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\title{SAM-T04: what's new for CASP6}

\author{Kevin Karplus\\
Richard Hughey\\
Jenny Draper, Sol Katzman, Martina Koeva, George Shackelford\\
Bret Barnes, Marcia Soriano}

\institution{Biomolecular Engineering\\
University of California, Santa Cruz}
\email{karplus@soe.ucsc.edu}

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\slideCaption{fragfinder and undertaker}

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\begin{document}
\maketitle

\begin{slide}{Steps of SAM-Txx Methods}
\hangindent 2em

\begin{itemize}
\item Iterative search and alignment [rewritten, minor improvements]

\item Local structure prediction [new alphabets, minor tweaks]

\item Multi-track HMMs [minor tweaks]

\item Contact prediction [all new]

\item Conformation generation (undertaker) [major changes]

\end{itemize}
\end{slide}



\begin{slide}{Iterative search using {\hmm}s}
\hangindent 2em
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 {\tt
  target04} script 

\begin{itemize}
\item uses perl modules to encapsulate programs, for greater flexibility.
\item uses fastacmd instead of grep for counting and retrieving sequences.
\item uses blastpgp on each iteration to prefetch sequences for hmmscore.
\item uses cheap\_gaps transition regularizer throughout.
\end{itemize}


\end{slide}



\begin{slide}{Local Structure Alphabets}
\begin{itemize}
\item Use more backbone alphabets:
    \begin{itemize}
    \item DSSP \& DSSP-ehl2
    \item Str2
    \item Stride
    \item Bystroff
    \item alpha
    \end{itemize}
\item Use burial alphabets:
    \begin{itemize}
    \item CB-14-7
    \item near-backbone-11
    \end{itemize}
\end{itemize}
\end{slide}

\begin{slide}{Neural Net}
\begin{itemize}
\item We use neural nets to predict local properties.
\item 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.
\item Neural nets were retrained using T04 alignments and better
  training set.
\end{itemize}


\end{slide}


\begin{slide}{Multi-track {\Hmm}s}

\begin{itemize}
\item Using more 2-track {\hmm}s: amino acid plus each local structure alphabet.
\item Using 3-track {\hmm}s:  amino acid, backbone (str2), burial (CB-14-7)
\item Generate many alignments for each potential template.
    \begin{itemize}
    \item use different {\hmm}s.
    \item use both local and global.
    \item use both Viterbi and posterior decoding.
    \end{itemize}
\end{itemize}


\end{slide}

\begin{slide}{Contact prediction: new in 2004!}

\begin{itemize}
\item Use mutual information between columns.
\item Thin alignments aggressively (30\%, 35\%, 40\%, 50\%, 62\%).
\item Compute e-value for mutual info (correcting for small-sample effects).
\item Compute z-score of \(\log \mbox{e-value}\) within protein.
\item Feed e-values, z-scores, conservation, amino-acid profile,
  separation along chain, into neural net.
\end{itemize}
\end{slide}

\begin{slide}{Evaluating contact prediction}

Two measures of contact prediction:
\begin{itemize}
\item
  Accuracy: 
  \begin{displaymath}
  \sum \chi(i,j) \over \sum 1
  \end{displaymath}
  (favors short-range predictions, where contact probability is higher)
\item 
  Weighted accuracy: 
  \begin{displaymath}
  \sum {\chi(i,j) \over   \condprob{\mbox{contact}}{\mbox{separation}=|i-j|}} 
  \over \sum 1
  \end{displaymath}
  (1 if predictions no better than chance based on separation).
\end{itemize}
\end{slide}

\begin{slide}{Contact prediction results}

\centerline{\psfig{figure=figures/accuracy.eps,width=0.5\textwidth}\hfill
\psfig{figure=figures/weight-accuracy.eps,width=0.5\textwidth}}

\end{slide}

\begin{slide}{Fragfinder}
Medium-length fragments (9 long) for every position 
\begin{itemize}
\item Generated from 3-track {\hmm}s.
\item Residues filtered to remove improbable \(\phi\)-\(\psi\) pairs 
  (creating smaller fragments).
\end{itemize}
\end{slide}

\begin{slide}{Undertaker}
Undertaker is UCSC's attempt at a fragment-packing program (named because it optimizes burial).
\begin{itemize}
\item New cost functions (especially H-bonds)
\item Improved clash detection.
\item New conformation change operators (tweaking torsion angles,
  rigid body movements of chunks).
\item New ways to specify constraints (Hbond, SSbond, HelixConstraint,
  	StrandConstraint, SheetConstraint).
\item Improved adaptation of genetic algorithm.
\end{itemize}
\end{slide}


\begin{slide}{Results: best vs. Robetta best }
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\end{slide}

\begin{slide}{Results: model1 vs. Robetta 1}
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\end{slide}

\begin{slide}{Results: model1 vs. automatic SAM-T04}
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\end{slide}

\begin{slide}{Results: model1 vs. alignment}
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\end{slide}

\begin{slide}{Target T0201}
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\end{slide}

\begin{slide}{Target T0214}
%\epsfh{figures/T0214.model1-real}{0.8}
\end{slide}

\begin{slide}{Target T0281}
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\end{slide}

\begin{slide}{Target Txxx}
%\epsfh{figures/Txxx.model1-real}{0.8}
\end{slide}

\begin{slide}{Web sites}
\small
\begin{description}

\item[UCSC bioinformatics degrees:] 
{\tiny\verb+http://www.soe.ucsc.edu/programs/bionformatics/+}

\item[UCSC bioinformatics research:]
{\small\verb+http://www.soe.ucsc.edu/research/compbio/+}

\item[SAM tool suite info:]
{\tiny\verb+http://www.soe.ucsc.edu/research/compbio/sam.html+}

\item[{\Hmm} servers:]
{\tiny\verb+http://www.soe.ucsc.edu/research/compbio/HMM-apps/+}

\item[These slides:]
{\tiny\verb+http://www.soe.ucsc.edu/~karplus/papers/casp6-slides.pdf+}

\end{description}

\end{slide}



\end{document}
