Spatial Monte Carlo model of integrin clustering

BC Bo Cheng WW Wanting Wan GH Guoyou Huang YL Yuhui Li GG Guy M. Genin MM Mohammad R. K. Mofrad TL Tian Jian Lu FX Feng Xu ML Min Lin

This protocol is extracted from research article:

Nanoscale integrin cluster dynamics controls cellular mechanosensing via FAKY397 phosphorylation

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Sci Adv**,
Mar 4, 2020;
DOI:
10.1126/sciadv.aax1909

Nanoscale integrin cluster dynamics controls cellular mechanosensing via FAKY397 phosphorylation

Procedure

We developed a multistep process of integrin clustering. In our model, single integrin has two states, i.e., inactive state where integrin has a low affinity with ligands, which are assumed to be uniformly distributed in our simulation domain, and active state where integrin has a high affinity with ligands. The configuration transition of integrin from the inactive state to the active state is the prerequisite for integrin clustering. The molecular mechanisms underlying the lateral clustering of integrins remain controversial. The integrin clustering submodel applied in this work was based on three clustering mechanisms: ITD-mediated clustering, PI(4,5)P_{2}-mediated clustering, and integrin cross-talk–mediated clustering. We note that many other mechanisms have been proposed, and the rationale for focusing on these three is described in the Supplementary Materials. We then built a coarse-grained model to simulate the integrin clustering process based on the multistep processes as described above.

We modeled the integrin clustering via a spatial Monte Carlo algorithm. All reactions including integrin activation and inactivation, integrin clustering and disassociation, and translational diffusion of integrin on the membrane were modeled on a 2D lattice plane with periodic boundary conditions. The integrin molecules were randomly placed in the lattice domains for the initial configuration. The simulation was implemented by randomly choosing an integrin molecule (an occupied lattice site), and then an event was determined (chemical reactions or translational diffusion) based on the probabilities. An event was chosen by calculating the probability distribution for all possible events$${P}_{i}^{M}=\frac{{\mathrm{\sigma}}_{i}^{M}}{{\mathrm{\sigma}}_{\text{tot}}}$$(6)where *i* is the integrin site and *M* is the event; ${\mathrm{\sigma}}_{i}^{M}$ is the transition rate for *M* event; and σ_{tot} is defined as$$\begin{array}{c}{\mathrm{\sigma}}_{\text{tot}}=6(\frac{{\mathrm{\sigma}}^{D}}{6}+\text{max}\{{\displaystyle \sum _{\text{all}\text{forward}\text{reaction}\text{events}}}{\mathrm{\sigma}}^{R}\left\}\right)\\ +\text{max}\left\{{\displaystyle \sum _{\text{all}\text{backward reaction events}}}{\mathrm{\sigma}}^{R}\right\}\end{array}$$(7)where σ* ^{D}* is the transition probability of translational diffusion, and σ

The dynamical parameters of the α_{5}β_{1} and α_{v}β_{3} integrins were taken from the literature. Four dynamical parameters differ for α_{5}β_{1} and α_{v}β_{3} integrins: (i) the diffusion coefficients of integrins (~0.1 μm^{2}/s for α_{5}β_{1} integrins and ~0.3 μm^{2}/s for α_{v}β_{3} integrins); (ii) the activation rate of α_{v}β_{3} integrin is 10-fold higher than that of α_{5}β_{1} integrin, as deduced from free-energy analysis; (iii) the intrinsic ligand binding affinity of the α_{5}β_{1} integrin is higher than that of the α_{v}β_{3} integrin; and (iv) α_{5}β_{1} integrins have longer lifetime than α_{v}β_{3} integrins, in response to the same bond tension. The two types of integrins with different dynamical parameters were dispersed within simulation regions. Note that, because lateral clustering of integrin remains controversial, we assumed that different integrins had the same lateral clustering affinity.

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