scholarly journals Functional and Mechanistic Characterization of an Enzyme Family Combining Bioinformatics and High-Throughput Microfluidics

2021 ◽  
Author(s):  
Michal Vasina ◽  
Pavel Vanacek ◽  
Jiri Hon ◽  
David Kovar ◽  
Hanka Faldynova ◽  
...  
Keyword(s):  
High Throughput ◽  
Sequence Data ◽  
Experimental Testing ◽  
Catalytic Efficiency ◽  
Industrial Applications ◽  
Quality Data ◽  
Soluble Form ◽  
Genomic Databases ◽  
Lower Protein ◽  
Enzyme Families

Abstract Next-generation sequencing doubles genomic databases every 2.5 years. The accumulation of sequence data raises the need to speed up functional analysis. Herein, we present a pipeline integrating bioinformatics and microfluidics and its application for high-throughput mining of novel haloalkane dehalogenases. We employed bioinformatics to identify 2,905 putative dehalogenases and selected 45 representative enzymes, of which 24 were produced in soluble form. Droplet-based microfluidics accelerates subsequent experimental testing up to 20,000 reactions per day while achieving 1,000-fold lower protein consumption. This resulted in doubling the dehalogenation “toolbox" characterized over three decades, yielding biocatalysts surpassing the efficiency of currently available enzymes. Combining microfluidics with modern global data analysis provided precious mechanistic information related to the high catalytic efficiency of new variants. This pipeline applied to other enzyme families can accelerate the identification of biocatalysts for industrial applications as well as the collection of high-quality data for machine learning.

scholarly journals Functional Annotation of an Enzyme Family by Integrated Strategy Combining Bioinformatics with Microanalytical and Microfluidic Technologies

2021 ◽  
Author(s):  
Pavel Vanacek ◽  
Michal Vasina ◽  
Jiri Hon ◽  
David Kovar ◽  
Hana Faldynova ◽  
...  
Keyword(s):  
Sequence Data ◽  
Bioinformatic Analysis ◽  
Industrial Applications ◽  
Genomic Databases ◽  
Functional Studies ◽  
Sequencing Technologies ◽  
Enzyme Family ◽  
Lower Protein ◽  
Enzyme Families ◽  
User Friendly

<p>Next-generation sequencing technologies enable doubling of the genomic databases every 2.5 years. Collected sequences represent a rich source of novel biocatalysts. However, the rate of accumulation of sequence data exceeds the rate of functional studies, calling for acceleration and miniaturization of biochemical assays. Here, we present an integrated platform employing bioinformatics, <a></a><a>microanalytics, </a>and microfluidics and its application for exploration of unmapped sequence space, using haloalkane dehalogenases as model enzymes. First, we employed bioinformatic analysis for identification of 2,905 putative dehalogenases and rational selection of 45 representative enzymes. Second, we expressed and experimentally characterized 24 enzymes showing sufficient solubility for microanalytical and microfluidic testing. Miniaturization increased the throughput to 20,000 reactions per day with 1000-fold lower protein consumption compared to conventional assays. A single run of the platform doubled dehalogenation toolbox of family members characterized over three decades. Importantly, the dehalogenase activities of nearly one-third of these novel biocatalysts far exceed that of most published HLDs. Two enzymes showed unusually narrow substrate specificity, never before reported for this enzyme family. The strategy is generally applicable to other enzyme families, paving the way towards the acceleration of the process of identification of novel biocatalysts for industrial applications but also for the collection of homogenous data for machine learning. The automated <i>in silico</i> workflow has been released as a user-friendly web-tool EnzymeMiner: https://loschmidt.chemi.muni.cz/enzymeminer/.</p>

scholarly journals Functional Annotation of an Enzyme Family by Integrated Strategy Combining Bioinformatics with Microanalytical and Microfluidic Technologies

