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PDB-REDO in Computational-Aided Drug Design (CADD)
Ida de Vries, Anastassis Perrakis, and Robbie P. Joosten
Oncode Institute and The Netherlands Cancer Institute, Department of Biochemistry, Plesmanlaan, 121 1066
CX Amsterdam, the Netherlands
PDB-REDO is both a pipeline that aims to optimize crystallographic macromolecular structure models and a databank oering optimized versions of Protein Data
Bank (PDB) models. The automated decision-making system renes, rebuilds, and
validates the models available in the PDB or provided by users, based on their original experimental diraction data. It returns a new structure model with rich metadata on model quality and structural changes. Optimized PDB models are saved to
the PDB-REDO databank, which contains “redone” structuremodels with their electron density maps and associated validation data. The PDB-REDO databank is a good
resource for structure models in a computer-aided drug design (CADD) project.
201
7.1 History and Concepts
7.1.1 X-ray Structure Models
The most commonly used technique to obtain macromolecular structure models is
X-raycrystallography (Figure 7.1). In the crystallographic process, the protein, DNA,
RNA, or complex is crystallized and irradiated with X-rays. This results in a set of
2D diraction images, which undergoes a series of complex operations to determine
the intensity and the associated error of diracted X-rays constrained by the symmetry of the crystal. This results into what we will refer to here as “experimental
data,” a list of intensities and their estimated errors. After the experimental data
are available, crystallographers need to retrieve the missing phases of the diracted
X-rays by computational methods that involve prior knowledge about the nature of
the macromolecular structure, often by collecting additional experimental data. The
experimental data and the phase estimates allow the construction of a 3D electron
density map, which is to construct an initial structure model [1]. Next, this atomic
model is rened using renement software [2–4] and validated against targets based
on independent knowledge of the macromolecular structure [5]. Important steps in
this process are dening the parametersfor the renement and judging the quality of
Open Access Databases and Datasets for Drug Discovery, First Edition.
Edited by Antoine Daina, Michael Przewosny, and Vincent Zoete.
© 2024 WILEY-VCH GmbH. Published 2024 by WILEY-VCH GmbH.
202 7 PDB-REDO in Computational-Aided Drug Design (CADD)
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Crystal Final modelDiffraction data
Figure 7.1 Workflow of an X-ray crystallographic experiment to obtain a macromolecular
structure model.
Initial structure
model
Model
rebuilding
Refinement
Validation
the newly obtained model based on the validation. There are various software packages available for the renement and validation [6–8], each with its own strengths
and weaknesses. The rebuilding, renement, and validationsteps should be repeated
because, due to the intricacies of the crystallographic process we briey explained
above, better estimates of the phases are computed and thus better electron density
maps when the atomic model improves. After many iterations, the structure model
cannot be improved any further and is then considered “optimal”[9]. This end-point
remains highly subjective to date [10].
For several decades (50 years at the moment of writing [11, 12]), crystallographers
have uploaded their structure models obtained from crystallography experiments
to the PDB [13]. This databank contains over 180,000 structure models and has
become a key resource for (computational) structural biology, biochemistry, and
drug design with 1.3G downloads in 2020 alone. Experimental techniques and also
the software used to analyze the diraction data continue to develop and improve.
This has resulted in overall more accurate models of macromolecular structures
that were mostly deposited more recently [14]. Usually, crystallographers continue
with other projects and do not update the deposited structure models after PDB
deposition. As a result, especially older structure models are not as accurate as they
could be with the current computational methods. Researchers interested in such
a structure may choose to optimize the structure themselves when experimental
data are available. The latter is not always the case, as only since 2008 the PDB has
made it mandatory to upload the experimental data when depositing a new structure model to this database [5]. Nevertheless, 86% of all X-raydiraction entries have
their experimental data available.As crystallographicskills do not necessarily belong
to the expertise of researchers in CADD, judging the quality of a structure model can
become problematic. For such structural biology research purposes, but also to help
active crystallographers determine better structures, the PDB-REDO procedure and
the associated databank have been developed [15].
7.1.2 PDB-REDO Development
The PDB-REDO databank contains alternative versions of the X-raycrystallographic
structure models deposited in the PDB that were updated with the PDB-REDO
software pipeline using the original experimental data that is also deposited with
most PDB entries. PDB-REDO entries (>155,000) are generally of better quality
than their PDB counterparts in terms of t to the experimental data and molecular
geometry [15]. An additional and crucial benet for CADD approaches is that the
7.1 History and Concepts 203
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(methodological) uniformity of the structure models is substantially improved, as
all models are generated using the same pipeline and validated against the same
targets. Both overall model quality and model uniformity have made considerable
steps forward over the course of PDB-REDO development.
7.1.2.1 First Uniformity
The rst version of the PDB-REDO databank contained optimized coordinatesof the
structure models as well as the model parameters used in renement for structures
with a resolution of 2.70 Å or better [15]. R-free was used as a model quality indicator and the quality of the model coordinates was veried using WHAT_CHECK [8].
