AIMC Topic: Protein Stability

Clear Filters Showing 21 to 30 of 59 articles

ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.

Journal of chemical information and modeling
Protein thermodynamic stability is essential to clarify the relationships among structure, function, and interaction. Therefore, developing a faster and more accurate method to predict the impact of the mutations on protein stability is helpful for p...

MEnTaT: A machine-learning approach for the identification of mutations to increase protein stability.

Proceedings of the National Academy of Sciences of the United States of America
Enhancing protein thermal stability is important for biomedical and industrial applications as well as in the research laboratory. Here, we describe a simple machine-learning method which identifies amino acid substitutions that contribute to thermal...

ProS-GNN: Predicting effects of mutations on protein stability using graph neural networks.

Computational biology and chemistry
Predicting protein stability change upon variation through a computational approach is a valuable tool to unveil the mechanisms of mutation-induced drug failure and develop immunotherapy strategies. Some previous machine learning-based techniques exh...

Rapid protein stability prediction using deep learning representations.

eLife
Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate pred...

DeepSTABp: A Deep Learning Approach for the Prediction of Thermal Protein Stability.

International journal of molecular sciences
Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, curren...

Large-scale design and refinement of stable proteins using sequence-only models.

PloS one
Engineered proteins generally must possess a stable structure in order to achieve their designed function. Stable designs, however, are astronomically rare within the space of all possible amino acid sequences. As a consequence, many designs must be ...

Machine Learning Approaches for Metalloproteins.

Molecules (Basel, Switzerland)
Metalloproteins are a family of proteins characterized by metal ion binding, whereby the presence of these ions confers key catalytic and ligand-binding properties. Due to their ubiquity among biological systems, researchers have made immense efforts...

Artificial intelligence challenges for predicting the impact of mutations on protein stability.

Current opinion in structural biology
Stability is a key ingredient of protein fitness, and its modification through targeted mutations has applications in various fields, such as protein engineering, drug design, and deleterious variant interpretation. Many studies have been devoted ove...

Coupling of Trastuzumab chromatographic profiling with machine learning tools: A complementary approach for biosimilarity and stability assessment.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Biosimilar products present a growing opportunity to improve the global healthcare systems. The amount of accepted variability during the comparative assessments of biosimilar products introduces a significant challenge for both the biosimilar develo...

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation.

The journal of physical chemistry. B
Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protei...