Disruption of energetic and dynamic base pairing cooperativity in DNA duplexes by an abasic site.

DNA duplex stability arises from cooperative interactions between multiple adjacent nucleotides that favor base pairing and stacking when formed as a continuous stretch rather than individually. Lesions and nucleobase modifications alter this stability in complex manners that remain challenging to understand despite their centrality to biology. Here, we investigate how an abasic site destabilizes small DNA duplexes and reshapes base pairing dynamics and hybridization pathways using temperature-jump infrared spectroscopy and coarse-grained molecular dynamics simulations. We show how an abasic site splits the cooperativity in a short duplex into two segments, which destabilizes small duplexes as a whole and enables metastable half-dissociated configurations. Dynamically, it introduces an additional barrier to hybridization by constraining the hybridization mechanism to a step-wise process of nucleating and zipping a stretch on one side of the abasic site and then the other.

Learning the relationship between nanoscale chemical patterning and hydrophobicity.

The hydrophobicity of proteins and similar surfaces, which display chemical heterogeneity at the nanoscale, drives countless aqueous interactions and assemblies. However, predicting how surface chemical patterning influences hydrophobicity remains a challenge. Here, we address this challenge by using molecular simulations and machine learning to characterize and model the hydrophobicity of a diverse library of patterned surfaces, spanning a wide range of sizes, shapes, and chemical compositions. We find that simple models, based only on polar content, are inaccurate, whereas complex neural network models are accurate but challenging to interpret. However, by systematically incorporating chemical correlations between surface groups into our models, we are able to construct a series of minimal models of hydrophobicity, which are both accurate and interpretable. Our models highlight that the number of proximal polar groups is a key determinant of hydrophobicity and that polar neighbors enhance hydrophobicity. Although our minimal models are trained on particular patch size and shape, their interpretability enables us to generalize them to rectangular patches of all shapes and sizes. We also demonstrate how our models can be used to predict hot-spot locations with the largest marginal contributions to hydrophobicity and to design chemical patterns that have a fixed polar content but vary widely in their hydrophobicity. Our data-driven models and the principles they furnish for modulating hydrophobicity could facilitate the design of novel materials and engineered proteins with stronger interactions or enhanced solubilities.

Data-driven prediction of αIIbß3 integrin activation paths using manifold learning and deep generative modeling.

The integrin heterodimer is a transmembrane protein critical for driving cellular process and is a therapeutic target in the treatment of multiple diseases linked to its malfunction. Activation of integrin involves conformational transitions between bent and extended states. Some of the conformations that are intermediate between bent and extended states of the heterodimer have been experimentally characterized, but the full activation pathways remain unresolved both experimentally due to their transient nature and computationally due to the challenges in simulating rare barrier crossing events in these large molecular systems. An understanding of the activation pathways can provide new fundamental understanding of the biophysical processes associated with the dynamic interconversions between bent and extended states and can unveil new putative therapeutic targets. In this work, we apply nonlinear manifold learning to coarse-grained molecular dynamics simulations of bent, extended, and two intermediate states of αIIbß3 integrin to learn a low-dimensional embedding of the configurational phase space. We then train deep generative models to learn an inverse mapping between the low-dimensional embedding and high-dimensional molecular space and use these models to interpolate the molecular configurations constituting the activation pathways between the experimentally characterized states. This work furnishes plausible predictions of integrin activation pathways and reports a generic and transferable multiscale technique to predict transition pathways for biomolecular systems.

Molecular insight into how the position of an abasic site modifies DNA duplex stability and dynamics.

Local perturbations to DNA base-pairing stability from lesions and chemical modifications can alter the stability and dynamics of an entire oligonucleotide. End effects may cause the position of a disruption within a short duplex to influence duplex stability and structural dynamics, yet this aspect of nucleic acid modifications is often overlooked. We investigate how the position of an abasic site (AP site) impacts the stability and dynamics of short DNA duplexes. Using a combination of steady-state and time-resolved spectroscopy and molecular dynamics simulations, we unravel an interplay between AP-site position and nucleobase sequence that controls energetic and dynamic disruption to the duplex. The duplex is disrupted into two segments by an entropic barrier for base-pairing on each side of the AP site. The barrier induces fraying of the short segment when an AP site is near the termini. Shifting the AP site inward promotes a transition from short-segment fraying to fully encompassing the barrier into the thermodynamics of hybridization, leading to further destabilization of the duplex. Nucleobase sequence determines the length scale for this transition by tuning the barrier height and base-pair stability of the short segment, and certain sequences enable out-of-register base-pairing to minimize the barrier height.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction.

