ESPRESSO: a system for estimating protein expression and solubility in protein expression systems

Area of Science:

• Computational biology
• Protein science
• Biotechnology

Background:

• Recombinant protein technology is vital for pharmaceutical and industrial applications.
• Achieving soluble protein expression remains a significant experimental challenge, impacting research efficiency and cost.
• Predictive knowledge of protein expression and solubility can streamline proteomics studies.

Purpose of the Study:

• To develop computational methods for predicting protein expression and solubility using only amino acid sequence information.
• To evaluate prediction accuracy for two distinct expression systems: in vivo Escherichia coli and wheat germ cell-free.
• To provide a freely accessible tool for researchers to aid in protein expression studies.

Main Methods:

• Implementation of two prediction approaches: a sequence/predicted structural property-based method and a sequence pattern-based method.
• Utilizing sequence data and predicted structural features for solubility estimation.
• Employing occurrence frequencies of sequence patterns to identify solubility-associated motifs.

Main Results:

• The developed computational methods achieved F-scores of approximately 70% in benchmark tests.
• The proposed methods demonstrated superior performance compared to existing publicly available servers.
• Analysis of genomic data indicated that translation and transcription-associated proteins exhibit high solubility in the E. coli system.

Conclusions:

• The computational approach effectively predicts protein expression and solubility from sequence data for common expression systems.
• The sequence pattern-based method shows potential for identifying regions within proteins that could be modified to enhance solubility.
• The ESPRESSO server offers a valuable, free resource for the scientific community to improve recombinant protein production.