The document outlines methods and results for protein structure prediction and refinement using various scoring functions and models. It details procedures for evaluating and selecting models based on contact potentials, amino acid similarity matrices, and simulations, along with a compilation of top-ranked models in different categories. The authors acknowledge contributions from various researchers in the field and reference several studies that support their methods.

1. Hyung-Rae Kim, Amit Roy,
Daisuke Kihara
http://kiharalab.org
333

2. Overall Prediction Procedure
Server
Models
PRESCO
Residue
Environment
Score
BLOSUM30
CC80
CCPC
QUIB
QUC2
Ranking by
AA matrices
Structure
Refinement by
CHARMM runs
Side-Chain
Modeling
5 models
Final 5
models
20-30 models
CABS 5 Clusters
Fragment-
interaction
-potential

3. Add H
Starting
Structure
Minimize in
Screened Coulomb
Potential (SCP)
10 x 10ns → 5000 snapshots
constraints on
secondary structure
MD 20 x 10ns
with SCP
CH27 force field
10 x 10ns → 5000 snapshots
constraints on
all Ca atoms
Dfire score,
RMSD with
initial structure
Corr (dfire,Irmsd) > 0.4
Discarded (very rare)
Average structure
from low dfire, Irmsd
snapshots from
set 1 and 2
Relax at low T MD
Model 1 and 2
Select structures with
Lowest dfire score
Model 3,4,5
Hassan, S. A., Guarnieri, F., & Mehler, E. L. (2000).
The Journal of Physical Chemistry B, 104(27), 6478-6489.
Mirjalili, V., Noyes, K., & Feig, M. (2014).
Proteins: Structure, Function, and Bioinformatics, 82(S2), 196-207.
Refinement Procedure

4. Side-Chain Depth Environment (SDE)
within a sphere of 6 or 8 Å
along the main-chain
Center
(Kim & Kihara, Proteins 2014)

5. Finding Similar SDE from Database
Structure
Database
2536 proteins
500 lowest
RMSD
fragments of 9
side-chain
centroids;
Superimposed
with the query
fragment
Select SDE
with the same
number of
side-chain
centroids in
the sphere of
8.0/6.0Å
Query SDE
Compute
residue-depth
RMSD for
corresponding
side-chain
centroids
Sort by depth RMSD
to the query
surface

6. (Kim & Kihara, Proteins, 2014)
Decoy Evaluation
with Protein
Residue
Environmental
Score (PRESCO)

7. CCPC, CC80 Matrices:
Contact definition of two residues: any pair of side-
chain heavy atoms or Cα atom less than 4.5 Å
Compute a knowledge-based residue contact
potential (Gaussian chain reference state,
composition correction averaging)
Correlation coefficients of residue pairs are used
as values of the amino acid similarity matrix
Residue Contact Potential-Based Matrix
(Tan, Huang, & Kihara, Proteins, 2006)

8. Structure-derived Amino Acid
Similarity Matrices in AAIndex
BLAJ010101 - Structural superposition data for identifying potential remote homologues
(Blake-Cohen, 2001)
HENS920101 - BLOSUM45 substitution matrix (Henikoff-Henikoff, 1992)
JOHM930101 - Structure-based amino acid scoring table (Johnson-Overington, 1993)
KOLA920101 - Conformational similarity weight matrix (Kolaskar-Kulkarni-Kale, 1992)
KOSJ950115 - Context-dependent optimal substitution matrices for all residues
(Koshi-Goldstein, 1995)
MIYS930101 - Base-substitution-protein-stability matrix (Miyazawa-Jernigan, 1993)
OVEJ920101 - STR matrix from structure-based alignments (Overington et al., 1992)
PRLA000101 - Structure derived matrix (SDM) for alignment of distantly related sequences
(Prlic et al., 2000)
PRLA000102 - Homologous structure derived matrix for alignment of distantly related sequences
(Prlic et al., 2000)
QU_C930101 - Cross-correlation coefficients of preference factors main chain (Qu et al., 1993)
QU_C930102 - Cross-correlation coefficients of preference factors side chain (Qu et al., 1993)
QUIB020101 - STROMA score matrix for the alignment of known distant homologs
(Qian-Goldstein, 2002)

9. Alignment Accuracy by AA Matrices
2761 Fold level protein sequence pairs, Lindahl & Eloffson Database
(Tan, Huang, Kihara, Proteins 2006)
Correct alignments: >50% of residues are correctly aligned