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

PACAP is a pathogen-inducible resident antimicrobial neuropeptide affording rapid and contextual molecular host defense of the brain.

Defense of the central nervous system (CNS) against infection must be accomplished without generation of potentially injurious immune cell-mediated or off-target inflammation which could impair key functions. As the CNS is an immune-privileged compartment, inducible innate defense mechanisms endogenous to the CNS likely play an essential role in this regard. Pituitary adenylate cyclase-activating polypeptide (PACAP) is a neuropeptide known to regulate neurodevelopment, emotion, and certain stress responses. While PACAP is known to interact with the immune system, its significance in direct defense of brain or other tissues is not established. Here, we show that our machine-learning classifier can screen for immune activity in neuropeptides, and correctly identified PACAP as an antimicrobial neuropeptide in agreement with previous experimental work. Furthermore, synchrotron X-ray scattering, antimicrobial assays, and mechanistic fingerprinting provided precise insights into how PACAP exerts antimicrobial activities vs. pathogens via multiple and synergistic mechanisms, including dysregulation of membrane integrity and energetics and activation of cell death pathways. Importantly, resident PACAP is selectively induced up to 50-fold in the brain in mouse models of Staphylococcus aureus or Candida albicans infection in vivo, without inducing immune cell infiltration. We show differential PACAP induction even in various tissues outside the CNS, and how these observed patterns of induction are consistent with the antimicrobial efficacy of PACAP measured in conditions simulating specific physiologic contexts of those tissues. Phylogenetic analysis of PACAP revealed close conservation of predicted antimicrobial properties spanning primitive invertebrates to modern mammals. Together, these findings substantiate our hypothesis that PACAP is an ancient neuro-endocrine-immune effector that defends the CNS against infection while minimizing potentially injurious neuroinflammation.

Thermodynamics and kinetics of DNA and RNA dinucleotide hybridization to gaps and overhangs.

Hybridization of short nucleic acid segments (<4 nt) to single-strand templates occurs as a critical intermediate in processes such as nonenzymatic nucleic acid replication and toehold-mediated strand displacement. These templates often contain adjacent duplex segments that stabilize base pairing with single-strand gaps or overhangs, but the thermodynamics and kinetics of hybridization in such contexts are poorly understood because of the experimental challenges of probing weak binding and rapid structural dynamics. Here we develop an approach to directly measure the thermodynamics and kinetics of DNA and RNA dinucleotide dehybridization using steady-state and temperature-jump infrared spectroscopy. Our results suggest that dinucleotide binding is stabilized through coaxial stacking interactions with the adjacent duplex segments as well as from potential noncanonical base-pairing configurations and structural dynamics of gap and overhang templates revealed using molecular dynamics simulations. We measure timescales for dissociation ranging from 0.2-40 µs depending on the template and temperature. Dinucleotide hybridization and dehybridization involve a significant free energy barrier with characteristics resembling that of canonical oligonucleotides. Together, our work provides an initial step for predicting the stability and kinetics of hybridization between short nucleic acid segments and various templates.

Computational and Experimental Determination of the Properties, Structure, and Stability of Peptoid Nanosheets and Nanotubes.

