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      Assessment of mutation probabilities of KRAS G12 missense mutants and their long-timescale dynamics by atomistic molecular simulations and Markov state modeling

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A mutated KRAS protein is frequently observed in human cancers. Traditionally, the oncogenic properties of KRAS missense mutants at position 12 (G12X) have been considered as equal. Here, by assessing the probabilities of occurrence of all KRAS G12X mutations and KRAS dynamics we show that this assumption does not hold true. Instead, our findings revealed an outstanding mutational bias. We conducted a thorough mutational analysis of KRAS G12X mutations and assessed to what extent the observed mutation frequencies follow a random distribution. Unique tissue-specific frequencies are displayed with specific mutations, especially with G12R, which cannot be explained by random probabilities. To clarify the underlying causes for the nonrandom probabilities, we conducted extensive atomistic molecular dynamics simulations (170 μs) to study the differences of G12X mutations on a molecular level. The simulations revealed an allosteric hydrophobic signaling network in KRAS, and that protein dynamics is altered among the G12X mutants and as such differs from the wild-type and is mutation-specific. The shift in long-timescale conformational dynamics was confirmed with Markov state modeling. A G12X mutation was found to modify KRAS dynamics in an allosteric way, which is especially manifested in the switch regions that are responsible for the effector protein binding. The findings provide a basis to understand better the oncogenic properties of KRAS G12X mutants and the consequences of the observed nonrandom frequencies of specific G12X mutations.

          Author summary

          The oncogene KRAS is frequently mutated in various cancers. When the amino acid glycine 12 is mutated, KRAS protein acquires oncogenic properties that result in tumor cell-growth and cancer progression. These mutations prevail especially in the pancreatic ductal adenocarcinoma, which is a cancer with an exceptionally dismal prognosis. To date, there is a limited understanding of the different mutations at the position 12, also regarding whether the different mutations would have different consequences. These discrepancies could have major implications for the future drug therapies targeting KRAS mutant harboring tumors. In this study, we made a critical assessment of the observed frequency of KRAS G12X mutations and the underlying causes for these frequencies. We also assessed KRAS G12X mutant discrepancies on an atomistic level by utilizing state-of-the-art molecular dynamics simulations. We found that the dynamics of the mutants does not only differ from the wild-type protein, but there is also a profound difference among the different mutants. These results emphasize that the different KRAS G12X mutations are not equal, and thereby they suggest that the future research related to mutant KRAS biology should account for these observations.

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          Most cited references 65

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          Canonical sampling through velocity-rescaling

          We present a new molecular dynamics algorithm for sampling the canonical distribution. In this approach the velocities of all the particles are rescaled by a properly chosen random factor. The algorithm is formally justified and it is shown that, in spite of its stochastic nature, a quantity can still be defined that remains constant during the evolution. In numerical applications this quantity can be used to measure the accuracy of the sampling. We illustrate the properties of this new method on Lennard-Jones and TIP4P water models in the solid and liquid phases. Its performance is excellent and largely independent on the thermostat parameter also with regard to the dynamic properties.
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            RAS Proteins and Their Regulators in Human Disease.

            RAS proteins are binary switches, cycling between ON and OFF states during signal transduction. These switches are normally tightly controlled, but in RAS-related diseases, such as cancer, RASopathies, and many psychiatric disorders, mutations in the RAS genes or their regulators render RAS proteins persistently active. The structural basis of the switch and many of the pathways that RAS controls are well known, but the precise mechanisms by which RAS proteins function are less clear. All RAS biology occurs in membranes: a precise understanding of RAS' interaction with membranes is essential to understand RAS action and to intervene in RAS-driven diseases.
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              The R.E.D. tools: advances in RESP and ESP charge derivation and force field library building.

              Deriving atomic charges and building a force field library for a new molecule are key steps when developing a force field required for conducting structural and energy-based analysis using molecular mechanics. Derivation of popular RESP charges for a set of residues is a complex and error prone procedure because it depends on numerous input parameters. To overcome these problems, the R.E.D. Tools (RESP and ESP charge Derive, ) have been developed to perform charge derivation in an automatic and straightforward way. The R.E.D. program handles chemical elements up to bromine in the periodic table. It interfaces different quantum mechanical programs employed for geometry optimization and computing molecular electrostatic potential(s), and performs charge fitting using the RESP program. By defining tight optimization criteria and by controlling the molecular orientation of each optimized geometry, charge values are reproduced at any computer platform with an accuracy of 0.0001 e. The charges can be fitted using multiple conformations, making them suitable for molecular dynamics simulations. R.E.D. allows also for defining charge constraints during multiple molecule charge fitting, which are used to derive charges for molecular fragments. Finally, R.E.D. incorporates charges into a force field library, readily usable in molecular dynamics computer packages. For complex cases, such as a set of homologous molecules belonging to a common family, an entire force field topology database is generated. Currently, the atomic charges and force field libraries have been developed for more than fifty model systems and stored in the RESP ESP charge DDataBase. Selected results related to non-polarizable charge models are presented and discussed.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                10 September 2018
                September 2018
                : 14
                : 9
                Affiliations
                [1 ] School of Pharmacy, University of Eastern Finland, Kuopio, Finland
                [2 ] Laboratory of Physics, Tampere University of Technology, Tampere, Finland
                [3 ] Department of Internal Medicine VIII, University Hospital Tuebingen, Tuebingen, Germany
                [4 ] Department of Physiology I, Institute of Physiology, Eberhard Karls University Tuebingen, Tuebingen, Germany
                [5 ] Department of Physics, University of Helsinki, Helsinki, Finland
                [6 ] MEMPHYS-Center for Biomembrane Physics, Helsinki, Finland
                National Cancer Institute, United States of America and Tel Aviv University, Israel, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Article
                PCOMPBIOL-D-18-00354
                10.1371/journal.pcbi.1006458
                6147662
                30199525
                © 2018 Pantsar et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Figures: 5, Tables: 1, Pages: 23
                Product
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100002341, Academy of Finland;
                Award ID: 276509
                Award Recipient :
                Funded by: Biocenter Finland/DDCB
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100002341, Academy of Finland;
                Award ID: 307415
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 290974
                Award Recipient :
                Funded by: Tampereen Teknillinen Yliopisto (FI)
                Award ID: Graduate School Program
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100008969, Alfred Kordelinin Säätiö;
                Award Recipient :
                The study was supported by the Academy of Finland ( http://www.aka.fi/en) (grant 276509 to AP) and Biocenter Finland/DDCB ( http://ddcb.fi/en/ddcb/biocenter_finland/) (TL). IV and SR acknowledge the Academy of Finland ( http://www.aka.fi/en) (Center of Excellence program (grant no. 307415)) and the European Research Council ( https://erc.europa.eu) (Advanced Grant CROWDED-PRO-LIPIDS (grant no. 290974)) for financial support. SR thanks the Graduate School program of Tampere University of Technology and Alfred Kordelin Foundation ( https://kordelin.fi) for financial support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Mutation
                Point Mutation
                Biology and Life Sciences
                Biochemistry
                Biochemical Simulations
                Biology and Life Sciences
                Computational Biology
                Biochemical Simulations
                Research and Analysis Methods
                Database and Informatics Methods
                Biological Databases
                Mutation Databases
                Biology and Life Sciences
                Genetics
                Mutation
                Mutation Databases
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Multivariate Analysis
                Principal Component Analysis
                Biology and Life Sciences
                Biochemistry
                Salt Bridges
                Physical Sciences
                Chemistry
                Electrochemistry
                Salt Bridges
                Biology and Life Sciences
                Anatomy
                Endocrine System
                Pancreas
                Medicine and Health Sciences
                Anatomy
                Endocrine System
                Pancreas
                Biology and Life Sciences
                Anatomy
                Exocrine Glands
                Pancreas
                Medicine and Health Sciences
                Anatomy
                Exocrine Glands
                Pancreas
                Physical Sciences
                Physics
                Condensed Matter Physics
                Solid State Physics
                Crystallography
                Crystal Structure
                Physical Sciences
                Mathematics
                Probability Theory
                Markov Models
                Custom metadata
                vor-update-to-uncorrected-proof
                2018-09-20
                Datasets related to the publication are available at https://doi.org/10.5281/zenodo.1346073. Other relevant data are included within the paper and its Supporting information files.

                Quantitative & Systems biology

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