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Amino Acid Sequence

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mACPpred 2.0: Stacked Deep Learning for Anticancer Peptide Prediction with Integrated Spatial and Probabilistic Feature Representations.

Journal of molecular biology
Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the pa...

PepNet: an interpretable neural network for anti-inflammatory and antimicrobial peptides prediction using a pre-trained protein language model.

Communications biology
Identifying anti-inflammatory peptides (AIPs) and antimicrobial peptides (AMPs) is crucial for the discovery of innovative and effective peptide-based therapies targeting inflammation and microbial infections. However, accurate identification of AIPs...

Decoding the impact of neighboring amino acids on ESI-MS intensity output through deep learning.

Journal of proteomics
Peptide-level quantification using mass spectrometry (MS) is no trivial task as the physicochemical properties affect both response and detectability. The specific amino acid (AA) sequence affects these properties, however the connection between sequ...

Impact of Multi-Factor Features on Protein Secondary Structure Prediction.

Biomolecules
Protein secondary structure prediction (PSSP) plays a crucial role in resolving protein functions and properties. Significant progress has been made in this field in recent years, and the use of a variety of protein-related features, including amino ...

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest.

Methods (San Diego, Calif.)
Interactions of biological molecules in organisms are considered to be primary factors for the lifecycle of that organism. Various important biological functions are dependent on such interactions and among different kinds of interactions, the protei...

Predicting Peptide Ionization Efficiencies for Electrospray Ionization Mass Spectrometry Using Machine Learning.

Journal of the American Society for Mass Spectrometry
Mass spectrometry (MS) is inherently an information-rich technique. In this era of big data, label-free MS quantification for nontargeted studies has gained increasing popularity, especially for complex systems. One of the cornerstones of successful ...

Pred-AHCP: Robust Feature Selection-Enabled Sequence-Specific Prediction of Anti-Hepatitis C Peptides via Machine Learning.

Journal of chemical information and modeling
Every year, an estimated 1.5 million people worldwide contract Hepatitis C, a significant contributor to liver problems. Although many studies have explored machine learning's potential to predict antiviral peptides, very few have addressed the probl...

All-Atom Protein Sequence Design Based on Geometric Deep Learning.

Angewandte Chemie (International ed. in English)
Designing sequences for specific protein backbones is a key step in creating new functional proteins. Here, we introduce GeoSeqBuilder, a deep learning framework that integrates protein sequence generation with side chain conformation prediction to p...

Development of Peptide Identification System for ToF-SIMS Spectra Using Supervised Machine Learning.

Journal of the American Society for Mass Spectrometry
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) data interpretation for organic materials is complicated because of various fragment ions produced from each molecule and the overlapping of certain mass peaks from different molecules. Fragme...

Generative AI Models for the Protein Scaffold Filling Problem.

Journal of computational biology : a journal of computational molecular cell biology
De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches ...