Application of modern reversed-phase peptide retention prediction algorithms to the Houghten and DeGraw dataset: Peptide helicity and its effect on prediction accuracy

Reimer, Janice M; Spicer, Vic; Krokhin, Oleg V.

doi:10.1016/j.chroma.2012.07.092

Cited by 22 publications

(19 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Peptides with large positive prediction errors are predominantly amphipathic 22 ( Figure 1B−D); therefore, their retention prediction error (ΔACN = HI exp − HI SSRCalc NonHel ) is dominated by the helical component: ΔACN ≈ HI Hel . We selected 5141 sequences with ΔACN >3% acetonitrile (shown in red in Figure 1A) for further analysis.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

N-Capping Motifs Promote Interaction of Amphipathic Helical Peptides with Hydrophobic Surfaces and Drastically Alter Hydrophobicity Values of Individual Amino Acids

et al. 2014

Self Cite

View full text Add to dashboard Cite

Capping rules, which govern interactions of helical peptides with hydrophobic surfaces, were never established before due to lack of methods for the direct measurement of polypeptide structure on the interphase boundary. We employed proteomic techniques and peptide retention modeling in reversed-phase chromatography to generate a data set sufficient for amino acid population analysis at helix ends. We found that interactions of amphipathic helical peptides with a hydrophobic C18 phase are induced by a unique motif featuring hydrophobic residues in the N1 and N2 positions adjacent to the N-cap (Asn, Asp, Ser, Thr, Gly), followed by Glu, Gln, or Asp in position N3 to complete a capping box. A favorable N-capping arrangement prior to amphipathic helix may result in the highest hydrophobicity (retention on C18 columns) of Asp/Asn (or Glu/Gln) peptide analogues among all naturally occurring amino acids when placed in N-cap or N3 position, respectively. These results contradict all previously reported hydrophobicity scales and provide new insights into our understanding of the phenomenon of hydrophobic interactions.

show abstract

Section: ■ Results and Discussionmentioning

confidence: 99%

N-Capping Motifs Promote Interaction of Amphipathic Helical Peptides with Hydrophobic Surfaces and Drastically Alter Hydrophobicity Values of Individual Amino Acids

et al. 2014

Self Cite

View full text Add to dashboard Cite

show abstract

“…Nevertheless, machine learning-based methods rely on peptide feature selection, which is usually performed manually upon personal knowledge. As numerous factors are involved in peptide separation, the lack of suitable representations of peptide features, such as secondary structure 19 , leads to prediction errors. For peptide MS/MS spectrum prediction, there have also been tools developed, including kinetic model-based methods such as MassAnalyzer 20 and MS-Simulator 21 , and machine learning-based methods like Pep-tideART 22 and MS 2 PIP 23 .…”

mentioning

confidence: 99%

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics

Yang

Liu

Shen³

et al. 2020

Nat Commun

180

199

View full text Add to dashboard Cite

Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by datadependent acquisition (DDA) experiments are required prior to DIA analysis, which is timeconsuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.

show abstract

“…These peptides have standardized RTs spanning a wide gradient and can be used to normalize the RT of individual experiments . Although RTs can be predicted through computational modeling, these predictions have a somewhat limited accuracy . Instead, the reference RTs of the iRT peptides can be used to correct for variations in the RT of the other peptides detected in a single experiment or to align RTs across multiple experiments.…”

Section: Managing Lc‐ms Variability Through Quality Controlmentioning

confidence: 99%

Quality control in mass spectrometry‐based proteomics

Bittremieux

Tabb

Impens

et al. 2017

Mass Spectrometry Reviews

105

107

View full text Add to dashboard Cite

Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.

show abstract

Application of modern reversed-phase peptide retention prediction algorithms to the Houghten and DeGraw dataset: Peptide helicity and its effect on prediction accuracy

Cited by 22 publications

References 27 publications

N-Capping Motifs Promote Interaction of Amphipathic Helical Peptides with Hydrophobic Surfaces and Drastically Alter Hydrophobicity Values of Individual Amino Acids

N-Capping Motifs Promote Interaction of Amphipathic Helical Peptides with Hydrophobic Surfaces and Drastically Alter Hydrophobicity Values of Individual Amino Acids

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics

Quality control in mass spectrometry‐based proteomics

Contact Info

Product

Resources

About