Tim Hubbard and Jong Park
Centre for Protein Engineering, MRC Centre, Cambridge, UK, th@mrc-lmb.cam.ac.uk
Our entries to the structure prediction competition are for those
sequences where there is no known homology with any sequence of known
structure, with the objective of either recognising similar folds in
the database of known structures (PDB) or predicting ab-initio a rough
3-D topology.
Both the fold recognition and ab-initio prediction algorithms used rely
on the information contained in multiple sequence alignments, so
predictions were not made in cases where the sequence family was very
small or the alignments ambiguous. Initial multiple sequence
alignments were generally obtained by mailing the target sequence to
the PHD secondary structure prediction server [1] and were improved by
adding additional related sequences obtained from running blast against
a number of databases and using a number of other automatic and manual
alignment methods.
For fold recognition hidden markov models (HMM) [2] were constructed
from the multiple sequence alignment. Models were then used to search
a subset of pdb chain sequences (pdb90), none of which has greater than
90% homology with any other. The sequence or alignment was also mailed
to the PHD server to obtain a secondary structure prediction [1]. For
each alignment a value was calculated measuring the degree of
similarity between predicted secondary structure segments and those
observed in the known structure (from DSSP [3]) using an algorithm
similar to [4]. By considering the hmm score, the secondary structure
overlap score and the ranking of similar folds in the list (using the
fold classification of scop [5], which is incorporated into pdb90) a
prediction of fold type was made. The predictions for xyla, kau,
synapto, bphc and l14 were based mainly on the results from this
approach.
In cases where a high beta sheet content was observed, an ab initio
beta-strand pairing prediction was made [6]. The results of this
prediction were combined with the PHD secondary structure prediction to
identify strands most likely to pair. Because the number of possible
pairings is proportional to the square of the number of strands,
whereas the number of observed pairs is linearly related, prediction
generally becomes less reliable as the number of stands increases. The
predictions of the small proteins, prosub and staufen3 were mainly made
using this approach. In the case of large proteins, the method was
used to guide the selection of the closest fold in PDB, based on the
similarities in predicted contact maps observed between target and
folds proposed to be similar.
For the cases of rtp and chmut, no fold could be recognised and the
sequences predicted to be mainly helical, however certain sequence
patterns suggesting leucine zippers were identified and speculative
topology predictions submitted based on this.
Structures Predicted Main Method Used
==================== ================
xyla hmm
kau hmm
prosub beta-strand topology
synapto hmm
staufen3 beta-strand topology + hmm
bphc hmm + beta-strand topology
rtp sequence patterns
chmut sequence patterns
l14 hmm + beta-strand topology
Structures not predicted Reason not predicted
======================== ====================
bhted too few homologous sequences
pcna too short notice (2 days)
smanucecs too few homologous sequences
ppdk not enough time
pbdg not enough time
mystery suspected to be some sort of joke!
We thank Burkhard Rost, Reinhard Schneider and Chris Sander for access
to the PHD secondary structure prediction server [1], the DSSP program
[3], and the HSSP database, to Sean Eddy for use of HMM program suite
[2], Erik Sonnhammer for the use of SWIR5, to Andrej Sali for use of
Modeller and to TH's collaborators Alexey Murzin, Brenner and Cyrus
Chothia for the development of SCOP [5]. TH is grateful to the MRC and
ZENECA for financial support.
[1] B. Rost, C. Sander: Prediction of protein structure at better than
70% accuracy. J. Mol. Biol., 1993, Vol. 232, pp. 584-599.
[2] S. Eddy, "HMM*: Hidden Markov Modeling of Proteins and Nucleic
Acids", software freely available via anonymous ftp to
cele.mrc-lmb.cam.ac.uk, in pub/sre, documentation from
http://logi.mrc-lmb.cam.ac.uk/.
Contact sre@mrc-lmb.cam.ac.uk for
information.
[3] W. Kabsch and C. Sander (1983). Dictionary of protein secondary
structure: pattern recognition of hydrogen-bonded and geometrical
features. Biopolymers, 22, 2577-637.
[4] B. Rost, C. Sander, R. Schneider (1994). Redefining the goals of
protein secondary structure. JMB, 235, 13-26.
[5] A. Murzin, S.E. Brenner, T.J.P. Hubbard, C. Chothia (1994). The
SCOP (Structural Classification of Proteins) database: available
world-wide over the internet through the world wide web (WWW) at
http://scop.mrc-lmb.cam.ac.uk/scop/.
[6] T.J.P Hubbard (1994). Use of beta-strand Interaction
Pseudo-Potentials in Protein Structure Prediction and Modelling. In
R.H. Lathrop (eds.), Proceedings of the Biotechnology Computing Track,
Protein Structure Prediction MiniTrack of the 27th HICSS. IEEE Computer
Society Press, pp. 336-354. (macintosh Postscript file available by
anonymous ftp to ind2.mrc-lmb.cam.ac.uk in /pub/th/beta/beta.hicss-27.ps.Z. Contact th@mrc-cpe.cam.ac.uk for
information.
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Last modified on 1-11-95