AIMC Topic: Protein Stability

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Prediction of protein biophysical traits from limited data: a case study on nanobody thermostability through NanoMelt.

mAbs
In-silico prediction of protein biophysical traits is often hindered by the limited availability of experimental data and their heterogeneity. Training on limited data can lead to overfitting and poor generalizability to sequences distant from those ...

PTSP-BERT: Predict the thermal stability of proteins using sequence-based bidirectional representations from transformer-embedded features.

Computers in biology and medicine
Thermophilic proteins, mesophiles proteins and psychrophilic proteins have wide industrial applications, as enzymes with different optimal temperatures are often needed for different purposes. Convenient methods are needed to determine the optimal te...

Linking Protein Stability to Pathogenicity: Predicting Clinical Significance of Single-Missense Mutations in Ocular Proteins Using Machine Learning.

International journal of molecular sciences
Understanding the effect of single-missense mutations on protein stability is crucial for clinical decision-making and therapeutic development. The impact of these mutations on protein stability and 3D structure remains underexplored. Here, we develo...

Particle formation in response to different protein formulations and containers: Insights from machine learning analysis of particle images.

Journal of pharmaceutical sciences
Subvisible particle count is a biotherapeutics stability indicator widely used by pharmaceutical industries. A variety of stresses that biotherapeutics are exposed to during development can impact particle morphology. By classifying particle morpholo...

AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.

Interdisciplinary sciences, computational life sciences
The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, whi...

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...