SAM-T04: what’s new for CASP6

Kevin Karplus Richard Hughey

Jenny Draper, Sol Katzman, Martina Koeva, George Shackelford

Bret Barnes, Marcia Soriano

karplus@soe.ucsc.edu

Biomolecular Engineering University of California, Santa Cruz

Steps of SAM-Txx Methods

Iterative search and alignment [rewritten, minor improvements] Local structure prediction [new alphabets, minor tweaks] Multi-track HMMs [minor tweaks] Finding medium-length fragments (fragfinder) [multi-track HMMs, filter implausible] Contact prediction [all new] Conformation generation (undertaker) [major changes]

Contact prediction: new in 2004!

Use mutual information between columns. Thin alignments aggressively (30%, 35%, 40%, 50%, 62%). Compute e-value for mutual info (correcting for small-sample effects). Compute z-score of log(e-value) within protein. Feed e-values, z-scores, conservation, amino-acid profile, separation along chain into neural net.

Evaluating contact prediction

Two measures of contact prediction:

Accuracy:

\frac{∑ χ(i, j)}{∑ 1}

(favors short-range predictions, where contact probability is higher) Weighted accuracy:

\frac{∑ \frac{χ(i,j)}{Prob(contact|separation =|i−j|)}}{∑ 1}

(1 if predictions no better than chance based on separation).

CASP6, SAM-T04 – p.4/43

Contact prediction results

CASP6, SAM-T04 – p.5/43

Undertaker

Undertaker is UCSC’s attempt at a fragment-packing program (named because it optimizes burial).

New cost functions (especially H-bonds)

Improved clash detection.

New conformation change operators (tweaking torsion angles, rigid body movements of chunks).

New ways to specify constraints (Hbond, SSbond, HelixConstraint, StrandConstraint, SheetConstraint).

Improved adaptation of genetic algorithm.

Model 1 vs. Robetta 1

CASP6, SAM-T04 – p.7/43

Good stuff from Murzin

We won’t discuss the following:

T0270: 1t0tA became available after servers ran.

T0213: Murzin suggested using 1t62A for T0213, T0214, and T0227. T04 scored 1t62A best—we messed up the good alignment.

T0214: We used 1t62A, but we never got a good alignment.

T0227: T04 scored 1t62A best, but 2^{∘} prediction was poor, so we had bad alignments.

T0240: We submitted both dimer and monomer, but mistakenly put the dimer first.

T0245: 1tljA became available, but we don’t have the true structure yet.

CASP6, SAM-T04 – p.8/43

Best vs. Robetta best (NF and FR/A)

CASP6, SAM-T04 – p.9/43

Good stuff from Robetta

We won’t discuss the following, because the good stuff in them seems to have come from better Robetta models:

T0209_2: sheet constraints from Robetta-model1

T0248 (all 3 domains): borrows heavily from Robetta-model2

Model 1 vs. alignment (NF and FR/A)

CASP6, SAM-T04 – p.11/43

Auto vs. align (NF and FR/A)

CASP6, SAM-T04 – p.12/43

Target T0201 (NF)

We tried forcing various sheet topologies and selected 4 by hand.

Model 1 has right topology (5.9117 all-atom RMSD).

Unconstrained cost function not good at choosing topology.

Contact prediction didn’t help, though first prediction right.

Helices were too short.

Highest GDT and lowest RMSD model (try41-opt2.repack-nonPC 5.4912 all-atom) has wrong topology.

Target T0201 (NF)

CASP6, SAM-T04 – p.14/43

Target T0201 (NF)

Wrong topology, but best scoring decoy.

Target T0230 (FR/A)

Good except for C-terminal loop and helix flopped wrong way.

We have secondary structure right, including phase of beta strands.

Contact prediction helped, but we put too much weight on it—decoys fit predictions better than real structure does.

Target T0230 (FR/A)

CASP6, SAM-T04 – p.17/43

Target T0230 (FR/A)

Real structure with contact predictions:

Third strand has off-by-one error.

Top T04 hit (1gefA) is good, T2K put it 3rd.

We submitted the best model we had (in GDT score, try7-opt1 had better rmsd).

