Fragmentation Spectra Prediction and DNA Adducts Structural Determination.

J Am Soc Mass Spectrom

Masonic Cancer Center, University of Minnesota, 2231 6th Street SE, Minneapolis, MN, 55455, USA.

Published: December 2019

In this work, chemical dynamics simulations were optimized and used to predict fragmentation mass spectra for DNA adduct structural determination. O-methylguanine (O-Me-G) was used as a simple model adduct to calculate theoretical spectra for comparison with measured high-resolution fragmentation data. An automatic protocol was established to consider the different tautomers accessible at a given energy and obtain final theoretical spectra by insertion of an initial tautomer. In the work reported here, the most stable tautomer was chosen as the initial structure, but in general, any structure could be considered. Allowing for the formation of the various possible tautomers during simulation calculations was found to be important to getting a more complete fragmentation spectrum. The calculated theoretical results reproduce the experimental peaks such that it was possible to determine reaction pathways and product structures. The calculated tautomerization network was crucial to correctly identifying all the observed ion peaks, showing that a mobile proton model holds not only for peptide fragmentation but also for nucleobases. Finally, first principles results were compared to simple machine learning fragmentation models.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s13361-019-02348-7DOI Listing

Publication Analysis

Top Keywords

structural determination
8
theoretical spectra
8
fragmentation
6
fragmentation spectra
4
spectra prediction
4
prediction dna
4
dna adducts
4
adducts structural
4
determination work
4
work chemical
4

Similar Publications

Validation of UniverSeg for Interventional Abdominal Angiographic Segmentation.

J Imaging Inform Med

January 2025

Department of Radiation Oncology, Henry Ford Health, Detroit, MI, USA.

Automatic segmentation of angiographic structures can aid in assessing vascular disease. While recent deep learning models promise automation, they lack validation on interventional angiographic data. This study investigates the feasibility of angiographic segmentation using in-context learning with the UniverSeg model, which is a cross-learning segmentation model that lacks inherent angiographic training.

View Article and Find Full Text PDF

Analysis of the symmetry of the brain hemispheres at the level of individual structures and dominant tissue features has been the subject of research for many years in the context of improving the effectiveness of imaging methods for the diagnosis of brain tumor, stroke, and Alzheimer's disease, among others. One useful approach is to reliably determine the midline of the brain, which allows comparative analysis of the hemispheres and uncovers information on symmetry/asymmetry in the relevant planes of, for example, CT scans. Therefore, an effective method that is robust to various geometric deformations, artifacts, varying noise characteristics, and natural anatomical variability is sought.

View Article and Find Full Text PDF

Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers.

J Imaging Inform Med

January 2025

College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.

This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.

View Article and Find Full Text PDF

Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.

View Article and Find Full Text PDF

We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!