AIMC Topic: Proteins

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Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Journal of biomolecular NMR
Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR paramete...

How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.

BioEssays : news and reviews in molecular, cellular and developmental biology
The performance of deep Neural Networks (NNs) in the text (ChatGPT) and image (DALL-E2) domains has attracted worldwide attention. Convolutional NNs (CNNs), Large Language Models (LLMs), Denoising Diffusion Probabilistic Models (DDPMs)/Noise Conditio...

GraphPI: Efficient Protein Inference with Graph Neural Networks.

Journal of proteome research
The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled ...

Exploring Protein Conformational Changes Using a Large-Scale Biophysical Sampling Augmented Deep Learning Strategy.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the de...

Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding...

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...

Stacking based ensemble learning framework for identification of nitrotyrosine sites.

Computers in biology and medicine
Protein nitrotyrosine is an essential post-translational modification that results from the nitration of tyrosine amino acid residues. This modification is known to be associated with the regulation and characterization of several biological function...

Protein representations: Encoding biological information for machine learning in biocatalysis.

Biotechnology advances
Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address th...

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.

Molecules (Basel, Switzerland)
The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computati...

Dataset from a human-in-the-loop approach to identify functionally important protein residues from literature.

Scientific data
We present a novel system that leverages curators in the loop to develop a dataset and model for detecting structure features and functional annotations at residue-level from standard publication text. Our approach involves the integration of data fr...