Sequence-Structure-Binding Relationships Reveal Adhesion Behavior of the Car9 Solid-Binding Peptide: An Integrated Experimental and Simulation Study.

Solid-binding peptides (SBPs) recognizing inorganic and synthetic interfaces have enabled a broad range of materials science applications and hold promise as adhesive or morphogenetic control units that can be genetically encoded within desirable or designed protein frameworks. To date, the underlying relationships governing both SBP-surface and SBP-SBP interactions and how they give rise to different adsorption mechanisms remain unclear. Here, we combine protein engineering, surface plasmon resonance characterization, and molecular dynamics (MD) simulations initiated from Rosetta predictions to gain insights on the interplay of amino acid composition, structure, self-association, and adhesion modality in a panel of variants of the Car9 silica-binding peptide (DSARGFKKPGKR) fused to the C-terminus of superfolder green fluorescent protein (sfGFP). Analysis of kinetics, energetics, and MD-predicted structures shows that the high-affinity binding of Car9 to the silanol-rich surface of silica is dominated by electrostatic contributions and a spectrum of several persistent interactions that, along with a high surface population of bound molecules, promote cooperative interactions between neighboring SBPs and higher order structure formation. Transition from cooperative to Langmuir adhesion in sfGFP-Car9 variants occurs in concert with a reduction of stable surface interactions and self-association, as confirmed by atomic force microscopy imaging of proteins exhibiting the two different binding behaviors. We discuss the implications of these results for the de novo design of SBP-surface binding systems.

Solid-State NMR and MD Study of the Structure of the Statherin Mutant SNa15 on Mineral Surfaces.

Elucidation of the structure and interactions of proteins at native mineral interfaces is key to understanding how biological systems regulate the formation of hard tissue structures. In addition, understanding how these same proteins interact with non-native mineral surfaces has important implications for the design of medical and dental implants, chromatographic supports, diagnostic tools, and a host of other applications. Here, we combine solid-state NMR spectroscopy, isotherm measurements, and molecular dynamics simulations to study how SNa15, a peptide derived from the hydroxyapatite (HAP) recognition domain of the biomineralization protein statherin, interacts with HAP, silica (SiO2), and titania (TiO2) mineral surfaces. Adsorption isotherms are used to characterize the binding affinity of SNa15 to HAP, SiO2, and TiO2. We also apply 1D 13C CP MAS, 1D 15N CP MAS, and 2D 13C-13C DARR experiments to SNa15 samples with uniformly 13C- and 15N-enriched residues to determine backbone and side-chain chemical shifts. Different computational tools, namely TALOS-N and molecular dynamics simulations, are used to deduce secondary structure from backbone and side-chain chemical shift data. Our results show that SNa15 adopts an α-helical conformation when adsorbed to HAP and TiO2, but the helix largely unravels upon adsorption to SiO2. Interactions with HAP are mediated in general by acidic and some basic amino acids, although the specific amino acids involved in direct surface interaction vary with surface. The integrated experimental and computational approach used in this study is able to provide high-resolution insights into adsorption of proteins on interfaces.

Biasing Smarter, Not Harder, by Partitioning Collective Variables into Families in Parallel Bias Metadynamics.

Molecular simulations of systems with multiple copies of identical atoms or molecules may require the biasing of numerous, degenerate collective variables (CVs) to accelerate sampling. Recently, a variation of metadynamics (MetaD) named parallel bias metadynamics (PBMetaD) has been shown to make biasing of many CVs more tractable. We extended the PBMetaD scheme so that it partitions degenerate CVs into families that share the same bias potential, consequently expediting convergence of the free-energy landscape. We tested our method, named parallel bias metadynamics with partitioned families, on 3, 21, and 78 CV systems and obtained an approximately proportional increase in convergence speed compared to standard PBMetaD.

Peptoid Backbone Flexibilility Dictates Its Interaction with Water and Surfaces: A Molecular Dynamics Investigation.