Peptoids (N-substituted glycines) are a group of highly controllable peptidomimetic polymers. Amphiphilic diblock peptoids have been engineered to assemble crystalline nanospheres, nanofibrils, nanosheets, and nanotubes with biochemical, biomedical, and bioengineering applications. The mechanical properties of peptoid nanoaggregates and their relationship to the emergent self-assembled morphologies have been relatively unexplored and are critical for the rational design of peptoid nanomaterials. In this work, we consider a family of amphiphilic diblock peptoids consisting of a prototypical tube-former (Nbrpm6Nc6, a NH2-capped hydrophobic block of six N-((4-bromophenyl)methyl)glycine residues conjugated to a polar NH3(CH2)5CO tail), a prototypical sheet-former (Nbrpe6Nc6, where the hydrophobic block comprises six N-((4-bromophenyl)ethyl)glycine residues), and an intermediate sequence that forms mixed structures ((NbrpeNbrpm)3Nc6). We combine all-atom molecular dynamics simulations and atomic force microscopy to determine the mechanical properties of the self-assembled 2D crystalline nanosheets and relate these properties to the observed self-assembled morphologies. We find good agreement between our computational predictions and experimental measurements of Young's modulus of crystalline nanosheets. A computational analysis of the bending modulus along the two axes of the planar crystalline nanosheets reveals bending to be more favorable along the axis in which the peptoids stack by interdigitation of the side chains compared to that in which they form columnar crystals with π-stacked side chains. We construct molecular models of nanotubes of the Nbrpm6Nc6 tube-forming peptoid and predict a stability optimum in good agreement with experimental measurements. A theoretical model of nanotube stability suggests that this optimum is a free energy minimum corresponding to a "Goldilocks" tube radius at which capillary wave fluctuations in the tube wall are minimized.

Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables.

Molecular dynamics simulations of microscopic phenomena are limited by the short integration time steps which are required for numerical stability but which limit the practically achievable simulation time scales. Collective variable (CV) enhanced sampling techniques apply biases to predefined collective coordinates to promote barrier crossing, phase space exploration, and sampling of rare events. The efficacy of these techniques is contingent on the selection of good CVs correlated with the molecular motions governing the long-time dynamical evolution of the system. In this work, we introduce Girsanov Reweighting Enhanced Sampling Technique (GREST) as an adaptive sampling scheme that interleaves rounds of data-driven slow CV discovery and enhanced sampling along these coordinates. Since slow CVs are inherently dynamical quantities, a key ingredient in our approach is the use of both thermodynamic and dynamical Girsanov reweighting corrections for rigorous estimation of slow CVs from biased simulation data. We demonstrate our approach on a toy 1D 4-well potential, a simple biomolecular system alanine dipeptide, and the Trp-Leu-Ala-Leu-Leu (WLALL) pentapeptide. In each case GREST learns appropriate slow CVs and drives sampling of all thermally accessible metastable states starting from zero prior knowledge of the system. We make GREST accessible to the community via a publicly available open source Python package.

Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories.

All atom molecular dynamics (MD) simulations offer a powerful tool for molecular modeling, but the short time steps required for numerical stability of the integrator place many interesting molecular events out of reach of unbiased simulations. The popular and powerful Markov state modeling (MSM) approach can extend these time scales by stitching together multiple short discontinuous trajectories into a single long-time kinetic model but necessitates a configurational coarse-graining of the phase space that entails a loss of spatial and temporal resolution and an exponential increase in complexity for multimolecular systems. Latent space simulators (LSS) present an alternative formalism that employs a dynamical, as opposed to configurational, coarse graining comprising three back-to-back learning problems to (i) identify the molecular system's slowest dynamical processes, (ii) propagate the microscopic system dynamics within this slow subspace, and (iii) generatively reconstruct the trajectory of the system within the molecular phase space. A trained LSS model can generate temporally and spatially continuous synthetic molecular trajectories at orders of magnitude lower cost than MD to improve sampling of rare transition events and metastable states to reduce statistical uncertainties in thermodynamic and kinetic observables. In this work, we extend the LSS formalism to short discontinuous training trajectories generated by distributed computing and to multimolecular systems without incurring exponential scaling in computational cost. First, we develop a distributed LSS model over thousands of short simulations of a 264-residue proteolysis-targeting chimera (PROTAC) complex to generate ultralong continuous trajectories that identify metastable states and collective variables to inform PROTAC therapeutic design and optimization. Second, we develop a multimolecular LSS architecture to generate physically realistic ultralong trajectories of DNA oligomers that can undergo both duplex hybridization and hairpin folding. These trajectories retain thermodynamic and kinetic characteristics of the training data while providing increased precision of folding populations and time scales across simulation temperature and ion concentration.

Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design.

A prophylactic or therapeutic vaccine offers the best hope to curb the HIV-AIDS epidemic gripping sub-Saharan Africa, but it remains elusive. A major challenge is the extreme viral sequence variability among strains. Systematic means to guide immunogen design for highly variable pathogens like HIV are not available. Using computational models, we have developed an approach to translate available viral sequence data into quantitative landscapes of viral fitness as a function of the amino acid sequences of its constituent proteins. Predictions emerging from our computationally defined landscapes for the proteins of HIV-1 clade B Gag were positively tested against new in vitro fitness measurements and were consistent with previously defined in vitro measurements and clinical observations. These landscapes chart the peaks and valleys of viral fitness as protein sequences change and inform the design of immunogens and therapies that can target regions of the virus most vulnerable to selection pressure.

Evolution of π-Peptide Self-Assembly: From Understanding to Prediction and Control.

Supramolecular materials derived from the self-assembly of engineered molecules continue to garner tremendous scientific and technological interest. Recent innovations include the realization of nano- and mesoscale particles (0D), rods and fibrils (1D), sheets (2D), and even extended lattices (3D). Our research groups have focused attention over the past 15 years on one particular class of supramolecular materials derived from oligopeptides with embedded π-electron units, where the oligopeptides can be viewed as substituents or side chains to direct the assembly of the central π-electron cores. Upon assembly, the π-systems are driven into close cofacial architectures that facilitate a variety of energy migration processes within the nanomaterial volume, including exciton transport, voltage transmission, and photoinduced electron transfer. Like many practitioners of supramolecular materials science, many of our initial molecular designs were designed with substantial inspiration from biologically occurring self-assembly coupled with input from chemical intuition and molecular modeling and simulation. In this feature article, we summarize our current understanding of the π-peptide self-assembly process as documented through our body of publications in this area. We address fundamental spectroscopic and computational tools used to extract information regarding the internal structures and energetics of the π-peptide assemblies, and we address the current state of the art in terms of recent applications of data science tools in conjunction with high-throughput computational screening and experimental assays to guide the efficient traversal of the π-peptide molecular design space. The abstract image details our integrated program of chemical synthesis, spectroscopic and functional characterization, multiscale simulation, and machine learning which has advanced the understanding and control of the assembly of synthetic π-conjugated peptides into supramolecular nanostructures with energy and biomedical applications.

Hierarchical Self-Assembly Pathways of Peptoid Helices and Sheets.

Peptoids (N-substituted glycines) are a class of tailorable synthetic peptidomic polymers. Amphiphilic diblock peptoids have been engineered to assemble 2D crystalline lattices with applications in catalysis and molecular separations. Assembly is induced in an organic solvent/water mixture by evaporating the organic phase, but the assembly pathways remain uncharacterized. We conduct all-atom molecular dynamics simulations of Nbrpe6Nc6 as a prototypical amphiphilic diblock peptoid comprising an NH2-capped block of six hydrophobic N-((4-bromophenyl)ethyl)glycine residues conjugated to a polar NH3(CH2)5CO tail. We identify a thermodynamically controlled assembly mechanism by which monomers assemble into disordered aggregates that self-order into 1D chiral helical rods then 2D achiral crystalline sheets. We support our computational predictions with experimental observations of 1D rods using small-angle X-ray scattering, circular dichroism, and atomic force microscopy and 2D crystalline sheets using X-ray diffraction and atomic force microscopy. This work establishes a new understanding of hierarchical peptoid assembly and principles for the design of peptoid-based nanomaterials.

Synthesis of polypeptides via bioinspired polymerization of in situ purified N-carboxyanhydrides.

