AIMC Topic: Peptides
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ACPScanner: Prediction of Anticancer Peptides by Integrated Machine Learning Methodologies.
Journal of chemical information and modeling
Jan 26, 2024
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
Jan 23, 2024
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
Jan 22, 2024
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...
AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics.
Journal of proteome research
Jan 22, 2024
In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of the PSMs identified are incorrect, and therefore various postproce...
Hollow CoFe Nanozymes Integrated with Oncolytic Peptides Designed via Machine-Learning for Tumor Therapy.
Small (Weinheim an der Bergstrasse, Germany)
Jan 17, 2024
Developing novel substances to synergize with nanozymes is a challenging yet indispensable task to enable the nanozyme-based therapeutics to tackle individual variations in tumor physicochemical properties. The advancement of machine learning (ML) ha...
Evaluating NetMHCpan performance on non-European HLA alleles not present in training data.
Frontiers in immunology
Jan 16, 2024
Bias in neural network model training datasets has been observed to decrease prediction accuracy for groups underrepresented in training data. Thus, investigating the composition of training datasets used in machine learning models with healthcare ap...
Computational Design of Peptide Assemblies.
Journal of chemical theory and computation
Jan 11, 2024
With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expandi...
Using deep learning and molecular dynamics simulations to unravel the regulation mechanism of peptides as noncompetitive inhibitor of xanthine oxidase.
Scientific reports
Jan 2, 2024
Xanthine oxidase (XO) is a crucial enzyme in the development of hyperuricemia and gout. This study focuses on LWM and ALPM, two food-derived inhibitors of XO. We used molecular docking to obtain three systems and then conducted 200 ns molecular dynam...
Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.
Nature communications
Jan 2, 2024
Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteo...
High-Yield Preparation of American Oyster Defensin (AOD) via a Small and Acidic Fusion Tag and Its Functional Characterization.
Marine drugs
Dec 20, 2023
The marine peptide, American oyster defensin (AOD), is derived from and exhibits a potent bactericidal effect. However, recombinant preparation has not been achieved due to the high charge and hydrophobicity. Although the traditional fusion tags suc...
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