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A machine learning approach for prediction of DNA and peptide HPLC retention times

Marc Sturm, Sascha Quinten, Christian G. Huber, Oliver Kohlbacher, Knut Reinert
2006
In 2002 Gilar et al. proposed a simple mathematical model for the prediction of DNA retention times, that reliably works at high temperatures only (at least 70°C).  ...  We used support vector regression (SVR) for the model generation and retention time prediction. A similar problem arises in shotgun proteomics.  ...  Correlation of experimental and predicted peptide retention times. Table 1 . 1 Comparison of prediction methods for DNA retention times. Both models perform very well for 80 • C.  ... 
doi:10.4230/dagsemproc.05471.3 fatcat:pmie4wnkbbaxdctauxjz4qnb5e

Deep learning in proteomics

Bo Wen, Wenfeng Zeng, Yuxing Liao, Zhiao Shi, Sara R Savage, Wen Jiang, Bing Zhang
2020 Proteomics  
Here, we provide a comprehensive overview of deep learning applications in proteomics including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility  ...  complex-peptide binding affinity prediction, and protein structure prediction.  ...  Table 1 . 1 List of deep learning-based retention time prediction tools. No.  ... 
doi:10.1002/pmic.201900335 pmid:32939979 fatcat:gq6yffww6reu7eqrky7u6l2454

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Bo Wen, Kai Li, Yun Zhang, Bing Zhang
2020 Nature Communications  
We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods.  ...  To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction.  ...  The source code of NeoFlow is available at https://github.com/bzhanglab/neoflow. AutoRT is available at https://github.com/bzhanglab/AutoRT/.  ... 
doi:10.1038/s41467-020-15456-w pmid:32273506 fatcat:igkfssucdjhbldacmxhtn5hury

Deep Directed Evolution of Solid Binding Peptides for Quantitative Big-Data Generation [article]

Deniz T Yucesoy, Siddharth S Rath, Jacob L Rodriguez, Jonathan T Francis-Landau, Oliver Nakano-Baker, Mehmet Sarikaya
2021 bioRxiv   pre-print
Recently, machine-learning approaches have been introduced to guide the evolution process that facilitates a deeper and denser search of the sequence-space.  ...  The established extensive groundwork here provides unique opportunities to further iterate and modify the technique to suit a wide variety of needs and generate various peptide and protein datasets.  ...  The first iteration of the CNN failed to predict a trend in affinity for the benchmark 706 peptides.  ... 
doi:10.1101/2021.01.26.428348 fatcat:dae4sz2asfagfdttgwgcdd4cqm

An introduction to deep learning on biological sequence data: examples and solutions

Vanessa Isabell Jurtz, Alexander Rosenberg Johansen, Morten Nielsen, Jose Juan Almagro Armenteros, Henrik Nielsen, Casper Kaae Sønderby, Ole Winther, Søren Kaae Sønderby, Alfonso Valencia
2017 Bioinformatics  
on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules.  ...  The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this  ...  Corporation for the donation of Titan X GPUs. Conflict of Interest: none declared. Alipanahi,B. et al. (2015) Predicting the sequence specificities of DNA-and RNAbinding proteins by deep learning.  ... 
doi:10.1093/bioinformatics/btx531 pmid:28961695 pmcid:PMC5870575 fatcat:z6qa5zlounhlrltzkt3tcxev2u

Computational Modeling of C-Terminal Tails to Predict the Calcium-Dependent Secretion of Endoplasmic Reticulum Resident Proteins

Kathleen A. Trychta, Bing Xie, Ravi Kumar Verma, Min Xu, Lei Shi, Brandon K. Harvey
2021 Frontiers in Chemistry  
Here, by combining computational prediction with machine learning-based models and experimental validation, we identify carboxy tail sequences of ER resident proteins divergent from the canonical "KDEL  ...  These ER resident proteins typically have a carboxy-terminal ER retention/retrieval sequence (ERS).  ...  A Machine Learning Algorithm Predicts ERS Given that the canonical KDEL primary sequence motif cannot reliably predict an ERS, we used a machine learning-based approach to identify potential ERS and their  ... 
doi:10.3389/fchem.2021.689608 fatcat:ivuhfuxsmbgfhbpfrhtlscfpuu

Computational modeling of C-terminal tails to predict the calcium-dependent secretion of ER resident proteins [article]

Kathleen A Trychta, Bing Xie, Ravi Verma, Min Xu, Lei Shi, Brandon K Harvey
2021 bioRxiv   pre-print
Here, by combining computational prediction with machine learning-based models and experimental validation, we identify carboxy tail sequences of ER resident proteins divergent from the canonical KDEL  ...  These ER resident proteins typically have a carboxy-terminal ER retention sequence (ERS).  ...  Acknowledgements This work was supported by the National Institute on Drug Abuse, Intramural Research Program (Z1A DA000606 to L.S. and Z1A DA000618 to B.K.H.).  ... 
doi:10.1101/2021.03.21.435734 fatcat:hya4mqnonbd4xn3y2ebyeyky3i

Identification and Characterization of Peptides and Proteins Using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry [chapter]

M. Palmblad, J. Bergquist
2003 Journal of Chromatography Library  
The non-covalent interaction between HIV-inhibitory peptides and the oligomerization of amyloid β-peptides were investigated, reporting several new findings with possible relevance for development of anti-HIV  ...  To identify proteins by short sequence tags, electron capture dissociation was implemented, improved and finally coupled on-line to liquid chromatography for the first time.  ...  These models are then used to predict the retention time for candidate peptides in a database.  ... 
doi:10.1016/s0301-4770(03)80012-x fatcat:ifplfwjxovbo7bvq4yqyk3bbky

