EVA: Evaluation of protein structure prediction servers.

EVA (http://cubic.bioc.columbia.edu/eva/) is a web server for evaluation of the accuracy of automated protein structure prediction methods. The evaluation is updated automatically each week, to cope with the large number of existing prediction servers and the constant changes in the prediction methods. EVA currently assesses servers for secondary structure prediction, contact prediction, comparative protein structure modelling and threading/fold recognition. Every day, sequences of newly available protein structures in the Protein Data Bank (PDB) are sent to the servers and their predictions are collected. The predictions are then compared to the experimental structures once a week; the results are published on the EVA web pages. Over time, EVA has accumulated prediction results for a large number of proteins, ranging from hundreds to thousands, depending on the prediction method. This large sample assures that methods are compared reliably. As a result, EVA provides useful information to developers as well as users of prediction methods.

On the role of structural information in remote homology detection and sequence alignment: new methods using hybrid sequence profiles.

Structural alignments often reveal relationships between proteins that cannot be detected using sequence alignment alone. However, profile search methods based entirely on structural alignments alone have not been found to be effective in finding remote homologs. Here, we explore the role of structural information in remote homolog detection and sequence alignment. To this end, we develop a series of hybrid multidimensional alignment profiles that combine sequence, secondary and tertiary structure information into hybrid profiles. Sequence-based profiles are profiles whose position-specific scoring matrix is derived from sequence alignment alone; structure-based profiles are those derived from multiple structure alignments. We compare pure sequence-based profiles to pure structure-based profiles, as well as to hybrid profiles that use combined sequence-and-structure-based profiles, where sequence-based profiles are used in loop/motif regions and structural information is used in core structural regions. All of the hybrid methods offer significant improvement over simple profile-to-profile alignment. We demonstrate that both sequence-based and structure-based profiles contribute to remote homology detection and alignment accuracy, and that each contains some unique information. We discuss the implications of these results for further improvements in amino acid sequence and structural analysis.

CAFASP3 in the spotlight of EVA.

We have analysed fold recognition, secondary structure and contact prediction servers from CAFASP3. This assessment was carried out in the framework of the fully automated, web-based evaluation server EVA. Detailed results are available at http://cubic.bioc.columbia.edu/eva/cafasp3/. We observed that the sequence-unique targets from CAFASP3/CASP5 were not fully representative for evaluating performance. For all three categories, we showed how careless ranking might be misleading. We compared methods from all categories to experts in secondary structure and contact prediction and homology modellers to fold recognisers. While the secondary structure experts clearly outperformed all others, the contact experts appeared to outperform only novel fold methods. Automatic evaluation servers are good at getting statistics right and at using these to discard misleading ranking schemes. We challenge that to let machines rule where they are best might be the best way for the community to enjoy the tremendous benefit of CASP as a unique opportunity for brainstorming.

Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling.

We participated in the fold recognition and homology sections of CASP5 using primarily in-house software. The central feature of our structure prediction strategy involved the ability to generate good sequence-to-structure alignments and to quickly transform them into models that could be evaluated both with energy-based methods and manually. The in-house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)-a profile-to-profile alignment method that is derived from sequence-enhanced multiple structure alignments in core regions, and sequence motifs in non-structurally conserved regions. b) NEST-a fast model building program that applies an "artificial evolution" algorithm to construct a model from a given template and alignment. c) GRASP2-a new structure and alignment visualization program incorporating multiple structure superposition and domain database scanning modules. These methods were combined with model evaluation based on all atom and simplified physical-chemical energy functions. All of these methods were under development during CASP5 and consequently a great deal of manual analysis was carried out at each stage of the prediction process. This interactive model building procedure has several advantages and suggests important ways in which our and other methods can be improved, examples of which are provided.

Continuous shifts in the active set of spinal interneurons during changes in locomotor speed.

The classic 'size principle' of motor control describes how increasingly forceful movements arise by the recruitment of motoneurons of progressively larger size and force output into the active pool. We explored the activity of pools of spinal interneurons in larval zebrafish and found that increases in swimming speed were not associated with the simple addition of cells to the active pool. Instead, the recruitment of interneurons at faster speeds was accompanied by the silencing of those driving movements at slower speeds. This silencing occurred both between and within classes of rhythmically active premotor excitatory interneurons. Thus, unlike motoneurons, there is a continuous shift in the set of cells driving the behavior, even though changes in the speed of the movements and the frequency of the motor pattern appear to be smoothly graded. We conclude that fundamentally different principles may underlie the recruitment of motoneuron and interneuron pools.

Experience-dependent changes in basal dendritic branching of layer 2/3 pyramidal neurons during a critical period for developmental plasticity in rat barrel cortex.

In rat barrel cortex, development of layer 2/3 receptive fields can be disrupted by sensory deprivation, with a critical period ending around postnatal day (PND) 14. To determine if experience-dependent plasticity of dendritic morphology could contribute to the reorganization of synaptic inputs, we analyzed dendritic structure in acute brain slices using two-photon laser scanning microscopy (2PLSM) and automated segmentation and analysis software. Layer 2/3 pyramidal cells from control and deprived rats were imaged from PND 9 to PND 20, spanning the critical period. Detailed analyses were performed on basal arbors, which receive the majority of synaptic input from layer 4. Some parameters (number of primary dendrites, volume subtended, aspect ratios) were stable, suggesting that development of several important properties of basal arbors has ceased by age PND 9. However, the spatial organization of secondary branching changed with age and experience. In older neurons there was a larger fraction of branch points farther from the soma. Deprivation from age PND 9 delayed these changes in secondary branching. This effect of deprivation was rapid (detectable at PND 10) and present at all ages observed. Deprivation initiated at PND 15 had no effect on basal branching measured at PND 20. Thus the spatial organization of secondary dendritic branching is experience-dependent and shares a critical period with receptive field plasticity.

An image analysis algorithm for dendritic spines.

The structure of neuronal dendrites and their spines underlie the connectivity of neural networks. Dendrites, spines, and their dynamics are shaped by genetic programs as well as sensory experience. Dendritic structures and dynamics may therefore be important predictors of the function of neural networks. Based on new imaging approaches and increases in the speed of computation, it has become possible to acquire large sets of high-resolution optical micrographs of neuron structure at length scales small enough to resolve spines. This advance in data acquisition has not been accompanied by comparable advances in data analysis techniques; the analysis of dendritic and spine morphology is still accomplished largely manually. In addition to being extremely time intensive, manual analysis also introduces systematic and hard-to-characterize biases. We present a geometric approach for automatically detecting and quantifying the three-dimensional structure of dendritic spines from stacks of image data acquired using laser scanning microscopy. We present results on the measurement of dendritic spine length, volume, density, and shape classification for both static and time-lapse images of dendrites of hippocampal pyramidal neurons. For spine length and density, the automated measurements in static images are compared with manual measurements. Comparisons are also made between automated and manual spine length measurements for a time-series data set. The algorithm performs well compared to a human analyzer, especially on time-series data. Automated analysis of dendritic spine morphology will enable objective analysis of large morphological data sets. The approaches presented here are generalizable to other aspects of neuronal morphology.