Hyperdimensional Analysis of Amino Acid Pair Distributions in Proteins

Our manuscript presents a novel approach to protein structure analyses. We have organized an 8-dimensional data cube with protein 3D-structural information from 8706 high-resolution non-redundant protein-chains with the aim of identifying packing rules at the amino acid pair level. The cube contains information about amino acid type, solvent accessibility, spatial and sequence distance, secondary structure and sequence length. We are able to pose structural queries to the data cube using program ProPack. The response is a 1, 2 or 3D graph. Whereas the response is of a statistical nature, the user can obtain an instant list of all PDB-structures where such pair is found. The user may select a particular structure, which is displayed highlighting the pair in question. The user may pose millions of different queries and for each one he will receive the answer in a few seconds. In order to demonstrate the capabilities of the data cube as well as the programs, we have selected well known structural features, disulphide bridges and salt bridges, where we illustrate how the queries are posed, and how answers are given. Motifs involving cysteines such as disulphide bridges, zinc-fingers and iron-sulfur clusters are clearly identified and differentiated. ProPack also reveals that whereas pairs of Lys residues virtually never appear in close spatial proximity, pairs of Arg are abundant and appear at close spatial distance, contrasting the belief that electrostatic repulsion would prevent this juxtaposition and that Arg-Lys is perceived as a conservative mutation. The presented programs can find and visualize novel packing preferences in proteins structures allowing the user to unravel correlations between pairs of amino acids. The new tools allow the user to view statistical information and visualize instantly the structures that underpin the statistical information, which is far from trivial with most other SW tools for protein structure analysis

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences.</p> <p>Results</p> <p>The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes.</p> <p>Conclusions</p> <p>The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at <url>http://biomine.ece.ualberta.ca/MODAS/</url>.</p

Objects with motor valence affect the visual processing of human body parts: Evidence from behavioural and ERP studies

Recent findings indicate that the mental representation of an object contains crucial information about the motor interactions relevant for its intended functional use, suggesting a possible action-specific link with body effectors. For example, in the visual system, the extrastriate body area (EBA) responds to full body and body part images according to a functional/semantic organizational principle. However, the pliancy of the relationship between objects and body parts remains under-investigated. The present study aims to i) investigate this relationship more directly by assessing whether recognition of specific body parts can be facilitated by a brief exposure to functionally-related objects (Experiment 1) and ii) whether the functional relationship between objects and body parts modulates a posterior body-sensitive ERP waveform, peaking around 200 ms, and the more centro-parietal P300, linked to item categorization processes and visual awareness (Experiment 2). Participants were asked to quickly recognize targets (pictures of hands or feet) preceded by a functionally related (e.g., drum for hand target), unrelated (e.g., drum for foot target), or neutral (e.g., unknown object for both targets) prime. Findings showed that participants’ performance was significantly more accurate with related than unrelated primes and that ERP amplitudes were modulated by the relationship between the prime and the target. These findings confirm the existence of action-specific links between objects and body parts and expand on recent findings on categorical organization of neural responses to human effectors in the visual system

Anabaena circadian clock proteins KaiA and KaiB reveal a potential common binding site to their partner KaiC

The cyanobacterial clock proteins KaiA and KaiB are proposed as regulators of the circadian rhythm in cyanobacteria. Mutations in both proteins have been reported to alter or abolish circadian rhythmicity. Here, we present molecular models of both KaiA and KaiB from the cyanobacteria Anabaena sp PCC7120 deduced by crystal structure analysis, and we discuss how clock-changing or abolishing mutations may cause their resulting circadian phenotype. The overall fold of the KaiA monomer is that of a four-helix bundle. KaiB, on the other hand, adopts an alpha–beta meander motif. Both proteins purify and crystallize as dimers. While the folds of the two proteins are clearly different, their size and some surface features of the physiologically relevant dimers are very similar. Notably, the functionally relevant residues Arg 69 of KaiA and Arg 23 of KaiB align well in space. The apparent structural similarities suggest that KaiA and KaiB may compete for a potential common binding site on KaiC