AIMC Topic: Proteins

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Performing protein fold recognition by exploiting a stack convolutional neural network with the attention mechanism.

Analytical biochemistry
Protein fold recognition is a critical step in protein structure and function prediction, and aims to ascertain the most likely fold type of the query protein. As a typical pattern recognition problem, designing a powerful feature extractor and metri...

Bioactivity assessment of natural compounds using machine learning models trained on target similarity between drugs.

PLoS computational biology
Natural compounds constitute a rich resource of potential small molecule therapeutics. While experimental access to this resource is limited due to its vast diversity and difficulties in systematic purification, computational assessment of structural...

Capturing surface complementarity in proteins using unsupervised learning and robust curvature measure.

Proteins
The structure of a protein plays a pivotal role in determining its function. Often, the protein surface's shape and curvature dictate its nature of interaction with other proteins and biomolecules. However, marked by corrugations and roughness, a pro...

Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine.

BioMed research international
Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundati...

DISTEMA: distance map-based estimation of single protein model accuracy with attentive 2D convolutional neural network.

BMC bioinformatics
BACKGROUND: Estimation of the accuracy (quality) of protein structural models is important for both prediction and use of protein structural models. Deep learning methods have been used to integrate protein structure features to predict the quality o...

Artificial intelligence in the experimental determination and prediction of macromolecular structures.

Current opinion in structural biology
Machine learning methods, in particular convolutional neural networks, have been applied to a variety of problems in cryo-EM and macromolecular crystallographic structure solution. However, they still have only limited acceptance by the community, ma...

Label-Free Differentiation of Cancer and Non-Cancer Cells Based on Machine-Learning-Algorithm-Assisted Fast Raman Imaging.

Biosensors
This paper proposes a rapid, label-free, and non-invasive approach for identifying murine cancer cells (B16F10 melanoma cancer cells) from non-cancer cells (C2C12 muscle cells) using machine-learning-assisted Raman spectroscopic imaging. Through quic...

Learning Multi-Scale Heterogeneous Representations and Global Topology for Drug-Target Interaction Prediction.

IEEE journal of biomedical and health informatics
Identification of interactions between drugs and target proteins plays a critical role not only in drug discovery but also in drug repositioning. Deep integration of inter-connections and intra-similarities between heterogeneous multi-source data abo...

Using Big Data Analytics to "Back Engineer" Protein Conformational Selection Mechanisms.

Molecules (Basel, Switzerland)
In the living cells, proteins bind small molecules (or "ligands") through a "conformational selection" mechanism, where a subset of protein structures are capable of binding the small molecules well while most other protein structures are not capable...

TransPhos: A Deep-Learning Model for General Phosphorylation Site Prediction Based on Transformer-Encoder Architecture.

International journal of molecular sciences
Protein phosphorylation is one of the most critical post-translational modifications of proteins in eukaryotes, which is essential for a variety of biological processes. Plenty of attempts have been made to improve the performance of computational pr...