AIMC Topic: Amino Acid Sequence

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Intelligent decoding platform for peptide sequences: SERS detection via high affinity self-assembled silver nanoparticles and machine learning analysis.

Analytica chimica acta
BACKGROUND: Peptides are compounds formed by the dehydration-condensation reaction of two or more amino acids which play an important role in the life functions of the organism. Changes in the structure of amino acids and peptides are vital for eluci...

Deep Learning-Assisted Discovery of Protein Entangling Motifs.

Biomacromolecules
Natural topological proteins exhibit unique properties including enhanced stability, controlled quaternary structures, and dynamic switching properties, highlighting topology as a unique dimension in protein engineering. Although artificial design an...

Deep Learning Predicts Non-Normal Transmission Distributions in High-Field Asymmetric Waveform Ion Mobility (FAIMS) Directly from Peptide Sequence.

Analytical chemistry
Peptide ion mobility adds an extra dimension of separation to mass spectrometry-based proteomics. The ability to accurately predict peptide ion mobility would be useful to expedite assay development and to discriminate true answers in a database sear...

Phyre2.2: A Community Resource for Template-based Protein Structure Prediction.

Journal of molecular biology
Template-based modelling, also known as homology modelling, is a powerful approach to predict the structure of a protein from its amino acid sequence. The approach requires one to identify a sequence similarity between the query sequence and that of ...

GraphPhos: Predict Protein-Phosphorylation Sites Based on Graph Neural Networks.

International journal of molecular sciences
Phosphorylation is one of the most common protein post-translational modifications. The identification of phosphorylation sites serves as the cornerstone for protein-phosphorylation-related research. This paper proposes a protein-phosphorylation site...

IL-6-Inducing Peptide Prediction Based on 3D Structure and Graph Neural Network.

Biomolecules
Interleukin-6 (IL-6) is a potent glycoprotein that plays a crucial role in regulating innate and adaptive immunity, as well as metabolism. The expression and release of IL-6 are closely correlated with the severity of various diseases. IL-6-inducing ...

ConoDL: a deep learning framework for rapid generation and prediction of conotoxins.

Journal of computer-aided molecular design
Conotoxins, being small disulfide-rich and bioactive peptides, manifest notable pharmacological potential and find extensive applications. However, the exploration of conotoxins' vast molecular space using traditional methods is severely limited, nec...

EnDM-CPP: A Multi-view Explainable Framework Based on Deep Learning and Machine Learning for Identifying Cell-Penetrating Peptides with Transformers and Analyzing Sequence Information.

Interdisciplinary sciences, computational life sciences
Cell-Penetrating Peptides (CPPs) are a crucial carrier for drug delivery. Since the process of synthesizing new CPPs in the laboratory is both time- and resource-consuming, computational methods to predict potential CPPs can be used to find CPPs to e...

Improved enzyme functional annotation prediction using contrastive learning with structural inference.

Communications biology
Recent years have witnessed the remarkable progress of deep learning within the realm of scientific disciplines, yielding a wealth of promising outcomes. A prominent challenge within this domain has been the task of predicting enzyme function, a comp...

Discovery of anticancer peptides from natural and generated sequences using deep learning.

International journal of biological macromolecules
Anticancer peptides (ACPs) demonstrate significant potential in clinical cancer treatment due to their ability to selectively target and kill cancer cells. In recent years, numerous artificial intelligence (AI) algorithms have been developed. However...