A High-Throughput Workflow to Analyze Sequence-Conformation Relationships and Explore Hydrophobic Patterning in...

Area of Science:

• Biophysics and Materials Science
• Computational Chemistry
• Chemical Biology

Background:

• Determining how a macromolecule's primary sequence dictates its 3D shape (conformational landscape) is vital for understanding its function.
• Principles governing sequence-conformation relationships are not well-established for intrinsically disordered macromolecules.

Purpose of the Study:

• To establish a high-throughput workflow for analyzing sequence-conformation relationships in macromolecules.
• To develop a generalizable algorithm for predicting macromolecule conformation based on sequence.
• To accelerate the discovery of novel materials with tunable conformational properties.

Main Methods:

• Implemented a colorimetric conformational assay and a semi-automated sequencing protocol using MALDI-MS/MS.
• Utilized a model system of 20mer peptidomimetics with varying glycine and N-butylglycine residues.
• Employed atomistic simulations and ion mobility spectrometry coupled with liquid chromatography for validation.
• Developed a machine learning algorithm integrating sequence variables and data-derived motifs for conformation prediction.

Main Results:

• Identified nine classifications of conformational disorder and isolated 122 unique sequences.
• Corroborated conformational distributions of selected sequences using advanced biophysical techniques.
• Demonstrated the efficacy of the machine learning algorithm in predicting conformation.

Conclusions:

• The developed workflow provides a powerful tool for understanding sequence-conformation relationships in macromolecules.
• This approach enhances the ability to design and discover materials with specific conformational control.
• The findings contribute to the broader understanding of intrinsically disordered macromolecules and their functions.