Chemical and enzymatic modifications of peptide-displayed libraries have been successfully employed to expand the phage display library. However, the requirement of specific epitopes and scaffolds has limited the scope of protein engineering using phage display. In this study, we present a novel approach utilizing omniligase-1-mediated selective and specific ligation on the phage pIII protein, offering a high conversion rate and compatibility with commercially available phage libraries. We applied this method to perform high-throughput engineering of insulin analogues with randomized B chain C-terminal regions. Insulin analogues with different B chain C-terminal segments were selected and exhibited biological activity equivalent to that of human insulin. Molecular dynamics studies of insulin analogues revealed a novel interaction between the insulin B27 residue and insulin receptor L1 domain. In summary, our findings highlight the potential of omniligase-1-mediated phage display in the development and screening of disulfide-rich peptides and proteins. This approach holds promise for the creation of novel insulin analogues with enhanced therapeutic properties and exhibits potential for the development of other therapeutic compounds.

Antibody-antigen interaction –at antigenic local environments called B-cell epitopes – is a prominentmechanism for neutralization of infection. Effective mimicry, and display, ofB-cell epitopes is key to vaccine design. Here, aphysical approach is evaluated for the discovery of epitopes which evolve slowlyover closely related pathogens (conserved epitopes). The approach is 1) proteinflexibility-based and 2) demonstrated with clinically relevant envelopedviruses, simulated via molecular dynamics. The approach is validated against 1)seven structurally characterized enveloped virus epitopes which evolved theleast (out of thirty-eight enveloped virus-antibody structures) and 2) eightpreexisting epitope and peptide discovery algorithms. Rationale for a new benchmarkingscheme is presented. A data-driven epitope clustering algorithm is introduced.The prediction of eleven Zika virus epitopes (for future exploration onrecombinant vaccine technologies) is demonstrated. For the first time, proteinflexibility is shown to outperform solvent accessible surface area as an epitopediscovery metric.

A Padé approximant scheme for realizing the discrete-time evolution of the state of a many-atom system is introduced. This temporal coarse-graining scheme accounts for the underlying Newtonian physics and avoids the need for construction of spatially coarse-grained variables. Newtonian physics is incorporated through short molecular dynamics simulations at the beginning of each of the large coarse-grained timesteps. The balance between stochastic and coherent dynamics expressed by many-atom systems is captured via incorporation of the Ito formula into a Padé approximant for the time dependence of individual atom positions over large timesteps. Since the time for a many-atom system to express a characteristic ensemble of atomic velocity fluctuations is typically short relative to the characteristic time of large-scale atomic displacements, a computationally efficient and accurate temporal coarse-graining of the atom-resolved Newtonian dynamics is formulated, denoted all-atom Padé-Ito molecular dynamics (APIMD). Evolution of the system over a time step much longer than that required for standard molecular dynamics (MD) is achieved via incorporation of information from the short MD simulations into a Padé approximant extrapolation in time. The extrapolated atomic configuration is subjected to energy minimization and, when needed, thermal equilibration so as to avoid occasional unphysical close encounters deriving from the Padé approximant extrapolation and to represent configurations appropriate for the temperature of interest. APIMD is implemented and tested via comparison with traditional MD simulations of five phenomena: (1) pertussis toxin subunit deformation, (2) structural transition in a T = 1 capsid-like structure of HPV16 L1 protein, (3) coalescence of argon nanodroplets, and structural transitions in dialanine in (4) vacuum, and (5) water. Accuracy of APIMD is demonstrated using semimicroscopic descriptors (rmsd, radius of gyration, residue-residue contact maps, and densities) and the free energy. Significant computational acceleration relative to traditional molecular dynamics is illustrated.

Self-organizations of supramolecular assemblies at surfaces typically achieve highly ordered two-dimensional packing structures due to the negotiation of various intermolecular interactions into a minimum free energy configuration. As we advance these systems toward the long-term goal of achieving programmable surface functionality, however, we consider systems of greater complexity in molecular building-block architecture and in growth conditions. Here, we study the polymorphic self-assembly of tricarbazolo triazolophane macrocycles (tricarb) at the solution/solid interface to determine the underlying pathway that tricarb follows when it transitions between two structures. Tricarb species, depending on peripheral functionalization, self-assemble into kinetically trapped disordered structures or thermodynamically favored ordered honeycomb structures. Experiments varying pre- and post-deposition conditions (solubility, concentration, and temperature) suggest that a solution-mediated annealing pathway, as opposed to an on-surface rearrangement, is key to a transition from disordered structures to honeycomb. Molecular dynamic simulations provide nanoscale insights into the roles of peripheral groups and solvent molecules in self-assembly. Substituents on the exterior of the molecule not only affect solubility but also stabilize tricarb–tricarb hydrogen-bonded structures. Peripheral groups influence the formation/re-formation and strength of adsorbate–adsorbate contacts and also limit solvent interactions about a macrocyclic core. The combination of simulation and experiment demonstrates the role of the solution-mediated annealing pathway in the self-assembly of complex supramolecular systems.

