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STEEP1 protein, human

Q: What are some common challenges in studying the STEEP1 protein?

One of the key challenges in studying the STEEP1 protein is identifying the most effective and reproducible experimental protocols from the available literature. With numerous studies and variations, it can be difficult to pinpoint the optimal methods for your specific research goals related to STEEP1.

STEEP1 (Serine/Threonine-rich and Extracellular Envelope Protein 1) is a protein involved in cellular processes related to the extracellular matrix and cell signaling.
It is expressed in various tissue types and has been implicated in developmental and disease-related pathways.
STEEP1 plays a role in cell adhesion, migration, and proliferation, making it a potential target for research in areas such as tissue engineering, cancer biology, and regenerative medicine.
The characterization of STEEP1 structure, function, and regulatory mechanisms can provide valuable insights into its physiological and pathological relevance.

Most cited protocols related to «STEEP1 protein, human»

Protein Simulation with CHARMM-GUI and NAMD

Cited 858 times

Protein simulation systems were prepared with the CHARMM-GUI.^{28 (link)} Briefly, protein structures taken from corresponding protein data bank^{29 (link)} files were solvated in pre-equilibrated cubic TIP3P water boxes of suitable sizes and counter-ions were added to keep systems neutral as detailed in Table 1. Periodic boundary conditions were applied and Lennard-Jones (LJ) interactions were truncated at 12 Å with a force switch smoothing function from 10 Å to 12 Å. The non-bonded interaction lists were generated with a distance cutoff of 16 Å and updated heuristically. Electrostatic interactions were calculated using the particle mesh Ewald method³⁰ with a real space cutoff of 12 Å on an approximately 1 Å grid with 6th order spline. Covalent bonds to hydrogen atoms were constrained by SHAKE.³¹ After a 200 step Steepest Descent (SD) minimization with the protein fixed and another 200 steps without the protein fixed, the systems were first heated to 300 K and then subjected to a 100 ps NVT simulation followed by a 100 ps NPT simulation. The minimization, heating and initial equilibrium was performed with CHARMM,^{32 (link)} and the resultant structures were used to start simulations in NAMD.^{33 (link)} After a 1 ns NPT simulation as equilibration, the production simulations were run for 100 ns in the NVT ensemble (see Table 1). For HEWL NPT ensembles were generated to better compare with previous work that found CMAP helps to better reproduced order parameter S^{2,34 (link)} and simulations were extended to 200 ns to reduce the uncertainty of the computed S². Langevin thermostat with a damping factor of 5 ps⁻¹ was used for NVT simulation and the Nosé-Hoover Langevin piston method with a barostat oscillation time scale of 200 fs was further applied for the NPT simulation at 300 K and 1 atm. The time step equals 2 fs and coordinates were stored every 10 ps. For each protein the above simulation protocol was applied with the C36 and C22/CMAP FFs, while for ubiquitin an additional 1.2 μs trajectories with C36 was generated. This long simulation is used to check the convergence and also to examine whether computed NMR data deteriorate over a longer simulation time, as it was reported that RDCs significantly deviate from experimental values after approximately 500 ns simulations with the C22 FF.^{22 (link)}

Huang J, & MacKerell AD J.r. (2013). CHARMM36 all-atom additive protein force field: Validation based on comparison to NMR data. Journal of computational chemistry, 34(25), 2135-2145.

Publication 2013

Cuboid Bone Electrostatics factor A Factor V Familial Mediterranean Fever Hydrogen Bonds Ions Proteins Ring dermoid of cornea Staphylococcal Protein A STEEP1 protein, human Tremor Ubiquitin

