SBMOpenMM: A Builder of Structure-Based Models for OpenMM.

Molecular dynamics (MD) simulations have become a standard tool to correlate the structure and function of biomolecules and significant advances have been made in the study of proteins and their complexes. A major drawback of conventional MD simulations is the difficulty and cost of obtaining converged results, especially when exploring potential energy surfaces containing considerable energy barriers. This limits the wide use of MD calculations to determine the thermodynamic properties of biomolecular processes. Indeed, this is true when considering the conformational entropy of such processes, which is ultimately critical in assessing the simulations' convergence. Alternatively, a wide range of structure-based models (SBMs) has been used in the literature to unravel the basic mechanisms of biomolecular dynamics. These models introduce simplifications that focus on the relevant aspects of the physical process under study. Because of this, SBMs incorporate the need to modify the force field definition and parameters to target specific biophysical simulations. Here we introduce SBMOpenMM, a Python library to build force fields for SBMs, that uses the OpenMM framework to create and run SBM simulations. The code is flexible and user-friendly and profits from the high customizability and performance provided by the OpenMM platform.

Enzyme Conformation Influences the Performance of Lipase-powered Nanomotors.

Enzyme-powered micro/nanomotors have myriads of potential applications in various areas. To efficiently reach those applications, it is necessary and critical to understand the fundamental aspects affecting the motion dynamics. Herein, we explored the impact of enzyme orientation on the performance of lipase-powered nanomotors by tuning the lipase immobilization strategies. The influence of the lipase orientation and lid conformation on substrate binding and catalysis was analyzed using molecular dynamics simulations. Besides, the motion performance indicates that the hydrophobic binding (via OTES) represents the best orienting strategy, providing 48.4 % and 95.4 % increase in diffusion coefficient compared to hydrophilic binding (via APTES) and Brownian motion (no fuel), respectively (with C[triacetin] of 100 mm). This work provides vital evidence for the importance of immobilization strategy and corresponding enzyme orientation for the catalytic activity and in turn, the motion performance of nanomotors, and is thus helpful to future applications.

Estimating conformational heterogeneity of tryptophan synthase with a template-based Alphafold2 approach.

The three-dimensional structure of the enzymes provides very relevant information on the arrangement of the catalytic machinery and structural elements gating the active site pocket. The recent success of the neural network Alphafold2 in predicting the folded structure of proteins from the primary sequence with high levels of accuracy has revolutionized the protein design field. However, the application of Alphafold2 for understanding and engineering function directly from the obtained single static picture is not straightforward. Indeed, understanding enzymatic function requires the exploration of the ensemble of thermally accessible conformations that enzymes adopt in solution. In the present study, we evaluate the potential of Alphafold2 in assessing the effect of the mutations on the conformational landscape of the beta subunit of tryptophan synthase (TrpB). Specifically, we develop a template-based Alphafold2 approach for estimating the conformational heterogeneity of several TrpB enzymes, which is needed for enhanced stand-alone activity. Our results show the potential of Alphafold2, especially if combined with molecular dynamics simulations, for elucidating the changes induced by mutation in the conformational landscapes at a rather reduced computational cost, thus revealing its plausible application in computational enzyme design.

Insights into the molecular determinants of thermal stability in halohydrin dehalogenase HheD2.

Halohydrin dehalogenases (HHDHs) are promising enzymes for application in biocatalysis due to their promiscuous epoxide ring-opening activity with various anionic nucleophiles. So far, seven different HHDH subtypes A to G have been reported with subtype D containing the by far largest number of enzymes. Moreover, several characterized members of subtype D have been reported to display outstanding characteristics such as high catalytic activity, broad substrate spectra or remarkable thermal stability. Yet, no structure of a D-type HHDH has been reported to date that could be used to investigate and understand those features on a molecular level. We therefore solved the crystal structure of HheD2 from gamma proteobacterium HTCC2207 at 1.6 Å resolution and used it as a starting point for targeted mutagenesis in combination with molecular dynamics (MD) simulation, in order to study the low thermal stability of HheD2 in comparison with other members of subtype D. This revealed a hydrogen bond between conserved residues Q160 and D198 to be connected with a high catalytic activity of this enzyme. Moreover, a flexible surface region containing two α-helices was identified to impact thermal stability of HheD2. Exchange of this surface region by residues of HheD3 yielded a variant with 10 °C higher melting temperature and reaction temperature optimum. Overall, our results provide important insights into the structure-function relationship of HheD2 and presumably for other D-type HHDHs. DATABASES: Structural data are available in PDB database under the accession number 7B73.