AIMC Topic: Protein Stability

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An end-to-end framework for the prediction of protein structure and fitness from single sequence.

Nature communications
Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction...

Computational design of soluble and functional membrane protein analogues.

Nature
De novo design of complex protein folds using solely computational means remains a substantial challenge. Here we use a robust deep learning pipeline to design complex folds and soluble analogues of integral membrane proteins. Unique membrane topolog...

Differentiating stable and unstable protein using convolution neural network and molecular dynamics simulations.

Computational biology and chemistry
Protein stability is a critical aspect of molecular biology and biochemistry, hinges on an intricate balance of thermodynamic and structural factors. Determining protein stability is crucial for understanding and manipulating biological machineries, ...

PMSPcnn: Predicting protein stability changes upon single point mutations with convolutional neural network.

Structure (London, England : 1993)
Protein missense mutations and resulting protein stability changes are important causes for many human genetic diseases. However, the accurate prediction of stability changes due to mutations remains a challenging problem. To address this problem, we...

Transfer learning to leverage larger datasets for improved prediction of protein stability changes.

Proceedings of the National Academy of Sciences of the United States of America
Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability can be important in research and medicine. Computational methods for predicting how mutations per...

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...