AI Medical Compendium Topic:
Proteins

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CPGL: Prediction of Compound-Protein Interaction by Integrating Graph Attention Network With Long Short-Term Memory Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Recent advancements of artificial intelligence based on deep learning algorithms have made it possible to computationally predict compound-protein interaction (CPI) without conducting laboratory experiments. In this manuscript, we integrated a graph ...

Using artificial neural networks to accelerate flowsheet optimization for downstream process development.

Biotechnology and bioengineering
An optimal purification process for biopharmaceutical products is important to meet strict safety regulations, and for economic benefits. To find the global optimum, it is desirable to screen the overall design space. Advanced model-based approaches ...

Mathematical and Machine Learning Approaches for Classification of Protein Secondary Structure Elements from Coordinates.

Biomolecules
Determining Secondary Structure Elements (SSEs) for any protein is crucial as an intermediate step for experimental tertiary structure determination. SSEs are identified using popular tools such as DSSP and STRIDE. These tools use atomic information ...

An end-to-end deep learning method for protein side-chain packing and inverse folding.

Proceedings of the National Academy of Sciences of the United States of America
Protein side-chain packing (PSCP), the task of determining amino acid side-chain conformations given only backbone atom positions, has important applications to protein structure prediction, refinement, and design. Many methods have been proposed to ...

Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method.

Computers in biology and medicine
The Src Homology 2 (SH2) domain plays an important role in the signal transmission mechanism in organisms. It mediates the protein-protein interactions based on the combination between phosphotyrosine and motifs in SH2 domain. In this study, we desig...

Residue-level error detection in cryoelectron microscopy models.

Structure (London, England : 1993)
Building accurate protein models into moderate resolution (3-5 Å) cryoelectron microscopy (cryo-EM) maps is challenging and error prone. We have developed MEDIC (Model Error Detection in Cryo-EM), a robust statistical model that identifies local back...

Targeting protein-protein interactions with low molecular weight and short peptide modulators: insights on disease pathways and starting points for drug discovery.

Expert opinion on drug discovery
INTRODUCTION: Protein-protein interactions (PPIs) have been often considered undruggable targets although they are attractive for the discovery of new therapeutics. The spread of artificial intelligence and machine learning complemented with experime...

Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing.

Genes
Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel therapeutics for disease-related targets. To this end, computation has transformed the field of peptide design ...

: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities.

Journal of chemical information and modeling
Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geom...

EvoRator2: Predicting Site-specific Amino Acid Substitutions Based on Protein Structural Information Using Deep Learning.

Journal of molecular biology
Multiple sequence alignments (MSAs) are the workhorse of molecular evolution and structural biology research. From MSAs, the amino acids that are tolerated at each site during protein evolution can be inferred. However, little is known regarding the ...