An important aspect of the process was that PDB-REDO optimized in a uniform way
the relative weight of the experimental data and the so-called “geometric restraints.”
The latter consists of a priori expectations of the covalent geometry of amino acids
and other chemical moieties that are found in macromolecular structures and in
molecular simulations terminology can be thought of as a basic “force eld.” At
least at that time, nding the optimal weight between these two factors, “experiment” and “geometry,” has been often up to each user of each software package.
Crucially, PDB-REDO was not only choosing the software package for optimization,
but was also proposing an objective algorithm for determining this weighting factor.
A key in the re-renement process that was done for each entry was the uniform
use of translation, liberation, and screw (TLS) displacement models [16] during the
renement [17]. The TLS models work on groups of atoms that behave as rigid bodies and provide a layer of information to the atomic displacement parameters. These
parameters describe anisotropic movement by adding only 20 model parameters per
group and without changing the characteristics of the input PDB entry in terms of
e.g. ligands, rotamers, and amino acids. This was then followed by general model
renement that netuned the atomic positions and B-factors. Although the structure
models improved in terms of t to the X-ray and to other model quality indicators,
more gross modeling errors (e.g. side chains out of density) were not handled in this
rst version of the PDB-REDO databank. Manual inspection and adjustments were
still required to resolve these errors [18].
7.1.2.2 Automatic Rebuilding of Protein Backbone and Side Chains
In further development of PDB-REDO, the programs pepip and SideAide were
adapted from the ARP/wARP package [19] and implemented in the pipeline [20].
These two were the rst fully automated tools to systematically check, correct, and
improve the protein with respect to the electron density maps if the electron density
maps indicate that this is needed. As the name implies, pepip systematically checks
for all peptide planes (i.e. the planes consisting of the Cα
backbone atoms) outside the cores of α-helices and β-strands whether alternative,
ipped orientations improve the t to the electron density as well as the position of
the two involved residues on the Ramachandran plot. If so, the adjustment is kept,
as it is considered to be an improvement of the model. SideAide was implemented
to optimize amino acid side chain conformations. This tool searches for the rotamer
conformation of a side chain that shows the best t to the electron density map.
i,Ci,Oi,Ni+1
,andCα
i+1
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During this process, the correctly modeled parts of the structure model are kept
rigid, so that no interference occurs. Furthermore, the Cα atoms are allowed to shift,
increasing the sampled search space and thus the detection rate. The best rotamer is
selected for each amino acid, after which renement of the structure model against
the electron density map is performed. Subsequent validation indicates whether
the change in rotamer have indeed improved the structure [20]. Additionally, side
chains of histidine, glutamine, and asparagine are ipped if this improves the local
hydrogen bonding network.
It should be noted that PDB-REDO by default completes missing side chains in
structure models even when the electron density is relatively poor. Although other
methods of dealing with poor side chain density are also used by crystallographers
(e.g. side chain truncation or manipulation of the occupancy of side chain atoms),
the completion of side chains in the most plausible conformation has the advantage that interpretation is still possible even without having the complete structural
model. The positional uncertainty of the added side chain atoms is, to a large extent,
captured in the atomic B-factors.
7.1.2.3 Automated Model Completion Approaches
The addition of missing side-chains was a rst step toward making structure
models more complete. In further PDB-REDO development, Loopwhole was
added to complete loops in protein models. When Loopwhole detects an unmodeled loop in the protein model (based on the deposited sequence), it looks for
homologs of the protein that do contain a modeled loop at that position in the
protein. If “homologous loops” are found, they are transferred to the structure
model of interest by local structural alignment. After real-space optimization,
the loop with the best t to the electron density is retained when it is of sufcient quality in terms of geometry and t to the density map [21]. Besides
auto-completion of the protein part of structures, PDB-REDO also works on
carbohydrates from N-glycosylation. Carbohydrates are often added to proteins
as post-translational modications and are important recognition parameters in
several biological processes (e.g. protein folding). Modeling of the carbohydrates
in protein structure models is often done relatively poorly [22, 23] because it
has received little attention in the past, and interactive tools for handling carbohydrates were not well-developed (that is, handling polysaccharides required
expertise far beyond normal use of the software available) and also because the
task contains particular challenges. The experimental data are commonly less
informative for carbohydrates than for protein and the tools for model building
of carbohydrates are not as well established as those for proteins. Additionally,
many carbohydrates present in structure models are often not the research interest of the depositor [24]. Carbivore was written and added to the PDB-REDO
pipeline to overcome these challenges by automating the extension of existing
carbohydrate trees [25] using, at that time, newly introduced functionality in the
popular model-building software COOT [26, 27]. Carbivore is also able to build
new trees at asparagine residues that are part of the so-called N-glycosylation
sequon Asn-X-Ser/Thr [28]. Additionally, carbohydrates that do not t any known
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