Assessing protein conformational sampling and structural stability via de novo design and molecular dynamics simulations

Abstract

Molecular dynamics and de novo techniques, associated to quality parameter sets, have excelled at determining the structure of small proteins with high accuracy. To achieve a detailed description of protein conformations, these methods must critically assess the thermodynamic features of the molecular ensembles. Here, a comparison of the conformational ensemble generated by molecular dynamics and de novo techniques were carried out for six Top7-based proteins carrying gp41 HIV-1 epitopes. The native Top7, a highly stable computationally designed protein, was used as benchmark. Structural stability, flexibility, and secondary structure content were assessed. The consistency of the latter was compared to experimental circular dichroism spectra for all proteins. While both methods are capable to identify the stable from unstable chimeric proteins, the sampled conformational space and flexibility differ significantly in both methods. Molecular dynamics simulations seem to better describe secondary structure content and identify regions responsible for conformational instability. The de novo method, as implemented in Rosetta-a prime tool for protein design, overestimates secondary structure content. On the other hand, its empirical energy function is capable to predict the threshold for protein stability.

Residues 315 and 369 in HN Protein Contribute to the Thermostability of Newcastle Disease Virus

“…MD simulation has widely been used to explore the thermalstable mechanism of industry enzymes (Purmonen et al, 2007;Rouhani et al, 2018). By calculating the RMSF values of backbone atoms, thermally sensitive regions or structural flexibility were identified (Cunha et al, 2015;Unal et al, 2019). In this study, we investigated the changes in the RMSF FIGURE 7 | Syncytium formation induced by different NDVs.…”

“…MD simulation has widely been used to explore the thermal-stable mechanism of industry enzymes ( Purmonen et al, 2007 ; Rouhani et al, 2018 ). By calculating the RMSF values of backbone atoms, thermally sensitive regions or structural flexibility were identified ( Cunha et al, 2015 ; Unal et al, 2019 ). In this study, we investigated the changes in the RMSF value of the HN protein by changing the temperature to 310 and 330 K. We found that residues 315–375 of the HN protein in the La Sota strain caused the major instability of the structure.…”

In silico studies on structural, functional, and evolutionary analysis of bacterial chromate reductase family responsible for high chromate bioremediation efficiency

“…As sampled conformational space and their distance are significantly related to the flexibility of protein and that a protein will be more stable if it has more conformational space with a less intermediate distance between them, the mutant (Y85N) form of E. coli quinone reductase appears as comparatively stable than wild type. Cunha et al [62], in their study, hypothesized a relation of protein conformational sampling with its structural stability via de novo design and molecular dynamics simulations. They also stated that the secondary structure of a protein is also coherently associated with the changes of conformational sampling during the protein dynamism [62].…”

“…Cunha et al [62], in their study, hypothesized a relation of protein conformational sampling with its structural stability via de novo design and molecular dynamics simulations. They also stated that the secondary structure of a protein is also coherently associated with the changes of conformational sampling during the protein dynamism [62]. In the current study, it has also been reflected from the PCA that in the case of mutant (Y85N) form of E. coli quinone reductase secondary structure integrity is maintained throughout the simulation trajectory compared to its wild-type form as the conformational spaces are linked more adjacent with each other alongside the two eigenvectors (PC1 and PC2).…”

Insights from molecular dynamics simulations for computational protein design

“…The second main source of error is the extent of conformational sampling obtained from the simulation, or the extent to which simulations sample the heterogeneous distribution of conformational states, which is due to constraints on computing time and methods that limit the flexibility and sampling of the protein, as described previously. 32 Determining the appropriate length of a simulation is a trade off between economy (shorter simulations) and sampling (longer simulations), constrained by the timescale(s) of the dynamics in question 33,34 . However, longer simulations are not always better.…”