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A machine learning approach for predicting methionine oxidation sites

Juan C. Aledo, Francisco R. Cantón, Francisco J. Veredas
2017 BMC Bioinformatics  
We use a machine learning approach to generate predictive models from these datasets.  ...  As a first approach to this matter, we have developed models based on random forests, support vector machines and neural networks, aimed at accurate prediction of sites of methionine oxidation.  ...  The source code to extract features and carry out predictions can be obtained at google.drive.scripts.  ... 
doi:10.1186/s12859-017-1848-9 pmid:28962549 pmcid:PMC5622526 fatcat:nus4ml35vfdkth2trr3w4rj2oy

Machine learning prediction of methionine and tryptophan photooxidation susceptibility

Jared A. Delmar, Eugen Buehler, Ashwin K. Chetty, Agastya Das, Guillermo Miro Quesada, Jihong Wang, Xiaoyu Chen
2021 Molecular Therapy: Methods & Clinical Development  
We show that our machine learning models predict Met and Trp photooxidation likelihood with 0.926 and 0.860 area under the curve (AUC), respectively, and Met photooxidation rate with a correlation coefficient  ...  To our knowledge, no predictive model for photooxidation of Met or Trp is currently available.  ...  Figure 2 .( 2 Regression machine learning model for predicting deamidation rate Predicted Met oxidation abundance (%) was plotted versus the experimental measured oxidation abundance for the independent  ... 
doi:10.1016/j.omtm.2021.03.023 pmid:33898635 pmcid:PMC8060516 fatcat:wkt2frbqfrddtauhvhlwqt2mci

Unraveling Oxidative Stress Resistance: Molecular Properties Govern Proteome Vulnerability [article]

Roger L. Chang, Julian A. Stanley, Matthew C. Robinson, Joel W. Sher, Zhanwen Li, Yujia A. Chan, Ashton R. Omdahl, Ruddy Wattiez, Adam Godzik, Sabine Matallana-Surget
2020 bioRxiv   pre-print
to oxidation, a mode of evolutionary adaptation for stress tolerance in bacteria.  ...  The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance  ...  for machine learning In addition to structure-derived features, we implemented simple sequence-alignmentbased feature engineering to predict oxidation sites (Fig. 4B ).  ... 
doi:10.1101/2020.03.09.983213 fatcat:7gf773mv6va25ivenmf5enifza

Sulphur Atoms from Methionines Interacting with Aromatic Residues Are Less Prone to Oxidation

Juan C. Aledo, Francisco R. Cantón, Francisco J. Veredas
2015 Scientific Reports  
Within a given protein, an examination of the sequence surrounding the non-oxidized methionine revealed a preference for neighbouring tyrosine and tryptophan residues, but not for phenylalanine residues  ...  Although solvent accessibility is a relevant factor, oxidation at particular sites cannot be unequivocally explained by accessibility alone.  ...  Acknowledgements The authors thank Alicia Esteban del Valle and José María Blanco for critical reading of the manuscript.  ... 
doi:10.1038/srep16955 pmid:26597773 pmcid:PMC4657052 fatcat:txeg6ih5fjgo5hdcfa2ygfb2zy

Protein structure, amino acid composition and sequence determine proteome vulnerability to oxidation‐induced damage

Roger L Chang, Julian A Stanley, Matthew C Robinson, Joel W Sher, Zhanwen Li, Yujia A Chan, Ashton R Omdahl, Ruddy Wattiez, Adam Godzik, Sabine Matallana‐Surget
2020 EMBO Journal  
The radiation-resistant bacterium Deinococcus radiodurans accumulates less carbonylation than sensitive organisms, making it a key model for deciphering properties governing oxidative stress resistance  ...  We integrated shotgun redox proteomics, structural systems biology, and machine learning to resolve properties determining protein damage by γ-irradiation in Escherichia coli and D. radiodurans at multiple  ...  Debora Marks (Harvard) for advice on machine learning, data analysis and feedback on the manuscript; Dr. James Collins (MIT) for facilitating c-irradiator access; Dr.  ... 
doi:10.15252/embj.2020104523 pmid:33073387 fatcat:vwspyunzabgo5gbcukrlmpf4zu

