Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into lead compounds, which in turn may ultimately lead to a marketed drug. A commonly used screening protocol used for this task is high-throughput screening (HTS). However, the performance of HTS against antibacterial targets has generally been unsatisfactory, with high costs and low rates of hit identification. Here, we present a novel computational methodology that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner. This virtual screening methodology was tested prospectively on two versions of an antibacterial target (type II dehydroquinase from Mycobacterium tuberculosis and Streptomyces coelicolor), for which HTS has not provided satisfactory results and consequently practically all known inhibitors are derivatives of the same core scaffold. Overall, our protocols identified 100 new inhibitors, with calculated K_i ranging from 4 to 250 μM (confirmed hit rates are 60% and 62% against each version of the target). Most importantly, over 50 new active molecular scaffolds were discovered that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification.     

以中央地方共建为契机建立生物信息综合实验平台

结合中央与地方共建特色实验室项目,以应用生物的理论和方法技术研究生物等问题为切入点,在将生物信息学院实验室提升为具有产学研一体化功能、特色鲜明的现代化生物信息实验教学中心方面进行了研究与实践,取得了阶段性成果。     

使用pFind软件深度检测蛋白质修饰

Tandem mass spectrometry-based protein identification provides a powerful means for large-scale mapping of post-translational modifications. However, due to the complexity of tandem mass spectra and the large number of modifications, comprehensive and efficient detection of modifications remains an unsolved problem. This paper briefly describes a new solution used by the pFind software for in-depth detection of modifications.     

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post‐translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post‐translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ～1 ng/mL. © 2010 Wiley Periodicals, Inc., Mass Spec Rev 30:685–732, 2011     

An Overview of Synergistic Data Tools for Biological Scrutiny

A network of biological databases is reviewed, supplying a framework for studies of human genes and the association of their genomic variations with human phenotypes. The network is composed of GeneCards, the human gene compendium, which provides comprehensive information on all known and predicted human genes, along with its suite members GeneDecks and GeneLoc. Two databases are shown that address genes and variations focusing on olfactory reception (HORDE) and transduction (GOSdb). In the realm of disease scrutiny, we portray MalaCards, a novel comprehensive database of human diseases and their annotations. Also shown is GeneKid, a tool aimed at generating novel kidney disease biomarkers using systems biology, as well as Xome, a database for whole-exome next-generation DNA sequences for human diseases in the Israeli population. Finally, we show LifeMap Discovery, a database of embryonic development, stem cell research and regenerative medicine, which links to both GeneCards and MalaCards.     

不同基因型丙型肝炎病毒E1、E2蛋白生物信息学分析

目的对不同基因型的丙型肝炎病毒（HCV）的E1、E2蛋白进行生物信息学比较分析,以找出重要的生物信息学数据。方法从Gene Bank中获取不同基因型HCV的E1、E2蛋白核苷酸与氨基酸序列,运用DNA Star,ClustalX,Bio Edit等国际通用的软件进行氨基酸和核苷酸的序列比对,计算核苷酸和氨基酸同源性。在线软件TMHMM v2.0分析E1、E2蛋白跨膜区。AntheProt5.0软件分析二级结构。结果 E1氨基酸起始于aa 193—aa196和终止于aa 382—aa 383,E2蛋白氨基酸起始于aa 384和终止于aa 744—aa 754。不同基因型间E1、E2蛋白基因核苷酸同源性为59.7%-77.0%,氨基酸同源性为60.6%-82.8%。E1、E2蛋白存在3个跨膜区：E1存在2个跨膜区,位于aa 273—aa 293和aa 363—aa 383;E2蛋白存在1个跨膜区,位于aa 723—aa 744。二级结构分析发现不同型HCV E1、E2蛋白富含α螺旋（21%-30%）,β折叠（26%-36%）和卷曲结构（43%-48%）。结论HCV E1、E2核苷酸和氨基酸序列表现为较大的异质性,其蛋白跨膜区富含α螺旋,胞外区以β折叠为主。     

CPL: Detecting Protein Complexes by Propagating Labels on Protein-Protein Interaction Network

Proteins usually bind together to form complexes, which play an important role in cellular activities. Many graph clustering methods have been proposed to identify protein complexes by finding dense regions in protein-protein interaction networks. We present a novel framework （CPL） that detects protein complexes by propagating labels through interactions in a network, in which labels denote complex identifiers. With proper propagation in CPL, proteins in the same complex will be assigned with the same labels. CPL does not make any strong assumptions about the topological structures of the complexes, as in previous methods. Tile CPL algorithm is tested on several publicly available yeast protein-protein interaction networks and compared with several state-of-the-art methods. The results suggest that CPL performs better than the existing methods. An analysis of the functional homogeneity based on a gene ontology analysis shows that the detected complexes of CPL are highly biologically relevant.