AI Medical Compendium Topic:
Proteins

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DP-AOP: A novel SVM-based antioxidant proteins identifier.

International journal of biological macromolecules
The identification of antioxidant proteins is a challenging yet meaningful task, as they can protect against the damage caused by some free radicals. In addition to time-consuming, laborious, and expensive experimental identification methods, efficie...

A Highly Sensitive Model Based on Graph Neural Networks for Enzyme Key Catalytic Residue Prediction.

Journal of chemical information and modeling
Determining the catalytic site of enzymes is a great help for understanding the relationship between protein sequence, structure, and function, which provides the basis and targets for designing, modifying, and enhancing enzyme activity. The unique l...

More than just pattern recognition: Prediction of uncommon protein structure features by AI methods.

Proceedings of the National Academy of Sciences of the United States of America
The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the...

A deep learning solution for crystallographic structure determination.

IUCrJ
The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallogr...

Finding functional motifs in protein sequences with deep learning and natural language models.

Current opinion in structural biology
Recently, prediction of structural/functional motifs in protein sequences takes advantage of powerful machine learning based approaches. Protein encoding adopts protein language models overpassing standard procedures. Different combinations of machin...

Combining pairwise structural similarity and deep learning interface contact prediction to estimate protein complex model accuracy in CASP15.

Proteins
Estimating the accuracy of quaternary structural models of protein complexes and assemblies (EMA) is important for predicting quaternary structures and applying them to studying protein function and interaction. The pairwise similarity between struct...

Emerging Pharmacotherapeutic Strategies to Overcome Undruggable Proteins in Cancer.

International journal of biological sciences
Targeted therapies in cancer treatment can improve efficacy and reduce adverse effects by altering the tissue exposure of specific biomolecules. However, there are still large number of target proteins in cancer are still undruggable, owing to the f...

A large expert-curated cryo-EM image dataset for machine learning protein particle picking.

Scientific data
Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. Howeve...

A Simple Way to Incorporate Target Structural Information in Molecular Generative Models.

Journal of chemical information and modeling
Deep learning generative models are now being applied in various fields including drug discovery. In this work, we propose a novel approach to include target 3D structural information in molecular generative models for structure-based drug design. Th...

DeepSP: A Deep Learning Framework for Spatial Proteomics.

Journal of proteome research
The study of protein subcellular localization (PSL) is a fundamental step toward understanding the mechanism of protein function. The recent development of mass spectrometry (MS)-based spatial proteomics to quantify the distribution of proteins acros...