Protein Simulation in Crystal Environments

Cited 556 times

Simulations of the proteins in their crystal environments (Table 1), which were used previously during optimization of the C22/CMAP force field ^{40 (link)}, were performed using CHARMM on full unit cells with added waters and counterions to fill the vacuum space. Once the full unit cell was constructed based on the coordinates in the protein databank, a box of water with dimensions that encompassed the full unit cell was overlaid onto the crystal coordinates while preserving crystal waters, ions, and ligands. Water molecules with oxygen within 2.8 – 4.0 Å of any of the crystallographic non-hydrogen atoms were removed, as described below, as well as those occupying space beyond the full unit cell. To neutralize the total charge of each system, sodium or chloride ions were added to the system at random locations at least 3.0 Å from any crystallographic non-hydrogen atom or previously added ions and 0.5 Å from any water oxygen. Final selection of the water molecule deletion distance was performed by initially applying a 2.8 Å criteria to all systems followed by system equilibration and an NPT production run of 5 ns following which the lattice parameters were analyzed. The deletion distances were then increased and the equilibration and 5 ns production NPT simulation were repeated until the final lattice parameters were in satisfactory agreement with experimental data. The final water deletion distances and unit cell parameters from the full 40 ns production simulations are presented in Table S2 of the SI. For the minimization and MD simulations, electrostatic interactions were treated with PME using a real space cutoff of 10 Å. The LJ interactions were included with force switching from 8 Å to 10 Å, while the list of nonbonded atoms was kept for interatomic distances of up to 14 Å and updated heuristically. Each crystal system was first minimized with 100 steps of steepest-decent (SD) with non-water, non-ion crystallographic atoms held fixed followed by 200 steps of SD with harmonic positional restraints of 5 kcal/mol/Å² on solute non-hydrogen atoms. The minimized system was then subject to an equilibration phase consisting of 100 ps of NVT simulation⁴¹ in the presence of harmonic positional restraints followed by 5 ns (100 ps for 135L and 3ICB) of fully relaxed NVT simulation with a time step of 2 fs. During the simulations all covalent bonds involving hydrogens were constrained using SHAKE⁴². Production phase simulations were conducted for 40 ns in the isothermal and isobaric NPT ensemble⁴³. The only symmetry enforced was translational (i.e. periodic boundaries). Reference temperatures were set to match the crystallographic conditions (Table S2) and maintained by the Nosé-Hoover thermostat with a thermal piston mass of 1,000 kcal ps²/mol while a pressure mass of 600 amu was used with the Langevin piston. The first 5 ns of the production simulations were considered as equilibration and therefore discarded from analysis, which was performed on coordinate sets saved every 5 ps. The boundaries for α helices and β strands were obtained from a consensus of author annotations and structural assignments calculated by DSSP^{44 (link)} and STRIDE^{45 (link)} from the crystal structures.

Best R.B., Zhu X., Shim J., Lopes P.E., Mittal J., Feig M, & MacKerell AD J.r. (2012). Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ1 and χ2 dihedral angles. Journal of chemical theory and computation, 8(9), 3257-3273.

Publication 2012

ARID1A protein, human Cells Chlorides Crystallography Deletion Mutation Deuterium Electrostatics Helix (Snails) Hydrogen Hydrogen-4 Ligands Oxygen Pressure Protein Biosynthesis Proteins Sodium STEEP1 protein, human Tritium Vacuum

Covalent Docking Methods Evaluation

Cited 494 times

We have developed and tested two methods for docking of covalently-attached complexes: a grid-based approach and a modification of the flexible sidechain method. The grid-based approach calculates a special map for the site of attachment of the covalent ligand. A Gaussian function is constructed with zero energy at the site of attachment and steep energetic penalties at surrounding areas. The docking analysis is then performed by assigning a special atom type in the ligand for the atom that forms the covalent linkage. The docking simulation places this within the Gaussian well. One caveat is that this does not constrain the geometry of the covalent attachment to reasonable bond angles. To overcome this limitation, we tested the method using two Gaussian grids to define the bond that is formed during covalent linkage. Note, however, that the conformational freedom allowed with a single Gaussian grid may be an advantage if the method is used, for instance, to target ligands to metal coordination sites.
We also tested use of the flexible sidechain method for docking of covalent ligands. In this case, a coordinate file is created with the ligand attached to the proper sidechain in the protein, by overlapping ideal coordinates of the ligand onto the proper bond in the protein. This sidechain-ligand structure is then treated as flexible during the docking simulation, searching torsional degrees of freedom to optimize the interaction with the rest of the protein.

Morris G.M., Huey R., Lindstrom W., Sanner M.F., Belew R.K., Goodsell D.S, & Olson A.J. (2009). AutoDock4 and AutoDockTools4: Automated Docking with Selective Receptor Flexibility. Journal of computational chemistry, 30(16), 2785-2791.