Machine Learning on Signal-to-Noise Ratios Improves Peptide Array Design in SAMDI Mass Spectrometry

Albert Y. Xue, Lindsey C. Szymczak, Milan Mrksich, Neda Bagheri
2017 Analytical Chemistry  
However, with proper peptide sampling, this study illustrates how machine learning can accurately predict the S/N of a peptide in an array, allowing for the efficient design of arrays through selection  ...  We apply supervised machine learning to predict peptide S/N based on amino acid sequence, and identify specific physical properties of the amino acids that govern variation of this metric.  ...  Machine Learning Model Predicts SAMDI-MS S/N as a Function of Amino Acid Sequence.  ... 
doi:10.1021/acs.analchem.7b01728 pmid:28719743 pmcid:PMC5588089 fatcat:ixrmwd7atbblhflpsk7qushlem

Protein oxidation, nitration and glycation biomarkers for early-stage diagnosis of osteoarthritis of the knee and typing and progression of arthritic disease

Usman Ahmed, Attia Anwar, Richard S. Savage, Paul J. Thornalley, Naila Rabbani
2016 Arthritis Research & Therapy  
Data-driven machine learning methods were employed to explore diagnostic utility of the measurements for detection and classifying early-stage OA and RA, non-RA and good skeletal health with training set  ...  Conclusions: Oxidized, nitrated and glycated amino acids combined with hydroxyproline and anti-CCP antibody status provided a plasma-based biochemical test of relatively high sensitivity and specificity  ...  Machine learning analysis To explore the diagnostic utility of protein damage measurements we analysed plasma amino acid analyte data by a machine learning approach.  ... 
doi:10.1186/s13075-016-1154-3 pmid:27788684 pmcid:PMC5081671 fatcat:ntqjxkpsxzgblfd66blrsac4t4

A MACHINE LEARNING APPROACH TO PREDICTING PEPTIDE FRAGMENTATION SPECTRA

RANDY J. ARNOLD, NARMADA JAYASANKAR, DIVYA AGGARWAL, HAIXU TANG, PREDRAG RADIVOJAC
2005 Biocomputing 2006  
To address this problem, here we used a datadriven approach to learn peptide fragmentation rules in mass spectrometry, in the form of posterior probabilities, for various fragment-ion types of doubly and  ...  Accordingly, it is expected that new approaches to de novo predicting peptide fragmentation spectra will enable more accurate peptide identification.  ...  Acknowledgements This study was partially funded by the Indiana University Office of the Vice President for Research through a Faculty Research Support grant awarded to RJA, HT and PR.  ... 
doi:10.1142/9789812701626_0021 fatcat:gvj4pfo4mze25apw5l5hensg3a

DeepDigest: prediction of protein proteolytic digestion with deep learning [article]

Jinghan Yang, Zhiqiang Gao, Xiuhan Ren, Jie Sheng, Ping Xu, Cheng Chang, Yan Fu
2020 bioRxiv   pre-print
Compared with traditional machine learning algorithms, DeepDigest showed superior performance for all the eight proteases on a variety of datasets.  ...  Here, we propose a novel sequence-based deep learning model - DeepDigest, which integrates convolutional neural networks and long-short term memory networks for digestibility prediction of peptides.  ...  machine learning methods.  ... 
doi:10.1101/2020.03.13.990200 fatcat:jtadg4dyzvhw5jx4cfq54o5tm4

Functional Genomics with a Comprehensive Library of Transposon Mutants for the Sulfate-Reducing Bacterium Desulfovibrio alaskensis G20

J. V. Kuehl, M. N. Price, J. Ray, K. M. Wetmore, Z. Esquivel, A. E. Kazakov, M. Nguyen, R. Kuehn, R. W. Davis, T. C. Hazen, A. P. Arkin, A. Deutschbauer
2014 mBio  
Additionally, we show that the entire choline utilization cluster is important for fitness in choline sulfate medium, which confirms that a functional microcompartment is required for choline oxidation  ...  Here, we describe a genetic approach to fill gaps in our knowledge of sulfate-reducing bacteria.  ...  We combined these sources of information with a semisupervised machine learning approach: to generate training data for each data source, we used the other two data sources to label potential TSS locations  ... 
doi:10.1128/mbio.01041-14 pmid:24865553 pmcid:PMC4045070 fatcat:w7hhxgh3efhjrdho7ioff4wgv4