Ribozymes synthesize proteins in a highly regulated local environment to minimize side reactions caused by various competing species. In contrast, it is challenging to prepare synthetic polypeptides from the polymerization of N-carboxyanhydrides (NCAs) in the presence of water and impurities, which induce monomer degradations and chain terminations, respectively. Inspired by natural protein synthesis, we herein report the preparation of well-defined polypeptides in the presence of competing species, by using a water/dichloromethane biphasic system with macroinitiators anchored at the interface. The impurities are extracted into the aqueous phase in situ, and the localized macroinitiators allow for NCA polymerization at a rate which outpaces water-induced side reactions. Our polymerization strategy streamlines the process from amino acids toward high molecular weight polypeptides with low dispersity by circumventing the tedious NCA purification and the demands for air-free conditions, enabling low-cost, large-scale production of polypeptides that has potential to change the paradigm of polypeptide-based biomaterials.

Determining Sequence-Dependent DNA Oligonucleotide Hybridization and Dehybridization Mechanisms Using Coarse-Grained Molecular Simulation, Markov State Models, and Infrared Spectroscopy.

A robust understanding of the sequence-dependent thermodynamics of DNA hybridization has enabled rapid advances in DNA nanotechnology. A fundamental understanding of the sequence-dependent kinetics and mechanisms of hybridization and dehybridization remains comparatively underdeveloped. In this work, we establish new understanding of the sequence-dependent hybridization/dehybridization kinetics and mechanism within a family of self-complementary pairs of 10-mer DNA oligomers by integrating coarse-grained molecular simulation, machine learning of the slow dynamical modes, data-driven inference of long-time kinetic models, and experimental temperature-jump infrared spectroscopy. For a repetitive ATATATATAT sequence, we resolve a rugged dynamical landscape comprising multiple metastable states, numerous competing hybridization/dehybridization pathways, and a spectrum of dynamical relaxations. Introduction of a G:C pair at the terminus (GATATATATC) or center (ATATGCATAT) of the sequence reduces the ruggedness of the dynamics landscape by eliminating a number of metastable states and reducing the number of competing dynamical pathways. Only by introducing a G:C pair midway between the terminus and the center to maximally disrupt the repetitive nature of the sequence (ATGATATCAT) do we recover a canonical "all-or-nothing" two-state model of hybridization/dehybridization with no intermediate metastable states. Our results establish new understanding of the dynamical richness of sequence-dependent kinetics and mechanisms of DNA hybridization/dehybridization by furnishing quantitative and predictive kinetic models of the dynamical transition network between metastable states, present a molecular basis with which to understand experimental temperature jump data, and furnish foundational design rules by which to rationally engineer the kinetics and pathways of DNA association and dissociation for DNA nanotechnology applications.

Computationally Guided Tuning of Peptide-Conjugated Perylene Diimide Self-Assembly.

Peptide-π-conjugated materials are important for biointerfacing charge-transporting applications due to their aqueous compatibility and formation of long-range π-electron networks. Perylene diimides (PDIs), well-established charge-transporting π systems, can self-assemble in aqueous solutions when conjugated with amino acids. In this work, we leveraged computational guidance from our previous work to access two different self-assembled architectures from PDI-amino acid conjugates. Furthermore, we expanded the design rule to other sequences to learn that the closest amino acids to the π core have a significant effect on the photophysical properties of the resulting assemblies. By simply altering glycine to alanine at the closest residue position, we observed significantly different electronic properties as revealed through UV-vis, photoluminescence, and circular dichroism spectroscopies. Accompanying molecular dynamics simulations revealed two distinct types of self-assembled architectures: cofacial structures when the smaller glycine residue is at the closest residue position to the π core versus rotationally shifted structures when glycine is substituted for the larger alanine. This study illustrates the use of tandem computations and experiments to unearth and understand new design rules for supramolecular materials and exposes a modest amino acid substitution as a means to predictably modulate the supramolecular organization and engineer the photophysical properties of π-conjugated peptidic materials.

Computationally Guided Tuning of Amino Acid Configuration Influences the Chiroptical Properties of Supramolecular Peptide-π-Peptide Nanostructures.

