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Biomolecular interactions frequently occur in orientation-specific manner. For example, prior nuclear magnetic resonance spectroscopy experiments in our lab have suggested the presence of a group of strongly binding residues on a particular face of the protein ubiquitin for interactions with Capto MMC multimodal ligands ("Capto" ligands) (Srinivasan, K.; et al. Langmuir 2014, 30 (44), 13205-13216). We present a clear confirmation of those studies by performing single-molecule force spectroscopy (SMFS) measurements of unbinding complemented with molecular dynamics (MD) calculations of the adsorption free energy of ubiquitin in two distinct orientations with self-assembled monolayers (SAMs) functionalized with "Capto" ligands. These orientations were maintained in the SMFS experiments by tethering ubiquitin mutants to SAM surfaces through strategically located cysteines, thus exposing the desired faces of the protein. Analogous orientations were maintained in MD simulations using suitable constraining methods. Remarkably, despite differences between the finer details of experimental and simulation methodologies, they confirm a clear preference for the previously hypothesized binding face of ubiquitin. Furthermore, MD simulations provided significant insights into the mechanism of protein binding onto this multimodal surface. Because SMFS and MD simulations both directly probe protein-surface interactions, this work establishes a key link between experiments and simulations at molecular scale through the determination of protein face-specific binding energetics. Our approach may have direct applications in biophysical systems where face- or orientation-specific interactions are important, such as biomaterials, sensors, and biomanufacturing.

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Silicon-photonic neural networks (SPNNs) offer substantial improvements in computing speed and energy efficiency compared to their digital electronic counterparts. However, the energy efficiency and accuracy of SPNNs are highly impacted by uncertainties that arise from fabrication-process and thermal variations. In this paper, we present the first comprehensive and hierarchical study on the impact of random uncertainties on the classification accuracy of a Mach-Zehnder Interferometer (MZI)based SPNN. We show that such impact can vary based on both the location and characteristics (e.g., tuned phase angles) of a non-ideal silicon-photonic device. Simulation results show that in an SPNN with two hidden layers and 1374 tunable-thermalphase shifters, random uncertainties even in mature fabrication processes can lead to a catastrophic 70% accuracy loss.

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