Strengths and limitations of in silico tools to assess physicochemical properties, bioactivity, and bioavailability of food-derived peptides
F Rivero-Pino, MC Millan-Linares… - Trends in Food Science …, 2023 - Elsevier
Background Bioactive peptides obtained from different food sources have been proved to
exert several bioactivities, such as antioxidant, antihypertensive, antimicrobial, or anti …
exert several bioactivities, such as antioxidant, antihypertensive, antimicrobial, or anti …
Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design
The global pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute
respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide …
respiratory syndrome coronavirus 2, has led to a dramatic loss of human life worldwide …
sAMPpred-GAT: prediction of antimicrobial peptide by graph attention network and predicted peptide structure
K Yan, H Lv, Y Guo, W Peng, B Liu - Bioinformatics, 2023 - academic.oup.com
Abstract Motivation Antimicrobial peptides (AMPs) are essential components of therapeutic
peptides for innate immunity. Researchers have developed several computational methods …
peptides for innate immunity. Researchers have developed several computational methods …
Accurately identifying hemagglutinin using sequence information and machine learning methods
Introduction Hemagglutinin (HA) is responsible for facilitating viral entry and infection by
promoting the fusion between the host membrane and the virus. Given its significance in the …
promoting the fusion between the host membrane and the virus. Given its significance in the …
Deepm5C: a deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy
As one of the most prevalent post-transcriptional epigenetic modifications, N5-
methylcytosine (m5C) plays an essential role in various cellular processes and disease …
methylcytosine (m5C) plays an essential role in various cellular processes and disease …
AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning
Amyloid proteins have the ability to form insoluble fibril aggregates that have important
pathogenic effects in many tissues. Such amyloidoses are prominently associated with …
pathogenic effects in many tissues. Such amyloidoses are prominently associated with …
DeepIPs: comprehensive assessment and computational identification of phosphorylation sites of SARS-CoV-2 infection using a deep learning-based approach
The rapid spread of SARS-CoV-2 infection around the globe has caused a massive health
and socioeconomic crisis. Identification of phosphorylation sites is an important step for …
and socioeconomic crisis. Identification of phosphorylation sites is an important step for …
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction
Protein post-translational modification (PTM) is an important regulatory mechanism that
plays a key role in both normal and disease states. Acetylation on lysine residues is one of …
plays a key role in both normal and disease states. Acetylation on lysine residues is one of …
TPpred-ATMV: therapeutic peptide prediction by adaptive multi-view tensor learning model
K Yan, H Lv, Y Guo, Y Chen, H Wu, B Liu - Bioinformatics, 2022 - academic.oup.com
Motivation Therapeutic peptide prediction is important for the discovery of efficient
therapeutic peptides and drug development. Researchers have developed several …
therapeutic peptides and drug development. Researchers have developed several …
H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA
O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a
crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high …
crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high …