Occlusion Surface Using the Occluded Surface and Fibonacci Occluded Surface.

FIBOS R (BETA)

The Occluded Surface (OS) algorithm is a widely used approach for analyzing atomic packing in biomolecules. Here, we introduce FIBOS, an R and Python package that extends the OS methodology with enhancements. It integrates efficient Fortran code from the original OS implementation and introduces an innovation: the use of Fibonacci spirals for surface point distribution. This modification reduces anisotropy and ensures a more uniform and even distribution of surface dots, improving the accuracy of the algorithm.

Python fibos version PyPI.

Operating Systems

FIBOS was designed to be multiplatform and run on Linux, Windows and Mac.
Tested on:

• Linux: Ubuntu ($\geq$ 20.04)
• Windows: Windows 11
• Mac: MacOS 15.0.1

Compilers

• gfortran
• gcc

R versions

Tested on: 4.4.1, 4.4.2

Instalations

Preliminary

Some preliminary actions according to OS:

Linux (Ubuntu)

Install gfortran:

$ sudo apt install gfortran

Windows

Install RTools from:

https://cran.r-project.org/bin/windows/Rtools

Install gfortran from (version $\geq$ 13.2):

http://www.equation.com/servlet/equation.cmd?fa=fortran

Set the PATH to the R bin folder as an administrator:

$ setx PATH "%PATH%;C:\Program Files\R\R-x.x.x\bin"

where x.x.x is the actual version number of your R installation.

MacOS

Install Homebrew:

$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/
HEAD/install.sh)”

In your shell, set the PATH to include the Homebrew bin folder by adding it into the .zshrc file

export PATH= "/path/to/homebrew/bin:$PATH"

where "/path/to/homebrew/bin" is the actual homebrew path in your system. So, reload it:

$ source ~/.zshrc

Some Mac versions (with Apple Silicon) may require Rosetta:

$ softwareupdate --install-rosetta --agree-to-license

Install xcode and gfortran from:

https://cran.r-project.org/bin/macosx/tools/

Official fibos Installation:

install.packages("fibos") 

Alternative fibos Installation:

install.packages("fibos")
library("devtools")
install_github("https://github.com/insilico-unifei/fibos-R.git")

Main functions:

1. occluded_surface(pdb, method = "FIBOS"): Implements the Occluded Surface algorithm, generating points, areas, and normals for each atom. As parameters it accepts a PDB id (or the path to a local PDB file) and a method selection — either the classic 'OS' or the default 'FIBOS' and the density dots values. The function returns the results as a table and creates a file named prot_PDBid.srf in the fibos_file directory.

2. osp(file): Implements the Occluded Surface Packing (OSP) metric for each residue. Accepts a path to an .srf file generated by occluded_surface as a parameter and returns the results as a table summarized by residue.

3. fibos_config(): Sets up the environment for using fibos by creating a virtual environment and installing the package. This function must be run before using any other functionality, as the remaining functions depend on it and will not work otherwise.

4. get_radii(): Returns the Van der Waals radii values employed in the surface occlusion calculations. These are the values used in each computation.

5. set_radii(radii_values): Enables customization of the radii values used in surface occlusion calculations. To specify new values, the function accepts a DataFrame as its parameter.

6. reset_radii(): Reverts all modifications made to the radii values, resetting them to their default settings.

Getting Started:

library(fibos)
fibos_config()

Quickstart:

library(fibos)

# Calculate FIBOS per atom and create .srf files in fibos_files folder
pdb_fibos <- occluded_surface("1fib", method = "FIBOS")

# Show first 3 rows of pdb_fibos table
pdb_fibos |> utils::head(3) |> print()

# A tibble: 3 × 6
#   ATOM                       NUMBER_POINTS  AREA RAYLENGTH DISTANCE
#   <chr>                              <int> <dbl>     <dbl>    <dbl>
# 1 GLN    1@N___>HIS   3@NE2_             6  1.29     0.791     5.49
# 2 GLN    1@N___>HIS   3@CE1_             1  0.2      0.894     6.06
# 3 GLN    1@N___>HIS   3@CG__             1  0.16     0.991     6.27

# Calculate OSP metric per residue from .srf file in fibos_files folder
pdb_osp <- osp(fs::path("fibos_files","prot_1fib.srf"))

# Show first 3 rows of pdb_osp table
pdb_osp |> utils::head(3) |> print()

# A tibble: 3 × 5
#   Resnum Resname    OS `os*[1-raylen]`   OSP
#    <dbl> <chr>   <dbl>           <dbl> <dbl>
# 1      1 GLN      36.8            22.0 0.157
# 2      2 ILE      49.4            36.2 0.317
# 3      3 HIS      64.2            43.2 0.335

A more complex example:

library(fibos)
library(furrr)

# source of PDB files
pdb_folder <- "PDB"

# fibos folder output
fibos_folder <- "fibos_files"

# Create PDB folder if it does not exist
if (!fs::dir_exists(pdb_folder)) fs::dir_create(pdb_folder)

# PDB ids list
pdb_ids = c("8RXN","1ROP") |> tolower()

# Get PDB files from RCSB and put them into the PDB folder 
pdb_paths <- pdb_ids |> bio3d::get.pdb(path = pdb_folder)
pdb_paths |> print()

# Save default environment variable "mc.cores" to recover later
default_cores <- getOption("mc.cores")

# Detect number of physical cores and update "mc.cores" according to pdb_ids size
ideal_cores <- min(parallel::detectCores(), length(pdb_ids))
if (ideal_cores > 0) options(mc.cores = ideal_cores)

# Calculate in parallel FIBOS per PDBid 
# Create .srf files in fibos_files folder
# Return FIBOS tables in pdb_fibos list
if (ideal_cores > 1) future::plan(multisession, workers = ideal_cores)
pdb_fibos <- pdb_paths |> furrr::future_map(\(x) occluded_surface(x, method = "FIBOS"), 
                                            .options = furrr_options(seed = 123))
# Recover default "mc.cores"
if (ideal_cores > 0) options(mc.cores = default_cores)

# Show first 3 rows of first pdb_fibos table
pdb_fibos[[1]] |> utils::head(3) |> print()

# Prepare paths for the generated .srf files in folder fibos_files
srf_paths <- pdb_ids |> purrr::map(\(x) fs::path(fibos_folder, paste0("prot_",x), ext = "srf")) |> 
             unlist()
srf_paths |> print()

# Calculate OSP metric by residue
# Return OSP tables in pdb_osp list
pdb_osp <- srf_paths |> purrr::map(\(x) osp(x))

# Show first 3 rows of the first pdb_osp table
pdb_osp[[1]] |> utils::head(3) |> print()

Case study:

Here we show a case study (currently only in R), aiming to compare the packing density between experimentally determined structures and the same structures predicted by AlphaFold (AF).

Authors

• Carlos Silveira ([email protected])
  Herson Soares ([email protected])
  Institute of Technological Sciences,
  Federal University of Itajubá,
  Campus Itabira, Brazil.

• João Romanelli ([email protected])
  Institute of Applied and Pure Sciences,
  Federal University of Itajubá,
  Campus Itabira, Brazil.

• Patrick Fleming ([email protected])
  Thomas C. Jenkins Department of Biophysics,
  Johns Hopkins University,
  Baltimore, MD, USA

References

Fleming PJ, Richards FM. Protein packing: Dependence on protein size, secondary structure and amino acid composition. J Mol Biol 2000;299:487–98.

Pattabiraman N, Ward KB, Fleming PJ. Occluded molecular surface: Analysis of protein packing. J Mol Recognit 1995;8:334–44.

Status

In Progress.