Stereoselectivity is a hallmark of biomolecular processes from catalysis to self-assembly, which predominantly occur between homochiral species. However, both homochiral and heterochiral complexes of synthetic polypeptides have been observed where stereoselectivity hinges on details of intermolecular interactions. This raises the question whether general rules governing stereoselectivity exist. A geometric ridges-in-grooves model of interacting helices indicates that heterochiral associations should generally be favored in this class of structures. We tested this principle using a simplified molecular screw – a collagen peptide triple- helix composed of either L- or D-proline with a cyclic aliphatic side chain. Calculated stabilities of like- and opposite- handed triple-helical pairings indicated a preference for heterospecific associations. Mixing left and right-handed helices drastically lowered solubility, resulting in micron-scale sheet-like assemblies that are one peptide-length thick as characterized with atomic force microscopy. X-ray scattering measurements of inter-helical spacing in these sheets support a tight ridges-in-grooves packing of left and right-handed triple helices.
Synthetic collagen mimetic peptides are used to probe the role of hydrophobic forces in mediating protein self-assembly. Higher order association is an integral property of natural collagens, which assemble into fibers and meshes that comprise the extracellular matrix of connective tissues. The unique triple-helix fold fully exposes two-thirds of positions in the protein to solvent, providing ample opportunities for engineering interaction sites. Inclusion of just a few hydrophobic groups in a minimal peptide promotes a rich variety of self-assembly behaviors, resulting in hundred-nanometer to micron size nanodiscs and nanofibers. Morphology depends primarily on the length of hydrophobic domains. Peptide discs contain lipophilic domains capable of sequestering small hydrophobic dyes. Combining multiple peptide types result in composite structures of discs and fibers ranging from stars to plates-on-a-string. These systems provide valuable tools to shed insight into the fundamental principles underlying hydrophobicity-driven higher order protein association that will facilitate the design of self-assembling systems in biomaterials and nanomedical applications.
The melting temperature (Tm) of a protein is the temperature at which half of the protein population is in a folded state. Therefore, Tm is a measure of the thermostability of a protein. Increasing the Tm of a protein is a critical goal in biotechnology and biomedicine. However, predicting the change in melting temperature (dTm) due to mutations at a single residue is difficult because it depends on an intricate balance of forces. Existing methods for predicting dTm have had similar levels of success using generally complex models. We find that training a machine learning model with a simple set of easy to calculate physicochemical descriptors describing the local environment of the mutation performed as well as more complicated machine learning models and is 2–6 orders of magnitude faster. Importantly, unlike in most previous publications, we perform a blind prospective test on our simple model by designing 96 variants of a protein not in the training set. Results from retrospective and prospective predictions reveal the limited applicability domain of each model. This study highlights the current deficiencies in the available dTm dataset and is a call to the community to systematically design a larger and more diverse experimental dataset of mutants to prospectively predict dTm with greater certainty.
Collagens are the most abundant proteins of the extracellular matrix, and the hierarchical folding and supramolecular assembly of collagens into banded fibers is essential for mediating cell-matrix interactions and tissue mechanics. Collagen extracted from animal tissues is a valuable commodity, but suffers from safety and purity issues, limiting its biomaterials applications. Synthetic collagen biomaterials could address these issues, but their construction requires molecular-level control of folding and supramolecular assembly into ordered banded fibers, comparable to those of natural collagens. Here, we show an innovative class of banded fiber-forming synthetic collagens that recapitulate the morphology and some biological properties of natural collagens. The synthetic collagens comprise a functional-driver module that is flanked by adhesive modules that effectively promote their supramolecular assembly. Multiscale simulations support a plausible molecular-level mechanism of supramolecular assembly, allowing precise design of banded fiber morphology. We also experimentally demonstrate that synthetic fibers stimulate osteoblast differentiation at levels comparable to natural collagen. This work thus deepens understanding of collagen biology and disease by providing a ready source of safe, functional biomaterials that bridge the current gap between the simplicity of peptide biophysical models and the complexity of in vivo animal systems.
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