Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are increasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are necessary in recognizing weak sequence-structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. A major new challenge for comparative modeling is the integration of it with the torrents of data from genome sequencing projects as well as from functional and structural genomics. In particular, there is a need to develop an automated, rapid, robust, sensitive, and accurate comparative modeling pipeline applicable to whole genomes. Such large-scale modeling is likely to encourage new kinds of applications for the many resulting models, based on their large number and completeness at the level of the family, organism, or functional network.

Comparative protein structure prediction is limited mostly by the errors in alignment and loop modeling. We describe here a new automated modeling technique that significantly improves the accuracy of loop predictions in protein structures. The positions of all nonhydrogen atoms of the loop are optimized in a fixed environment with respect to a pseudo energy function. The energy is a sum of many spatial restraints that include the bond length, bond angle, and improper dihedral angle terms from the CHARMM-22 force field, statistical preferences for the main-chain and side-chain dihedral angles, and statistical preferences for nonbonded atomic contacts that depend on the two atom types, their distance through space, and separation in sequence. The energy function is optimized with the method of conjugate gradients combined with molecular dynamics and simulated annealing. Typically, the predicted loop conformation corresponds to the lowest energy conformation among 500 independent optimizations. Predictions were made for 40 loops of known structure at each length from 1 to 14 residues. The accuracy of loop predictions is evaluated as a function of thoroughness of conformational sampling, loop length, and structural properties of native loops. When accuracy is measured by local superposition of the model on the native loop, 100, 90, and 30% of 4-, 8-, and 12-residue loop predictions, respectively, had Ͻ2 Å RMSD error for the mainchain N, C a , C, and O atoms; the average accuracies were 0.59 6 0.05, 1.16 6 0.10, and 2.61 6 0.16 Å, respectively. To simulate real comparative modeling problems, the method was also evaluated by predicting loops of known structure in only approximately correct environments with errors typical of comparative modeling without misalignment. When the RMSD distortion of the main-chain stem atoms is 2.5 Å, the average loop prediction error increased by 180, 25, and 3% for 4-, 8-, and 12-residue loops, respectively. The accuracy of the lowest energy prediction for a given loop can be estimated from the structural variability among a number of low energy predictions. The relative value of the present method is gauged by~1! comparing it with one of the most successful previously described methods, and~2! describing its accuracy in recent blind predictions of protein structure. Finally, it is shown that the average accuracy of prediction is limited primarily by the accuracy of the energy function rather than by the extent of conformational sampling.

No abstract

VISTA suppresses T cell proliferation and cytokine production and can influence autoimmunity and antitumor responses in mice.

The server is freely accessible to academic users at http://salilab.org/modloop

The following resources for comparative protein structure modeling and analysis are described (http://salilab.org): MODELLER, a program for comparative modeling by satisfaction of spatial restraints; MODWEB, a web server for automated comparative modeling that relies on PSI-BLAST, IMPALA and MODELLER; MODLOOP, a web server for automated loop modeling that relies on MODELLER; MOULDER, a CPU intensive protocol of MODWEB for building comparative models based on distant known structures; MODBASE, a comprehensive database of annotated comparative models for all sequences detectably related to a known structure; MODVIEW, a Netscape plugin for Linux that integrates viewing of multiple sequences and structures; and SNPWEB, a web server for structure-based prediction of the functional impact of a single amino acid substitution.

In selecting a method to produce a recombinant protein, a researcher is faced with a bewildering array of choices as to where to start. To facilitate decision-making, we describe a consensus 'what to try first' strategy based on our collective analysis of the expression and purification of over 10,000 different proteins. This review presents methods that could be applied at the outset of any project, a prioritized list of alternate strategies and a list of pitfalls that trip many new investigators.

The antitumor drug Taxol stabilizes microtubules and reduces their dynamicity, promoting mitotic arrest and cell death. Upon assembly of the ␣͞␤-tubulin heterodimer, GTP bound to ␤-tubulin is hydrolyzed to GDP reaching a steady-state equilibrium between free tubulin dimers and microtubules. The binding of Taxol to ␤-tubulin in the polymer results in cold-stable microtubules at the expense of tubulin dimers, even in the absence of exogenous GTP. However, there is little biochemical insight into the mechanism(s) by which Taxol stabilizes microtubules. Here, we analyze the structural changes occurring in both ␤-and ␣-tubulin upon microtubule stabilization by Taxol. Hydrogen͞deuterium exchange (HDX) coupled to liquid chromatography-electrospray ionization MS demonstrated a marked reduction in deuterium incorporation in both ␤-and ␣-tubulin when Taxol was present. Decreased local HDX in peptic peptides was mapped on the tubulin structure and revealed both expected and new dimer-dimer interactions. The increased rigidity in Taxol microtubules was distinct from and complementary to that due to GTP-induced polymerization. The Taxol-induced changes in tubulin conformation act against microtubule depolymerization in a precise directional way. These results demonstrate that HDX coupled to liquid chromatography-electrospray ionization MS can be effectively used to study conformational effects induced by small ligands on microtubules. The present study also opens avenues for locating drug and protein binding sites and for deciphering the mechanisms by which their interactions alter the conformation of microtubules and tubulin dimers.hydrogen͞deuterium exchange