DOI: 10.5061/dryad.wstqjq2zv
title: Data from: Molecular exaptation by the integrin αI domain

description: Integrins bind ligands between their alpha (α) and beta (β) subunits and
 transmit signals through conformational changes. Early in chordate
 evolution, some α subunits acquired an "inserted" (I) domain
 that expanded integrin’s ligand-binding repertoire but obstructed the
 ancestral ligand pocket, seemingly blocking conventional integrin
 activation. To understand the ancestral conformational dynamics of the
 integrin I domain, we performed all-atom molecular dynamics simulations on
 a representative sample of integrin I domains, sister clade
 collagen-derived vWFA domains, and the ancestral reconstruction AncI. We
 further investigated the effect of amino acid substitution on the internal
 ligand of αLβ2 bound with ICAM-1 and αEβ7 bound to E-cadherin. All
 simulations were performed using OpenMM 7.7.0 (I-domain systems) or OpenMM
 8.2.0 (integrin headpiece systems) employing AMBER19ffSB force field, OPC
 water model, and 12-6-4 Li Merz ion parameters. Simulations were performed
 on either the Fred Hutch Cancer Center clusters on Nvidia GTX 2080 or
 in-house workstations on Nvidia GTX 3090. Trajectory snapshots were saved
 every 100 ps during production simulations.
description: # Data from: Molecular exaptation by the integrin αI domain ## Provided
 Files The dataset contains molecular dynamics trajectories analyzed for
 the manuscript titled "Molecular exaptation by the integrin αI
 domain", Science Advances, 2015. Two sets of simulations were
 performed: 1) simulations of various integrin I-domains initially bound to
 different divalent cations (`i-domains/`), and 2) simulations of the αEβ7
 and LFA1 headpiece simulations (`aEb7-LFA1/`). * `i-domains.zip/` files
 for I-domain simulations. Includes: * `strip-traj/` - trajectories in
 `.xtc` file format * `ref/` - reference trajectories for RMS analysis *
 `prmtop/` - topology file in `.prmtop` file format * `aEb7_LFA1.zip/`
 files for αEβ7 and LFA1 simulations. Includes: * `wrap-traj/` -
 trajectories in `.xtc` file format * `ref/` - reference trajectories for
 RMS analysis * `prmtop/` - topology file in `.prmtop` file format ###
 Simulation details All simulations were performed using OpenMM 7.7.0
 (I-domain systems) (Ref. 1) or OpenMM 8.2.0 (Ref 2.) (integrin headpiece
 systems) employing AMBER19ffSB force field (Ref. 3), OPC water model, and
 12-6-4 Li Merz ion parameters (Ref. 4). The proteins were solvated in a
 periodic truncated octahedron with OPC water molecules and 150mM NaCl.
 Langevin dynamics was performed using a Langevin integrator with an
 integration timestep of 4 fs and collision rate of √2 ps-1 and hydrogen
 mass repartitioning (Ref. 5). Pressure was maintained using a Monte Carlo
 barostat with an update frequency of 100 steps. Nonbonded interactions
 were calculated with a distance cutoff of 10Å. Trajectory snapshots were
 saved every 100 ps during production simulations. Trajectory lengths and
 number of replicates of each simulated system are listed in the Table
 below. #### Integrin headpiece | System | Ligand | Initial Model |
 Replicates | Trajectory Length (per replicate, μs) | | ----------- |
 ---------- | ------------- | ---------- |
 ------------------------------------- | | αEβ7 | E-cadherin | This study |
 4 | 1.0 | | αEβ7\_E372D | E-cadherin | This study | 4 | 1.0 | |
 αEβ7\_E372A | E-cadherin | This study | 4 | 1.0 | #### LFA1 | System |
 Ligand | Initial Model | Replicates | Trajectory Length (per replicate,
 μs) | | ---------- | ------ | -------------- | ---------- |