2021 ◽  
Author(s):  
Pavel Vanacek ◽  
Michal Vasina ◽  
Jiri Hon ◽  
David Kovar ◽  
Hana Faldynova ◽  
...  
Keyword(s):  
Sequence Data ◽  
Bioinformatic Analysis ◽  
Industrial Applications ◽  
Genomic Databases ◽  
Functional Studies ◽  
Sequencing Technologies ◽  
Enzyme Family ◽  
Lower Protein ◽  
Enzyme Families ◽  
User Friendly

<p>Next-generation sequencing technologies enable doubling of the genomic databases every 2.5 years. Collected sequences represent a rich source of novel biocatalysts. However, the rate of accumulation of sequence data exceeds the rate of functional studies, calling for acceleration and miniaturization of biochemical assays. Here, we present an integrated platform employing bioinformatics, <a></a><a>microanalytics, </a>and microfluidics and its application for exploration of unmapped sequence space, using haloalkane dehalogenases as model enzymes. First, we employed bioinformatic analysis for identification of 2,905 putative dehalogenases and rational selection of 45 representative enzymes. Second, we expressed and experimentally characterized 24 enzymes showing sufficient solubility for microanalytical and microfluidic testing. Miniaturization increased the throughput to 20,000 reactions per day with 1000-fold lower protein consumption compared to conventional assays. A single run of the platform doubled dehalogenation toolbox of family members characterized over three decades. Importantly, the dehalogenase activities of nearly one-third of these novel biocatalysts far exceed that of most published HLDs. Two enzymes showed unusually narrow substrate specificity, never before reported for this enzyme family. The strategy is generally applicable to other enzyme families, paving the way towards the acceleration of the process of identification of novel biocatalysts for industrial applications but also for the collection of homogenous data for machine learning. The automated <i>in silico</i> workflow has been released as a user-friendly web-tool EnzymeMiner: https://loschmidt.chemi.muni.cz/enzymeminer/.</p>

scholarly journals ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data

2009 ◽  
Vol 25 (19) ◽  
pp. 2607-2608 ◽  
Author(s):  
M. Morgan ◽  
S. Anders ◽  
M. Lawrence ◽  
P. Aboyoun ◽  
H. Pages ◽  
...  
Keyword(s):  
Quality Assessment ◽  
High Throughput ◽  
Sequence Data ◽  
Bioconductor Package ◽  
Input Quality

scholarly journals Hill number as a bacterial diversity measure framework with high-throughput sequence data

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Sanghoon Kang ◽  
Jorge L. M. Rodrigues ◽  
Justin P. Ng ◽  
Terry J. Gentry
Keyword(s):  
Bacterial Diversity ◽  
High Throughput ◽  
Sequence Data ◽  
Diversity Measure ◽  
Hill Number

scholarly journals A sensitive, accurate, and high-throughput gluco-oligosaccharide oxidase based HRP colorimetric method for lytic polysaccharide monooxygenase activity assay

2021 ◽  
Author(s):  
Gang Liu
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Direct Method ◽  
Total Concentration ◽  
Colorimetric Method ◽  
Industrial Applications ◽  
Assay Method ◽  
Activity Assay ◽  
Lytic Polysaccharide Monooxygenase ◽  
Monooxygenase Activity

