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converged? stop : otherwise back to (1)
Bates-Paul (P0096) - 72 predictions: 72 3D
Comparative Modelling By In Silico Recombination of
Templates, Alignments and Models
This is a standard genetic algorithm with two genetic operators (recombination
and mutation) and a fitness function acting as an artificial selection agent. We
will now briefly describe each step in the protocol.
Bruno Contreras-Moreira, Paul W. Fitzjohn, Marc Offman,
Graham R. Smith and Paul A. Bates
Initial population of models. Initially, our server Domain Fishing [3]
(www.bmm.icnet.uk/servers/
3djigsaw/dom_fish) was used to define protein domains within each target
sequence and to find suitable modelling templates. Resulting alignments were
inspected and corrected if suspected to be incorrect. If reasonable alternative
alignments could be found they too were added to the pool. When possible,
only alignments with bit-scores (average pssm-logodds+secondary structure
agreement/residue) around 2 were selected. In several cases annotations from
the templates or their corresponding PFAM families were used to check the
correctness of the alignment in active/binding sites. Usually several models
were built using the same template changing parts in the alignment. Models
from these alignments were built using our server 3D-JIGSAW [4]
(www.bmm.icnet.uk/servers/3djigsaw). Additional models were obtained from
the CAFASP3 server after inspection of the alignments to gain extra variability
in sequence alignments, templates used and exposed loops. These models were
taken from different sources, including
FAMS (physchem.pharm.kitasatou.ac.jp/FAMS),
Pmodeller (www.sbc.su.se/~arne/pcons) and
EsyPred3D (www.fundp.ac.be/urbm/bioinfo/esypred).
Models were inspected and missing parts, typically loops, added using in-house
software before going to the next step. In essence, this software explores phi/psi
space to allow a peptide (the missing loop) to connect a gap in a protein fold.
Biomolecular Modelling Laboratory
Cancer Research UK - London Research Institute
paul.bates@cancer.org.uk
After the CASP4 assessment it was concluded that template selection and
sequence alignment remain the main problems awaiting solution in the field of
comparative modelling [1]. Models were rarely found to be closer to the
experimental structures than the optimal template and often manual
intervention only marginally mproved their quality. Similar problems were
found in the fold recognition category [2,4], suggesting that the same approach
may be applied in the search for possible solutions in both fields. During
CASP5 our group has tested a novel procedure to tackle these problems. This
new method was used to generate models for all 67 targets, with roughly half of
them classified as fold recognition targets by the CAFASP3 meta-server
(www.cs.bgu.ac.il/~dfischer/CAFASP3).
This procedure is named in silico protein recombination, as it is a
computational implementation of genetic recombination, a well known
mechanism for generating population variability, but at the protein level. For
each CASP5 target a population of models was generated from a variety of
templates and sequence alignments. Care was taken to assure that models had
similar length and were complete, adding missing loops when necessary and
smoothing their phi/psi geometry to permit later energy calculations and
minimizations. The algorithm can be outlined as:
1. Growing the population by recombination and mutation. The initial
population was grown by randomly selecting pairs of protein models and
applying one of the two possible operators. In the case of recombination, the
models were superimposed based on their sequence alignment and a crossover
point drawn. Crossover was not permitted inside secondary structure elements.
The resulting recombinant model inherits the N-terminus from one parent and
the C-terminus from the other. In mutation events (occurring with frequency 1r, where r is the recombination probability) a new protein model was obtained
initial population of models
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(1) grow population: r recombination + (1-r) mutation
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(2) select best proportion according to fitness
A-1
by simply averaging its parents' coordinates after superimposition. In many
cases this process obtained distorted side-chain conformations.
2. Sippl M.J., Lackner P., Domingues F.S., Prlic A., Malik R., Andreeva A.
2. Selecting the best proportion. Fitness function. The whole idea of the
algorithm is that it should be possible to obtain optimized mosaic models by
shuffling them in a rational way. The key point in this approach is thus the
choice of an appropriate fitness function. After some benchmarking
experiments (unpublished results) we chose a function that calculates a free
energy estimate based on two terms: protein contact pair-potentials and sidechain solvation energies estimated from their solvent accessible area. This
function seems to yield a consistent measure of protein structural quality.
When each population reaches the upper limit (between 2 and 4 times its initial
size), this energy function is used to rank its members. Only the worst 25% of
the population is discarded at this point, to assure that quality models are not
lost prematurely.
3.
and Wiederstein M.(2001) Assessment of the CASP4 Fold Recognition
Category. Protein suppl 5, 55-67.
Contreras-Moreira B. and Bates P.A. (2002) Domain Fishing: a first step in
protein comparative modelling. Bioinformatics 18, 1141-1142.
4. Bates P.A., Kelley L.A., MacCallum R.M. and Sternberg
M.J.E. (2001) Enhancement of Protein Modelling by Human
Intervention in Applying the Automatic Programs 3DJIGSAW and 3D-PSSM. Proteins suppl 5, 39-46.
(www.bmm.icnet.uk/servers/3djigsaw)
3. Convergence criterion and final refinements. When the population has
converged to similar energies, there is no room for further generation of
variability and the evolution process stops. At this point the final population is
inspected. In most cases this consists of several representations of the same
protein conformation with average backbone deviations in the order of 0.1Å.
One of these representatives is then taken as the final model, which is carefully
inspected to detect unfavorable peptide conformations and a final energy
minimization using the CHARMM22 force field is performed. This procedure
is able to fix distorted side-chains. At this point we have a CASP5 unrefined
model.
In addition, for targets T0134, T0165, T0177 and T0185 we tested a further
refinement step consisting of running an all-atom, molecular dynamics
simulation inside a water box, with neutral total charge for around 0.5ns. For
these simulations we used the GROMACS package (www.gromacs.org) and
the OPLSAA force field. Snapshots taken from the trajectory were clustered
according to average backbone deviations and one conformation from the most
populated cluster was selected. After a few rounds of CHARMM22 energy
minimization, it was submitted as a refined model.
Insufficient computer resources prevented us from refining all targets.
1. Tramontano A., Leplae R. and Morea V. (2001) Analysis and Assessment
of Comparative Modeling Predictions in CASP4.. Proteins suppl 5, 22-38
A-2