 ------------------------------------- | | LFA1 | ICAM-1 | Homology model |
 6 | 1.0 | | LFA1\_IDGT | ICAM-1 | Homology model | 6 | 1.0 | | LFA1\_VDGT
 | ICAM-1 | Homology model | 6 | 1.0 | | LFA1\_IAGT | ICAM-1 | Homology
 model | 6 | 1.0 | | LFA1\_SNTD | ICAM-1 | Homology model | 6 | 1.0 | |
 LFA1\_GRPQ | ICAM-1 | Homology model | 6 | 1.0 | #### I domain | System |
 Ligand | Initial Model | Replicates | Trajectory Length (per replicate,
 μs) | | ------------- | ------ | ------------- | ---------- |
 ------------------------------------- | | αE | Mg²⁺ | This study | 10 |
 2.4 | | αE | Mn²⁺ | This study | 10 | 2.4 | | αE | Ca²⁺ | This study | 10
 | 2.4 | | αL | Mg²⁺ | PDB: 3F74 | 10 | 2.4 | | αL | Mn²⁺ | PDB: 3F74 | 10
 | 2.4 | | αL | Ca²⁺ | PDB: 3F74 | 10 | 2.4 | | α2 | Mg²⁺ | PDB: 5HJ2 | 10
 | 2.4 | | α2 | Mn²⁺ | PDB: 5HJ2 | 10 | 2.4 | | α2 | Ca²⁺ | PDB: 5HJ2 | 10
 | 2.4 | | XP\_032834731 | Mg²⁺ | Alphafold2 | 10 | 2.4 | | XP\_032834731 |
 Mn²⁺ | Alphafold2 | 10 | 2.4 | | XP\_032834731 | Ca²⁺ | Alphafold2 | 10 |
 2.4 | | co6a6 | Mg²⁺ | Alphafold3 | 10 | 2.4 | | co6a6 | Mn²⁺ | Alphafold3
 | 10 | 2.4 | | co6a6 | Ca²⁺ | Alphafold3 | 10 | 2.4 | | co6a5 | Mg²⁺ |
 Alphafold3 | 10 | 2.4 | | co6a5 | Mn²⁺ | Alphafold3 | 10 | 2.4 | | co6a5 |
 Ca²⁺ | Alphafold3 | 10 | 2.4 | | vwa129 | Mg²⁺ | Alphafold3 | 10 | 2.4 | |
 vwa129 | Mn²⁺ | Alphafold3 | 10 | 2.4 | | vwa129 | Ca²⁺ | Alphafold3 | 10
 | 2.4 | | coea1 | Mg²⁺ | Alphafold3 | 10 | 2.4 | | coea1 | Mn²⁺ |
 Alphafold3 | 10 | 2.4 | | coea1 | Ca²⁺ | Alphafold3 | 10 | 2.4 | | AncI |
 Mg²⁺ | Alphafold3 | 10 | 2.4 | | AncI | Mn²⁺ | Alphafold3 | 10 | 2.4 | |
 AncI | Ca²⁺ | Alphafold3 | 10 | 2.4 | | RPN10 | None | PDB: 5LN1 | 10 |
 2.4 | References: 1. P. Eastman, J. Swails, J. D. Chodera, R. T. McGibbon,
 Y. Zhao, K. A. Beauchamp, L.-P. Wang, A. C. Simmonett, M. P. Harrigan, C.
 D. Stern, R. P. Wiewiora, B. R. Brooks, V. S. Pande, OpenMM 7: Rapid
 development of high performance algorithms for molecular dynamics. PLoS
 Comput. Biol. 13, e1005659 (2017). 2. P. Eastman, R. Galvelis, R. P.
 Peláez, C. R. A. Abreu, S. E. Farr, E. Gallicchio, A. Gorenko, M. M.
 Henry, F. Hu, J. Huang, A. Krämer, J. Michel, J. A. Mitchell, V. S. Pande,
 J. P. Rodrigues, J. Rodriguez-Guerra, A. C. Simmonett, S. Singh, J.
 Swails, P. Turner, Y. Wang, I. Zhang, J. D. Chodera, G. De Fabritiis, T.
 E. Markland, OpenMM 8: Molecular dynamics simulation with machine learning
 potentials. J. Phys. Chem. B 128, 109–116 (2024). 3. C. Tian, K.
 Kasavajhala, K. A. A. Belfon, L. Raguette, H. Huang, A. N. Migues, J.
 Bickel, Y. Wang, J. Pincay, Q. Wu, C. Simmerling, ff19SB:
 Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum
 Mechanics Energy Surfaces in Solution. J. Chem. Theory Comput. 16, 528–552
 (2020). 4. A. Sengupta, Z. Li, L. F. Song, P. Li, K. M. Merz Jr,
 Parameterization of Monovalent Ions for the OPC3, OPC, TIP3P-FB, and
 TIP4P-FB Water Models. J. Chem. Inf. Model. 61, 869–880 (2021). 5. C. W.
 Hopkins, S. Le Grand, R. C. Walker, A. E. Roitberg, Long-Time-Step
 Molecular Dynamics through Hydrogen Mass Repartitioning. J. Chem. Theory
 Comput. 11, 1864–1874 (2015). ### Dataset usage Trajectories (`.xtc`) may
 be visualized using [VMD](https://www.ks.uiuc.edu/Research/vmd/) by
 loading the corresponding topology file (`.prmtop`). Alternatively,
 trajectories and topologies may be loaded in open-sourced analysis
 packages such as [MDTraj](https://www.mdtraj.org/1.9.8.dev0/index.html),
 [MDAnalysis](https://www.mdanalysis.org/), and
 [pytraj](https://amber-md.github.io/pytraj/latest/index.html).