Analyzing and Characterizing Heme Binding Peptides
Session Number
CHEM 04
Advisor(s)
Chris Fry, Dr. Henry Chan, Dr. Jesse Prelesnik, Argonne National Laboratory
Discipline
Chemistry
Start Date
17-4-2025 11:25 AM
End Date
17-4-2025 11:40 AM
Abstract
Self-assembling peptides have a variety of uses in biological materials science, from conductive nanowires to applications in biomedicine. This research hopes to facilitate future heme binding ⍺-helical peptide designs by employing machine learning methods to identify patterns in peptide sequences that promote heme binding while maintaining an ⍺-helical structure to mitigate the bias in previous research towards β-sheet forming peptides.
Physical and structural properties of the peptides determined from their sequences, including charge distribution, ⍺-helical propensity, heme binding propensity, and hydrophobic interactions motivated the rational design of initial experimental peptide sequences. >200 ixteen-amino-acid-long peptide sequences were synthesised using Solid Phase Peptide Synthesis, and characterized using Fourier Transformation Infrared spectroscopy, Circular Dichroism (CD) spectroscopy and Ultraviolet-visible (UV-Vis) spectroscopy to assess secondary structure and heme binding efficiency.
A linear combination analysis routine of the CD and UV/vis data was developed using python to automate and quantify the fidelity of ⍺-helix formation and heme binding. These data will then be used as part of the experimental validation method in our machine learning protocol, consisting of a Long Short Term Memory model that was trained on the Protein Data Bank and experimental data, aimed to generate potential patterns of successful structures for experimentation.
Analyzing and Characterizing Heme Binding Peptides
Self-assembling peptides have a variety of uses in biological materials science, from conductive nanowires to applications in biomedicine. This research hopes to facilitate future heme binding ⍺-helical peptide designs by employing machine learning methods to identify patterns in peptide sequences that promote heme binding while maintaining an ⍺-helical structure to mitigate the bias in previous research towards β-sheet forming peptides.
Physical and structural properties of the peptides determined from their sequences, including charge distribution, ⍺-helical propensity, heme binding propensity, and hydrophobic interactions motivated the rational design of initial experimental peptide sequences. >200 ixteen-amino-acid-long peptide sequences were synthesised using Solid Phase Peptide Synthesis, and characterized using Fourier Transformation Infrared spectroscopy, Circular Dichroism (CD) spectroscopy and Ultraviolet-visible (UV-Vis) spectroscopy to assess secondary structure and heme binding efficiency.
A linear combination analysis routine of the CD and UV/vis data was developed using python to automate and quantify the fidelity of ⍺-helix formation and heme binding. These data will then be used as part of the experimental validation method in our machine learning protocol, consisting of a Long Short Term Memory model that was trained on the Protein Data Bank and experimental data, aimed to generate potential patterns of successful structures for experimentation.