithm, which is applied to a Voronoi mesh. Tests on a wide variety of biomolecular systems including gramicidine, acetylcholinesterase, cytochromes P450, potassium channels, DNA quadruplexes, ribozymes, and the large ribosomal subunit have demonstrated that the MOLE algorithm performs well. MOLE is thus a powerful tool for exploring large molecular channels, complex networks of channels, and molecular dynamics trajectories in which analysis of a large number of snapshots is required.     

Molecular docking of phenolic compounds and screening of antioxidant and antidiabetic potential of Moringa oleifera ethanolic leaves extract from Qassim region,Saudi Arabia

IntroductionOxidative stress is crucial in diabetic pathophysiology, hence the prerequisite of ingesting naturally derived antioxidants as a remedial target. This study investigates the naturally occurring antioxidant and antidiabetic potential of Moringa oleifera ethanolic leaves extract.MethodsMoringa oleifera leaves were macerated (MOLE) by using 70% ethanol. Physiochemical and phytochemical examinations of MOLE was assayed using standard methods. The antioxidant activity was analyzed by DPPH (1, 1-diphenyl-2-picrylhydrazil) radical scavenging assay. In vitro antidiabetic was analyzed by pancreatic α-amylase enzyme inhibitory assay. The molecular docking was performed using AutoDock Vina v1.1.2 in PyRx 30.8.ResultsEthanolic extraction of MOLE by maceration technique, 14 % yield. Loss on drying, foreign organic matters and total ash value of OLE showed 0.27 w/w, 0.8 % and 19 %, respectively. Phytochemical test on MOLE confirmed starch, carbohydrate, flavonoid, gum, glycoside, saponin, tannin, and phenol presences. The total phenolic and flavonoid contents of MOLE are 260 mg GAE/g and 755 mg RUE/g of extract. MOLE (IC 50 55.6 ± 0.18 µg/mL) showed functional DPPH scavenging assay comparable to ascorbic acid (IC 50 46.71 ± 0.24 µg/mL). In the alpha-amylase inhibitory activity, Acarbose showed an IC 50 value of 19.45 ± 0.26 µg/mL, while MOLE portrayed an IC 50 value of 27.54 ± 0.07 µg/mL. Docking studies revealed that most phenolic compounds found within MOLE have minimum docking scores and high binding affinity against Human pancreatic alpha-amylase.ConclusionsThe invitro and docking results suggest that MOLE has been a viable natural bioactive source and might be a great potential source for future antidiabetic medicine.     

Proteomics data repositories: Providing a safe haven for your data and acting as a springboard for further research

Despite the fact that data deposition is not a generalised fact yet in the field of proteomics, several mass spectrometry (MS) based proteomics repositories are publicly available for the scientific community. The main existing resources are: the Global Proteome Machine Database (GPMDB), PeptideAtlas, the PRoteomics IDEntifications database (PRIDE), Tranche, and NCBI Peptidome. In this review the capabilities of each of these will be described, paying special attention to four key properties: data types stored, applicable data submission strategies, supported formats, and available data mining and visualization tools. Additionally, the data contents from model organisms will be enumerated for each resource. There are other valuable smaller and/or more specialized repositories but they will not be covered in this review. Finally, the concept behind the ProteomeXchange consortium, a collaborative effort among the main resources in the field, will be introduced.     

Technical device for obtaining Raman spectra of ultrathin films of phospholipids.

The association of a sample illumination apparatus for supporting ultra thin films with a microprobe (MOLE) spectral analysis system allowed us to obtain non-resonant Raman spectra of phospholipid films. Films as thin as 75 Å have yielded useful spectra.     

SCOPExplorer: a tool for browsing and analyzing structural classification of proteins (SCOP) data

We have developed a tool, named "SCOPExplorer", for browsing and analyzing SCOP information. SCOPExplorer 1) contains a tree-style viewer to display an overview of protein structure data, 2) is able to employ a variety of options to analyze SCOP data statistically, and 3) provides a function to link protein domains to protein data bank (PDB) resources. SCOPExplorer uses an XML-based structural document format, named "SCOPML", derived from the SCOP data. To evaluate SCOPExplorer, proteins containing more than 20 domains were analyzed. The Skp1-Skp2 protein complex and the Fab fragment of IgG2 contain the largest numbers of domains in the current eukaryotic SCOP database. These proteins are known to either bind to various proteins or generate diversity. This suggests that the more domains a protein has, the more interactions or more variability it will be capable of. (SCOPExplorer is available for download at http://scopexplorer.ulsan.ac.kr).     

PRINCESS, a protein interaction confidence evaluation system with multiple data sources

Advances in proteomics technologies have enabled novel protein interactions to be detected at high speed, but they come at the expense of relatively low quality. Therefore, a crucial step in utilizing the high throughput protein interaction data is evaluating their confidence and then separating the subsets of reliable interactions from the background noise for further analyses. Using Bayesian network approaches, we combine multiple heterogeneous biological evidences, including model organism protein-protein interaction, interaction domain, functional annotation, gene expression, genome context, and network topology structure, to assign reliability to the human protein-protein interactions identified by high throughput experiments. This method shows high sensitivity and specificity to predict true interactions from the human high throughput protein-protein interaction data sets. This method has been developed into an on-line confidence scoring system specifically for the human high throughput protein-protein interactions. Users may submit their protein-protein interaction data on line, and the detailed information about the supporting evidence for query interactions together with the confidence scores will be returned. The Web interface of PRINCESS (protein interaction confidence evaluation system with multiple data sources) is available at the website of China Human Proteome Organisation.     

