A structural alphabet for local protein structures: improved prediction methods

Three-dimensional protein structures can be described with a library of 3D fragments that define a structural alphabet. We have previously proposed such an alphabet, composed of 16 patterns of five consecutive amino acids, called Protein Blocks (PBs). These PBs have been used to describe protein backbones and to predict local structures from protein sequences. The Q16 prediction rate reaches 40.7% with an optimization procedure. This article examines two aspects of PBs. First, we determine the effect of the enlargement of databanks on their definition. The results show that the geometrical features of the different PBs are preserved (local RMSD value equal to 0.41 A on average) and sequence-structure specificities reinforced when databanks are enlarged. Second, we improve the methods for optimizing PB predictions from sequences, revisiting the optimization procedure and exploring different local prediction strategies. Use of a statistical optimization procedure for the sequence-local structure relation improves prediction accuracy by 8% (Q16 = 48.7%). Better recognition of repetitive structures occurs without losing the prediction efficiency of the other local folds. Adding secondary structure prediction improved the accuracy of Q16 by only 1%. An entropy index (Neq), strongly related to the RMSD value of the difference between predicted PBs and true local structures, is proposed to estimate prediction quality. The Neq is linearly correlated with the Q16 prediction rate distributions, computed for a large set of proteins. An "expected" prediction rate QE16 is deduced with a mean error of 5%.     

A pharmacophore-based evolutionary approach for screening selective estrogen receptor modulators

We developed a pharmacophore-based evolutionary approach for virtual screening. This tool, termed the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach with a new pharmacophore-based scoring function. The former integrates discrete and continuous global search strategies with local search strategies to expedite convergence. The latter, integrating an empirical-based energy function and pharmacological preferences (binding-site pharmacological interactions and ligand preferences), simultaneously serves as the scoring function for both molecular docking and postdocking analyses to improve screening accuracy. We apply pharmacological interaction preferences to select the ligands that form pharmacological interactions with target proteins, and use the ligand preferences to eliminate the ligands that violate the electrostatic or hydrophilic constraints. We assessed the accuracy of our approach using human estrogen receptor (ER) and a ligand database from the comparative studies of Bissantz et al. (J Med Chem 2000;43:4759-4767). Using GEMDOCK, the average goodness-of-hit (GH) score was 0.83 and the average false-positive rate was 0.13% for ER antagonists, and the average GH score was 0.48 and the average false-positive rate was 0.75% for ER agonists. The performance of GEMDOCK was superior to competing methods such as GOLD and DOCK. We found that our pharmacophore-based scoring function indeed was able to reduce the number of false positives; moreover, the resulting pharmacological interactions at the binding site, as well as ligand preferences, were important to the screening accuracy of our experiments. These results suggest that GEMDOCK constitutes a robust tool for virtual database screening.     

Using string kernel to predict signal peptide cleavage site based on subsite coupling model

Summary. Owing to the importance of signal peptides for studying the molecular mechanisms of genetic diseases, reprogramming cells for gene therapy, and finding new drugs for healing a specific defect, it is in great demand to develop a fast and accurate method to identify the signal peptides. Introduction of the so-called {−3,−1, +1} coupling model (Chou, K. C.: Protein Engineering, 2001, 14–2, 75–79) has made it possible to take into account the coupling effect among some key subsites and hence can significantly enhance the prediction quality of peptide cleavage site. Based on the subsite coupling model, a kind of string kernels for protein sequence is introduced. Integrating the biologically relevant prior knowledge, the constructed string kernels can thus be used by any kernel-based method. A Support vector machines (SVM) is thus built to predict the cleavage site of signal peptides from the protein sequences. The current approach is compared with the classical weight matrix method. At small false positive ratios, our method outperforms the classical weight matrix method, indicating the current approach may at least serve as a powerful complemental tool to other existing methods for predicting the signal peptide cleavage site. The software that generated the results reported in this paper is available upon requirement, and will appear at http://www.pami.sjtu.edu.cn/wm. An erratum to this article is available at .     

Toward consistent assignment of structural domains in proteins

The assignment of protein domains from three-dimensional structure is critically important in understanding protein evolution and function, yet little quality assurance has been performed. Here, the differences in the assignment of structural domains are evaluated using six common assignment methods. Three human expert methods (AUTHORS (authors' annotation), CATH and SCOP) and three fully automated methods (DALI, DomainParser and PDP) are investigated by analysis of individual methods against the author's assignment as well as analysis based on the consensus among groups of methods (only expert, only automatic, combined). The results demonstrate that caution is recommended in using current domain assignments, and indicates where additional work is needed. Specifically, the major factors responsible for conflicting domain assignments between methods, both experts and automatic, are: (1) the definition of very small domains; (2) splitting secondary structures between domains; (3) the size and number of discontinuous domains; (4) closely packed or convoluted domain-domain interfaces; (5) structures with large and complex architectures; and (6) the level of significance placed upon structural, functional and evolutionary concepts in considering structural domain definitions. A web-based resource that focuses on the results of benchmarking and the analysis of domain assignments is available at     

A proposal for using the ensemble approach to understand genetic regulatory networks

