DOI: 10.5061/dryad.905qfttxf
title: Fragment and torsion biasing algorithms for construction of small organic molecules in proteins using DOCK

description: The computational construction of small organic molecules (de novo
 design), directly in a protein binding site, is an effective means for
 generating novel ligands tailored to fit the pocket environment. In this
 work, we present two new methods, which aim to improve de novo design
 outcomes using (1) biasing algorithms to prioritize selection and/or
 acceptance of fragments and torsions during growth, and (2) parallel‐based
 clustering and pruning algorithms to remove duplicate molecules as
 candidate fragment are added. Large‐scale testing encompassing thousands
 of simulations were employed to interrogate the methods in terms of
 multiple metrics which include numbers of duplicate molecules generated,
 pairwise‐similarity, focused library reconstruction rates, fragment and
 torsion frequencies, fragment and torsion rank scores, interaction energy
 and drug‐likeness scores, and 3D pose comparisons. The biasing algorithms,
 particularly those that include fragment and torsion components
 simultaneously, led to molecules that more closely mimicked the
 distributions of fragments and torsions found in drug‐like libraries. The
 new parallel‐based clustering and pruning algorithms, compared with the
 existing serial approach, also led to larger ensembles comprised of
 topologically unique molecules with much greater efficiency by removing
 redundant growth paths.
description: # Fragment and torsion biasing algorithms for construction of small
 organic molecules in proteins using DOCK [Access
 this dataset on Dryad](https://doi.org/10.5061/dryad.905qfttxf) This
 dataset corresponds to several experiments performed in the [parent
 manuscript](https://doi.org/10.1002/jcc.27508) of the same title. Each
 compressed directory corresponds to different sections of the manuscript
 as follows: ## Description of the data and file structure All data
 collected for this set were collected using DOCK6's De Novo Design
 algorithm (DOCK_DN) under various conditions to examine the effects of
 biasing fragment library selection and acceptance by a distribution of
 populations. All input files provided are for experiments using 1AJV with
 anchor0, but all anchors are provided in 001_de_novo_inputs. ### Files and
 variables #### **000_1AJV_system_files.tar.gz**: All base files (protein +
 cognate ligand setup) needed to run a DOCK6 experiment on HIV Protease PDB
 1AJV. These files (and those of the other 56 systems used in the
 manuscript) were generated using a standard process to generate the
 [SB2012 test set](https://doi.org/10.1021/ci1001982). 1AJV was the
 individual test system used for parallel vs. serial analysis in *Section
 4.1* and *Figures 5 and 6* of the parent manuscript. *1AJV.lig.am1bcc.mol*
 & *1AJV.lig.gast.mol2*: The cognate crystallized ligand for PDB 1AJV
 with two different charge models. While most of our experiments are done
 with AM1BCC charges, DOCK_DN experiments use a Gasteiger model, so both
 are provided for appropriate comparison. *1AJV_gridbox.pdb*: The grid box
 used to calculate the energy grid. *1AJV.rec.bmp*, *1AJV.rec.nrg*:
 Calculated energy grid files for running a DOCK experiment.
 *1AJV.rec.clean.mol2*: The cleaned receptor file that is docked or built,
 used for initial energy grid calculations and eventual Cartesian scoring.
 *1AJV.rec.clust.close.sph*: Processed spheres defining the binding pocket,
 utilized for orienting initial anchors. ####
 **001_de_novo_inputs.tar.gz**: Fragment library used for standard de novo
 design experiments with DOCK6 (DOCK_DN). Includes the 30 anchors used for
 initial seeds to begin growth. Along with *000_1AJV_system_files.tar.gz*
 and an input file, these files can be used to perform a basic de novo
 design experiment. *anchors*: 30 most populous fragments used as anchors
 for DOCK_DN *fraglib*: The standard ZINC13M Fragment Library used for
 DOCK_DN *fraglib_linker.mol2*, *fraglib_scaffold.mol2*,
 *fraglib_sidechain.mol2*: Multi-mol2 files of the fragments.
 *fraglib_torenv.dat*: All torsion environments, used to ensure chemical
 sanity during growth. #### **002_serial_vs_parallel.tar.gz** Input files
 for the *Serial Vs. Parallel* section of the manuscript (*Sections 4.1*
 and *4.2*, *Figures 5* and *6*, and *Table 2*), and results for the Serial
 portion of the experiments. The parallel portion of the experiments are
 included as the *R* data sets in 003. \**dock_input.in*: DOCK input files
 for Serial or Parallel experiments. \**duplicate_info.dat*: Results for
 duplicate analysis of all tested anchors for 1AJV in Serial and Parallel.
 Used to plot *Figure 5B*. *1AJV_serial_results*: The output fragment
 library and number of generated molecules for the serial experiment of
 1AJV. #### **003_single_biasing_experiments.tar.gz** Input files and
 output results for a "standard" DOCK_DN experiment (Random, R)
 as well as for the individual biasing methods outlined in the manuscript
 (*FAF, FSF, TAF*). \**.in*: Input file used for DOCK_DN experiments for
 each biasing type. *FAF, FSF, R, TAF directories*: Fragment library
 values, number of molecules constructed, and Grid Scores for individual
 molecules. These were used in the generation of *Figures 7 and 9* of the
 parent manuscript. #### **004_combination_biasing_experiments.tar.gz**
 Input files and output results for for the combination biasing methods
 outlined in the manuscript (*FAF_TAF, FSF_FAF, FSF_FAF_TAF, FSF_TAF*).
 \**.in*: Input file used for DOCK_DN experiments for each biasing
 combination. *FAF_TAF, FSF_FAF, FSF_FAF_TAF, FSF_TAF, directories*:
 Fragment library values, number of molecules constructed, and Grid Scores
 for individual molecules. These were used in the generation of *Figures 8
 and 9* of the parent manuscript. ## Sharing/Access information The
 remaining data for the remaining 56 systems is available upon request from
 authors, though the working example here is sufficient for replication of
 the work. ## Code/Software All molecular construction performed for this
 data set was done using a development branch of DOCK6.12, with code
 modifications to be released as DOCK6.13. Analyses performed are described
 in the [parent manuscript](https://doi.org/10.1002/jcc.27508), and were
 done utilizing standard Python packages for scientific computing
 (matplotlib, numpy, pandas). For accessing files:  * *.pdb, *.mol2, *.sph,
 *.dat, *.num, and *.in files are readable and editable via standard text
 editors such as Notepad, Sublime Text, and VIM. They are also readable via
 Python and other languages' file reading methods. * Structures found
 in *.pdb, *.mol2, and *.sph files are also visualizable in most molecular
 visualization softwares (such as
 [Chimera](https://www.cgl.ucsf.edu/chimera/download.html),
 [ChimeraX](https://www.cgl.ucsf.edu/chimerax/) or
 [PyMol)](https://www.pymol.org/). - *.nrg and *.bmp files are generated by
 Grid and readable
 by [DOCK6](https://dock.compbio.ucsf.edu/DOCK_6/index.htm) (free academic
 license) for use in experiments and
 by [Chimera](https://www.cgl.ucsf.edu/chimera/download.html) for
 visualization.