Simulating and validating proteomics data and search results

The computational simulation of complete proteomic data sets and their utility to validate detection and interpretation algorithms, to aid in the design of experiments and to assess protein and peptide false discovery rates is presented. The simulation software has been developed for emulating data originating from data-dependent and data-independent LC-MS workflows. Data from all types of commonly used hybrid mass spectrometers can be simulated. The algorithms are based on empirically derived physicochemical liquid and gas phase models for proteins and peptides. Sample composition in terms of complexity and dynamic range, as well as chromatographic, experimental and MS conditions, can be controlled and adjusted independently. The effect of on-column amounts, gradient length, mass resolution and ion mobility on search specificity will be demonstrated using tryptic peptides from human and yeast cellular lysates simulated over five orders of magnitude in dynamic range. Initial justification of the simulated data sets is achieved by comparing and contrasting the in silico simulated data to experimentally derived results from a 48 protein mixture, spanning a similar magnitude of five orders of magnitude. Additionally, experimental data from replicate and dilutions series experiments will be utilized to determine error rates at the peptide and protein level with respect to mass, area, retention and drift time. The data presented reveal a high degree of similarity at the ion detection, peptide and protein level when analyzed under similar conditions.     

非蛋白氨基酸的生物合成及其生物学作用

解释了蛋白氨基酸和非蛋白氨基酸,并着重论述了非蛋白氨基酸的生物合成及其生物学作用。非蛋白氨基酸的生物合成主要通过基本氨基酸合成后的修饰、代谢及消旋作用产生,其生物学作用主要表现在能合成其他含氮物质、储藏氮和运输氮、储能、组成细菌细胞壁、毒性作用及药物作用等方面。     

A proposal for a flow cytometric data file standard

The increasing complexity of multiparameter data collection and analysis in flow cytometry and the development of relatively inexpensive arc-lamp-based flow cytometers, which increases the probability that laboratories or institutions may have more than one type of instrument, creates a need for shareable analysis programs and for the transport of flow cytometric data files within an installation or from one institution to another. To address this need, we propose a standard file format to be used for all flow cytometric data. The general principles of this proposal are: (1) The data file will contain a minimum of three segments, TEXT, DATA, and ANALYSIS; (2) The TEXT and ANALYSIS segments consist of KEYWORDS, which are the names of data fields, and their values; (3) All TEXT is encoded in ASCII; (4) KEYWORDS and their values may be of any length; (5) Certain KEYWORDS will be standard, i.e., having specified formats to be recognized by all programs. The structure of the DATA segment will be uniquely defined by the values of KEYWORDS in the TEXT area. It may be in any bit resolution, facilitating compatibility between machines with different word length and/or allowing bit compression of the data. The structured nature of the TEXT area should facilitate management of flow cytometric data using existing data base management systems. The proposed file format has been implemented on VAX, PDP-11, and HP9920 based flow cytometry data acquisition systems.     

Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining

An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. Further details are accessible at the Sting_RDB demo web page: http://www.cbi.cnptia.embrapa.br/StingRDB.     

171 Protein structure prediction with limited data

Proteins need to interact with other molecules in order to carry out their biological role. Knowing the protein structure is crucial to study these interactions and it can for example lead to drug design. However, the cost of determining the structure of a protein with current experimental techniques is very high – both in time and money. In the absence of experimental structures, current computational tools are not always able to correctly predict the native fold. We are using a physics based computational framework to determine the structure of proteins. The pipeline is designed to handle sparse data coming from evolution, bioinformatics or experiments (solid state NMR, cross linking, …). The data is transformed into a set of restraints used in our physical simulations. However, we require that only a subset of the input information is satisfied. This is done to account for uncertainties in the input data. The framework uses a Hamiltonian-temperature replica exchange formalism that allows the system to choose what data is compatible with the physics of the system. I will show some results on how this methodology can help us in both protein structure refinement and protein structure prediction.     

Reproducible quantitative proteotype data matrices for systems biology

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.     