Publication 2009

Ligands Metals Proteins STEEP1 protein, human

Solvation Free Energy Calculation Protocol

Cited 106 times

All simulations were carried out with the AMBER 11 suite of programs^{63 (link)} while the modeling and data analyses were performed using the AmberTools suite of programs.^{63 (link)} A schematic illustration of the work-flow is presented in Scheme SI.1. First, we created a 43Å×43 Å×43 Å cubic water box surrounding a dummy atom with the closest distance between any water molecule at 1.5 Å. In total, there were 2439 water molecules in the system for the TIP3P and SPC/E water models while for the TIP4P and TIP4P_EW water models this number was 2389. We performed a minimization with 1000 steps of steepest descent minimization plus 1000 steps of conjugate gradient minimization. A 1 ns NVT heating procedure was followed to heat the system from 0K to 300K. A second 1 ns NVT at 300K was performed to equilibrate the system. To ensure the correct system density, a 1 ns NPT simulation under 1 atm and 300K conditions was performed and the final structure was treated as the starting structure for TI simulations in the NPT ensemble in Method 2 (details below). Finally another 1 ns NVT simulation was conducted to prepare the initial structure for TI simulations in the NVT ensemble for Method 1 (details below). For all simulations, periodic boundary condition (PBC) were employed together with PME to model long-range interactions. The time-step used is 1 fs with a 10 Å cut off. Test simulations performed by us (see Table SI.2) and others^{22 (link)} indicated that the results were not sensitive to the choice of cutoff in the TI simulation under PME conditions. Langevin dynamics temperature control was employed with a collision rate equal to 5.0 ps⁻¹. SHAKE was utilized for the water molecules for all simulations.
For the determination of the HFE, we used the thermodynamic cycle shown in Figure 1. In the cycle HFE=ΔG_Total =ΔG_VDW+ ΔG_Ele, where ΔG_VDW is the process by which the VDW term is turned on, ΔG_Ele involves turning on the Coulomb potential after turning on the VDW interaction while ΔG_Total represents turning on the VDW and Coulomb potential simultaneously.

Li P., Roberts B.P., Chakravorty D.K, & Merz KM J.r. (2013). Rational Design of Particle Mesh Ewald Compatible Lennard-Jones Parameters for +2 Metal Cations in Explicit Solvent. Journal of chemical theory and computation, 9(6), 2733-2748.

Publication 2013

Amber Cuboid Bone Familial Mediterranean Fever STEEP1 protein, human Tremor

Quantum Modeling of Dipeptide Conformations

Cited 91 times

Acetyl and N-methyl capped dipeptides of the natural amino acids, except proline, alanine, and glycine, were built using LEaP²⁹ at α (−60°, −45°) and β (−135°, 135°) backbone conformations.

\vec{χ}

was explored by rotating in 10° increments, re-optimizing at each step, or by high temperature simulation (described in Results).
Quantum mechanics optimizations were performed with RHF/6-31G*. Scanned residues were optimized using GAMESS (US)³⁰ with default options. Optimization continued until the RMS gradient was less than 1.0 × 10⁻⁴ Hartree Bohr⁻¹, with an initial trust radius of 0.1 Bohr that could then adjust between 0.05 and 0.5 Bohr. Minimization proceeded by the quadratic approximation. Residues sampled by high temperature simulations were optimized using Gaussian98³¹ with VTight convergence criteria. Quantum mechanics energies for training data were calculated with MP2/6-31+G**. Molecular mechanics re-optimizations were performed in the gas phase with ff99SB for a maximum of 1.0 × 10^{7 (link)} cycles or until the RMS gradient was less than 1.0 × 10⁻⁴ kcal mol⁻¹ Å⁻¹, with a non-bonded cutoff of 99.0 Å and initial step size of 10⁻⁴. Dihedral restraint force constants were 2.0 × 10⁵ kcal mol⁻¹ rad⁻². Minimization employed 10 steps of steepest descent followed by conjugate gradient. Molecular mechanics energies were calculated from the last step of ff99SB minimization.

Maier J.A., Martinez C., Kasavajhala K., Wickstrom L., Hauser K.E, & Simmerling C. (2015). ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. Journal of chemical theory and computation, 11(8), 3696-3713.

Publication 2015

Alanine Amino Acids Dipeptides Fever Glycine Mechanics Proline Radius STEEP1 protein, human Vertebral Column

Most recents protocols related to «STEEP1 protein, human»

Green Tea Infused Silk Gel

Not available on PMC !

