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Amino Acid Sequence

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RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning.

Journal of chemical information and modeling
Microbial rhodopsins (MRs) are a diverse and abundant family of photoactive membrane proteins that serve as model systems for biophysical techniques. Optogenetics utilizes genetic engineering to insert specialized proteins into specific neurons or br...

Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences.

Proteins
We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed ...

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

PLoS computational biology
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models des...

Machine learning designs new GCGR/GLP-1R dual agonists with enhanced biological potency.

Nature chemistry
Several peptide dual agonists of the human glucagon receptor (GCGR) and the glucagon-like peptide-1 receptor (GLP-1R) are in development for the treatment of type 2 diabetes, obesity and their associated complications. Candidates must have high poten...

Predicting Antimicrobial Peptides Using ESMFold-Predicted Structures and ESM-2-Based Amino Acid Features with Graph Deep Learning.

Journal of chemical information and modeling
Currently, antimicrobial resistance constitutes a serious threat to human health. Drugs based on antimicrobial peptides (AMPs) constitute one of the alternatives to address it. Shallow and deep learning (DL)-based models have mainly been built from a...

Interface design of SARS-CoV-2 symmetrical nsp7 dimer and machine learning-guided nsp7 sequence prediction reveals physicochemical properties and hotspots for nsp7 stability, adaptation, and therapeutic design.

Physical chemistry chemical physics : PCCP
The COVID-19 pandemic, driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), necessitates a profound understanding of the virus and its lifecycle. As an RNA virus with high mutation rates, SARS-CoV-2 exhibits genetic variability lea...

Co-Mutations and Possible Variation Tendency of the Spike RBD and Membrane Protein in SARS-CoV-2 by Machine Learning.

International journal of molecular sciences
Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, SARS-CoV-2 variants capable of breakthrough infections have attracted global attention. These variants have significant mutations in the receptor-binding domain (RBD) of the spike p...

Computational scoring and experimental evaluation of enzymes generated by neural networks.

Nature biotechnology
In recent years, generative protein sequence models have been developed to sample novel sequences. However, predicting whether generated proteins will fold and function remains challenging. We evaluate a set of 20 diverse computational metrics to ass...

Model fusion for predicting unconventional proteins secreted by exosomes using deep learning.

Proteomics
Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins a...

KEGG orthology prediction of bacterial proteins using natural language processing.

BMC bioinformatics
BACKGROUND: The advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by ...