Kensert, A. | Search bioRxiv

bioRxiv2026

Predicting and Elucidating Peptide Retention Mechanisms with Graph Attention Networks

Liquid chromatography (LC) is a key technology in bottom-up proteomics, separating proteolytic peptides to decrease sample complexity, enhance coverage, and increase the robustness of protein identification and quantification. Although high-resolution mass spectrometry has advanced significantly, comparable progress in LC has lagged, primarily due to a limited understanding of peptide-column interactions. To bridge this knowledge gap, we introduce a novel deep learning model (PeptideGNN) based on a Graph Neural Network (GNN) architecture to model and elucidate peptide behaviors across various separation conditions. Trained to accurately predict peptide retention times on ten diverse proteomic datasets, the model subsequently employed a saliency mapping technique to interpret the underlying retention mechanisms. Our model consistently outperformed existing retention-time predictors across multiple datasets, while the saliency mapping, importantly, revealed insights into peptide-stationary phase interactions, highlighting the effects of neighboring amino acids, post-translational modifications (PTMs), chromato-graphic columns, and mobile phase additives on peptide retention.

bioinformatics↗

bioRxiv2018

Transfer learning with deep convolutional neural network for classifying cellular morphological changes

Quantification and identification of cellular phenotypes from high content microscopy images have proven to be very useful for understanding biological activity in response to different drug treatments. The traditional approach has been to use classical image analysis to quantify changes in cell morphology, which requires several non-trivial and independent analysis steps. Recently convolutional neural networks have emerged as a compelling alternative, offering good predictive performance and the possibility to replace traditional workflows with a single network architecture. In this study we applied the pre-trained deep convolutional neural networks ResNet50, InceptionV3 and InceptionResnetV2 to predict cell mechanisms of action in response to chemical perturbations for two cell profiling datasets from the Broad Bioimage Benchmark Collection. These networks were pre-trained on ImageNet enabling much quicker model training. We obtain higher predictive accuracy than previously reported, between 95 and 97% based on \"leave-one-compound-out\" cross-validation. The ability to quickly and accurately distinguish between different cell morphologies from a scarce amount of labelled data illustrates the combined benefit of transfer learning and deep convolutional neural networks for interrogating cell-based images.

bioinformatics↗