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Cited by 7Protein Kinase Regulation and GTPase SignalingProtein Structure and DynamicsReceptor Mechanisms and Signaling

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Comparative MD simulations and advanced analytics based studies on wild-type and hot-spot mutant A59G HRas

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Sharma, Neeru, Sonavane, Uddhavesh, Joshi, Rajendra·2020·PLOS ONE
DOI 10.1371/journal.pone.0234836PMC PMC7567374

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Abstract

The Ras family of proteins is known to play an important role in cellular signal transduction. The oncoprotein Ras is also found to be mutated in ~90% of the pancreatic cancers, of which G12V, G13V, A59G and Q61L are the known hot-spot mutants. These ubiquitous proteins fall in the family of G-proteins, and hence switches between active GTP bound and inactive GDP bound states, which is hindered in most of its oncogenic mutant counterparts. Moreover, Ras being a GTPase has an intrinsic property to hydrolyze GTP to GDP, which is obstructed due to mutations and lends the mutants stuck in constitutively active state leading to oncogenic behavior. In this regard, the present study aims to understand the dynamics involved in the hot-spot mutant A59G-Ras using long 10μs classical MD simulations (5μs for each of the wild-type and mutant systems) and comparing the same with its wild-type counterpart. Advanced analytics using Markov State Model (MSM) based approach has been deployed to comparatively understand the transition path for the wild-type and mutant systems. Roles of crucial residues like Tyr32, Gln61 and Tyr64 have also been established using multivariate PCA analyses. Furthermore, this multivariate PCA analysis also provides crucial features which may be used as reaction coordinates for biased simulations for further studies. The absence of formation of pre-hydrolysis network is also reported for the mutant conformation, using the distance-based analyses (between crucial residues) of the conserved regions. The implications of this study strengthen the hypothesis that the disruption of the pre-hydrolysis network in the mutant A59G ensemble might lead to permanently active oncogenic conformation in the mutant conformers.