Prediction of LC-MS/MS properties of peptides from sequence by deep learning

Shenheng Guan, Michael Moran, Bin Ma
2019 Molecular & Cellular Proteomics  
Deep learning models for prediction of three key LC-MS/MS properties from peptide sequences were developed.  ...  A common core deep supervised learning architecture, bidirectional long-short term memory (LSTM) recurrent neural networks was used to construct the three prediction models.  ...  DATA AVAILABILITY Training and testing data, trained models, prediction results, sample codes for training and accessing data are provided in the following link: https://zenodo.org/record/ 2652602#.XP67VlxKi70  ... 
doi:10.1074/mcp.tir119.001412 pmid:31249099 pmcid:PMC6773555 fatcat:nyhwis633raadcjucc7gpduk2e

Proceedings of the 2013 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference

Jonathan D Wren, Mikhail G Dozmorov, Dennis Burian, Rakesh Kaundal, Andy Perkins, Ed Perkins, Doris M Kupfer, Gordon K Springer
2013 BMC Bioinformatics  
Declarations Funding for publication of this editorial came from the Midsouth Computational Biology and Bioinformatics Society (MCBIOS).  ...  Discovery in a sea of data. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcbioinformatics/supplements/ 14/S14.  ...  Zhendong Zhao et al. present an approach to predict drug activity based upon a machine-learning approach to analyze structural conformers [15] .  ... 
doi:10.1186/1471-2105-14-s14-s1 pmid:24267415 pmcid:PMC3851158 fatcat:gy4v5j5xjvdoten44h4bkwcrsu

Comprehensive de Novo Peptide Sequencing from MS/MS Pairs Generated through Complementary Collision Induced Dissociation and 351 nm Ultraviolet Photodissociation

Andrew P. Horton, Scott A. Robotham, Joe R. Cannon, Dustin D. Holden, Edward M. Marcotte, Jennifer S. Brodbelt
2017 Analytical Chemistry  
We describe a strategy for de novo peptide sequencing based on matched pairs of tandem mass spectra (MS/MS) obtained by collision induced dissociation (CID) and 351 nm ultraviolet photodissociation (UVPD  ...  Each precursor ion is isolated twice with the mass spectrometer switching between CID and UVPD activation modes to obtain a complementary MS/MS pair.  ...  UVnovo uses a novel machine learning approach to spectral interpretation.  ... 
doi:10.1021/acs.analchem.7b00130 pmid:28234449 pmcid:PMC5480239 fatcat:bd4h2ndn5jghpgkj25ksjem6ru

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data

Osama Hamzeh, Abedalrhman Alkhateeb, Julia Zheng, Srinath Kandalam, Luis Rueda
2020 BMC Bioinformatics  
In this work, a combination of efficient machine learning methods for feature selection and classification are proposed to analyze gene activity and select them as relevant biomarkers for different laterality  ...  A data set that consists of 450 samples was used in this study. The samples were divided into three laterality classes (left, right, bilateral).  ...  The authors would like to thank the School of Computer Science and the Office of Research Services at the University of Windsor for their financial support too. This research was enabled  ... 
doi:10.1186/s12859-020-3345-9 pmid:32164523 fatcat:xt57zmqp3bac5pphvjb7ercjni

Potential neutralizing antibodies discovered for novel corona virus using machine learning

Rishikesh Magar, Prakarsh Yadav, Amir Barati Farimani
2021 Scientific Reports  
To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV  ...  We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response.  ...  This work is supported by Center for Machine Learning in Health (CMLH) (47247.1.5007162) at Carnegie Mellon University (https ://www.cs.cmu.edu/cmlh-cfp) and start-up fund from Mechanical Engineering Department  ... 
doi:10.1038/s41598-021-84637-4 pmid:33664393 fatcat:hvuw56tfznhbbd6fzuzel2ekjy
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