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The server can be accessed by non-commercial users at http://bioinf.cs.ucl.ac.uk/disopred/

A number of state-of-the-art protein structure prediction servers have been developed by researchers working in the Bioinformatics Unit at University College London. The popular PSIPRED server allows users to perform secondary structure prediction, transmembrane topology prediction and protein fold recognition. More recent servers include DISOPRED for the prediction of protein dynamic disorder and DomPred for domain boundary prediction. These servers are available from our software home page at .

The spindle midzone – composed of antiparallel microtubules, microtubule-associated proteins (MAPs), and motors – is the structure responsible for microtubule organization and sliding during anaphase B. In general, MAPs and motors stabilize the midzone and motors produce sliding. We show that fission yeast kinesin-6 motor klp9p binds to the microtubule antiparallel bundler ase1p at the midzone at anaphase B onset. This interaction depends upon the phosphorylation states of klp9p and ase1p. The cyclin-dependent kinase cdc2p phosphorylates and its antagonist phosphatase clp1p dephosphorylates klp9p and ase1p to control the position and timing of klp9p-ase1p interaction. Failure of klp9p-ase1p binding leads to decreased spindle elongation velocity. The ase1p-mediated recruitment of klp9p to the midzone accelerates pole separation, as suggested by computer simulation. Our findings indicate that a phosphorylation switch controls the spatial-temporal interactions of motors and MAPs for proper anaphase B, and suggest a mechanism whereby a specific motor-MAP conformation enables efficient microtubule sliding.

We describe here the results of using a neural network based method (DISOPRED) for predicting disordered regions in 55 proteins in the 5(th) CASP experiment. A set of 715 highly resolved proteins with regions of disorder was used to train the network. The inputs to the network were derived from sequence profiles generated by PSI-BLAST. A post-filter was applied to the output of the network to prevent regions being predicted as disordered in regions of confidently predicted alpha helix or beta sheet structure. The overall two-state prediction accuracy for the method is very high (90%) but this is highly skewed by the fact that most residues are observed to be ordered. The overall Matthews' correlation coefficient for the submitted predictions is 0.34, which gives a more realistic impression of the overall accuracy of the method, though still indicates significant predictive power.

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Dynamic instability, in which abrupt transitions occur between growing and shrinking states, is an intrinsic property of microtubules that is regulated by both mechanics and specialized proteins. We discuss a model of dynamic instability based on the popular idea that growth is maintained by a cap at the tip of the fiber. The loss of this cap is thought to trigger the transition from growth to shrinkage, called a catastrophe. The model includes longitudinal interactions between the terminal tubulins of each protofilament and ''gating rescues'' between neighboring protofilaments. These interactions allow individual protofilaments to transiently shorten during a phase of overall microtubule growth. The model reproduces the reported dependency of the catastrophe rate on tubulin concentration, the time between tubulin dilution and catastrophe, and the induction of microtubule catastrophes by walking depolymerases. The model also reproduces the comet tail distribution that is characteristic of proteins that bind to the tips of growing microtubules.cell biology ͉ cytoskeleton ͉ kinesin ͉ modeling

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