AIMC Topic: Proteins

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Deep learning for protein structure prediction and design-progress and applications.

Molecular systems biology
Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms th...

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...

Numerical stability of DeepGOPlus inference.

PloS one
Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well wi...

Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure.

Biophysical journal
Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achie...

Deep Learning Enables Automatic Correction of Experimental HDX-MS Data with Applications in Protein Modeling.

Journal of the American Society for Mass Spectrometry
Observed mass shifts associated with deuterium incorporation in hydrogen-deuterium exchange mass spectrometry (HDX-MS) frequently deviate from the initial signals due to back and forward exchange. In typical HDX-MS experiments, the impact of these di...

Analyzing domain features of small proteins using a machine-learning method.

Proteomics
Small proteins (SPs) are a unique group of proteins that play crucial roles in many important biological processes. Exploring the biological function of SPs is necessary. In this study, the InterPro tool and the maximum correlation method were utiliz...

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics.

Journal of the American Chemical Society
The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciph...

Approximating Projections of Conformational Boltzmann Distributions with AlphaFold2 Predictions: Opportunities and Limitations.

Journal of chemical theory and computation
Protein thermodynamics is intimately tied to biological function and can enable processes such as signal transduction, enzyme catalysis, and molecular recognition. The relative free energies of conformations that contribute to these functional equili...

LPI-SKMSC: Predicting LncRNA-Protein Interactions with Segmented k-mer Frequencies and Multi-space Clustering.

Interdisciplinary sciences, computational life sciences
 Long noncoding RNAs (lncRNAs) have significant regulatory roles in gene expression. Interactions with proteins are one of the ways lncRNAs play their roles. Since experiments to determine lncRNA-protein interactions (LPIs) are expensive and time-con...

PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.

Interdisciplinary sciences, computational life sciences
The protein S-nitrosylation (SNO) is a significant post-translational modification that affects the stability, activity, cellular localization, and function of proteins. Therefore, highly accurate prediction of SNO sites aids in grasping biological f...