Understanding the genetic regulatory network comprising genes, RNA, proteins and the network connections and dynamical control rules among them, is a major task of contemporary systems biology. I focus here on the use of the ensemble approach to find one or more well-defined ensembles of model networks whose statistical features match those of real cells and organisms. Such ensembles should help explain and predict features of real cells and organisms. More precisely, an ensemble of model networks is defined by constraints on the "wiring diagram" of regulatory interactions, and the "rules" governing the dynamical behavior of regulated components of the network. The ensemble consists of all networks consistent with those constraints. Here I discuss ensembles of random Boolean networks, scale free Boolean networks, "medusa" Boolean networks, continuous variable networks, and others. For each ensemble, M statistical features, such as the size distribution of avalanches in gene activity changes unleashed by transiently altering the activity of a single gene, the distribution in distances between gene activities on different cell types, and others, are measured. This creates an M-dimensional space, where each ensemble corresponds to a cluster of points or distributions. Using current and future experimental techniques, such as gene arrays, these M properties are to be measured for real cells and organisms, again yielding a cluster of points or distributions in the M-dimensional space. The procedure then finds ensembles close to those of real cells and organisms, and hill climbs to attempt to match the observed M features. Thus obtains one or more ensembles that should predict and explain many features of the regulatory networks in cells and organisms.     

阔叶丰花草——浙江茜草科一新归化种

首次记载阔叶丰花草 (Spermacoce latifolia Aubl.),一种原产南美热带地区的茜草科植物,为浙江归化植物一新记录,并通过实地调查和访问对其入侵途径进行了分析,认为可能是由于植物园引种或农作物种子贸易而无意引进浙江温州的。     

An integrative approach to species discovery in odonates: from character‐based DNA barcoding to ecology

Modern taxonomy requires an analytical approach incorporating all lines of evidence into decision‐making. Such an approach can enhance both species identification and species discovery. The character‐based DNA barcode method provides a molecular data set that can be incorporated into classical taxonomic data such that the discovery of new species can be made in an analytical framework that includes multiple sources of data. We here illustrate such a corroborative framework in a dragonfly model system that permits the discovery of two new, but visually cryptic species. In the African dragonfly genus Trithemis three distinct genetic clusters can be detected which could not be identified by using classical taxonomic characters. In order to test the hypothesis of two new species, DNA‐barcodes from different sequence markers (ND1 and COI) were combined with morphological, ecological and biogeographic data sets. Phylogenetic analyses and incorporation of all data sets into a scheme called taxonomic circle highly supports the hypothesis of two new species. Our case study suggests an analytical approach to modern taxonomy that integrates data sets from different disciplines, thereby increasing the ease and reliability of both species discovery and species assignment.     

Enzymatic hydrolysis of waste cellulose

Interstitial flow (IF) modulates both the biochemical and biophysical cues surrounding cells. It represents a very important regulating mechanism for cell/tissue function and has been commonly utilized in tissue engineering (TE). This article discusses the possible regulating mechanisms of IF on fibroblasts, the various fibroblast responses to IF, the current challenges in understanding the IF–fibroblast relationship and the application of IF for fibroblast involved TE. In particular, IF can affect fibroblast growth at both intracellular (e.g., calcium signaling, protein/proteinase secretion) and cellular (e.g., autocrine/paracrine signaling, proliferation, differentiation, alignment, adhesion, migration) levels. One major challenge for understanding IF–fibroblast interaction has been the determination of the flow and cell growth condition at microlevel especially in a three‐dimensional environment. To utilize IF and optimize the fluidic environment for TE, several influencing factors in the system including perfusate composition, flow profile, nutrient supply, signaling molecule effect, scaffold property, and fibroblast type should be considered. Biotechnol. Bioeng. 2010;107: 1–10. © 2010 Wiley Periodicals, Inc.     

Differentiation-dependent expression of hypothetical proteins in the neuroblastoma cell line N1E-115

Several protein cascades, including signaling, cytoskeletal, chaperones, metabolic, and antioxidant proteins, have been shown to be involved in the process of neuronal differentiation (ND) of neuroblastoma cell lines. No systematic approach to detect hitherto unknown and unnamed proteins or structures that have been predicted upon nucleic acid sequences in ND has been published so far. We therefore decided to screen hypothetical protein (HP) expression by protein profiling. Two-dimensional gel electrophoresis with subsequent matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF/TOF) identification was used for expression analysis of undifferentiated and dimethylsulfoxide-induced neuronally differentiated N1E-115 cells. We unambiguously identified six HPs: Q8C520, Q99LF4, Q9CXS1, Q9DAF8, Q91WT0, and Q8C5G2. A prefoldin domain in Q91WT0, a t-SNARE domain in Q9CXS1, and a bromodomain were observed in Q8C5G2. For the three remaining proteins, no putative function using Pfam, BLOCKS, PROSITE, PRINTS, InterPro, Superfamily, CoPS, and ExPASy could be assigned. While two proteins were present in both cell lines, Q9CXS1 was switched off (i.e., undetectably low) in differentiated cells only, and Q9DAF8, Q91WT0, and Q8C5G2 were switched on in differentiated cells exclusively. Herein, using a proteomic approach suitable for screening and identification of HP, we present HP structures that have been only predicted so far based upon nucleic acid sequences. The four differentially regulated HPs may play a putative role in the process of ND.