AIMC Topic: Amino Acid Sequence

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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 ...

sAMP-VGG16: Force-field assisted image-based deep neural network prediction model for short antimicrobial peptides.

Proteins
During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. T...

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis.

Journal of chemical theory and computation
The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenerative diseases, and thus, stabilization of Tau protein is a promising avenue for tauopathies therapy. To dissect the underlying aggregation mechanisms ...

Using protein language models for protein interaction hot spot prediction with limited data.

BMC bioinformatics
BACKGROUND: Protein language models, inspired by the success of large language models in deciphering human language, have emerged as powerful tools for unraveling the intricate code of life inscribed within protein sequences. They have gained signifi...

xCAPT5: protein-protein interaction prediction using deep and wide multi-kernel pooling convolutional neural networks with protein language model.

BMC bioinformatics
BACKGROUND: Predicting protein-protein interactions (PPIs) from sequence data is a key challenge in computational biology. While various computational methods have beenĀ proposed, the utilization of sequence embeddings from protein language models, wh...

MMDB: Multimodal dual-branch model for multi-functional bioactive peptide prediction.

Analytical biochemistry
Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play important roles in treating diverse diseases and disorders. While most of the methods focus on single-functional bioactive peptides and have obtained prom...

AbDPP: Target-oriented antibody design with pretraining and prior biological structure knowledge.

Proteins
Antibodies represent a crucial class of complex protein therapeutics and are essential in the treatment of a wide range of human diseases. Traditional antibody discovery methods, such as hybridoma and phage display technologies, suffer from limitatio...

Convolutions are competitive with transformers for protein sequence pretraining.

Cell systems
Pretrained protein sequence language models have been shown to improve the performance of many prediction tasks and are now routinely integrated into bioinformatics tools. However, these models largely rely on the transformer architecture, which scal...

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

Interdisciplinary sciences, computational life sciences
Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP predicti...

Automated model building and protein identification in cryo-EM maps.

Nature
Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs. Here we present ModelAngelo, a machine-learning approa...