Biology subjects

Chen, Q.

Publications and source records attributed to Chen, Q..

491 records · Page 28Linked to original sources

bioRxiv2017

Expressing Biologically Active Membrane Proteins in a Cell-Free Transcription-Translation Platform

Cell-free transcription-translation platforms have been widely utilized to express soluble proteins in basic synthetic biological circuit prototyping. From a synthetic biology point of view, it is critical to express membrane proteins in cell-free transcription-translation systems, and use them directly in biocircuits, considering the fact that histidine kinases, G-protein coupled receptors (GPCRs) and other important biosensors are all membrane proteins. Previous studies have expressed membrane proteins in cell-free systems with the help of detergents, liposomes or nanodiscs, but have not demonstrated the ability to prototype circuit behavior for the purpose of testing more complex circuit functions involving membrane-bound proteins. Built on previous efforts, in this work we demonstrated that we could co-translationally express solubilized and active membrane proteins in our cell-free TX-TL platform with membrane-like materials. We first tested the expression of several constructs with {beta}1 and {beta}2 adrenergic receptors in TX-TL and observed significant insoluble membrane protein production. The addition of nanodiscs to the cell free expression system enabled solubilization of membrane proteins. Nanodisc is lipoprotein-based membrane-like material. The activity of {beta}2 adrenergic receptor was tested with both fluorescence and Surface Plasmon Resonance (SPR) binding assays by monitoring the specific binding response of small-molecule binders, carazolol and norepinephrine. Our results suggest that it is promising to use cell-free expression systems to prototype synthetic biocircuits involving single chain membrane proteins without extra procedures. This data made us one step closer to testing complex membrane protein circuits in cell-free environment.

bioengineering↗

bioRxiv2017

An overexpressed Q allele leads to increased spike density and improved processing quality

Spike density and processing quality are important traits during the evolution of wheat, which is controlled by multiple gene loci. The associated gene loci have been heavily studied with slow progress. A common wheat mutant with extremely compact spikes and good processing quality was isolated. The gene (Qc1) responsible for the mutant phenotype was mapped and cloned, and the cellular mechanism for the mutant phenotype was investigated. Qc1 originated from a point mutation that interferes with the miR172-directed cleavage of the Q gene, leading to its overexpression. Qc1 reduces the longitudinal cell size of rachises, resulting in an increased spike density. Qc1 increases the number of vascular bundles, which suggests a higher efficiency in the transportation of assimilates in the spikes of the mutant than in the WT. This could account for the improved processing quality. The effects of Qc1 on spike density and wheat processing quality were confirmed by the identification of nine common wheat mutants having four different Qc alleles. These results deepen our understanding of the key role of Q gene, one of the most important domestication gene for wheat, and provide new insights for the potential application of Qc allele in wheat breeding.

genetics↗

bioRxiv2016

Duplicates, redundancies, and inconsistencies in the primary nucleotide databases: a descriptive study

GenBank, the EMBL European Nucleotide Archive, and the DNA DataBank of Japan, known collectively as the International Nucleotide Sequence Database Collaboration or INSDC, are the three most significant nucleotide sequence databases. Their records are derived from laboratory work undertaken by different individuals, by different teams, with a range of technologies and assumptions, and over a period of decades. As a consequence, they contain a great many duplicates, redundancies, and inconsistencies, but neither the prevalence nor the characteristics of various types of duplicates have been rigorously assessed. Existing duplicate detection methods in bioinformatics only address specific duplicate types, with inconsistent assumptions; and the impact of duplicates in bioinformatics databases has not been carefully assessed, making it difficult to judge the value of such methods. Our goal is to assess the scale, kinds, and impact of duplicates in bioinformatics databases, through a retrospective analysis of merged groups in INSDC databases. Our outcomes are threefold: (1) We analyse a benchmark dataset consisting of duplicates manually identified in INSDC - a dataset of 67,888 merged groups with 111,823 duplicate pairs across 21 organisms from INSDC databases - in terms of the prevalence, types, and impacts of duplicates. (2) We categorise duplicates at both sequence and annotation level, with supporting quantitative statistics, showing that different organisms have different prevalence of distinct kinds of duplicate. (3) We show that the presence of duplicates has practical impact via a simple case study on duplicates, in terms of GC content and melting temperature. We demonstrate that duplicates not only introduce redundancy, but can lead to inconsistent results for certain tasks. Our findings lead to a better understanding of the problem of duplication in biological databases.

bioinformatics↗

bioRxiv2016

Benchmarks for Measurement of Duplicate Detection Methods in Nucleotide Databases

Duplication of information in databases is a major data quality challenge. The presence of duplicates, implying either redundancy or inconsistency, can have a range of impacts on the quality of analyses that use the data. To provide a sound basis for research on this issue in databases of nucleotide sequences, we have developed new, large-scale validated collections of duplicates, which can be used to test the effectiveness of duplicate detection methods. Previous collections were either designed primarily to test efficiency, or contained only a limited number of duplicates of limited kinds. To date, duplicate detection methods have been evaluated on separate, inconsistent benchmarks, leading to results that cannot be compared and, due to limitations of the benchmarks, of questionable generality.\n\nIn this study we present three nucleotide sequence database benchmarks, based on information drawn from a range of resources, including information derived from mapping to Swiss-Prot and TrEMBL. Each benchmark has distinct characteristics. We quantify these characteristics and argue for their complementary value in evaluation. The benchmarks collectively contain a vast number of validated biological duplicates; the largest has nearly half a billion duplicate pairs (although this is probably only a tiny fraction of the total that is present). They are also the first benchmarks targeting the primary nucleotide databases. The records include the 21 most heavily studied organisms in molecular biology research. Our quantitative analysis shows that duplicates in the different benchmarks, and in different organisms, have different characteristics. It is thus unreliable to evaluate duplicate detection methods against any single benchmark. For example, the benchmark derived from Swiss-Prot mappings identifies more diverse types of duplicates, showing the importance of expert curation, but is limited to coding sequences. Overall, these benchmarks form a resource that we believe will be of great value for development and evaluation of the duplicate detection methods that are required to help maintain these essential resources.\n\nAvailability: The benchmark data sets are available at https://bitbucket.org/biodbqual/benchmarks.

bioinformatics↗

bioRxiv2016

Experimental and Computational Investigation of the Structure of Peptide Monolayers on Gold Nanoparticles

The self-assembly and self-organization of small molecules at the surface of nanoparticles constitute a potential route towards the preparation of advanced protein-like nanosystems. However, their structural characterization, critical to the design of bio-nanomaterials with well-defined biophysical and biochemical properties, remains highly challenging. Here, a computational model for peptide-capped gold nanoparticles is developed using experimentally characterized CALNN-and CFGAILSS-capped gold nanoparticles as a benchmark. The structure of CALNN and CFGAILSS monolayers is investigated by both structural biology techniques and molecular dynamics simulations. The calculations reproduce the experimentally observed dependence of the monolayer secondary structure on peptide capping density and on nanoparticle size, thus giving us confidence in the model. Furthermore, the computational results reveal a number of new features of peptide-capped monolayers, including the importance of sulfur movement for the formation of secondary structure motifs, the presence of water close to the gold surface even in tightly packed peptide monolayers, and the existence of extended 2D parallel {beta}-sheet domains in CFGAILSS monolayers. The model developed here provides a predictive tool that may assist in the design of further bio-nanomaterials.

biochemistry↗