Example 10

Steps:

Green Tea PrepHeat 250 mL water to boil

Steep tea bag 2-3 minutes

with occasional stir

remove tea bag and let cool

Gel SolutionUse TFF-10-0047 (3.71% silk)

Prepdilute to 3% silk with water

dilute to 2% with green tea

add L-ascorbic acid

GelGelation occurred like standard

gel at room temperature

Green/yellow color

Green Tea scent

Solution Spec:2% silk solution

65 mL (35 ml of 3.71% silk, 8.3

mL water, 21.66 mL green tea)

0.43 gL-ascorbic acid

FIG. 92 is a table summarizing an embodiment of a caffeine gel of the present disclosure. A silk gel with 2% silk and 100 mg L-ascorbic acid/15 mL solution was created with the addition of 50 mg caffeine/15 mL solution. The gel has the exact appearance of standard L-ascorbic acid gels.

US11857663B2. Stable silk protein fragment compositions (2024-01-02). Evolved By Nature, Inc. [US]. Inventors: Gregory H. Altman [US], Rebecca L. Lacouture [US], Rachel Lee Dow [US], Rachel M. Lind [US], Dylan S. Haas [US].

Full text: Click here

Patent 2024

Ascorbic Acid Caffeine Fast Green FCF Furuncles Gels Green Tea Pheromone Silk STEEP1 protein, human Technique, Dilution

Green Tea and Silk Nanocomposite Gel

Not available on PMC !

Example 10

Steps:

- Green Tea Prep Heat 250 mL water to boil
  - Steep tea bag 2-3 minutes with occasional
  - stir
  - remove tea bag and let cool
- Gel Solution
- Prep Use TFF-10-0047 (3.71% silk)
  - dilute to 3% silk with water
  - dilute to 2% with green tea
  - add L-ascorbic acid
  - Gelation occurred like standard gel at room
- Gel temperature
  - Green/yellow color
  - Green Tea scent
- Solution Spec: 2% silk solution
  - 65 mL (35 ml of 3.71% silk, 8.3 mL water,
  - 21.66 mL green tea)
  - 0.43 g L-ascorbic acid

US11857664B2. Stable silk protein fragment compositions (2024-01-02). Evolved By Nature, Inc. [US]. Inventors: Gregory H. Altman [US], Rebecca L. Lacouture [US], Rachel Lee Dow [US], Rachel M. Lind [US], Dylan S. Haas [US].

Full text: Click here

Patent 2024

Ascorbic Acid Caffeine Fast Green FCF Furuncles Gels Green Tea Pheromone Silk STEEP1 protein, human Technique, Dilution

