AIMC Topic: Viral Proteins
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High-throughput Kinetics using capillary Electrophoresis and Robotics (HiKER) platform used to study T7, T3, and Sp6 RNA polymerase misincorporation.
PloS one
Dec 2, 2024
T7 RNA Polymerase (RNAP) is a widely used enzyme with recent applications in the production of RNA vaccines. For over 50 years denaturing sequencing gels have been used as key analysis tools for probing the nucleotide addition mechanisms of T7 RNAP a...
Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.
BMC bioinformatics
Nov 9, 2024
Viral proteins that evade the host's innate immune response play a crucial role in pathogenesis, significantly impacting viral infections and potential therapeutic strategies. Identifying these proteins through traditional methods is challenging and ...
Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques.
Scientific reports
Nov 7, 2024
Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor in the etiology of various cancers. Traditionally, identifying these oncoproteins is both time-consuming and costly. With advancem...
Machine learning, network pharmacology, and molecular dynamics reveal potent cyclopeptide inhibitors against dengue virus proteins.
Molecular diversity
Sep 4, 2024
The dengue virus is a major global health hazard responsible for an estimated 390 million diseases yearly. This study focused on identifying cyclopeptide inhibitors for envelope structural proteins E, NS1, NS3, and NS5. Additionally, 5579 cyclopeptid...
Benchmarking machine learning robustness in Covid-19 genome sequence classification.
Scientific reports
Mar 13, 2023
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome-millions of sequences and counting. This amount of data, while being orders of magnitude beyond the capacity of traditional ap...
PhageLeads: Rapid Assessment of Phage Therapeutic Suitability Using an Ensemble Machine Learning Approach.
Viruses
Feb 8, 2022
The characterization of therapeutic phage genomes plays a crucial role in the success rate of phage therapies. There are three checkpoints that need to be examined for the selection of phage candidates, namely, the presence of temperate markers, anti...
Super.Complex: A supervised machine learning pipeline for molecular complex detection in protein-interaction networks.
PloS one
Dec 31, 2021
Characterization of protein complexes, i.e. sets of proteins assembling into a single larger physical entity, is important, as such assemblies play many essential roles in cells such as gene regulation. From networks of protein-protein interactions, ...
Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy.
Scientific reports
Dec 21, 2021
Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, a...
Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 6, 2021
Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrov...
DeepEMhancer: a deep learning solution for cryo-EM volume post-processing.
Communications biology
Jul 15, 2021
Cryo-EM maps are valuable sources of information for protein structure modeling. However, due to the loss of contrast at high frequencies, they generally need to be post-processed to improve their interpretability. Most popular approaches, based on g...
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