Aqueous peptide-TiO2 interfaces: iso-energetic binding via either entropically- or enthalpically-driven mechanisms

A major barrier to the systematic improvement of biomimetic peptide-mediated strategies for the controlled growth of inorganic nanomaterials in environmentally benign conditions lies in the lack of clear conceptual connections between the sequence of the peptide and its surface binding affinity, with binding being facilitated by non-covalent interactions. Peptide conformation, both in the adsorbed and non-adsorbed state, is the key relationship that connects peptide-materials binding with peptide sequence. Here, we combine experimental peptide–titania binding characterization with state-of-the-art conformational sampling via molecular simulations to elucidate these structure/binding relationships for two very different titania-binding peptide sequences. The two sequences (Ti-1: QPYLFATDSLIK and Ti-2: GHTHYHAVRTQT) differ in their overall hydropathy, yet via quartz-crystal microbalance measurements and predictions from molecular simulations, we show these sequences both support very similar, strong titania-binding affinities. Our molecular simulations reveal that the two sequences exhibit profoundly different modes of surface binding, with Ti-1 acting as an entropically-driven binder while Ti-2 behaves as an enthalpically-driven binder. The integrated approach presented here provides a rational basis for peptide sequence engineering to achieve the in-situ growth and organization of titania nanostructures in aqueous media and for the design of sequences suitable for a range of technological applications that involve the interface between titania and biomolecules

The low-lying excitations of polydiacetylene

The Pariser-Parr-Pople Hamiltonian is used to calculate and identify the nature of the low-lying vertical transition energies of polydiacetylene. The model is solved using the density matrix renormalisation group method for a fixed acetylenic geometry for chains of up to 102 atoms. The non-linear optical properties of polydiacetylene are considered, which are determined by the third-order susceptibility. The experimental 1Bu data of Giesa and Schultz are used as the geometric model for the calculation. For short chains, the calculated E(1Bu) agrees with the experimental value, within solvation effects (ca. 0.3 eV). The charge gap is used to characterise bound and unbound states. The nBu is above the charge gap and hence a continuum state; the 1Bu, 2Ag and mAg are not and hence are bound excitons. For large chain lengths, the nBu tends towards the charge gap as expected, strongly suggesting that the nBu is the conduction band edge. The conduction band edge for PDA is agreed in the literature to be ca. 3.0 eV. Accounting for the strong polarisation effects of the medium and polaron formation gives our calculated E(nBu) ca. 3.6 eV, with an exciton binding energy of ca. 1.0 eV. The 2Ag state is found to be above the 1Bu, which does not agree with relaxed transition experimental data. However, this could be resolved by including explicit lattice relaxation in the Pariser- Parr-Pople-Peierls model. Particle-hole separation data further suggest that the 1Bu, 2Ag and mAg are bound excitons, and that the nBu is an unbound exciton.Comment: LaTeX, 23 pages, 4 postscript tables and 8 postscript figure

Category Theoretic Analysis of Hierarchical Protein Materials and Social Networks

Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a “concept web” or “semantic network” except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine.Presidential Early Career Award for Scientists and Engineers (N000141010562)United States. Army Research Office. Multidisciplinary University Research Initiative (W911NF0910541)United States. Office of Naval Research (grant N000141010841)Massachusetts Institute of Technology. Dept. of MathematicsStudienstiftung des deutschen VolkesClark BarwickJacob Luri

A review of combined experimental and computational procedures for assessing biopolymer structure–process–property relationships

Tailored biomaterials with tunable functional properties are desirable for many applications ranging from drug delivery to regenerative medicine. To improve the predictability of biopolymer materials functionality, multiple design parameters need to be considered, along with appropriate models. In this article we review the state of the art of synthesis and processing related to the design of biopolymers, with an emphasis on the integration of bottom-up computational modeling in the design process. We consider three prominent examples of well-studied biopolymer materials – elastin, silk, and collagen – and assess their hierarchical structure, intriguing functional properties and categorize existing approaches to study these materials. We find that an integrated design approach in which both experiments and computational modeling are used has rarely been applied for these materials due to difficulties in relating insights gained on different length- and time-scales. In this context, multiscale engineering offers a powerful means to accelerate the biomaterials design process for the development of tailored materials that suit the needs posed by the various applications. The combined use of experimental and computational tools has a very broad applicability not only in the field of biopolymers, but can be exploited to tailor the properties of other polymers and composite materials in general.National Institutes of Health (U.S.) (U01 EB014976)National Science Foundation (U.S.)United States. Dept. of Defense (United States. Air Force Office of Scientific Research)United States. Dept. of Defense (Multidisciplinary University Research Initiative)German National Academic FoundationDr. Jurgen-Ulderup-Foundatio