Biomedical subjects

Zhiping Weng

Publications and source records attributed to Zhiping Weng.

At least 37 records · Page 2Linked to original sources

PubMed2005

Identification of conserved structural features at sequentially degenerate locations in transcription factor binding sites.

UNLABELLED: Many locations within transcription factor binding sites are not sequentially conserved and appear to be degenerate. We hypothesize that some of these positions contain essential structural codes that are recognized by the transcription factors that bind to them. The structural codes can be defined by base-pair step parameters that describe the relative displacement and orientation of two adjacent base pairs in a nucleic acid structure. We have developed a method, Identification of Conserved Structural Features (ICSF), which uses base-pair step parameters obtained from a collection of high-resolution DNA crystal structures to discover structural conservation that exists in the sequentially degenerate areas within a binding site and produce profiles of the structural features along the entire site. We have focused our study on the transcription factor binding sites in the JASPAR database and have found that one-third (P-value > or = 0.05) of the binding sites contain sequentially degenerate locations with highly conserved structural features as described by the base-pair step parameters. These results will help us to gain a better understanding of the process by which transcription factors recognize their binding sites and possibly lead to an improvement in our ability to find these sites in genomic sequences. AVAILABILITY: ICSF is freely available to academic users at http://zlab.bu.edu/ICSF . SUPPLEMENTARY INFORMATION: http://zlab.bu.edu/ICSF .

Algorithms↗

PubMed2005

Improvement of TRANSFAC matrices using multiple local alignment of transcription factor binding site sequences.

This paper describes a novel approach to constructing Position-Specific Weight Matrices (PWMs) based on the transcription factor binding site (TFBS) data provide by the TRANSFAC database and comparison of the newly generated PWMs with the original TRANSFAC matrices. Multiple local sequence alignment was performed on the TFBSs of each transcription factor. Several different alignment programs were tested and their matrices were compared to the original TRANSFAC matrices. One of the alignment programs, GLAM, produced comparable matrices in terms of the average ranking of true positive sites across the whole test set of sequences.

Algorithms↗

PubMed2004

M-ZDOCK: a grid-based approach for Cn symmetric multimer docking.

UNLABELLED: Computational protein docking is a useful technique for gaining insights into protein interactions. We have developed an algorithm M-ZDOCK for predicting the structure of cyclically symmetric (Cn) multimers based on the structure of an unbound (or partially bound) monomer. Using a grid-based Fast Fourier Transform approach, a space of exclusively symmetric multimers is searched for the best structure. This leads to improvements both in accuracy and running time over the alternative, which is to run a binary docking program ZDOCK and filter the results for near-symmetry. The accuracy is improved because fewer false positives are considered in the search, thus hits are not as easily overlooked. By searching four instead of six degrees of freedom, the required amount of computation is reduced. This program has been tested on several known multimer complexes from the Protein DataBank, including four unbound multimers: three trimers and a pentamer. For all of these cases, M-ZDOCK was able to find at least one hit, whereas only two of the four testcases had hits when using ZDOCK and a symmetry filter. In addition, the running times are 30-40% faster for M-ZDOCK. AVAILABILITY: M-ZDOCK is freely available to academic users at http://zlab.bu.edu/m-zdock/ CONTACT: zhiping@bu.edu SUPPLEMENTARY INFORMATION: http://zlab.bu.edu/m-zdock.

Algorithms↗

PubMed2004

Limited agreement among three global gene expression methods highlights the requirement for non-global validation.

MOTIVATION: DNA microarrays have revolutionized biological research, but their reliability and accuracy have not been extensively evaluated. Thorough testing of microarrays through comparison to dissimilar gene expression methods is necessary in order to determine their accuracy. RESULTS: We have systematically compared three global gene expression methods on all available histologically normal samples from five human organ types. The data included 25 Affymetrix high-density oligonucleotide array experiments, 23 expressed sequence tag based expression (EBE) experiments and 5 SAGE experiments. The reported gene-by-gene expression patterns showed a wide range of correlations between pairs of methods. This level of agreement was sufficient for accurate clustering of datasets from the same tissue and dissimilar methods, but highlights the need for thorough validation of individual gene expression measurements by alternate, non-global methods. Furthermore, analyses of mRNA abundance distributions indicate limitations in the EBE and SAGE methods at both high- and low-expression levels.

Algorithms↗

PubMed2004

CARRIE web service: automated transcriptional regulatory network inference and interactive analysis.

We present an intuitive and interactive web service for CARRIE (Computational Ascertainment of Regulatory Relationships Inferred from Expression). CARRIE is a computational method that analyzes microarray and promoter sequence data to infer a transcriptional regulatory network from the response to a specific stimulus. This service displays an interactive graph of the inferred network and provides easy access to the evidence for the involvement of each gene in the network. We provide functionality to include network data in KEGG XML (KGML) format in this graph. Our service also provides Gene Ontology annotation to aid the user in forming hypotheses about the role of each gene in the cellular response. The CARRIE web service is freely available at http://zlab.bu.edu/CARRIE-web.