Protein loop closure using orientational restraints from NMR data

Protein loops often play important roles in biological functions. Modeling loops accurately is crucial to determining the functional specificity of a protein. Despite the recent progress in loop prediction approaches, which led to a number of algorithms over the past decade, few rigorous algorithmic approaches exist to model protein loops using global orientational restraints, such as those obtained from residual dipolar coupling (RDC) data in solution nuclear magnetic resonance (NMR) spectroscopy. In this article, we present a novel, sparse data, RDC‐based algorithm, which exploits the mathematical interplay between RDC‐derived sphero‐conics and protein kinematics, and formulates the loop structure determination problem as a system of low‐degree polynomial equations that can be solved exactly, in closed‐form. The polynomial roots, which encode the candidate conformations, are searched systematically, using provable pruning strategies that triage the vast majority of conformations, to enumerate or prune all possible loop conformations consistent with the data; therefore, completeness is ensured. Results on experimental RDC datasets for four proteins, including human ubiquitin, FF2, DinI, and GB3, demonstrate that our algorithm can compute loops with higher accuracy, a three‐ to six‐fold improvement in backbone RMSD, versus those obtained by traditional structure determination protocols on the same data. Excellent results were also obtained on synthetic RDC datasets for protein loops of length 4, 8, and 12 used in previous studies. These results suggest that our algorithm can be successfully applied to determine protein loop conformations, and hence, will be useful in high‐resolution protein backbone structure determination, including loops, from sparse NMR data. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Power consumption evaluation of all-optical data center networks

Cloud computing and web emerging applications have created the need for more powerful data centers. These data centers need high bandwidth interconnects that can sustain the high interaction between the web-, application- and database-servers. Data center networks based on electronic packet switches will have to consume excessive power in order to satisfy the required communication bandwidth of future data centers. Optical interconnects have gained attention recently as a promising energy efficient solution offering high throughput, low latency and reduced energy consumption compared to current networks based on commodity switches. This paper presents a comparison on the power consumption of several optical interconnection schemes based on AWGRs, Wavelength Selective Switches (WSS) or Semiconductor Optical Amplifiers (SOAs). Based on a thorough analysis of each architecture, it is shown that optical interconnects can achieve at least an order of magnitude higher energy efficiency compared to current data center networks based on electrical packet based switches and they could contribute to greener IT network infrastructures.     

SAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments

We present a statistical method SAINT-MS1 for scoring protein-protein interactions based on the label-free MS1 intensity data from affinity purification-mass spectrometry (AP-MS) experiments. The method is an extension of Significance Analysis of INTeractome (SAINT), a model-based method previously developed for spectral count data. We reformulated the statistical model for log-transformed intensity data, including adequate treatment of missing observations, that is, interactions identified in some but not all replicate purifications. We demonstrate the performance of SAINT-MS1 using two recently published data sets: a small LTQ-Orbitrap data set with three replicate purifications of single human bait protein and control purifications and a larger drosophila data set targeting insulin receptor/target of rapamycin signaling pathway generated using an LTQ-FT instrument. Using the drosophila data set, we also compare and discuss the performance of SAINT analysis based on spectral count and MS1 intensity data in terms of the recovery of orthologous and literature-curated interactions. Given rapid advances in high mass accuracy instrumentation and intensity-based label-free quantification software, we expect that SAINT-MS1 will become a useful tool allowing improved detection of protein interactions in label-free AP-MS data, especially in the low abundance range.     

Enhanced information output from shotgun proteomics data by protein quantification and peptide quality control (PQPQ)

We present a tool to improve quantitative accuracy and precision in mass spectrometry based on shotgun proteomics: protein quantification by peptide quality control, PQPQ. The method is based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Dissonant patterns arise either from outlier peptides or because of the presence of different protein species. By correlation analysis, protein quantification by peptide quality control identifies and excludes outliers and detects the existence of different protein species. Alternative protein species are then quantified separately. By validating the algorithm on seven data sets related to different cancer studies we show that data processing by protein quantification by peptide quality control improves the information output from shotgun proteomics. Data from two labeling procedures and three different instrumental platforms was included in the evaluation. With this unique method using both peptide sequence data and quantitative data we can improve the quantitative accuracy and precision on the protein level and detect different protein species.     

Comparative study of ammonium transporters in different organisms by study of a large number of structural protein features via data mining algorithms

Ammonium is an excellent nitrogen source, and ammonium transfer is a fundamental process in most organisms. Membrane transport of ammonium is the key component of nitrogen metabolism mediated by Ammonium Transporter/Methylamine Permease/Rhesus (AMT/MEP/Rh) protein family. Ammonium transporters play different physiological roles in various organisms. Here, we looked at the protein characteristics of ammonium transporters in different organisms to create a link between protein characteristics and the organism. In order to increase the accuracy and precision of the employed models, for the first time, an attempt was made to cover all structural aspects of ammonium transporters in animals, bacteria, fungi, plants, and human by extracting and calculating 874 protein attributes of primary, secondary, and tertiary structures for each ammonium transporter. Then, various weighting and modeling algorithms were applied to determine how structural protein features change between organisms. Considering a large number of protein attributes made it possible to detect key protein characteristics in the structure of ammonium transporters. The results, for the first time, indicated that His-based features including count/frequency of His and frequency/count of Ile-His were the most significant features generating different types of ammonium transporters within organisms. Within different tested models, the C5.0 model was the most efficient and precise model for discrimination of organism type, based on ammonium transporter sequence, with the precision of 94.85%. The determination of protein characteristics of ammonium transporters in different organisms provides a new vista for understanding the evolution of transporters based on the modulation of protein characteristics and facilitates engineering of new transporters. In our point of view, dissecting a large number of structural protein characteristics through data mining algorithms provides a novel functional strategy for studying evolution and phylogeny. This research will serve as a basis for future studies on engineering novel ammonium transporters.