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Peptide Identification Using Tandem Mass Spectrometry

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...

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Updated: Jun 26, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

Published on: January 26, 2024

Machine learning algorithms to predict spray dried protein/peptide formulations.

Liping Wei¹, Jiayin Deng², Yang Sun¹

¹Wuya College of Innovation, Shenyang Pharmaceutical University, No. 103, Wenhua Road, 110016 Shenyang, China; Joint International Research Laboratory of Intelligent Drug Delivery Systems, Ministry of Education, 110016 Shenyang, China.

International Journal of Pharmaceutics

|June 24, 2026

Summary

This summary is machine-generated.

Machine learning models accurately predict properties of spray-dried protein and peptide formulations, accelerating drug development. This approach optimizes process parameters and reduces trial-and-error for stable biomacromolecule delivery.

Keywords:

Aggregation Machine learning Particle size Protein/peptide formulations Residual solvent content Solid state properties Spray drying Yield

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Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions

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Published on: November 10, 2016

Area of Science:

Biopharmaceutical formulation
Computational chemistry
Process engineering

Background:

Proteins and peptides show therapeutic potential but face stability challenges in drug development.
Spray drying is a method to stabilize these biomacromolecules into solid formulations.
Current spray drying methods rely on extensive trial-and-error, increasing resource demands.

Purpose of the Study:

To develop machine learning (ML) models for predicting key properties of spray-dried protein and peptide powders.
To accelerate formulation development and optimize spray drying process parameters.
To identify critical factors influencing the properties of spray-dried protein/peptide formulations.

Main Methods:

Collected data on yield, particle size, residual solvent content, solid-state properties, and aggregation for spray-dried protein/peptide powders.
Utilized various molecular descriptors for model building.
Tested seven ML algorithms, including Light Gradient Boosting Machine (LightGBM) and logistic regression.
Validated model generalizability using alpha-lactalbumin formulations.

Main Results:

LightGBM demonstrated superior performance in regression tasks, especially for residual solvent content (MAE=0.841).
Logistic regression excelled in predicting solid-state characteristics and aggregation.
Feature importance analysis highlighted protein type, excipients, processing, and environmental conditions as critical factors.
Experimental validation showed good predictive accuracy for yield, particle size, residual solvent content, solid states, and aggregation.

Conclusions:

Machine learning offers a powerful, data-driven approach to streamline the development of spray-dried protein formulations.
This methodology provides a material- and time-saving solution for optimizing spray drying processes.
The developed ML models can serve as a valuable reference for future biopharmaceutical formulation studies.