Direct Comparison of Lysine versus Site-Specific Protein Surface Immobilization in Single-molecule Mechanical Assays

Engineering the Mechanical Stability of a Therapeutic Affibody/PD-L1 Complex by Anchor Point Selection

Protein-protein complexes can vary in mechanical stability depending on the direction from which force is applied. Here we investigated the anisotropic mechanical stability of a molecular complex between a therapeutic non-immunoglobulin scaffold called Affibody and the extracellular domain of the immune checkpoint protein PD-L1. We used a combination of single-molecule AFM force spectroscopy (AFM-SMFS) with bioorthogonal clickable peptide handles, shear stress bead adhesion assays, molecular modeling, and steered molecular dynamics (SMD) simulations to understand the pulling point dependency of mechanostability of the Affibody:(PD-L1) complex. We observed diverse mechanical responses depending on the anchor point. For example, pulling from residue #22 on Affibody generated an intermediate unfolding event attributed to partial unfolding of PD-L1, while pulling from Affibody's N-terminus generated force-activated catch bond behavior. We found that pulling from residue #22 or #47 on Affibody generated the highest rupture forces, with the complex breaking at up to ~ 190 pN under loading rates of ~104-105 pN/sec, representing a ~4-fold increase in mechanostability as compared with low force N-terminal pulling. SMD simulations provided consistent tendencies in rupture forces, and through visualization of force propagation networks provided mechanistic insights. These results demonstrate how mechanostability of therapeutic protein-protein interfaces can be controlled by informed selection of anchor points within molecules, with implications for optimal bioconjugation strategies in drug delivery vehicles.

Multifunctional Poly(2-ethyl-2-oxazoline) Copolymers Containing Dithiolane and Pentafluorophenyl Esters as Effective Reactive Linkers for Gold Surface Coatings

Surface functionalization with biological macromolecules is an important task for the development of sensor materials, whereby the interaction with other biological materials should be suppressed. In this work, we developed a novel multifunctional poly(2-ethyl-2-oxazoline)-dithiolane conjugate as a versatile linker for gold surface immobilization of amine-containing biomolecules, containing poly(2-ethyl-2-oxazoline) as antifouling polymer, dithiolane for surface immobilization, and activated esters for protein conjugation. First, a well-defined carboxylic acid containing copoly(2-ethyl-2-oxazoline) was synthesized by cationic ring-opening copolymerization of 2-ethyl-2-oxazoline with a methyl ester-containing 2-oxazoline monomer, followed by postpolymerization modifications. The side-chain carboxylic groups were then converted to amine-reactive pentafluorophenyl (PFP) ester groups. Part of the PFP groups was used for the attachment of the dithiolane moiety, which can efficiently bind to gold surfaces. The final copolymer contained 1.4 mol% of dithiolane groups and 4.5 mol% of PFP groups. The copolymer structure was confirmed by several analytical techniques, including NMR spectroscopy and size-exclusion chromatography. The kinetics of the PFP ester aminolysis and hydrolysis demonstrated significantly faster amidation compared to hydrolysis, which is essential for subsequent protein conjugation. Successful coating of gold surfaces with the polymer was confirmed by spectroscopic ellipsometry, showing a polymer brush thickness of 4.77 nm. Subsequent modification of the coated surfaces was achieved using bovine serum albumin as a model protein. This study introduces a novel reactive polymer linker for gold surface functionalization and offers a versatile polymer platform for various applications including biosensing and surface functionalization.

Iterative Machine Learning for Classification and Discovery of Single-Molecule Unfolding Trajectories from Force Spectroscopy Data

We report the application of machine learning techniques to expedite classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression, and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) learning where a user classifies a small number of repeatable unfolding patterns encoded as images, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multidomain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways that were undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis and generate new insights in protein biophysics.