Peptoids are peptide-mimetic biopolymers that are easy to synthesize and adaptable for use in drugs, chemical scaffolds, and coatings. However, there is insufficient information about their structural preferences and interactions with the environment in various applications. We conducted a study to understand the fundamental differences between peptides and peptoids using molecular dynamics simulations with semiempirical (PM6) and empirical (AMBER) potentials, in conjunction with metadynamics enhanced sampling. From studies of single molecules in water and on surfaces, we found that sarcosine (model peptoid) is much more flexible than alanine (model peptide) in different environments. However, the sarcosine and alanine interact similarly with a hydrophobic or a hydrophilic. Finally, this study highlights the conformational landscape of peptoids and the dominant interactions that drive peptoids toward these conformations.

Essential slow degrees of freedom in protein-surface simulations: A metadynamics investigation.

Many proteins exhibit strong binding affinities to surfaces, with binding energies much greater than thermal fluctuations. When modelling these protein-surface systems with classical molecular dynamics (MD) simulations, the large forces that exist at the protein/surface interface generally confine the system to a single free energy minimum. Exploring the full conformational space of the protein, especially finding other stable structures, becomes prohibitively expensive. Coupling MD simulations with metadynamics (enhanced sampling) has fast become a common method for sampling the adsorption of such proteins. In this paper, we compare three different flavors of metadynamics, specifically well-tempered, parallel-bias, and parallel-tempering in the well-tempered ensemble, to exhaustively sample the conformational surface-binding landscape of model peptide GGKGG. We investigate the effect of mobile ions and ion charge, as well as the choice of collective variable (CV), on the binding free energy of the peptide. We make the case for explicitly biasing ions to sample the true binding free energy of biomolecules when the ion concentration is high and the binding free energies of the solute and ions are similar. We also make the case for choosing CVs that apply bias to all atoms of the solute to speed up calculations and obtain the maximum possible amount of information about the system.

Investigating the Role of Phosphorylation in the Binding of Silaffin Peptide R5 to Silica with Molecular Dynamics Simulations.

Biomimetic silica formation, a process that is largely driven by proteins, has garnered considerable interest in recent years due to its role in the development of new biotechnologies. However, much remains unknown of the molecular-scale mechanisms underlying the binding of proteins to biomineral surfaces such as silica, or even of the key residue-level interactions between such proteins and surfaces. In this study, we employ molecular dynamics (MD) simulations to study the binding of R5-a 19-residue segment of a native silaffin peptide used for in vitro silica formation-to a silica surface. The metadynamics enhanced sampling method is used to converge the binding behavior of R5 on silica at both neutral (pH 7.5) and acidic (pH 5) conditions. The results show fundamental differences in the mechanism of binding between the two cases, providing unique insight into the pH-dependent ability of R5 and native silaffin to precipitate silica. We also study the effect of phosphorylation of serine residues in R5 on both the binding free energy to silica and the interfacial conformation of the peptide. Results indicate that phosphorylation drastically decreases the binding free energy and changes the structure of silica-adsorbed R5 through the introduction of charge and steric repulsion. New mechanistic insights from this work could inform rational design of new biomaterials and biotechnologies.

Tuning crystallization pathways through sequence engineering of biomimetic polymers.

Two-step nucleation pathways in which disordered, amorphous, or dense liquid states precede the appearance of crystalline phases have been reported for a wide range of materials, but the dynamics of such pathways are poorly understood. Moreover, whether these pathways are general features of crystallizing systems or a consequence of system-specific structural details that select for direct versus two-step processes is unknown. Using atomic force microscopy to directly observe crystallization of sequence-defined polymers, we show that crystallization pathways are indeed sequence dependent. When a short hydrophobic region is added to a sequence that directly forms crystalline particles, crystallization instead follows a two-step pathway that begins with the creation of disordered clusters of 10-20 molecules and is characterized by highly non-linear crystallization kinetics in which clusters transform into ordered structures that then enter the growth phase. The results shed new light on non-classical crystallization mechanisms and have implications for the design of self-assembling polymer systems.