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Peptides

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Deep Learning-Based Label-Free Surface-Enhanced Raman Scattering Screening and Recognition of Small-Molecule Binding Sites in Proteins.

Analytical chemistry
Identification of small-molecule binding sites in proteins is of great significance in analysis of protein function and drug design. Modified sites can be recognized via proteolytic cleavage followed by liquid chromatography-mass spectrometry (LC-MS)...

Impact of ANN in Revealing of Viral Peptides.

BioMed research international
All organisms contain antimicrobial peptides (AMPs), which are a critical component of the innate immune system. These chemicals have the ability to suppress the growth of a variety of fungi, bacteria, and viruses. Because AMPs interact with structur...

DeepSeqPanII: An Interpretable Recurrent Neural Network Model With Attention Mechanism for Peptide-HLA Class II Binding Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Human leukocyte antigen (HLA) complex molecules play an essential role in immune interactions by presenting peptides on the cell surface to T cells. With significant deep learning progress, a series of neural network-based models have been proposed a...

From data to noise to data for mixing physics across temperatures with generative artificial intelligence.

Proceedings of the National Academy of Sciences of the United States of America
Using simulations or experiments performed at some set of temperatures to learn about the physics or chemistry at some other arbitrary temperature is a problem of immense practical and theoretical relevance. Here we develop a framework based on stati...

cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model.

Artificial intelligence in medicine
Cancer is a Toxic health concern worldwide, it happens when cellular modifications cause the irregular growth and division of human cells. Several traditional approaches such as therapies and wet laboratory-based methods have been applied to treat ca...

Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides.

Journal of chemical information and modeling
The discovery of therapeutic peptides is often accelerated by means of virtual screening supported by machine learning-based predictive models. The predictive performance of such models is sensitive to the choice of data and its representation scheme...

Multi-Branch-CNN: Classification of ion channel interacting peptides using multi-branch convolutional neural network.

Computers in biology and medicine
Ligand peptides that have high affinity for ion channels are critical for regulating ion flux across the plasma membrane. These peptides are now being considered as potential drug candidates for many diseases, such as cardiovascular disease and cance...

Accurate Prediction of y Ions in Beam-Type Collision-Induced Dissociation Using Deep Learning.

Analytical chemistry
Peptide fragmentation spectra contain critical information for the identification of peptides by mass spectrometry. In this study, we developed an algorithm that more accurately predicts the high-intensity peaks among the peptide spectra. The trainin...

Prosit-TMT: Deep Learning Boosts Identification of TMT-Labeled Peptides.

Analytical chemistry
The prediction of fragment ion intensities and retention time of peptides has gained significant attention over the past few years. However, the progress shown in the accurate prediction of such properties focused primarily on unlabeled peptides. Tan...

Accelerating All-Atom Simulations and Gaining Mechanistic Understanding of Biophysical Systems through State Predictive Information Bottleneck.

Journal of chemical theory and computation
An effective implementation of enhanced sampling algorithms for molecular dynamics simulations requires knowledge of the approximate reaction coordinate describing the relevant mechanisms in the system. In this work, we focus on the recently develop...