AIMC Topic: Protein Stability

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Pan-Specific Prediction of Peptide-MHC Class I Complex Stability, a Correlate of T Cell Immunogenicity.

Journal of immunology (Baltimore, Md. : 1950)
Binding of peptides to MHC class I (MHC-I) molecules is the most selective event in the processing and presentation of Ags to CTL, and insights into the mechanisms that govern peptide-MHC-I binding should facilitate our understanding of CTL biology. ...

Biosimilarity Assessments of Model IgG1-Fc Glycoforms Using a Machine Learning Approach.

Journal of pharmaceutical sciences
Biosimilarity assessments are performed to decide whether 2 preparations of complex biomolecules can be considered "highly similar." In this work, a machine learning approach is demonstrated as a mathematical tool for such assessments using a variety...

Prediction and design of thermostable proteins with a desired melting temperature.

Scientific reports
The stability of proteins at higher temperatures is crucial for their functionality, which is measured by their melting temperature (Tm). The Tm is the temperature at which 50% of the protein loses its native structure and activity. Existing methods ...

Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.

Microbial biotechnology
Antimicrobial peptides (AMPs) face stability and toxicity challenges in clinical use. Stapled modification enhances their stability and effectiveness, but its application in peptide design is rarely reported. This study built ten prediction models fo...

DDGemb: predicting protein stability change upon single- and multi-point variations with embeddings and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: The knowledge of protein stability upon residue variation is an important step for functional protein design and for understanding how protein variants can promote disease onset. Computational methods are important to complement experimen...

PROSTATA: a framework for protein stability assessment using transformers.

Bioinformatics (Oxford, England)
MOTIVATION: Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is...

DDMut: predicting effects of mutations on protein stability using deep learning.

Nucleic acids research
Understanding the effects of mutations on protein stability is crucial for variant interpretation and prioritisation, protein engineering, and biotechnology. Despite significant efforts, community assessments of predictive tools have highlighted ongo...

BayeStab: Predicting effects of mutations on protein stability with uncertainty quantification.

Protein science : a publication of the Protein Society
Predicting protein thermostability change upon mutation is crucial for understanding diseases and designing therapeutics. However, accurately estimating Gibbs free energy change of the protein remained a challenge. Some methods struggle to generalize...

On the critical review of five machine learning-based algorithms for predicting protein stability changes upon mutation.

Briefings in bioinformatics
A review, recently published in this journal by Fang (2019), showed that methods trained for the prediction of protein stability changes upon mutation have a very critical bias: they neglect that a protein variation (A- > B) and its reverse (B- > A) ...