Sol’s hand work helped, but my attempts to force M1-P4 as a first strand and to remove the bulge at R22 were misguided.

Target T0281 (FR/A)

Red is real structure.

Secondary structure good, but helix packing angles wrong.

Need helix packing info in undertaker—hand-added constraints were wrong.

Too few homologs for contact prediction.

Target T0215 (FR/A)

Red is real structure.

We tried to force a jelly-roll structure with the N-terminal strand omitted.

Swapping the N- and C-terminal strands of our model would make it almost right.

Strand T60-A66 is off by one.

Target T0212 (FR/A)

CASP6, SAM-T04 – p.24/43

Web sites

UCSC bioinformatics degrees:

http://www.soe.ucsc.edu/programs/bionformatics/

SAM tool suite info:

http://www.soe.ucsc.edu/research/compbio/sam.html
Hmm servers: http://www.soe.ucsc.edu/research/compbio/HMM-apps/

These slides:

http://www.soe.ucsc.edu/~karplus/papers/casp6-slides.pdf
CASP6 all working files: http://www.soe.ucsc.edu/~karplus/casp6

Iterative search using hmms

SAM-T98, T99, T2K, and T04 methods all use similar method for building a target hmm, given a single sequence (or a seed alignment). The target04 script

uses perl modules to encapsulate programs, for greater flexibility.

uses fastacmd instead of grep for counting and retrieving sequences.

uses blastpgp on each iteration to prefetch sequences for hmmscore.

uses cheap_gaps transition regularizer throughout.

Local Structure Alphabets

Use more backbone alphabets: DSSP & DSSP-ehl2 Str2 Stride Bystroff alpha

Use burial alphabets:

CB-14-7 near-backbone-11

Neural Net

We use neural nets to predict local properties.

Input is profile with probabilities of amino acids at each position of target chain, plus insertion and deletion probabilities. New in 2004 is additional 20 inputs with one-hot encoding of amino acid in the target sequence.

Neural nets were retrained using T04 alignments and better training set.

Multi-track Hmms

Using more 2-track hmms: amino acid plus each local structure alphabet.

Using 3-track hmms: amino acid, backbone (str2), burial (CB-14-7)

Generate many alignments for each potential template. use different hmms. use both local and global. use both Viterbi and posterior decoding.

Fragfinder

Medium-length fragments (9 long) for every position Generated from 3-track hmms. Residues filtered to remove improbable ϕ-ψ pairs (creating smaller fragments).

Best vs. Robetta best

CASP6, SAM-T04 – p.31/43

SAM-T04 auto vs. Robetta 1

CASP6, SAM-T04 – p.32/43

Model 1 vs. SAM-T04 auto

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Model 1 vs. alignment

CASP6, SAM-T04 – p.34/43

Undertaker sidechains vs. Rosetta

CASP6, SAM-T04 – p.35/43

Undertaker sidechains vs. SCWRL

CASP6, SAM-T04 – p.36/43

Target T0197 (FR/H)

Robetta did surprisingly poorly for an FR/H model.

Our scores indicated more distant relationship, and meta-servers got wrong family.

SAM-T04’s secondary prediction better than SAM-T02’s.

We tried assembling sheets into various barrels, based on top few fold-recognition hits.

We used conserved residues, but not contact predictions.

Target T0197 (FR/H)

Real structure is red.

Target T0209_2 (NF)

Our best model was try15-opt2 (model3) (5.7115 Ang all-atom RMSD). Good, but final strand misregistered (off by 2). Model is more complete than crystal. Sheet constraints came from robetta-model1, which outperformed it.

Target T0209_2 (NF)

Real structure is red.

Target T0235_2 (FR/A)

43-residue inserted domaim—not fully resolved in crystal.

We had made separate predictions for P347-P426, and had a good alignment to 1occJ, which we then messed up. We ended up not using the separate domain prediction.

Good score only because first and last helix constrained by surrounding domain.

We made last helix of domain too short, despite prediction that it was longer.

Target T0235_2 (FR/A)

Real structure is red.

Target T0248

Borrows heavily from robetta model2, which beats it.