Binding Sites↗

PubMed2004

MotifViz: an analysis and visualization tool for motif discovery.

Detecting overrepresented known transcription factor binding motifs in a set of promoter sequences of co-regulated genes has become an important approach to deciphering transcriptional regulatory mechanisms. In this paper, we present an interactive web server, MotifViz, for three motif discovery programs, Clover, Rover and Motifish, covering most available flavors of algorithms for achieving this goal. For comparison, we have also implemented the simple motif-matching program Possum. MotifViz provides uniform and intuitive input and output formats for all four programs. It can be accessed at http://biowulf.bu.edu/MotifViz.

Algorithms↗

PubMed2004

PromoSer: improvements to the algorithm, visualization and accessibility.

PromoSer is a web service that provides an easy and efficient approach to the batch retrieval of a large number of proximal promoters. Since its introduction last year, it has undergone continued development and expansion. At the core, there have been improvements in the filtering of the raw mRNA/EST sequences upon which all predictions are built, improvements in the alignments clustering and transcription start site prediction algorithms, and improvements in the backing database for increased performance. At the user interface level, improvements include enhanced functionality and user options, better integration with other resources on the web and a new visualization tool. PromoSer now also supports queries using a SOAP-based interface and XML-based responses. The service is publicly available at http://biowulf.bu.edu/zlab/PromoSer.

Algorithms↗

PubMed2004

SeqVISTA: a new module of integrated computational tools for studying transcriptional regulation.

Transcriptional regulation is one of the most basic regulatory mechanisms in the cell. The accumulation of multiple metazoan genome sequences and the advent of high-throughput experimental techniques have motivated the development of a large number of bioinformatics methods for the detection of regulatory motifs. The regulatory process is extremely complex and individual computational algorithms typically have very limited success in genome-scale studies. Here, we argue the importance of integrating multiple computational algorithms and present an infrastructure that integrates eight web services covering key areas of transcriptional regulation. We have adopted the client-side integration technology and built a consistent input and output environment with a versatile visualization tool named SeqVISTA. The infrastructure will allow for easy integration of gene regulation analysis software that is scattered over the Internet. It will also enable bench biologists to perform an arsenal of analysis using cutting-edge methods in a familiar environment and bioinformatics researchers to focus on developing new algorithms without the need to invest substantial effort on complex pre- or post-processors. SeqVISTA is freely available to academic users and can be launched online at http://zlab.bu.edu/SeqVISTA/web.jnlp, provided that Java Web Start has been installed. In addition, a stand-alone version of the program can be downloaded and run locally. It can be obtained at http://zlab.bu.edu/SeqVISTA.

Algorithms↗

PubMed2004

Characterization of genomic organization of the adenosine A2A receptor gene by molecular and bioinformatics analyses.

The adenosine A(2A) receptor (A(2A)R) is abundantly expressed in brain and emerging as an important therapeutic target for Parkinson's disease and potentially other neuropsychiatric disorders. To understand the molecular mechanisms of A(2A)R gene expression, we have characterized the genomic organization of the mouse and human A(2A)R genes by molecular and bioinformatic analyses. Three new exons (m1A, m1B and m1C) encoding the 5' untranslated regions (5'-UTRs) of mouse A(2A)R mRNA were identified by rapid amplification of 5' cDNA end (5' RACE), RT-PCR analysis and genome sequence analyses. Similar bioinformatics analysis also suggested six variants of the non-coding "exon 1" (h1A, h1B, h1C, h1D, h1E and h1F) in the human A(2A)R gene, which were confirmed by RT-PCR analysis, while three of the human exon 1 variants (h1D, h1E and h1F) were likewise verified by 5' oligonucleotide capping analysis suggesting multiple transcription start sites. Importantly, RT-PCR and quantitative PCR analysis demonstrated that the A(2A)R transcripts with different exon 1 variants displayed tissue-specific expression patterns. For instance, the mouse exon m1A mRNA was detected only in brain (specifically striatum) and the human exon h1D mRNA in lymphoreticular system. Furthermore, the determination of the three new transcription start sites of human A(2A)R gene by 5' oligonucleotide capping and bioinformatics analyses led to the identification of three corresponding promoter regions which contain several important cis elements, providing additional target for further molecular dissection of A(2A)R gene expression. Finally, our analysis indicates that A(2A)R mRNA and a novel transcript partially overlapping with the 3' exon h3, but in opposite orientation to the A(2A)R gene, could conceivably form duplexes to mutually regulate transcript expression. Thus, combined molecular and bioinformatics analyses revealed a new A(2A)R genomic structure, with conserved coding exons 2 and 3 and divergent, tissue-specific exon 1 variants encoding for 5'-UTR. This raises the possibility of generating multiple tissue-specific A(2A)R mRNA species by alternative promoters with varying regulatory susceptibility.

