We explore the design of metal binding sites to modulate triple-helix stability of collagen and collagen-mimetic peptides. Globular proteins commonly utilize metals to connect tertiary structural elements that are well separated in sequence, constraining structure and enhancing stability. It is more challenging to engineer structural metals into fibrous protein scaffolds, which lack the extensive tertiary contacts seen in globular proteins. In the collagen triple helix, the structural adjacency of the carboxy-termini of the three chains makes this region an attractive target for introducing metal binding sites. We engineered His3 sites based on structural modeling constraints into a series of designed homotrimeric and heterotrimeric peptides, assessing the capacity of metal binding to improve stability and in the case of heterotrimers, affect specificity of assembly. Notable enhancements in stability for both homo and heteromeric systems were observed upon addition of zinc(II) and several other metal ions only when all three histidine ligands were present. Metal binding affinities were consistent with the expected Irving-Williams series for imidazole. Unlike other metals tested, copper(II) also bound to peptides lacking histidine ligands. Acetylation of the peptide N-termini prevented copper binding, indicating proline backbone amide metal-coordination at this site. Copper similarly stabilized animal extracted Type I collagen in a metal specific fashion, highlighting the potential importance of metal homeostasis within the extracellular matrix.

Net-negatively-charged heterospecific A:B:C collagen peptide heterotrimers were designed using an automated computational approach. The design algorithm considers both target stability and the energy gap between the target states and misfolded competing states. Structural characterization indicates the net-negative charge balance on the new designs enhances the specificity of the target state at the expense of its stability.

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multiobjective protein design, the development of Pareto optimization methods, and present a specific case study using multiobjective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

Traditional simulation-based protein design considers energy minimization of candidate conformations as a singleobjective combinatorial optimization problem. In this paper we consider a challenging protein design problem, producing twelve protein species based on collagen that uniquely assort into four groups of three: a problem de ned herein as a 4-level heterotrimer. We formulate a bi-objective combinatorial minimization problem that targets both stability and speci city of the 4-level heterotrimer. In order to approximate its Pareto frontier, we utilize both evolutionary and nonevolutionary approaches, operating in either Pareto or aggregation fashions. Our practical observations suggest that the SMS-EMOA with Evolution Strategies' operators is more e ective than standard heuristics deployed in computational protein design, such as Simulated Annealing, Replica Exchange or the Canonical Genetic Algorithm. We investigate the a ained Pareto optimal sets using Barrier Tree analysis, aiming to provide insights into the chemical search-space, as well as to explain the observed algorithmic trends. In particular, we identify Replica Exchange as a promising non-evolutionary technique for this problem class, due to its ecient exploration capabilities. Overall, a common high-level protocol for simultaneous landscape analysis of evolutionary and nonevolutionary search methodologies is put forward for the rst time.