The Role of Calcium in Regulating the Conformational Dynamics of d-Galactose/d-Glucose-Binding Protein Revealed by Markov State Model Analysis

Elucidating the Role of a Calcium-Binding Loop in an x-Prolyl Aminodipeptidase from Lb. helveticus

Prolyl aminodipeptidase (PepX) is an α/β hydrolase that cleaves at penultimate N-terminal prolyl peptide bonds. The crystal structure of PepX from Lactobacillus helveticus exhibits a calcium-binding loop within the catalytic domain. The calcium-binding sequence of xDxDxDGxxD within this loop is highly conserved in PepX proteins among lactic acid bacteria, but its purpose remains unknown. Enzyme activity is not significantly affected in the presence of the metal chelator ethylenediaminetetraacetic acid (EDTA), nor in the presence of excess calcium ions. To eliminate calcium binding, D196A and D194A/D196A mutations were constructed within the conserved calcium-binding sequence motif. Enzyme activity and stability of the D196A mutant were comparable to the wild-type enzyme by colorimetric kinetic assays and protein thermal shift assays. However, the D194A/D196A mutant was inactive though it retained native-like structure and thermal stability, contradicting the EDTA and calcium titration results. This suggests calcium binding to PepX may be essential for activity.

"Dividing and Conquering" and "Caching" in Molecular Modeling

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes "dividing and conquering" and/or "caching" in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of "dividing and conquering" and "caching" along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution "caching" of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for "dividing and conquering" and "caching" in complex molecular systems.