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Peptides

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MMDB: Multimodal dual-branch model for multi-functional bioactive peptide prediction.

Analytical biochemistry
Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play important roles in treating diverse diseases and disorders. While most of the methods focus on single-functional bioactive peptides and have obtained prom...

Building trust in deep learning-based immune response predictors with interpretable explanations.

Communications biology
The ability to predict whether a peptide will get presented on Major Histocompatibility Complex (MHC) class I molecules has profound implications in designing vaccines. Numerous deep learning-based predictors for peptide presentation on MHC class I m...

EnAMP: A novel deep learning ensemble antibacterial peptide recognition algorithm based on multi-features.

Journal of bioinformatics and computational biology
Antimicrobial peptides (AMPs), as the preferred alternatives to antibiotics, have wide application with good prospects. Identifying AMPs through wet lab experiments remains expensive, time-consuming and challenging. Many machine learning methods have...

Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations.

Nature communications
We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Sub...

SME-MFP: A novel spatiotemporal neural network with multiangle initialization embedding toward multifunctional peptides prediction.

Computational biology and chemistry
As a promising alternative to conventional antibiotic drugs in the biomedical field, functional peptide has been widely used in disease treatment owing to its low toxicity, high absorption rate, and biological activity. Recently, several machine lear...

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design.

European journal of medicinal chemistry
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and li...

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.

Journal of chemical information and modeling
Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for s...

ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies.

Journal of chemical information and modeling
Novel therapeutic alternatives for cancer treatment are increasingly attracting global research attention. Although chemotherapy remains a primary clinical solution, it often results in significant side effects for patients. In recent years, anticanc...

iDVEIP: A computer-aided approach for the prediction of viral entry inhibitory peptides.

Proteomics
With the notable surge in therapeutic peptide development, various peptides have emerged as potential agents against virus-induced diseases. Viral entry inhibitory peptides (VEIPs), a subset of antiviral peptides (AVPs), offer a promising avenue as e...

Deep2Pep: A deep learning method in multi-label classification of bioactive peptide.

Computational biology and chemistry
Functional peptides are easy to absorb and have low side effects, which has attracted increasing interest from pharmaceutical scientists. However, due to the limitations in the laboratory funding and human resources, it is difficult to screen the fun...