Evidence map›Paper›PMID 28083757›Full record

ArticleJournal of the American Society for Mass Spectrometry2017

Finding Biomass Degrading Enzymes Through an Activity-Correlated Quantitative Proteomics Platform (ACPP).

Hongyan Ma, Daniel G Delafield, Zhe Wang, Jianlan You, Si Wu

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Hongyan MaDepartment of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
Daniel G DelafieldDepartment of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
Zhe WangDepartment of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
Jianlan YouDepartment of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA.
Si WuDepartment of Chemistry and Biochemistry, University of Oklahoma, Norman, OK, 73019, USA. si.wu@ou.edu.ORCID 0000-0002-6346-7359

Funding

Defining the role of altered cytokine signaling pathways on autoimmunityU01AI101990 · NIAID · BENAROYA RESEARCH INST AT VIRGINIA MASON · PI BUCKNER, JANE HOYT · 2012 to 2016
$12.1M
Computational tools for top down mass spectrometry based proteoform identification and proteogenomicsR01GM118470 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Xiaowen Liu · 2016 to 2026
$2.7M
NIAID NIH HHS U01 AI101990NIGMS NIH HHS R01 GM118470
6 · The paper itself

Abstract

The microbial secretome, known as a pool of biomass (i.e., plant-based materials) degrading enzymes, can be utilized to discover industrial enzyme candidates for biofuel production. Proteomics approaches have been applied to discover novel enzyme candidates through comparing protein expression profiles with enzyme activity of the whole secretome under different growth conditions. However, the activity measurement of each enzyme candidate is needed for confident "active" enzyme assignments, which remains to be elucidated. To address this challenge, we have developed an Activity-Correlated Quantitative Proteomics Platform (ACPP) that systematically correlates protein-level enzymatic activity patterns and protein elution profiles using a label-free quantitative proteomics approach. The ACPP optimized a high performance anion exchange separation for efficiently fractionating complex protein samples while preserving enzymatic activities. The detected enzymatic activity patterns in sequential fractions using microplate-based assays were cross-correlated with protein elution profiles using a customized pattern-matching algorithm with a correlation R-score. The ACPP has been successfully applied to the identification of two types of "active" biomass-degrading enzymes (i.e., starch hydrolysis enzymes and cellulose hydrolysis enzymes) from Aspergillus niger secretome in a multiplexed fashion. By determining protein elution profiles of 156 proteins in A. niger secretome, we confidently identified the 1,4-α-glucosidase as the major "active" starch hydrolysis enzyme (R = 0.96) and the endoglucanase as the major "active" cellulose hydrolysis enzyme (R = 0.97). The results demonstrated that the ACPP facilitated the discovery of bioactive enzymes from complex protein samples in a high-throughput, multiplexing, and untargeted fashion. Graphical Abstract ᅟ.

Indexed as

alpha-GlucosidasesAspergillus nigerBiomassCelluloseFungal ProteinsHydrolysisProteomicsalpha-GlucosidasesCelluloseFungal ProteinsCorrelationEnzyme activityLC-MS/MSPeptide countingProteomics

Identifiers

PMID28083757
PMCPMC5373979

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.