Animals↗

PubMed2004

CisML: an XML-based format for sequence motif detection software.

SUMMARY: CisML is an XML-based format for sequence motif detection software. This proposed standard is applicable to many types of sequence motif detection programs. It is intended to facilitate the integration of data and the comparison of results from different software packages, and to simplify the development of downstream tools. XSL stylesheets are provided for easy generation of text, html and graphical reports from CisML-formatted data. AVAILABILITY: http://zlab.bu.edu/CisML/ SUPPLEMENTARY INFORMATION: Example CisML-formatted data and XSL stylesheets for report generation are available along with the sample output.

Amino Acid Motifs↗

PubMed2004

Detection of functional DNA motifs via statistical over-representation.

The interaction of proteins with DNA recognition motifs regulates a number of fundamental biological processes, including transcription. To understand these processes, we need to know which motifs are present in a sequence and which factors bind to them. We describe a method to screen a set of DNA sequences against a precompiled library of motifs, and assess which, if any, of the motifs are statistically over- or under-represented in the sequences. Over-represented motifs are good candidates for playing a functional role in the sequences, while under-representation hints that if the motif were present, it would have a harmful dysregulatory effect. We apply our method (implemented as a computer program called Clover) to dopamine-responsive promoters, sequences flanking binding sites for the transcription factor LSF, sequences that direct transcription in muscle and liver, and Drosophila segmentation enhancers. In each case Clover successfully detects motifs known to function in the sequences, and intriguing and testable hypotheses are made concerning additional motifs. Clover compares favorably with an ab initio motif discovery algorithm based on sequence alignment, when the motif library includes only a homolog of the factor that actually regulates the sequences. It also demonstrates superior performance over two contingency table based over-representation methods. In conclusion, Clover has the potential to greatly accelerate characterization of signals that regulate transcription.

Animals↗

PubMed2004

Site2genome: locating short DNA sequences in whole genomes.

SUMMARY: Many biological papers describe short, functional DNA sites without specifying their exact positions in the genome. We have developed a Web server that automates the tedious task of locating such sites in eukaryotic genomes, thus giving access to the context of rich annotations that are increasingly available for genome sequences. AVAILABILITY: http://zlab.bu.edu/site2genome/

Algorithms↗

PubMed2004

Finding functional sequence elements by multiple local alignment.

Algorithms that detect and align locally similar regions of biological sequences have the potential to discover a wide variety of functional motifs. Two theoretical contributions to this classic but unsolved problem are presented here: a method to determine the width of the aligned motif automatically; and a technique for calculating the statistical significance of alignments, i.e. an assessment of whether the alignments are stronger than those that would be expected to occur by chance among random, unrelated sequences. Upon exploring variants of the standard Gibbs sampling technique to optimize the alignment, we discovered that simulated annealing approaches perform more efficiently. Finally, we conduct failure tests by applying the algorithm to increasingly difficult test cases, and analyze the manner of and reasons for eventual failure. Detection of transcription factor-binding motifs is limited by the motifs' intrinsic subtlety rather than by inadequacy of the alignment optimization procedure.

Algorithms↗

PubMed2004

Computational inference of transcriptional regulatory networks from expression profiling and transcription factor binding site identification.

We have developed a computational method for transcriptional regulatory network inference, CARRIE (Computational Ascertainment of Regu latory Relationships Inferred from Expression), which combines microarray and promoter sequence analysis. CARRIE uses sources of data to identify the transcription factors (TFs) that regulate gene expression changes in response to a stimulus and generates testable hypotheses about the regulatory network connecting these TFs to the genes they regulate. The promoter analysis component of CARRIE, ROVER (Relative OVER-abundance of cis-elements), is highly accurate at detecting the TFs that regulate the response to a stimulus. ROVER also predicts which genes are regulated by each of these TFs. CARRIE uses these transcriptional interactions to infer a regulatory network. To demonstrate our method, we applied CARRIE to six sets of publicly available DNA microarray experiments on Saccharomyces cerevisiae. The predicted networks were validated with comparisons to literature sources, experimental TF binding data, and gene ontology biological process information.

Binding Sites↗

PubMed2004

Optimizing protein representations with information theory.

