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author:("Jiang, taiji")
1.  Analysis of interactions between SNARE proteins using imaging ellipsometer coupled with microfluidic array 
Scientific Reports  2014;4:5341.
The soluble N-ethylmaleimide-sensitive factor attachment receptor (SNARE) proteins are small and abundant membrane-bound proteins, whose specific interactions mediate membrane fusion during cell fusion or cellular trafficking. In this study, we report the use of a label-free method, called imaging ellipsometer to analyze the interactions among three SNAREs, namely Sec22p, Ykt6p and Sso2p. The SNAREs were immobilized on the silicon wafer and then analyzed in a pairwise mode with microfluidic array, leading us to discover the interactions between Ykt6p and Sso2p, Sec22p and Sso2p. Moreover, by using the real-time function of the imaging ellipsometer, we were able to obtain their association constants (KA) of about 104 M−1. We argue that the use of imaging ellipsometer coupled with microfluidic device will deepen our understanding of the molecular mechanisms underlying membrane fusion process.
PMCID: PMC4061542  PMID: 24938428
4.  Improvement in Low-Homology Template-Based Modeling by Employing a Model Evaluation Method with Focus on Topology 
PLoS ONE  2014;9(2):e89935.
Many template-based modeling (TBM) methods have been developed over the recent years that allow for protein structure prediction and for the study of structure-function relationships for proteins. One major problem all TBM algorithms face, however, is their unsatisfactory performance when proteins under consideration are low-homology. To improve the performance of TBM methods for such targets, a novel model evaluation method was developed here, and named MEFTop. Our novel method focuses on evaluating the topology by using two novel groups of features. These novel features included secondary structure element (SSE) contact information and 3-dimensional topology information. By combining MEFTop algorithm with FR-t5, a threading program developed by our group, we found that this modified TBM program, which was named FR-t5-M, exhibited significant improvements in predictive abilities for low-homology protein targets. We further showed that the MEFTop could be a generalized method to improve threading programs for low-homology protein targets. The softwares (FR-t5-M and MEFTop) are available to non-commercial users at our website:
PMCID: PMC3935967  PMID: 24587135
5.  Coiled-coil networking shapes cell molecular machinery 
Molecular Biology of the Cell  2012;23(19):3911-3922.
Coiled coil is a principal oligomerization motif. A comprehensive map of coiled-coil interactions (CCIs) in yeast is reported. Computational analysis reveals that CCIs are extensively involved in cell machinery organization. Disrupting the CCIs in the kinetochore leads to defects in kinetochore assembly and cell division.
The highly abundant α-helical coiled-coil motif not only mediates crucial protein–protein interactions in the cell but is also an attractive scaffold in synthetic biology and material science and a potential target for disease intervention. Therefore a systematic understanding of the coiled-coil interactions (CCIs) at the organismal level would help unravel the full spectrum of the biological function of this interaction motif and facilitate its application in therapeutics. We report the first identified genome-wide CCI network in Saccharomyces cerevisiae, which consists of 3495 pair-wise interactions among 598 predicted coiled-coil regions. Computational analysis revealed that the CCI network is specifically and functionally organized and extensively involved in the organization of cell machinery. We further show that CCIs play a critical role in the assembly of the kinetochore, and disruption of the CCI network leads to defects in kinetochore assembly and cell division. The CCI network identified in this study is a valuable resource for systematic characterization of coiled coils in the shaping and regulation of a host of cellular machineries and provides a basis for the utilization of coiled coils as domain-based probes for network perturbation and pharmacological applications.
PMCID: PMC3459866  PMID: 22875988
6.  Modeling influenza sequence evolution for vaccination 
BMC Proceedings  2012;6(Suppl 6):P44.
PMCID: PMC3467554
7.  Correlation of Influenza Virus Excess Mortality with Antigenic Variation: Application to Rapid Estimation of Influenza Mortality Burden 
PLoS Computational Biology  2010;6(8):e1000882.
The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention.
Author Summary
In epidemiology, investigators usually rely on surveillance data to assess the impact of an influenza virus on human health. However, accurate assessment of the influenza mortality burden at the early stage of influenza infection is rather challenging because the early influenza surveillance data are very limited and prone to bias as well. This speaks to an urgent need for the development of a more effective method for rapid and accurate estimation of influenza mortality burden. By proposing a novel host-virus interaction model, we have established a quantitative relationship between the antigenic variation of human influenza virus and its mortality burden. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. We believe that our work will help develop a timely and sensible influenza preparedness programme that balances the gains of public health with the social and economic costs.
PMCID: PMC2920844  PMID: 20711361
8.  AVID: An integrative framework for discovering functional relationships among proteins 
BMC Bioinformatics  2005;6:136.
Determining the functions of uncharacterized proteins is one of the most pressing problems in the post-genomic era. Large scale protein-protein interaction assays, global mRNA expression analyses and systematic protein localization studies provide experimental information that can be used for this purpose. The data from such experiments contain many false positives and false negatives, but can be processed using computational methods to provide reliable information about protein-protein relationships and protein function. An outstanding and important goal is to predict detailed functional annotation for all uncharacterized proteins that is reliable enough to effectively guide experiments.
We present AVID, a computational method that uses a multi-stage learning framework to integrate experimental results with sequence information, generating networks reflecting functional similarities among proteins. We illustrate use of the networks by making predictions of detailed Gene Ontology (GO) annotations in three categories: molecular function, biological process, and cellular component. Applied to the yeast Saccharomyces cerevisiae, AVID provides 37,451 pair-wise functional linkages between 4,191 proteins. These relationships are ~65–78% accurate, as assessed by cross-validation testing. Assignments of highly detailed functional descriptors to proteins, based on the networks, are estimated to be ~67% accurate for GO categories describing molecular function and cellular component and ~52% accurate for terms describing biological process. The predictions cover 1,490 proteins with no previous annotation in GO and also assign more detailed functions to many proteins annotated only with less descriptive terms. Predictions made by AVID are largely distinct from those made by other methods. Out of 37,451 predicted pair-wise relationships, the greatest number shared in common with another method is 3,413.
AVID provides three networks reflecting functional associations among proteins. We use these networks to generate new, highly detailed functional predictions for roughly half of the yeast proteome that are reliable enough to drive targeted experimental investigations. The predictions suggest many specific, testable hypotheses. All of the data are available as downloadable files as well as through an interactive website at . Thus, AVID will be a valuable resource for experimental biologists.
PMCID: PMC1177925  PMID: 15929793

Results 1-8 (8)