Self-assembled supramolecular materials derived from peptidic macromolecules with π-conjugated building blocks are of enormous interest because of their aqueous solubility and biocompatibility. The design rules to achieve tailored optoelectronic properties from these types of materials can be guided by computation and virtual screening rather than intuition-based experimental trial-and-error. Using machine learning, we reported previously that the supramolecular chirality in self-assembled aggregates from VEVAG-π-GAVEV type peptidic materials was most strongly influenced by hydrogen bonding and hydrophobic packing of the alanine and valine residues. Herein, we build upon this idea to demonstrate through molecular-level experimental characterization and all-atom molecular modeling that varying the stereogenic centers of these residues has a profound impact on the optoelectronic properties of the supramolecular aggregates, whereas the variation of stereogenic centers of other residues has only nominal influence on these properties. This study highlights the synergy between computational and experimental insight relevant to the control of chiroptical or other electronic properties associated with supramolecular materials.

In search of a novel chassis material for synthetic cells: emergence of synthetic peptide compartment.

Giant lipid vesicles have been used extensively as a synthetic cell model to recapitulate various life-like processes, including in vitro protein synthesis, DNA replication, and cytoskeleton organization. Cell-sized lipid vesicles are mechanically fragile in nature and prone to rupture due to osmotic stress, which limits their usability. Recently, peptide vesicles have been introduced as a synthetic cell model that would potentially overcome the aforementioned limitations. Peptide vesicles are robust, reasonably more stable than lipid vesicles and can withstand harsh conditions including pH, thermal, and osmotic variations. This mini-review summarizes the current state-of-the-art in the design, engineering, and realization of peptide-based chassis materials, including both experimental and computational work. We present an outlook for simulation-aided and data-driven design and experimental realization of engineered and multifunctional synthetic cells.

Reconstruction of protein structures from single-molecule time series.

Single-molecule experimental techniques track the real-time dynamics of molecules by recording a small number of experimental observables. Following these observables provides a coarse-grained, low-dimensional representation of the conformational dynamics but does not furnish an atomistic representation of the instantaneous molecular structure. Takens's delay embedding theorem asserts that, under quite general conditions, these low-dimensional time series can contain sufficient information to reconstruct the full molecular configuration of the system up to an a priori unknown transformation. By combining Takens's theorem with tools from statistical thermodynamics, manifold learning, artificial neural networks, and rigid graph theory, we establish an approach, Single-molecule TAkens Reconstruction, to learn this transformation and reconstruct molecular configurations from time series in experimentally measurable observables such as intramolecular distances accessible to single molecule Förster resonance energy transfer. We demonstrate the approach in applications to molecular dynamics simulations of a C24H50 polymer chain and the artificial mini-protein chignolin. The trained models reconstruct molecular configurations from synthetic time series data in the head-to-tail molecular distances with atomistic root mean squared deviation accuracies better than 0.2 nm. This work demonstrates that it is possible to accurately reconstruct protein structures from time series in experimentally measurable observables and establishes the theoretical and algorithmic foundations to do so in applications to real experimental data.

Polymeric "Clickase" Accelerates the Copper Click Reaction of Small Molecules, Proteins, and Cells.

Recent work has shown that polymeric catalysts can mimic some of the remarkable features of metalloenzymes by binding substrates in proximity to a bound metal center. We report here an unexpected role for the polymer: multivalent, reversible, and adaptive binding to protein surfaces allowing for accelerated catalytic modification of proteins. The catalysts studied are a group of copper-containing single-chain polymeric nanoparticles (CuI-SCNP) that exhibit enzyme-like catalysis of the copper-mediated azide-alkyne cycloaddition reaction. The CuI-SCNP use a previously observed "uptake mode", binding small-molecule alkynes and azides inside a water-soluble amphiphilic polymer and proximal to copper catalytic sites, but with unprecedented rates. Remarkably, a combined experimental and computational study shows that the same CuI-SCNP perform a more efficient click reaction on modified protein surfaces and cell surface glycans than do small-molecule catalysts. The catalysis occurs through an "attach mode" where the SCNPs reversibly bind protein surfaces through multiple hydrophobic and electrostatic contacts. The results more broadly point to a wider capability for polymeric catalysts as artificial metalloenzymes, especially as it relates to bioapplications.