Biology subjects

Abdul-Khalek, N.

Publications and source records attributed to Abdul-Khalek, N..

4 recordsLinked to original sources

bioRxiv2025

Whats left from the brew? Investigating residual barley proteins in spent grains for downstream valorization opportunities

Brewers spent grain (BSG) is the major side-stream from beer production but remains highly underutilized. While the direct use of BSG as a food ingredient is limited due to subpar techno- functionality, the vast amounts and fairly high protein content of up to 30% makes it a high potential source for production of protein-based ingredient by valorization through e.g. enzymatic hydrolysis. However, little attention has been put towards the protein-level composition of BSG, which is essential for developing hydrolysis strategies for improving functionality in a targeted manner. Here, we present an in-depth characterization of the BSG proteome and investigate dynamic proteome changes from malting and mashing in the initial phases of beer production. We show dynamic and selective changes in the proteome across the different process steps, where 29% of reproducibly identified proteins display differential abundance. BSG represents a significantly higher proportion of intracellular protein compared to both barley and malt and has a nutritionally favorable amino acid composition. The major constituent of the BSG proteome is B3-Hordein, constituting more than 30% of the BSG protein. Moreover, we find that a large proportion (> 45%) of the BSG protein is associated with potential food safety concerns, being classified as potential allergens and antinutritional factors. Our analysis emphasizes the need for downstream processing of BSG to produce safe and functional food ingredients, while also providing protein-level insights for development of targeted hydrolysis strategies to achieve this. HighlightsO_LIAn optimized sample preparation for proteomics analysis has been developed C_LIO_LI29% of barley proteins are differentially abundant across malting and mashing C_LIO_LIB3-Hordein is the major protein in BSG with an abundance over 30% C_LIO_LIBSG contains a high content of potential allergenic and antinutritional proteins C_LIO_LIA protein-level basis for targeted downstream processing of BSG is presented C_LI

plant biology↗

bioRxiv2024

To fly, or not to fly, that is the question: A deep learning model for peptide detectability prediction in mass spectrometry

Identifying detectable peptides, known as flyers, is key in mass spectrometry-based proteomics. Peptide detectability is strongly related with the peptide sequence and its resulting physicochemical properties. Moreover, the high variability in MS data, particularly in peptide detectability and intensity across multiple analyses and samples, makes the development of a generic model for detectability prediction unfeasible. This underlines the need for tools that can be refined for specific experimental conditions. To address this need, we present Pfly, a deep learning model developed to predicts peptide detectability based solely on peptide sequence. Pfly distinguishes itself as a versatile and reliable state-of-the-art tool, offering high performance, accessibility, and easy customizability for end-users. This adaptability allows researchers to tailor the model to their specific experimental conditions, facilitating the creation of lab-specific models. This, in turn, can lead to more accurate results and expand the models applicability across various research fields. The models architecture is an encoder-decoder with an attention mechanism. This tool classifies peptides as either flyers or non-flyers, providing both binary probabilities and detailed categorical probabilities for four distinct classes defined in this study: non-flyer, weak flyer, intermediate flyer, and strong flyer. The model was initially trained on a synthetic peptide library and subsequently fine-tuned with a biological dataset to mitigate bias towards synthesizability, improving the predictive capacity and outperforming state-of-the-art predictors in a benchmark comparison. The study further investigates the influence of protein abundance and the search engine, illustrating the negative impact on peptide identification due to misclassification. Pfly has been integrated in the DLOmix framework and it is accessible on GitHub at https://github.com/wilhelm-lab/dlomix.

bioinformatics↗

bioRxiv2024

Decoding the Impact of Neighboring Amino Acid on MS Intensity Output through Deep Learning

Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the link from sequence to intensity output remains poorly understood. In this work, we explore combinations of amino acid pairs (i.e., dimer motifs) to determine a potential relationship between the local amino acid environment and MS1 intensity. For this purpose, a deep learning (DL) model, consisting of an encoder-decoder with an attention mechanism, was built. The attention mechanism allowed to identify the most relevant motifs. Specific patterns were consistently observed where a bulky/aromatic and hydrophobic AA followed by a cationic AA as well as consecutive bulky/aromatic and hydrophobic AAs were found important for the MS1 intensity. Correlating attention weights to mean MS1 intensities revealed that some important motifs, particularly containing Trp, His, and Cys, were linked with low responding peptides whereas motifs containing Lys and most bulky hydrophobic AAs were often associated with high responding peptides. Moreover, Asn-Gly was associated with low MS1 response. The model could predict MS1 response with a mean average percentage error of [~]11% and a Pearson correlation coefficient of [~]0.68.

bioinformatics↗

bioRxiv2023

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS proteomics: A deep learning approach

Accurate and absolute quantification of individual peptides in complex mixtures is a challenge not easily overcome. A potential solution is the use of quantitative mass spectrometry (MS) based methods, however, current state of the art requires foreground knowledge and isotopically labeled standards for each peptide to be accurately quantified. This increases analytical expenses, time consumption, and labor, limiting the number of peptides that can be quantified. A key step in developing less restrictive label-free quantitative peptidomics methods is understanding of the physicochemical properties of peptides that influence the MS response. In this work, a deep learning model was developed to identify the most relevant physicochemical properties based on repository MS data from equimolar peptide pools. Using an autoencoder with attention mechanism and correlating attention weights with corresponding physicochemical property indices from AAindex1, we were able to obtain insight on the properties governing the peptide-level MS1 response. These properties can be grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, we present a model for predicting the MS1 intensity output based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 11%.

bioinformatics↗