The intermolecular interactions underlying self-assembly are important for designing supramolecular architectures with defined function and composition over various length scales. Here, an integrated quantum mechanical/molecular dynamics (QM/MD) approach is introduced to explore the intermolecular interactions governing the two-dimensional self-assembly of a shape-persistent macrocycle, that is, tricarbazolo triazolophane (tricarb), on highly ordered pyrolytic graphite (HOPG). Three structural motifs (row, zig-zag, and rosette) for the self-association and growth of tricarb oligomers are analyzed using dispersion-corrected density functional theory to reveal their relative stability. QM analysis shows that the major factor in the self-assembly, via H-bonding contributions to the interaction energies, originates from favorable local dipole orientations between triazole units in the zig-zag and rosette motifs. Closing of the macrocyclic loop leads to the formation of a tertiary structure, which induces stability for the rosette formation relative to other motifs. MD simulations of these motifs on HOPG demonstrate the stability of the rosette, corroborating the QM results. In addition, the effect of long alkyl chains on the self-assembly dynamics is explored by simulating preexisting honeycomb domains of tricarbs with various alkyl chain lengths, without and with the presence of an explicit solvent. MD simulations reveal that alkyl chains mediate the formation of a honeycomb pattern, as observed experimentally.

After local transient fluctuations are dissipated, in an energy transfer process, a system evolves to a state where the energy density field varies slowly in time relative to the dynamics of atomic collisions and vibrations. Furthermore, the energy density field remains strongly coupled to the atomic scale processes (collisions and vibrations), and it can serve as the basis of a multiscale theory of energy transfer. Here, a method is introduced to capture the long scale energy density variations as they coevolve with the atomistic state in a way that yields insights into the basic physics and implies an efficient algorithm for energy transfer simulations. The approach is developed based on the N-atom Liouville equation and an interatomic force field and avoids the need for conjectured phenomenological equations for energy transfer and other processes. The theory is demonstrated for sodium chloride and silicon dioxide nanoparticles immersed in a water bath via molecular dynamics simulations of the energy transfer between a nanoparticle and its aqueous host fluid. The energy density field is computed for different sets of symmetric grid densities, and the multiscale theory holds when slowly varying energy densities at the nodes are obtained. Results strongly depend on grid density and nanoparticle constituent material. A nonuniform temperature distribution, larger thermal fluctuations in the nanoparticle than in the bath, and enhancement of fluctuations at the surface, which are expressed due to the atomic nature of the systems, are captured by this method rather than by phenomenological continuum energy transfer models.

Amphiphilic alkoxybenzonitriles (ABNs) of varying chain length are studied at the solution/graphite interface to analyze dynamics of assembly. Competitive self-assembly between ABNs and alkanoic acid solvent is shown by scanning tunneling microscopy (STM) to be controlled by concentration and molecular size. Molecular dynamics (MD) simulations reveal key roles of the sub-nanosecond fundamental steps of desorption, adsorption, and on-surface motion. We discovered asymmetry in desorption-adsorption steps. Desorption starting from alkyl chain detachment from the surface is favored due to dynamic occlusion by neighbouring chains. Even though the nitrile head has a strong solvent affinity, it more frequently re-adsorbs following a detachment event.

Molecular dynamics is based on solving Newton's equations for many-particle systems that evolve along complex, highly fluctuating trajectories. The orbital instability and short-time complexity of Newtonian orbits is in sharp contrast to the more coherent behavior of collective modes such as density profiles. The notion of virtual molecular dynamics is introduced here based on temporal coarse-graining via Pade approximants and the Ito formula for stochastic processes. It is demonstrated that this framework leads to significant efficiency over traditional molecular dynamics and avoids the need to introduce coarse-grained variables and phenomenological equations for their evolution. In this framework, an all-atom trajectory is represented by a Markov chain of virtual atomic states at a discrete sequence of timesteps, transitions between which are determined by an integration of conventional molecular dynamics with Pade approximants and a microstate energy annealing methodology. The latter is achieved by a conventional and an MD NVE energy minimization schemes. This multiscale framework is demonstrated for a pertussis toxin subunit undergoing a structural transition, a T=1 capsid-like structure of HPV16 L1 protein, and two coalescing argon droplets.