The problem of describing a protein representation by breaking up the amino acids atoms into functionally similar atom groups has been addressed by many researchers in the past 25 years. They have used a variety of physical, chemical and biological criteria of varying degrees of rigor to essentially impose our understanding of protein structures onto various atom-typing schemes used in studies of protein folding, protein-protein and protein-ligand interactions, and others. Here, instead, we have chosen to rely primarily on the data and use information-theoretic techniques to dissect it. We show that we can obtain an optimized protein representation for a given alphabet size from protein monomers or protein interface datasets that are in agreement with general concepts of protein energetics. Closer inspection of the atom partitions led to interesting observations pointing to the greater importance of the hydrophobic interactions in protein monomers compared to interfaces and, conversely, greater importance of polar/charged interaction in protein interfaces. Comparing the atom partitions from the two datasets we show that the two are strikingly similar at alphabet size of five, proving that despite some differences, the general energetic concepts are very similar for folding and binding. Implications for further structural studies are discussed.

Binding Sites↗

PubMed2003

Atomic contact vectors in protein-protein recognition.

The ability to analyze and compare protein-protein interactions on the structural level is critical to our understanding of various aspects of molecular recognition and the functional interplay of components of biochemical networks. In this study, we introduce atomic contact vectors (ACVs) as an intuitive way to represent the physico-chemical characteristics of a protein-protein interface as well as a way to compare interfaces to each other. We test the utility of ACVs in classification by using them to distinguish between homodimers and crystal contacts. Our results compare favorably with those reported by other authors. We then apply ACVs to mine the PDB for all known protein-protein complexes and separate transient recognition complexes from permanent oligomeric ones. Getting at the basis of this difference is important for our understanding of recognition and we achieved a success rate of 91% for distinguishing these two classes of complexes. Although accessible surface area of the interface is a major discriminating feature, we also show that there are distinct differences in the contact preferences between the two kinds of complexes. Illustrating the superiority of ACVs as a basic comparison measure over a sequence-based approach, we derive a general rule of thumb to determine whether two protein-protein interfaces are redundant. With this method, we arrive at a nonredundant set of 209 recognition complexes--the largest set reported so far.

Crystallization↗

PubMed2003

RDOCK: refinement of rigid-body protein docking predictions.

We present a simple and effective algorithm RDOCK for refining unbound predictions generated by a rigid-body docking algorithm ZDOCK, which has been developed earlier by our group. The main component of RDOCK is a three-stage energy minimization scheme, followed by the evaluation of electrostatic and desolvation energies. Ionic side chains are kept neutral in the first two stages of minimization, and reverted to their full charge states in the last stage of brief minimization. Without side chain conformational search or filtering/clustering of resulting structures, RDOCK represents the simplest approach toward refining unbound docking predictions. Despite its simplicity, RDOCK makes substantial improvement upon the top predictions by ZDOCK with all three scoring functions and the improvement is observed across all three categories of test cases in a large benchmark of 49 non-redundant unbound test cases. RDOCK makes the most powerful combination with ZDOCK2.1, which uses pairwise shape complementarity as the scoring function. Collectively, they rank a near-native structure as the number-one prediction for 18 test cases (37% of the benchmark), and within the top 4 predictions for 24 test cases (49% of the benchmark). To various degrees, funnel-like energy landscapes are observed for these 24 test cases. To the best of our knowledge, this is the first report of binding funnels starting from global searches for a broad range of test cases. These results are particularly exciting, given that we have not used any biological information that is specific to individual test cases and the whole process is entirely automated. Among three categories of test cases, the best results are seen for enzyme/inhibitor, with a near-native structure ranked as the number-one prediction for 48% test cases, and within the top 10 predictions for 78% test cases. RDOCK is freely available to academic users at http://zlab.bu.edu/ approximately rong/dock.

Algorithms↗

PubMed2003

Examining the independent binding assumption for binding of peptide epitopes to MHC-I molecules.

MOTIVATION: Various methods have been proposed to predict the binding affinities of peptides to Major Histocompatibility Complex class I (MHC-I) molecules based on experimental binding data. They can be classified into two groups: (1) AIB methods that assume independent contributions of all peptide positions to the binding to MHC-I molecule (e.g. scoring matrices) and (2) general methods which can take into account interactions between different positions (e.g. artificial neural networks). We aim to compare the prediction accuracies of these methods, and quantify the impact of interactions between peptide positions. RESULTS: We compared several previously published and widely used methods and discovered that the best AIB methods gave significantly better predictions than three previously published general methods, possibly due to the lack of a sufficient training data for the general methods. The best results, however, were achieved with our newly developed general method, which combined a matrix describing independent binding with pair coefficients describing pair-wise interactions between peptide positions. The pair coefficients consistently but only slightly improved prediction accuracy, and were much smaller than the matrix entries. This explains why neglecting them-as is done in AIB methods-can still lead to good predictions. AVAILABILITY: The new prediction model is implemented at http://zlab.bu.edu/SMM. The underlying matrix and pair coefficients are also available as supplementary materials.

Algorithms↗