Native or non-native protein-protein docking models? Molecular dynamics to the rescue

Abstract

Molecular docking excels at creating a plethora of potential models of protein-protein complexes. To correctly distinguish the favourable, native-like models from the remaining ones remains, however, a challenge. We assessed here if a protocol based on molecular dynamics (MD) simulations would allow to distinguish native from non-native models to complement scoring functions used in docking. To this end, first models for 25 protein-protein complexes were generated using HADDOCK. Next, MD simulations complemented with machine learning were used to discriminate between native and non-native complexes based on a combination of metrics reporting on the stability of the initial models. Native models showed higher stability in almost all measured properties, including the key ones used for scoring in the CAPRI competition, namely the positional root mean square deviations and fraction of native contacts from the initial docked model. A Random Forest classifier was trained, reaching 0.85 accuracy in correctly distinguishing native from non-native complexes. Reasonably modest simulation lengths in the order of 50 to 100 ns are already sufficient to reach this accuracy, which makes this approach applicable in practice.

Functional antibody characterization via direct structural analysis and information‐driven protein–protein docking

“…Previous studies showed that the trends that allow to differentiate between native and nonnative complexes are already evident within the first 5 ns, and are only augmented during the rest of the simulation. 64 Our analysis of the trajectory during the 50 ns simulation showed limited variation in the epitopic region and emphasized the stability of the Van der Waal contacts established by L313, which are central for the functional KD035 complex formation. On the other hand, marginal H-bond and salt bridge mediated interactions showed a greater instability.…”

“…[61][62][63] Understandably, the inherent flexibility of each monomer contributes to the energy landscape of the complex. 61 MD simulations are frequently utilized as a means to complement the information obtained by static analysis of protein-protein docking 64 and also to correct errors in the crystal structure mainly due to packing forces. The inspection of the symmetry mates for our B1 and KD035 structures show no involvement in the packing interfaces of the CDR loops or areas which could otherwise affect their conformation.…”

“…71 In this regard, descriptions of structural and energetic properties generated by the MD simulations will undoubtedly contribute to the development of more accurate scoring methods. 64 Overall, the reliability of our model, supported by MD simulation and mutagenesis analyses, sets up a protocol for a multidisciplinary approach where protein-protein docking output can be effectively restrained by the supplementary experimental information.…”

Investigating the competition between ACE2 natural molecular interactors and SARS-CoV-2 candidate inhibitors

“…For each of the four selected ACE2-interactor complexes, we performed a 100 ns long molecular dynamics simulation. The aim of this procedure is both to refine the predicted docking model [ 47 , 48 ] and to statistically study the structural stability of the estimated complex by analyzing the inter-molecular interactions during the simulation. Indeed, the greater the stability of the interaction between the ACE2 receptor and the corresponding molecular partner (each of the 4 selected), the greater the probability that the two macromolecules interact with high-affinity [ 16 , 25 ].…”

Structural insights into recognition of chemokine receptors by Staphylococcus aureus leukotoxins

“…For both complexes, we monitored the number of inter-chain hydrogen bonds (H-bonds), buried surface area (BSA) and interface R.M.S.D. (iRMSD) relative to the starting model, as these parameters were recently shown to be useful in discriminating native from non-native docked models by MD (Jandova, Vargiu et al 2021 (Figure 5C). Consistent with our modeling results, ACKR1 Cys51 was previously identified by mutagenesis as critical for LukED intoxication of erythrocytes (Spaan, Reyes-Robles et al 2015), as well as LukE loops 1, 3 and 4 (Peng, Takeshita et al 2018, Vasquez, Lubkin et al 2020.…”