Using sequence data to predict the self-assembly of supramolecular collagen structures

Collagen fibrils are the major constituents of the extracellular matrix, which provides structural support to vertebrate connective tissues. It is widely assumed that the superstructure of collagen fibrils is encoded in the primary sequences of the molecular building blocks. However, the interplay between large-scale architecture and small-scale molecular interactions makes the ab initio prediction of collagen structure challenging. Here, we propose a model that allows us to predict the periodic structure of collagen fibers and the axial offset between the molecules, purely on the basis of simple predictive rules for the interaction between amino acid residues. With our model, we identify the sequence-dependent collagen fiber geometries with the lowest free energy and validate the predicted geometries against the available experimental data. We propose a procedure for searching for optimal staggering distances. Finally, we build a classification algorithm and use it to scan 11 data sets of vertebrate fibrillar collagens, and predict the periodicity of the resulting assemblies. We analyzed the experimentally observed variance of the optimal stagger distances across species, and find that these distances, and the resulting fibrillar phenotypes, are evolutionary well preserved. Moreover, we observed that the energy minimum at the optimal stagger distance is broad in all cases, suggesting a further evolutionary adaptation designed to improve the assembly kinetics. Our periodicity predictions are not only in good agreement with the experimental data on collagen molecular staggering for all collagen types analyzed, but also for synthetic peptides. We argue that, with our model, it becomes possible to design tailor-made, periodic collagen structures, thereby enabling the design of novel biomimetic materials based on collagen-mimetic trimers.