The structure of proteins has long been recognized to
hold the
key to understanding and engineering their function, and rapid advances
in structural biology and protein structure prediction are now supplying
researchers with an ever-increasing wealth of structural information.
Most of the time, however, structures can only be determined in free
energy minima, one at a time. While conformational flexibility may
thus be inferred from static end-state structures, their interconversion
mechanisms—a central ambition of structural biology—are
often beyond the scope of direct experimentation. Given the dynamical
nature of the processes in question, many studies have attempted to
explore conformational transitions using molecular dynamics (MD).
However, ensuring proper convergence and reversibility in the predicted
transitions is extremely challenging. In particular, a commonly used
technique to map out a path from a starting to a target conformation
called steered MD (SMD) can suffer from starting-state dependence
(hysteresis) when combined with techniques such as umbrella sampling
(US) to compute the free energy profile of a transition. Here, we
study this problem in detail on conformational changes of increasing
complexity. We also present a new, history-independent approach that
we term “MEMENTO” (Morphing End states by Modelling
Ensembles with iNdependent TOpologies) to generate paths that alleviate
hysteresis in the construction of conformational free energy profiles.
MEMENTO utilizes template-based structure modelling to restore physically
reasonable protein conformations based on coordinate interpolation
(morphing) as an ensemble of plausible intermediates, from which a
smooth path is picked. We compare SMD and MEMENTO on well-characterized
test cases (the toy peptide deca-alanine and the enzyme adenylate
kinase) before discussing its use in more complicated systems (the
kinase P38α and the bacterial leucine transporter LeuT). Our
work shows that for all but the simplest systems SMD paths should
not in general be used to seed umbrella sampling or related techniques,
unless the paths are validated by consistent results from biased runs
in opposite directions. MEMENTO, on the other hand, performs well
as a flexible tool to generate intermediate structures for umbrella
sampling. We also demonstrate that extended end-state sampling combined
with MEMENTO can aid the discovery of collective variables on a case-by-case
basis.