(3) Fragment Library Construction: For template-free prediction, we first build
a set of 10-residue segments by comparing the feature vector of a target
sequence with them of library containing 2598 known-structure proteins thatshare <25% sequence identity. The vector contains PSSM of PSI-BLAST,
grouped chemical property of a residue, and a SSE. Two types of fragment
libraries are generated. Type I; a correlation coefficient scores top 200
segments for each overlapping 10-residue fragment of a target. Type II; fivescoring functions including the correlation coefficient pick over 200 segmentsby considering the degree of dominated level (often called “Pareto Frontier” inmulti-objective optimization field).

ROKKO
Template-free Prediction by Fragment Assembly withSimFold Energy Function at CASP7
S.J. Park1, N. Hori2, K. Okazaki2 and S. Takada12
1- Faculty of Sci, Kobe Univ, 2 - Grad School, Sci & Tech Kobe Univ
stakada@kobe-u.ac.jp

Team ROKKO primarily focused on predicting structures that need templatefree modeling and could have previously unseen folds. Prediction method,statistics, and short description for each target are available at
http://www.proteinsilico.org/ROKKO/casp7/rokko_casp7_strategy.html.
(1) General Workflow: All targets automatically stream to the general sequence
analysis procedure. BLAST1 package first searches homologous templates
through NRDB, and then mainly PSI-PRED2 predicts secondary structureelements (SSEs) using filtered NRDB. Some of DBs used are weekly updated.
If significant templates for a target are found, all available information on thetemplates is gathered for selecting high-resolution structural templates (See (2)).
When templates do not exist, 3D-Jury3 templates and alignments are gathered.
When we did not get reliable templates, we performed fragment assembly
simulations either by MCFA and/or GAFA (See (5) and (6)).
(2) Template-based Prediction: If we are satisfied with the quality of a template
BLAST found, we sample template-target sequence alignments using thestochastic backtracking procedure4 (over 100 sub-optimal alignments). Whenseveral templates are covering distinct target regions, we randomly pickalignments from each ensemble of the sub-optimal alignments, and input themas initial alignments of the progressive multiple sequence alignment(approximately 1000-3000 alignments). We also use template-target profilealignments when PSI-BLAST found templates with relatively higher E-value(>0.001). We convert the alignments to 3D structures by running MODELLER5,and evaluate them using both of Verify3D6 and Prosa7 to check the initialalignment quality. We iteratively run MODELLER with seemly goodalignments, and repeatedly checked SSEs and the quality of local/globalstructures. After ending this iterative procedure, we select final models from the2D score distribution generated by Verify3D and Prosa.

(4) SimFold Energy Function: For fragment assembly simulations, we solely
used a coarse-grained model, SimFold8,9, in which side chain atoms are
replaced with a center of mass. SimFold contains van der Waals interaction,
secondary structure propensity, hydrogen bond interaction, hydrophobicinteraction, and pairwise interaction. The latter three terms depend on thedegree of burial of interacting atoms. No protein specific potential such as
secondary structure prediction based potential is used in the energy function.
Parameters in SimFold are optimized by Z-score optimization method.
(5) Multi-Canonical Ensemble Fragment Assembly (MCFA): Using Type I
fragment library, we performed the reversible MCFA10 that fulfills detailedbalance condition. The predictive accuracy of our MCFA in de novo prediction
has been proved in CASP6. On the other hand, to define a reasonable weightfunction of MCFA is very time-consuming and human-dependant. We applied,therefore, Wang-Landau algorithm11 to the MCFA with a slight modification.
We arranged the reducing schedule of the Wang-Landau factor by using ourempirical data, and defined a weight function through approximately 2-3 billion
Monte Carlo steps in a MCFA. Independent MCFA for a target sampled
conformations as many as possible by the given time limit.
(6) Genetic Algorithm Fragment Assembly (GAFA): Using Type II fragment
library, we test a Genetic Algorithm newly developed (its basic code came fromthe earlier study12). With 100 initial random coils, the GA randomly selects aresidue as a crossover point. Based on this point, GA shuffles the two parents
randomly selected from the current population, and replaces a segment (4-10residues long) to a fragment coming from the library. After generating 200
offspring, GA updates the parents with the lowest-energy child and random one.
Thus, the initial coils hopefully evolve to the lowest-energy conformations

Abstracts - 1

through 5000 update steps. The final conformations of independent GAFA runs
are gathered as many as possible, and are analyzed.

increase the predictive accuracy. This will be a steppingstone to better
prediction of new folds.

(7) How We did in CASP7: We performed the template-based procedure forpredicting targets that have significant PSI-BLAST E-value (< 0.001) or 3Djury jscore (>50.0). For all remaining targets, we conducted MCFA and/orGAFA with the different types of fragment libraries. When there exist longalignment gaps (>20 residues) or probably unseen domains (e.g. T0311, T0347,etc.), we first predicted a full-length model with a template and ran FA topredict these broken regions. For a target that is likely to have multiple domains,
we parsed it into monomers based on domain DBs, and combined them into asingle chain by FA. 13 targets were predicted by the consensus of MCFA andGAFA; by using cluster analysis and visual inspection, we selected five models
from independently sampled models by each FA method.

1.

Interestingly, we often
found that some of models
FA predicted have highstructuralsimilarity
toknown proteins. In suchcases, we added a templatebased model to the finalmodels if we were confident.
For example, in T0363 case,
we first selected five models
fromMCFAsamples.
MAMMOTH13 said that all
five models are considerably
similar to a Beta Grasp Fold.
Particularly, model_5 washighly similar to 1MG4_A (z_score=4.92) that is akin to 3D-Jury templates.
We believed, consequently, that 1VJK_A (jscore=46.88) is the best template
for T0363. Such conspicuous structural similarity with remote homology was
found from FA models of several targets (e.g. T0304, T0349, T0353, T0361,T0382, etc.). Surprisingly, a model of SimFold FA for T0383 culled 1QYN_A(jscore=6.25) that is 3.66 Angstroms over 70 residues of the T0383 native.

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It is deemed again that SimFold FA is feasible to capture the native-likeinteractions from high quality fragment library. Therefore, the reliability ofstructural templates fold recognition servers detected can be confirmed toAbstracts - 2