A Survey of Computational Intelligence Techniques in Protein Function Prediction

Arvind Kumar Tiwari, Rajeev Srivastava
2014 International Journal of Proteomics  
, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein  ...  The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.  ...  Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.  ... 
doi:10.1155/2014/845479 pmid:25574395 pmcid:PMC4276698 fatcat:p3vwanr2nran7arwzotirhgyte

Effective Design of Multifunctional Peptides by Combining Compatible Functions

Christian Diener, Georgina Garza Ramos Martínez, Daniel Moreno Blas, David A. Castillo González, Gerardo Corzo, Susana Castro-Obregon, Gabriel Del Rio, Lilia M. Iakoucheva
2016 PLoS Computational Biology  
To identify compatible activities in peptide sequences, we used a machine-learning approach and discovered that a penetrating activity should be compatible with DNA-binding and antimicrobial activities  ...  To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible  ...  Gabriel Muciño Hernández, the IT core facility of the Instituto de Fisiologia Celular at UNAM and the Laboratorio Nacional de Microscopía Avanzada at UNAM provided technical assistance relevant for this  ... 
doi:10.1371/journal.pcbi.1004786 pmid:27096600 pmcid:PMC4838304 fatcat:zfjt35xvpzheliuwqiqfaqjeve

Machine learning for metabolic engineering: A review

Chris Lawson, Jose Manuel Martí, Tijana Radivojevic, Sai Vamshi R. Jonnalagadda, Reinhard Gentz, Nathan J. Hillson, Sean Peisert, Joonhoon Kim, Blake A. Simmons, Christopher J. Petzold, Steven W. Singer, Aindrila Mukhopadhyay (+3 others)
2020 Metabolic Engineering  
Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable.  ...  Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed.  ...  The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide  ... 
doi:10.1016/j.ymben.2020.10.005 pmid:33221420 fatcat:hac34yggd5hnrhkikd7elbzz3m

Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology [chapter]

Stéphane Ballereau, Enrico Glaab, Alexei Kolodkin, Amphun Chaiboonchoe, Maria Biryukov, Nikos Vlassis, Hassan Ahmed, Johann Pellet, Nitin Baliga, Leroy Hood, Reinhard Schneider, Rudi Balling (+1 others)
2013 Systems Biology  
Methods for clustering, feature selection, prediction analysis, text mining and pathway analysis used to analyse and integrate the data produced are then presented.  ...  This chapter introduces Systems Biology, its context, aims, concepts and strategies, then describes approaches used in genomics, epigenomics, transcriptomics, proteomics, metabolomics and lipidomics, and  ...  of Allergy, Grant Agreement FP7 N°264357) and the U-BIOPRED consortium (Unbiased Biomarkers for the PREDiction of respiratory disease outcomes, Grant Agreement IMI 115010).  ... 
doi:10.1007/978-94-007-6803-1_1 fatcat:toqji65vxzbejo6wdfxuncwnmq

An Integrated Approach toward NanoBRET Tracers for Analysis of GPCR Ligand Engagement

Michael P. Killoran, Sergiy Levin, Michelle E. Boursier, Kristopher Zimmerman, Robin Hurst, Mary P. Hall, Thomas Machleidt, Thomas A. Kirkland, Rachel Friedman Ohana
2021 Molecules  
Here, we devised an integrated tracer discovery workflow that couples machine learning-guided in silico screening for scaffolds displaying promiscuous binding to GPCRs with a blend of synthetic strategies  ...  However, development of fluorescent tracers is often challenging and remains the principal bottleneck for this approach.  ...  Wood for useful discussions, Barrie Kellam and Sarah Mistry for assistance with synthetic chemistry and Emily R. Lackner for assistance with generation of DNA constructs.  ... 
doi:10.3390/molecules26102857 pmid:34065854 fatcat:mkxvgobz65bphf4qbsklnrcvpi

Proteomics, lipidomics, metabolomics: a mass spectrometry tutorial from a computer scientist's point of view

Rob Smith, Andrew D Mathis, Dan Ventura, John T Prince
2014 BMC Bioinformatics  
For decades, mass spectrometry data has been analyzed to investigate a wide array of research interests, including disease diagnostics, biological and chemical theory, genomics, and drug development.  ...  Conclusions: This paper will facilitate contributions from mathematicians, computer scientists, and statisticians to MS-omics that will fundamentally improve results over existing approaches and inform  ...  Many peptide retention time prediction strategies exist [38] .  ... 
doi:10.1186/1471-2105-15-s7-s9 pmid:25078324 pmcid:PMC4110734 fatcat:lit2fe3omvevjoso5esvmuhlau

PROTEIN SUBCELLULAR LOCALIZATION PREDICTION [chapter]

Paul Horton, Yuri Mukai, Kenta Nakai
2004 The Practical Bioinformatician  
This chapter discusses various aspects of protein subcellular localization in the context of bioinformatics and reviews the twenty years of progress in predicting protein subcellular localization.  ...  Next we mention some of the general issues involved in predicting protein subcellular localization, such as what are the sites? how many sites per protein? how good are the predictions? and so on.  ...  Sequence Based Machine Learning Approaches with Architectures Designed to Reflect Localization Signals A series of works by Nielsen, Emanuelsson, and colleagues have taken an approach in which sophisticated  ... 
doi:10.1142/9789812562340_0009 fatcat:tqw45wob3fhihb4xi5jggrlzye
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