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Amino Acid Sequence

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AMP: Species-Specific Prediction of Anti-microbial Peptides Using Zero and Few Shot Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Evolution of drug-resistant microbial species is one of the major challenges to global health. Development of new antimicrobial treatments such as antimicrobial peptides needs to be accelerated to combat this threat. However, the discovery of novel a...

Boltzmann Machine Learning and Regularization Methods for Inferring Evolutionary Fields and Couplings From a Multiple Sequence Alignment.

IEEE/ACM transactions on computational biology and bioinformatics
The inverse Potts problem to infer a Boltzmann distribution for homologous protein sequences from their single-site and pairwise amino acid frequencies recently attracts a great deal of attention in the studies of protein structure and evolution. We ...

A two-step ensemble learning for predicting protein hot spot residues from whole protein sequence.

Amino acids
Protein hot spot residues are functional sites in protein-protein interactions. Biological experimental methods are traditionally used to identify hot spot residues, which is laborious and time-consuming. Thus a variety of computational methods were ...

Positional SHAP (PoSHAP) for Interpretation of machine learning models trained from biological sequences.

PLoS computational biology
Machine learning with multi-layered artificial neural networks, also known as "deep learning," is effective for making biological predictions. However, model interpretation is challenging, especially for sequential input data used with recurrent neur...

ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.

Journal of computational biology : a journal of computational molecular cell biology
Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under predict...

Enhancing protein inter-residue real distance prediction by scrutinising deep learning models.

Scientific reports
Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during conformational search. In this context, prediction of large in...

GCRNN: graph convolutional recurrent neural network for compound-protein interaction prediction.

BMC bioinformatics
BACKGROUND: Compound-protein interaction prediction is necessary to investigate health regulatory functions and promotes drug discovery. Machine learning is becoming increasingly important in bioinformatics for applications such as analyzing protein-...

Effective prediction of short hydrogen bonds in proteins via machine learning method.

Scientific reports
Short hydrogen bonds (SHBs), whose donor and acceptor heteroatoms lie within 2.7 Å, exhibit prominent quantum mechanical characters and are connected to a wide range of essential biomolecular processes. However, exact determination of the geometry an...

Harnessing protein folding neural networks for peptide-protein docking.

Nature communications
Highly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been develop...

Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning.

Scientific reports
In biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrom...