Title : The multidimensional topological shift of the KRASG12D proteins in catalytic environments and pertinent drugs-targetting
Abstract:
The properties of the KRASG12D proteins are analyzed; the behaviour in catalytic environment is recalled. The proposed Markov Models are reviewed after [F. Liang, Z. Kang, X. Sun, J. Chen, X. Duan, H. He, J. Cheng, Inhibition mechanism of MRTX1133 on KRASG12D: a molecular dynamics simulation and Markov state model study, Journal of Computer-Aided Molecular Design 37, 157 (2023).], from where the
advisement for drugs-targetting are scrutinized. The new analytical formulation of the multi-dimensional topological shift of the KRASG12D proteins in catalytic environments is presented; it is proven to fulfill a multidimensional Markov chain, after the application of the Borovkov First-Passage-Times method. Accordingly, the time evolution of the eigenvalues, the errors and the time evolution of the errors is newly
analytically written. The modern trends in genomics and in epigenomics and of the drugs-targetting are newly summoned. The numerical simulations to analyse the effects of the drug MRTX1133 on KRASG12D protein are here newly studied. The behaviour of KRASG12D protein catalytic environments is newly reappraised.
The experimental data are newly proven to be consistent with a description of a multidimensional Markov Chain. The close-ness of the newly-found multidimensional chain to that from which the one-dimensional Markov-State-Models of liang et al. Is issued in the Galerking representation is studied; the errors are analytically written. More in detail, the experimental data are demonstrated to exhibit a modification of the mean-first passage time from the first state of the possible one-dimensional models, from which the two-states Markov models are compared within the multidimensional framework. The calculations are performed in the Galerkin representation, is it is straightforward proven that the committors become orthogonal for the considered states: the time evolution of the eigenvalues and those of the errors are thus calculated form Laplace kernel with Radon measure.
The possibility of memory processes in the modifications of the mean-first passage times is excluded.
The analysis is aimed at outlining the characteristics of the First-Passage-Times, which dramatically determine the Markov-chain models of such proteins such as the KRAS as far as the definition of the hidden-Markov models are concerned, i.e. such as pharmaceutical applications.
In the work of Liang et al., the KRAS12G protien is found to be found into different conformational states, which are numerically analysed to be apt to define 12 different Markov States models.\\
In the work Lu et al. [ S. Lu, H. Jang, R. Nussinov, J Zhang, The structural basis of oncogenic mutations G12, G13 and Q61 in small GTPase K-Ras4B, Sci. Rep. 6(1):1-15 (2016). ], only one inactive states was considered in the exposition. More in particular, only one inactive state was take into account as far as the description of the catalytic domain in aqueous solutions considered within the framework of KRAS4B mutations.