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Fig 1 2D-plot showing RMSD trend for the wild-type and mutant simulationW and simulationM, respectively. The mutant system showed a fluctuating SwII region as compared to its wild-type counterpart.
Fig 1 2D-plot showing RMSD trend for the wild-type and mutant simulationW and simulationM, respectively. The mutant system showed a fluctuating SwII region as compared to its wild-type counterpart.CC BY · PubMed Central
Fig 2 2D-plot showing RMSF trend for the wild-type and mutant simulationW (black line) and simulationM (red line), respectively. The regions showing changes are labelled accordingly.
Fig 2 2D-plot showing RMSF trend for the wild-type and mutant simulationW (black line) and simulationM (red line), respectively. The regions showing changes are labelled accordingly.CC BY · PubMed Central
Fig 3 Overlaps of the wild-type and mutant simulation trajectories to depict the deviation in the selected regions in dotted ovals (gray for wild-type and yellow for A59G mutant). The wild-type and mutant trajectories are shown in blue and red cartoon representations, respectively.
Fig 3 Overlaps of the wild-type and mutant simulation trajectories to depict the deviation in the selected regions in dotted ovals (gray for wild-type and yellow for A59G mutant). The wild-type and mutant trajectories are shown in blue and red cartoon representations, respectively.CC BY · PubMed Central
Fig 4 2-D plot showing number of conformers for Gln61 residue’s interaction with Lys16, Ser17, Thr35, Tyr32 and Tyr64. Black and red lines represent trends for the wild-type and mutant systems, respectively.
Fig 4 2-D plot showing number of conformers for Gln61 residue’s interaction with Lys16, Ser17, Thr35, Tyr32 and Tyr64. Black and red lines represent trends for the wild-type and mutant systems, respectively.CC BY · PubMed Central
Fig 5 2-D plot showing number of conformers for Gly60 residue’s interaction with Lys16, and Ser17. Black and red lines represent trends for the wild-type and mutant systems, respectively.
Fig 5 2-D plot showing number of conformers for Gly60 residue’s interaction with Lys16, and Ser17. Black and red lines represent trends for the wild-type and mutant systems, respectively.CC BY · PubMed Central
Fig 6 2-D plot showing number of conformers for Tyr64 residue’s interaction with Lys16, Ser17, Thr35 and Tyr32. Black and red lines represent trends for the wild-type and mutant systems, respectively.
Fig 6 2-D plot showing number of conformers for Tyr64 residue’s interaction with Lys16, Ser17, Thr35 and Tyr32. Black and red lines represent trends for the wild-type and mutant systems, respectively.CC BY · PubMed Central
Fig 7 2-D plot showing the segregation of wild-type and mutant simulation trajectories, on the basis of phi(Φ) and psi(ψ) dihedrals values. Black color depicts the wild-type and red color represents the A59G-mutant cluster.
Fig 7 2-D plot showing the segregation of wild-type and mutant simulation trajectories, on the basis of phi(Φ) and psi(ψ) dihedrals values. Black color depicts the wild-type and red color represents the A59G-mutant cluster.CC BY · PubMed Central
Fig 8 The 2-D plot showing the projections of wild-type (green) and mutant (red) trajectories, using multivariate PCA approach. A combination of dihedral, interactions and energy were used as features, as labelled on the X and Y axis.
Fig 8 The 2-D plot showing the projections of wild-type (green) and mutant (red) trajectories, using multivariate PCA approach. A combination of dihedral, interactions and energy were used as features, as labelled on the X and Y axis.CC BY · PubMed Central
Fig 9 Projections of PC1 on PC2, of the features chosen for multivariate PCA. Cyan and orange color clusters are for wild-type and mutant trajectories, respectively. The individual arrows represent the eight features used for this analysis.
Fig 9 Projections of PC1 on PC2, of the features chosen for multivariate PCA. Cyan and orange color clusters are for wild-type and mutant trajectories, respectively. The individual arrows represent the eight features used for this analysis.CC BY · PubMed Central
Fig 10 Depiction of metastable clusters for the wild-type and mutant trajectories, represented by numbers, as captured using PyEMMA while performing MSM based analysis. The feature used was Cadist (distances between the C-alpha atoms of the respective trajectories).
Fig 10 Depiction of metastable clusters for the wild-type and mutant trajectories, represented by numbers, as captured using PyEMMA while performing MSM based analysis. The feature used was Cadist (distances between the C-alpha atoms of the respective trajectories).CC BY · PubMed Central
Fig 11 The 2D-plot demonstrating the probable pathways for the wild-type and mutant simulations, calculated using PyEMMA. The most probable pathway is denoted by a bold gray arrow (rightmost to left most cluster). The committor probability of each state transition is written along the respective arrow.
Fig 11 The 2D-plot demonstrating the probable pathways for the wild-type and mutant simulations, calculated using PyEMMA. The most probable pathway is denoted by a bold gray arrow (rightmost to left most cluster). The committor probability of each state transition is written along the respective arrow.CC BY · PubMed Central
Fig 12 Fig showing the overlap of representative structures of the cluster 5 (mutant), cluster 1 (wild-type and mutant), from the respective MSM based analysis. Yellow CPK representation shows the mutant conformation from cluster 5 (sparsely populated) and the most probable conformations of wild-type and mutant trajectories, from the respective cluster 1.
Fig 12 Fig showing the overlap of representative structures of the cluster 5 (mutant), cluster 1 (wild-type and mutant), from the respective MSM based analysis. Yellow CPK representation shows the mutant conformation from cluster 5 (sparsely populated) and the most probable conformations of wild-type and mutant trajectories, from the respective cluster 1.CC BY · PubMed Central

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Citations

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Metadata

Authors
Sharma, N., Sonavane, U., & Joshi, R.
Affiliations
Centre for Development of Advanced Computing (IN)
Corresponding author
Joshi, Rajendra
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2020-10-16
Volume
15
Issue
10
Pages
e0234836
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0234836
PMC
PMC7567374
OpenAlex
W3092863994
Cited by
7
References
6
Citations by year
2025: 1 · 2024: 2 · 2023: 4
FWCI
0.32
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Patient Safety Foundation
  • Ministry of Electronics and Information technology
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
Keywords
HRAS, Ras GTPase, GTP hydrolysis, GTP/GDP binding, Markov state models, transition pathways, reaction coordinate, pancreatic cancer
MeSH terms
Guanosine Diphosphate, Guanosine Triphosphate, Humans, Hydrolysis, Mutation, Proto-Oncogene Proteins p21(ras), Molecular Dynamics Simulation

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CoChat. (2026). Citation check: Comparative MD simulations and advanced analytics based studies on wild-type and hot-spot mutant A59G HRas. https://app.cochat.ai/papers/10.1371/journal.pone.0234836
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Sharma, N., Sonavane, U., & Joshi, R. (2020). Comparative MD simulations and advanced analytics based studies on wild-type and hot-spot mutant A59G HRas. PLOS ONE. https://doi.org/10.1371/journal.pone.0234836