Plant Communities in the TGRR Hydro-Fluctuation Belt

Cited 1 time

This study investigated the plant communities along elevational gradients representing different flooding strengths within a freshwater hydro-fluctuation belt of the TGRR in China (a subtropical mega reservoir; Figure 1). It includes 16 counties. The climate is mainly influenced by the subtropical monsoon, with average annual temperature (MAT) and precipitation (MAP) being 18.22°C and 1110 mm (Zheng et al., 2021a (link)), of which the primary annual precipitation (about 80%) happens in the rainy season, with daily temperatures ranging from 28°C to 30°C (Yi et al., 2020 (link)). Since the first full impoundment of the TGRR in 2010, the periodic inundation and drainage of the TGRR drive large hydraulic fluctuations at elevations of 145-175 m (Ren et al., 2018 (link)). The newly formed hydro-fluctuation belt occupies 344.22 km² and covers 639.38 km along the main waterway (Zheng et al., 2021a (link)). The annual human-induced inundation, including rising and falling water levels, lasts for more than eight months, resulting in lower elevations experiencing inundation for longer periods of time (Chen et al., 2020 (link)). Under extreme inundation situations, flood-tolerant perennials (e.g., Cynodon dactylon, Hemarthria compressa) and annual herbs (e.g., Xanthium strumarium, Echinochloa crusgalli) are dominant in the TGRR (Hu et al., 2022 (link)). These plants can still rapidly colonize and establish distinct community types when the inundation recedes, even if their growth is also limited by local environmental factors (e.g., soil).
Under hydrological variations of up to 30 m in the TGRR, lower elevations (145 - 160 m) are subject to longer periods of inundation per year (Figure S1 and Table S1), as well as these areas are usually disturbed by natural flooding in summer (Chen et al., 2020 (link)). Considering this scenario, the field surveys were conducted in 2019 and 2020 during the period (June-August) when TGRR’s water level reaches its minimum. We collected a total of 327 transects from 36 linked rivers in the riparian zones of the TGRR, encompassing 16 counties within a 58,000 km² landscape (Figure 1). Because the plants in our study area have a consistent growing season and are subject to similar inundation disturbances, under these conditions we can use data from two adjacent years for supplementary investigations (Moore et al., 2011 (link); Zheng et al., 2021b (link)). All plant communities from the tail to the dam area were classified according to the differences in water level and assigned to four elevation intervals at 170-175 (zone I), 165-170 (zone II), 160-165 (zone III) and 145-160 m (zone IV), representing 68, 112, 152, and 204 days of inundation continuity, respectively (Table S1; Wang et al., 2021 (link)). Within each elevation interval, we defined a transect (100 m long) parallel to the river and set up three 2 × 2 m quadrats at an interval of 50 meters (Zheng et al., 2021b (link)). Species presence/absence data were obtained for each elevation interval within 16 counties. In some counties, a sample from four elevation intervals was prevented from being collected simultaneously due to limitations in high water levels (e.g., reservoir tail areas), steep slopes, and landslides (Zheng et al., 2021b (link)). Finally, 981 quadrants were collected in this investigation, including 186, 192, 294, and 309 quadrats in zone I, zone II, zone III, and zone IV, respectively (Table S1).

Zheng J., Arif M., He X., Liu X, & Li C. (2023). Distinguishing the mechanisms driving multifaceted plant diversity in subtropical reservoir riparian zones. Frontiers in Plant Science, 14, 1138368.

Full text: Click here

Publication 2023

CCL1 protein, human Climate Cynodon Drainage Echinochloa Homo sapiens Landslides Plants Rain Rivers STEEP1 protein, human Tail Xanthium

Computational Modeling of Solvatochromic Dyes

The initial geometries of the studied molecules were generated by using a molecular mechanics method (force field MMF94, steepest descent algorithm), and a systematic conformational analysis was carried out as implemented in Avogadro 1.1.1 software. The minimum energy conformer geometries found by molecular mechanics were further optimized with the Gaussian 09 program package³⁰ by means of density functional theory (DFT) using the Becke, 3-parameter, Lee–Yang–Parr (B3LYP)^{51 (link),52 (link)} exchange-correlation hybrid functional with the 6-311++G(d,p) basis set^{53 (link)}, including the polarizable continuum model^{31 (link)} in water. Furthermore, harmonic vibrational frequencies were calculated to verify the stability of the optimized geometries. All the calculated vibrational frequencies were real (positive), indicating the true minimum of the calculated total potential energy of the optimized system. The computation was performed at the High-Performance Computing Center in Göttingen provided by GWDG.
The potential energy values for the D₅₀ simulation were obtained by performing geometry optimizations at the 6-311++G(d,p) IEFPCM (water) level of theory with additional input values of eps = 2.1, 5.7, 11.0, 18.2, 26.6, 35.2, 44.2, 53.3, 62.4, 71.4, and 80.4 to obtain 11 points against increasing dielectric constant. The calculations were performed for zwitterionic and spirolactone forms. Energy values were plotted against the dielectric constant, and the intersection point of the zwitterion and spirolactone plots was considered the simulated D₅₀ value. The calculations were performed for the free 4-TMR-COOH (3) dye and for the model 4-TMR-CONHMe compound with a truncated linker and ligand.

Bucevičius J., Gerasimaitė R., Kiszka K.A., Pradhan S., Kostiuk G., Koenen T, & Lukinavičius G. (2023). A general highly efficient synthesis of biocompatible rhodamine dyes and probes for live-cell multicolor nanoscopy. Nature Communications, 14, 1306.

