Protein and Antibody Modeling
MOE's industry-leading applications
for protein structure prediction are powerful, intuitive and easy to use, both
for experts and occasional users. Powerful homologue identification,
alignment technology and refinement methodology make high quality sequence-to-structure
predictions routinely possible.
MOE includes a number of important structure
databases for use in protein modeling. A structurally clustered
version of the PDB allows for rapid fold detection. The GPCR
database streamlines GPCR structure prediction with automatic trans-membrane
region annotations. The Kinase database is a clustered and aligned
collection of publically available kinase structures with biologically relevant
annotations. The antibody database contains structurally clustered
an annotated antibody structures. Automatically augment the provided databases
with proprietary structures.
- Predict 3D structure from sequence with
- Fold detection /
template selection with searchable structural databases
- Advanced loop modeling methodology: loop grafting
or in situ LowModeMD
- Advanced alignment methods for sequence,
structure and sequence and structure
- Powerful alignment visualizer and editor
Search the Structural Family Database to
identify protein families relevant to structure prediction. The
search uses a FastA-type local alignment followed by a family membership test
based upon full multiple alignment and Z-score significance testing. Folds
of even distantly related homologues can be reliably identified with few
false positives (unlike pairwise searches). Run the search in parallel
with MOE/smp cluster technology to perform timely whole-genome identifications.
Use the Domain Motif Search to identify structural homologues with low
Find optimal alignments of protein
sequences, given both sequence-only and structural data using CCG's unique
technology. The number of sequences and structures is not limited. Use
arbitrary constraints and secondary structure weights for the
alignments. Use both sequence and 3D structure information to enhance the
quality of the resulting alignment, especially with low sequence identities. Use
the Sequence Editor to adjust alignments interactively.
Dynamically color residues by function, sequence similarity or
structural proximity. Automatically annotate important regions of GPCRs,
kinases and antibodies to streamline multiple alignments.
Discover accessible amino acid side chain
conformations with MOE's Rotamer Explorer. Predict the structure
of amino acid mutations in a 3D protein structure and candidate rotamers
using an energy-based scoring function and visually analyze them using MOE's
Build homology models including multimer
models from an amino-acid sequence by assembling fragments of experimentally
determined backbone structures from one or more templates, selection of
sidechain conformations from a rich rotamer library, followed by a refinement
protocol based on forcefield energy minimization. Use specialized
protocols for antibody modeling [Almagro 2011]. Specify a customizable
loop dictionary for knowledge based loop modeling. The
homology models are scored with various scoring functions including MM/GBVI.
Include environment units such as bound ligands, antigens
and conserved waters in the structural template for induced fit.
Search loop conformations with LowModeMD
[Labute 2010] which efficiently perturbs molecular systems along
low-frequency vibrational modes. Large-scale conformational changes can
be induced in situ that cannot be observed with molecular dynamics
(unless prohibitively long runs are conducted). Ligands and explicit
waters can be included with little additional overhead.
Assess the reliability of predicted
structures with statistical measures of quality derived from X-ray
crystallographic data. Interactive visualization plots such as Ramachandran
plots, rotamer strain energy, solvation energy, non-bonded atom clashes
and backbone geometry can be used in combination with raw data tables for
detailed geometric analysis to identify and isolate regions of predicted
structures that require further treatment. Export textual reports and
graphics to image files.
The Structure Preparation application
identifies and automatically corrects many problems encountered in
crystallographic data such as missing loops, empty residues, chain
termini or breaks, missing disulfide bonds or atom names, picking alternate
conformations, etc. Optimizing the hydrogen bonding network can be
done by navigating through different tautomer/ionization states or
automatically by using Protonate3D [Labute 2008]. Protonate3D calculates optimal protonation states,
including titration, rotamer and “flips” using a large-scale
Almagro, J.C., Beavers, M.P., Hernandez-Guzman, Maier,J., Shaulsky, J.,
Butenhof, K., Labute, P., Thorsteinson, N., Kelly, K., Teplyakov, A., Luo, J.,
Sweet, R., Gilliland, G.L.; Antibody Modeling Assessment; Proteins: Struct.
Func. Bioinf. 79 (2011) 3050–3066.
Feldman, H.J., Labute, P.; Pocket Similarity: Are Alpha Carbons Enough? J. Chem.
Inf. Model. 50 (2010) 1466–1475.
[Labute 2008] Labute, P.; Protonate3D:
Assignment of Ionization States and Hydrogen Coordinates to Macromolecular
Structures; Proteins 75 (2008) 187–205.
[Labute 2008] Labute, P.; The Generalized Born / Volume Integral (GB/VI) Implicit Solvent
Model: Estimation of the Free Energy of Hydration Using London Dispersion
Instead of Atomic Surface Area; J. Comput. Chem. 29 (2008)
[Labute 2010] Labute, P.; LowModeMD –
Implicit Low Mode Velocity Filtering Applied to Conformational Search of
Macrocycles and Protein Loops; J. Chem. Inf. Model. 50 (2010)