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Peptides

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Deep learning approaches for data-independent acquisition proteomics.

Expert review of proteomics
INTRODUCTION: Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.

Scientific reports
Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) h...

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.

International journal of molecular sciences
Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly....

Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO.

Journal of chemical theory and computation
An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we appl...

Amino acid environment affinity model based on graph attention network.

Journal of bioinformatics and computational biology
Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that ...

A Deep Learning Approach with Data Augmentation to Predict Novel Spider Neurotoxic Peptides.

International journal of molecular sciences
As major components of spider venoms, neurotoxic peptides exhibit structural diversity, target specificity, and have great pharmaceutical potential. Deep learning may be an alternative to the laborious and time-consuming methods for identifying these...

Prediction of antimicrobial peptides toxicity based on their physico-chemical properties using machine learning techniques.

BMC bioinformatics
BACKGROUND: Antimicrobial peptides are promising tools to fight against ever-growing antibiotic resistance. However, despite many advantages, their toxicity to mammalian cells is a critical obstacle in clinical application and needs to be addressed.

pValid 2: A deep learning based validation method for peptide identification in shotgun proteomics with increased discriminating power.

Journal of proteomics
Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. ...

CL-ACP: a parallel combination of CNN and LSTM anticancer peptide recognition model.

BMC bioinformatics
BACKGROUND: Anticancer peptides are defence substances with innate immune functions that can selectively act on cancer cells without harming normal cells and many studies have been conducted to identify anticancer peptides. In this paper, we introduc...

Connecting MHC-I-binding motifs with HLA alleles via deep learning.

Communications biology
The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of bin...