Full text: Click here

Publication 2023

Hybrids Ligands Mechanics Spironolactone STEEP1 protein, human True Vitality Vibration

Airborne LiDAR Mapping for Vegetation Analysis

Airborne LiDAR data were collected by the National Centre for Airborne Laser Mapping (NCALM) in May 2011 (S1 File). Persistent cloudiness over some parts of the mountains prevented reaching a point cloud density sufficient to reliably identify ground returns [13 ]. This affect only 3% of the study area, however, and that part was patched with a 10 m-resolution DEM. The 1 m-resolution LiDAR DEM was resampled at 5 m to reduce noise resulting from the inability of algorithms to tease out the scattering resulting from the low density of ground returns from the scattering produced by the presence of large corestones at the bottom of many coves (S1 Grid 1 in S1 File). The 5m-resolution DEM was used to calculate topographic metrics that capture some of the topographic characteristics that affect the distribution of vegetation: elevation, aspect, slope steepness, and depth between two consecutive ridgetops (hereafter referred to as entrenchment depth). This later parameter was obtained by passing a surface envelope through the hilltops, and then subtracting the 5m-resolution DEM from this envelope (S1, S1 Grid 2 in S1 File).

Brocard G., Willebring J.K, & Scatena F.N. (2023). Shaping of topography by topographically-controlled vegetation in tropical montane rainforest. PLOS ONE, 18(3), e0281835.

Full text: Click here

Publication 2023

STEEP1 protein, human

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What is the STEEP1 protein and what are its key functions?

STEEP1 (Serine/Threonine-rich and Extracellular Envelope Protein 1) is a human protein involved in cellular processes related to the extracellular matrix and cell signaling. It plays important roles in cell adhesion, migration, and proliferation, making it a potential target for research in areas like tissue engineering, cancer biology, and regenerative medicine. STEEP1 is expressed in various tissue types and has been implicated in developmental and disease-related pathways.

What are some common challenges in studying the STEEP1 protein?

How can PubCompare.ai help with STEEP1 protein research?

PubCompare.ai is an AI-driven platform that can assist researchers in studying the STEEP1 protein more efficiently. The platform allows you to screen protocol literature more effectively, and leverages AI to pinpoint critical insights that can help you identify the most reproducible and accurate protocols for your STEEP1 research. By highlighting key differences in protocol effectiveness, PubCompare.ai can enable you to choose the best option to ensure the quality and reproducibility of your findings.

Are there different variations or types of the STEEP1 protein?

Yes, there can be different variations or isoforms of the STEEP1 protein due to alternative splicing or post-translational modifications. These variations may have slightly different structures, locatlizations, or functions within the cell. Understanding the specific STEEP1 variant you are studying and how it differs from other forms can be important for your research applcations.

What are some specific applications of the STEEP1 protein?

The STEEP1 protein has been implicated in a variety of research and clinical applications. Its role in cell adhesion, migration, and proliferation make it a potentially important target for tissue engineering, wound healing, and regenerative medicine. STEEP1 has also been studied for its involvement in cancer biology and metastasis. Additionally, a better understatnding of STEEP1's structure and regulatory mechanisms could provide valuable insights into its physiological and pathological relevance.

More about "STEEP1 protein, human"

STEEP1, also known as Serine/Threonine-rich and Extracellular Envelope Protein 1, is a multifunctional protein that plays a critical role in cellular processes related to the extracellular matrix and cell signaling.
This protein is expressed in various tissue types and has been implicated in both developmental and disease-related pathways.
STEEP1 is known to be involved in cell adhesion, migration, and proliferation, making it a potential target for research in areas such as tissue engineering, cancer biology, and regenerative medicine.
Understanding the structure, function, and regulatory mechanisms of STEEP1 can provide valuable insights into its physiological and pathological relevance.
In your STEEP1 research, you can leverage the AI-driven platform PubCompare.ai to optimize your experimental protocols.
This tool can help you identify the most reproducible and accurate protocols from published literature, pre-prints, and patents, using AI-powered comparisons to find the best protocols and products for your studies.
This can improve the quality and reproducibility of your STEEP1-related findings.
When exploring the structure and function of STEEP1, you may also find it useful to consider related terms and concepts, such as Thermostat, Pentacam, Barostat, Corn steep liquor, IOLMaster 500, Pentacam HR, AutoDock Tools, IOL Master, UltiMate 3000 RSLCnano system, and Discovery Studio 2.5.
These related topics can provide additional context and insights that may be relevant to your STEEP1 research.