Cargando…

SCMRSA: a New Approach for Identifying and Analyzing Anti-MRSA Peptides Using Estimated Propensity Scores of Dipeptides

[Image: see text] Staphylococcus aureus is deemed to be one of the major causes of hospital and community-acquired infections, especially in methicillin-resistant S. aureus (MRSA) strains. Because antimicrobial peptides have captured attention as novel drug candidates due to their rapid and broad-sp...

Descripción completa

Detalles Bibliográficos
Autores principales:	Charoenkwan, Phasit, Kanthawong, Sakawrat, Schaduangrat, Nalini, Li’, Pietro, Moni, Mohammad Ali, Shoombuatong, Watshara
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	American Chemical Society 2022
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9476499/ https://www.ncbi.nlm.nih.gov/pubmed/36120041 http://dx.doi.org/10.1021/acsomega.2c04305

Ejemplares similares

iAMAP-SCM: A Novel Computational Tool for Large-Scale Identification of Antimalarial Peptides Using Estimated Propensity Scores of Dipeptides
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method
por: Charoenkwan, Phasit, et al.
Publicado: (2020)

Empirical comparison and analysis of machine learning-based predictors for predicting and analyzing of thermophilic proteins
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens
por: Charoenkwan, Phasit, et al.
Publicado: (2023)

Large-scale comparative review and assessment of computational methods for phage virion proteins identification
por: Kabir, Muhammad, et al.
Publicado: (2022)

TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides
por: Charoenkwan, Phasit, et al.
Publicado: (2023)

Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

StackPR is a new computational approach for large-scale identification of progesterone receptor antagonists using the stacking strategy
por: Schaduangrat, Nalini, et al.
Publicado: (2022)

SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties
por: Charoenkwan, Phasit, et al.
Publicado: (2019)

DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists
por: Schaduangrat, Nalini, et al.
Publicado: (2023)

Correction: Shoombuatong, W., et al. iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. Int. J. Mol. Sci. 2020, 21, 75
por: Charoenkwan, Phasit, et al.
Publicado: (2020)

TROLLOPE: A novel sequence-based stacked approach for the accelerated discovery of linear T-cell epitopes of hepatitis C virus
por: Charoenkwan, Phasit, et al.
Publicado: (2023)

Recent development of machine learning-based methods for the prediction of defensin family and subfamily
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

SCMCRYS: Predicting Protein Crystallization Using an Ensemble Scoring Card Method with Estimating Propensity Scores of P-Collocated Amino Acid Pairs
por: Charoenkwan, Phasit, et al.
Publicado: (2013)

iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

Unraveling the bioactivity of anticancer peptides as deduced from machine learning
por: Shoombuatong, Watshara, et al.
Publicado: (2018)

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides
por: Schaduangrat, Nalini, et al.
Publicado: (2019)

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation
por: Schaduangrat, Nalini, et al.
Publicado: (2019)

Towards understanding aromatase inhibitory activity via QSAR modeling
por: Shoombuatong, Watshara, et al.
Publicado: (2018)

Empirical comparison and analysis of machine learning-based approaches for druggable protein identification
por: Shoombuatong, Watshara, et al.
Publicado: (2023)

AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning
por: Charoenkwan, Phasit, et al.
Publicado: (2022)

iPMI: Machine Learning-Aided Identification of Parametrial Invasion in Women with Early-Stage Cervical Cancer
por: Charoenkwan, Phasit, et al.
Publicado: (2021)

DeepThal: A Deep Learning-Based Framework for the Large-Scale Prediction of the α(+)-Thalassemia Trait Using Red Blood Cell Parameters
por: Phirom, Krittaya, et al.
Publicado: (2022)

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins
por: Ahmad, Saeed, et al.
Publicado: (2022)

SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides
por: Liou, Yi-Fan, et al.
Publicado: (2015)

SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides
por: Liou, Yi-Fan, et al.
Publicado: (2014)

TargetAntiAngio: A Sequence-Based Tool for the Prediction and Analysis of Anti-Angiogenic Peptides
por: Laengsri, Vishuda, et al.
Publicado: (2019)

Probing the origin of estrogen receptor alpha inhibition via large-scale QSAR study
por: Suvannang, Naravut, et al.
Publicado: (2018)

Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition
por: Huang, Hui-Ling, et al.
Publicado: (2012)

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides
por: Vasylenko, Tamara, et al.
Publicado: (2016)

Assessment of in vitro activities of novel modified antimicrobial peptides against clarithromycin resistant Mycobacterium abscessus
por: Sudadech, Phantitra, et al.
Publicado: (2021)

PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features
por: Nilamyani, Andi Nur, et al.
Publicado: (2021)

Rhinacanthin-C Extracted from Rhinacanthus nasutus (L.) Inhibits Cholangiocarcinoma Cell Migration and Invasion by Decreasing MMP-2, uPA, FAK and MAPK Pathways
por: Boueroy, Parichart, et al.
Publicado: (2018)

SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method
por: Vasylenko, Tamara, et al.
Publicado: (2015)

Extracellular DNA facilitates bacterial adhesion during Burkholderia pseudomallei biofilm formation
por: Pakkulnan, Rattiyaphorn, et al.
Publicado: (2019)

Prediction of aromatase inhibitory activity using the efficient linear method (ELM)
por: Shoombuatong, Watshara, et al.
Publicado: (2015)

Cannot write session to /tmp/vufind_sessions/sess_61uprr5df4iadivc2el0hsfqf7