Results 1 - 1 of 1
Results 1 - 1 of 1. Search took: 0.015 seconds
[en] Highlights: • A new computational method is introduced for polymer self-assembly. • The method searches in the subspace of symmetric conformations, which is biologically relevant. • The method predicts self-assembly of a lattice tetramer, an otherwise intractable problem. • The method is used to construct the statistically convergent free energy landscape. • Chain aggregation is shown to be a three-state thermodynamic process. • The method can be applied to atomistic simulations. Protein self-assembly is fundamental to biological function and disease. Experimentally, the atomic-level structure is difficult to obtain and the assembly mechanism is poorly understood. The large number of possible states accessible to such systems limits computational prediction. Here, I introduce a new computational approach that enforces conformational symmetry, whereby all chains in the system adopt the same conformation. Using this approach on a 2D lattice, a designed multi-chain conformation is found more than four orders of magnitude faster than existing approaches. Furthermore, the free energy landscape can be efficiently computed, showing potential for enabling atomistic prediction of protein self-assembly.