# Solvent Accessible Surface Area

**URL:** <https://discourse.julialang.org/t/solvent-accessible-surface-area/132751>\
**Category:** General Usage\
**Tags:** molecular-simulation\
**Created:** [September 29, 2025, 7:38pm UTC](https://discourse.julialang.org/t/solvent-accessible-surface-area/132751 "2025-09-29T19:38:23Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![lmiq](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/lmiq/32/18314_2.png) [@lmiq](https://discourse.julialang.org/u/lmiq)\
**Post date:** [September 29, 2025, 7:38pm UTC](https://discourse.julialang.org/t/solvent-accessible-surface-area/132751/1 "2025-09-29T19:38:23Z")

</div>

For anyone that might be interested, we implemented a molecular Solvent Accessible Surface Area calculator in PDBTools.jl:

> **[Tools · PDBTools.jl](https://m3g.github.io/PDBTools.jl/stable/tools/#Solvent-Accessible-Surface-Area-(SASA))**
>
> Documentation for PDBTools.jl.

The interface is useful (for us) for allowing extracting contributions to the SASA very easily:

```julia-repl
julia> using PDBTools

julia> pdb = wget("1BSX", "chain A");

julia> sasa_per_atom = atomic_sasa(pdb) # array of atoms containing SASA per atom 
   Vector{Atom{PDBTools.SASA}} with 1905 atoms with fields:
   index name resname chain resnum residue x y z occup beta model segname index_pdb
       1 N LYS A 211 1 52.884 24.022 35.587 1.00 53.10 1 1
       2 CA LYS A 211 1 52.916 24.598 36.993 1.00 53.10 1 2
       3 C LYS A 211 1 52.017 23.754 37.944 1.00 53.10 1 3
       4 O LYS A 211 1 51.522 22.675 37.539 1.00 53.10 1 4
⋮
    1902 CG ASP A 461 243 16.862 50.003 45.475 1.00 97.43 1 1902
    1903 OD1 ASP A 461 243 16.488 49.747 44.304 1.00 97.43 1 1903
    1904 OD2 ASP A 461 243 17.538 51.009 45.748 1.00 97.43 1 1904
    1905 OXT ASP A 461 243 14.506 47.082 47.528 1.00 97.43 1 1905

julia> sasa(sasa_per_atom) # all atoms (Angs^2)
11900.575f0

julia> sasa(sasa_per_atom, "backbone") 
2391.8325f0

julia> sasa(sasa_per_atom, "sidechain and resname GLU") 
1632.0673f0

```

This can also be useful to run calculations on trajectories within Julia, like in the following example.

> **SASA on trajectory**
>
> ```julia-auto
> using MolSimToolkit # uses Chemfiles.jl under the hood
> using PDBTools
> using ProgressMeter
> using DelimitedFiles
> 
> function sasa_traj(sim)
> prot = select(atoms(sim), "protein")
> index_prot = index.(prot)
> sasa_total = Float32[]
> @showprogress for frame in sim
> p = @view(positions(frame)[index_prot])
> for i in eachindex(prot, p)
> prot[i].x = p[i].x
> prot[i].y = p[i].y
> prot[i].z = p[i].z
> end
> sasa_atoms = atomic_sasa(prot; n_dots=100)
> push!(sasa_total, sasa(sasa_atoms))
> end
> return sasa_total
> end
> 
> # Rodar com julia -t auto --project sasa.jl
> function (@main)(ARGS)
> sim = Simulation(
> "/home/leandro/Documents/vinicius/EMIMClBF4/3.00/08/system.pdb",
> "/home/leandro/Documents/vinicius/EMIMClBF4/3.00/08/processed.xtc"
> )
> sasa_total = sasa_traj(sim)
> writedlm("sasa_julia.txt", sasa_total)
> end
> 
> ```

Performance is “ok” (something like 2x slower than what Gromacs or VMD tools provide, considering the same “precision” - number of dots - used).

One thing is that moving the calculations out of PDBTools.jl into an independent package is very easy, and probably something I will do in the future, such that other packages, like BioStructures.jl, can just plug it in with their own atom data structures.

---

<div class="post-metadata">

**Author:** ![lmiq](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/lmiq/32/18314_2.png) [@lmiq](https://discourse.julialang.org/u/lmiq)\
**Post date:** [October 1, 2025, 4:58pm UTC](https://discourse.julialang.org/t/solvent-accessible-surface-area/132751/2 "2025-10-01T16:58:25Z")

</div>

Update:

After the [help from you](https://discourse.julialang.org/t/nerd-sniping-can-you-make-this-faster/132793/24), now we are in a different ballpark here.

For small structures the SASA here implemented takes similar time than Gromacs and VMD.

For large structures, it is much faster than Gromacs, and similar to VMD without the julia startup time (6co8.pdb is a ~700k virus capsid):

Gromacs:

```bash
$ time gmx sasa -s ./6co8.pdb -o sasa_output.xvg -ndots 100
real 1m15,536s
user 1m15,412s
sys 0m0,123s

```

VMD:

```bash
$ time vmd -dispdev text -e sasa_big.vmd
real 0m3,941s
user 0m3,716s
sys 0m0,543s

```

SASA from PDBTools.jl:

```bash
$ time julia --project -e "using PDBTools; sasa(atomic_sasa(read_pdb(\"6co8.pdb\")))"
real 0m7,134s
user 0m8,000s
sys 0m0,400s

#or
julia> @b sasa(atomic_sasa(pdb; parallel=false))
3.635 s (40045204 allocs: 1.186 GiB, 17.71% gc time, without a warmup)

```

To be released soon in PDBTools.jl 3.5.2

(all these are single-threaded, I’m not sure if VMD is using the GPU)

---

<div class="post-metadata">

**Author:** ![jgreener64](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/jgreener64/32/2483_2.png) [@jgreener64](https://discourse.julialang.org/u/jgreener64)\
**Post date:** [October 1, 2025, 9:36pm UTC](https://discourse.julialang.org/t/solvent-accessible-surface-area/132751/3 "2025-10-01T21:36:06Z")

</div>

Looks like a useful tool!