10. Native Structure Recognition
10
Decoy Sets DFIRE dDFIRE DOPE RW RWplus OPUS-
PSP
GOAP MRE
(CC80)
SDE
(QUIB)
Combinations of
MRE & SDE
#
Targe
tsBLSM3
0+QU_
C2
BLSM3
0+QU_
C2
CC80+
QU_C1
4state_redu
ced
6 7 7 6 6 7 7 7 7 7 7 7 7
Fisa 3 3 3 3 3 3 3 2 2 2 2 3 4
Fisa_casp3 4 4 3 4 4 5 5 2 1 3 3 4 5
Lmds 7 6 7 7 7 8 7 10 6 10 10 10 10
Lattice_ssfit 8 8 8 8 8 8 8 8 8 8 8 8 8
hg_structal 12 16 ---- ---- 12 18 22 28 11 27 27 27 29
ig_structal 0 26 ---- ---- 0 20 47 61 6 61 61 61 61
ig_structal_
hires
0 16 ---- ---- 0 14 18 20 6 20 20 20 20
Moulder 19 18 19 19 19 19 19 20 16 20 20 20 20
ROSETTA 20 12 21 20 20 39 45 25 31 41 41 39 58
I-TASSER 49 48 30 53 56 55 45 56 47 56 56 56 56
#Total
(Z-score)
128
(-1.94)
164
(-2.52)
98/168
(-2.47)
120/168
(-3.23)
135
(-2.13)
196
(-2.86)
226
(-3.57)
239
(6.78)
141
(2.14)
255
(5.70)
255
(5.76)
255
(5.65)
278

11. Scoring Function Models only Native included
Average
Rank
Ranked 1 Average
Rank
ranked 1
MRE (CC80) 6.77 29 1.32 131
SDE (QUIB) 2.89 56 1.98 97
Combinations of
MRE & SDE
BLSM30+QU_C1 6.79 31 1.18 139
CC80(SDE)+BLSM
30(SDE)
2.82 66 1.99 89
QMEAN6 2.87 85 1.71 113
RWplus 2.97 57 1.78 106
RW 3.08 51 1.71 110
QMEANall_atom 3.59 74 1.71 119
QMEANSSE_agree 3.74 62 3.72 39
RF_HA_SRS 4.65 49 1.38 137
OPUS_CA 4.72 79 5.13 55
RF_HA 5.44 62 2.78 112
DOPE 5.77 54 3.27 95
DFIRE 6.03 50 5.69 33
Floudas-CM 7.75 38 7.05 42
Melo-ANOLEA 9.62 19 5.19 86
Random 9.72 13.9 10.1 8.3
Benchamark
on Ryukumov
& Fiser CASP
Set
Comparison
against36
scoring
functions.
Only showing
results of 13
functions.
Best Second best Third best

12. Side-Chain Building
(Peterson, Kang, Kihara, Proteins 2014)

13. T0804 Top 1 models
Kiharalab: TS333_1
Boniecki_pred: TS301_1
Skwark: TS358_1

14. T0804 Kiharalab Top 1 Model
Native (Coordinates not available)
Kiharalab_1
GDT-TS: 31.44 GOAP: -18178.22
QUARK_5
GDT-TS: 30.93 GOAP: -14959.68
Best in Top 1 Models

15. T0804 Server Models Selected by
PRESCO
Final Selection
Rank Model GDT-TS
1 QUARK_TS5 30.93
2 myprotein-me_TS4 12.63
3 Zhang-server_TS5 29.77
4 Seok-server_TS2 11.86
5 BAKER_ROSETTASERVER_TS3 12.37
QU_C2 + BLOSUM30
Rank Model
1 QUARK_TS5
2 TASSER-VMT_TS5
3 myprotein-me_TS4
4 BAKER_ROSETTAS_TS3
5 Zhang-Server-TS1
QU_C1 + QUIB
Rank Model
1 QUARK_TS5
2 SAM-T08-server_TS3
3 myprotein-me_TS1
4 myprotein-me_TS4
5 TASSER-VMT-TS1
CC80+ BLOSUM30
Rank Model
1 BAKER_ROSETTAS_TS3
2 myprotein-me_TS4
3 QUARK_TS5
4 myprotein-me_TS1
5 RBO_Aleph_TS3
CCPC+ BLOSUM30
Rank Model
1 QUARK_TS5
2 BAKER_ROSETTAS_TS3
3 myprotein-me_TS4
4 myprotein-me_TS1
5 TASSER-VMT_TS5
QUIB
Rank Model
1 SAM-T08-server_TS3
2 myprotein-me_TS4
3 QUARK_TS5
4 BAKER_ROSETTAS_TS3
5 BAKER_ROSETTAS_TS2

16. T0799-D1 Kiharalab Top 1 Model
Native (Coordinates not available)Kiharalab_1
GDT-TS: 19.86 GOAP: -33178.17
BAKER-ROSETTASERVER_3
GDT-TS: 19.86 GOAP: -31360.77
3rd Best in Top 1 Models

17. T0834-D1 Kiharalab Top1 Model
3rd Best in Top 1 Models
Kiharalab_1
GDT-TS: 37.12 GOAP: -26474.14
RBO_ALeph_5
GDT-TS: 37.88 GOAP: -26865.67
Superimposition with the native (130-192) (D1 also includes 2-37)

18. Acknowledgements
http://kiharalab.org@kiharalab
Hyung-Rae Kim
Amit Roy
Lenna Peterson
Daisuke Kihara