Abstract Background The AA9 (auxiliary activities) family of lytic polysaccharide monooxygenases (AA9 LPMOs) are ubiquitous and diverse group of enzymes amongst the fungal kingdom. They catalyze the oxidative cleavage of glycosidic bonds in lignocellulose and exhibit great potential for secondary biorefinery applications. Screening of AA9 LPMOs for desirable properties is crucial for biorefinery industrial applications. However, robust, high-throughput and direct method for AA9 LPMO activity assay, which is prerequisite for screening of LPMOs with excellent properties, is still lacking. Here, we have described a gluco-oligosaccharide oxidase (GOOX) based horseradish peroxidase (HRP) colorimetric method for AA9 LPMO activity assay. Results We cloned and expressed a GOOX gene from Sarocladium strictum in Trichoderma reesei, purified the recombinant SsGOOX, validated its properties, and set up a SsGOOX based HRP colorimetric method for cellobiose concentration assay. Then we expressed two AA9 LPMOs from Thielavia terrestris, TtAA9F and TtAA9G in T. reesei, purified the recombinant proteins, and analyzed their product profiles and regioselectivity towards phosphoric acid swollen cellulose (PASC). TtAA9F was characterized as a C1 type (class 1) LPMO, while TtAA9G was characterized as a C4 type (class 2) LPMO. Finally, the SsGOOX based HRP colorimetric method was used to quantify the total concentration of reducing lytic products from LPMO reaction, and consequently, the activities of both C1 and C4 types of LPMOs were analyzed. These LPMOs could be effectively analyzed with limits of detection (LoDs) lower than 30 nmol/L, and standard curves between A515 and LPMO concentrations with determination coefficients greater than 0.994 were obtained. Conclusions A novel, sensitive and accurate assay method that directly targets the main activity of both C1 and C4 type of AA9 LPMOs was established. This method is easy to use and could be performed on a microtiter plate ready for high-throughput screening of AA9 LPMOs with high properties.

scholarly journals A Need for Improved Cellulase Identification from Metagenomic Sequence Data

2020 ◽  
Vol 87 (1) ◽  
Author(s):  
Rebecca Co ◽  
Laura A. Hug
Keyword(s):  
Sequence Data ◽  
Industrial Applications ◽  
Data Sets ◽  
Metagenomic Sequence ◽  
Environmental Sequence ◽  
Sequencing Technologies ◽  
Current Classification ◽  
Applied Microbiology ◽  
Environmental Surveys ◽  
Metagenomic Sequence Data

ABSTRACT Improved sequencing technologies and the maturation of metagenomic approaches allow the identification of gene variants with potential industrial applications, including cellulases. Cellulase identification from metagenomic environmental surveys is complicated by inconsistent nomenclature and multiple categorization systems. Here, we summarize the current classification and nomenclature systems, with recommendations for improvements to these systems. Addressing the issues described will strengthen the annotation of cellulose-active enzymes from environmental sequence data sets—a rapidly growing resource in environmental and applied microbiology.

Two-Level Parallel Alignment Based on Sequence Parallel Vectorization

2014 ◽  
Vol 490-491 ◽  
pp. 757-762
Author(s):  
Guo Li Ji ◽  
Long Teng Chen ◽  
Liang Liang Chen
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Sequence Alignment ◽  
High Throughput ◽  
Sequence Data ◽  
Cell Receptor ◽  
Alignment Accuracy ◽  
Gpu Acceleration ◽  
Alignment Algorithm ◽  
Alignment Result

This paper proposed a way of two-level parallel alignment based on sequence parallel vectorization with GPU acceleration on the Fermi architecture, which integrates sequence parallel vectorization, parallel k-means clustering approximate alignment and parallel Smith-Waterman algorithm. The method converts sequence alignment into vector alignment by first. Then it uses k-means alignment to divide sequences into several groups and reduce the size of sequence data. The expected accurate alignment result is achieved using parallel Smith-Waterman algorithm. The high-throughput mouse T-cell receptor (TCR) sequences were used to validate the proposed method. Under the same hardware condition, comparing to serial Smith-Waterman algorithm and CUDASW++2.0 algorithm, our method is the most efficient alignment algorithm with high alignment accuracy.

scholarly journals Publisher Correction: Optimal compressed representation of high throughput sequence data via light assembly

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Antonio A. Ginart ◽  
Joseph Hui ◽  
Kaiyuan Zhu ◽  
Ibrahim Numanagić ◽  
Thomas A. Courtade ◽  
...  
Keyword(s):  
High Throughput ◽  
Sequence Data
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