Improving the accuracy of protein secondary structure prediction using structural alignment

BACKGROUND: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high. RESULTS: We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4–5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%. CONCLUSION: By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at . For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally

Understanding the Effect of Cognitive Reference Frames on Unmanned Aircraft Operations

As an ever-greater share of our national military airborne resources transition from manned to unmanned aircraft (UA) the issues associated with unmanned aircraft operations become more and more important. This study seeks to understand the difficulties associated with controlling both the unmanned aircraft and an onboard video sensor. Traditional unmanned aircraft involve multiple operators controlling multiple control displays that are often oriented on misaligned reference frames. One example unmanned aircraft mission includes a target described on a north-up reference frame, such as a map. The pilot plans a flight path, to this target, on a north-up map, but controls the aircraft along that flight path using an aircraft-view reference frame that offers a forward-looking cockpit view. Finally, the sensor operator controls the sensor to point at the target area using a sensor-view reference frame that offers a sensor viewfinder perspective. Any unmanned aircraft operator or team of operators is required to manage tasks across these multiple reference frames (north-up, aircraft-view, and sensor-view). This study investigated several display design techniques that had the potential to reduce the cognitive burden associated with correlating information from multiple reference frames. Orientation aids, reference frame alignment, display integration, and reduced display redundancy were all evaluated with human subject simulator experiments. During four separate experiments, a total of 80 subjects were asked to complete a series of representative unmanned aircraft operational tasks involving target acquisition, imagery orientation, target tracking, and flight path control. A simulator was developed to support this effort and allow for modification of display characteristics. Over all four experiments the reference frame alignment technique reduced basic orientation time and improved target acquisition time along with other performance and workload measures. The currently accepted practice of placing an orientation aid, such as a north arrow, on the displayed sensor video was only significant on the basic imagery orientation task and did not have a significant impact on the more involved target acquisition task. This research introduced a potential benefit of reference frame alignment on unmanned aircraft operations.This material is based upon work supported by the Air Force Rapid Capabilities Office under Air Force Contract No. FA8721-05-C-0002. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the United States Air Force, Department of Defense, or the US Government

Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels

BACKGROUND: Studies of the structure-function relationship in proteins for which no 3D structure is available are often based on inspection of multiple sequence alignments. Many functionally important residues of proteins can be identified because they are conserved during evolution. However, residues that vary can also be critically important if their variation is responsible for diversity of protein function and improved phenotypes. If too few sequences are studied, the support for hypotheses on the role of a given residue will be weak, but analysis of large multiple alignments is too complex for simple inspection. When a large body of sequence and functional data are available for a protein family, mature data mining tools, such as machine learning, can be applied to extract information more easily, sensitively and reliably. We have undertaken such an analysis of voltage-gated potassium channels, a transmembrane protein family whose members play indispensable roles in electrically excitable cells. RESULTS: We applied different learning algorithms, combined in various implementations, to obtain a model that predicts the half activation voltage of a voltage-gated potassium channel based on its amino acid sequence. The best result was obtained with a k-nearest neighbor classifier combined with a wrapper algorithm for feature selection, producing a mean absolute error of prediction of 7.0 mV. The predictor was validated by permutation test and evaluation of independent experimental data. Feature selection identified a number of residues that are predicted to be involved in the voltage sensitive conformation changes; these residues are good target candidates for mutagenesis analysis. CONCLUSION: Machine learning analysis can identify new testable hypotheses about the structure/function relationship in the voltage-gated potassium channel family. This approach should be applicable to any protein family if the number of training examples and the sequence diversity of the training set that are necessary for robust prediction are empirically validated. The predictor and datasets can be found at the VKCDB web site [1]

Systems Theory Based Architecture Framework for Complex System Governance

The purpose of this research was to develop a systems theory based framework for complex system governance using grounded theory approach. Motivation for this research includes: 1) the lack of research that identifies modeling characteristics for complex system governance, 2) the lack of a framework rooted in systems theory to support performance of complex system governance functions for maintaining system viability. This research focused on answering: What systems theoretic framework can be developed to inform complex system governance and enable articulation of governance function performance? The grounded theory research approach utilized three phases. First, the literature in systems theory, management cybernetics, governance and enterprise architecture was synthesized and open-coded to generalize main themes using broad analysis in NVivo software, researcher note taking in EndNote, and cataloging in Excel spreadsheets. Second, the literature underwent axial-coding to identify interconnections and relevance to systems theory and complex system governance, primarily using Excel spreadsheets. Finally, selective coding and interrelationships were identified and the complex system governance architecture framework was shaped, reviewed, and validated by qualified experts. This research examined a grounded theory approach not traditionally used in systems theory research. It produced a useful systems theory based framework for practical application, bridging the gap between theory and practice in the emerging field of complex system governance. Theoretical implications of this research include identifying the state of knowledge in each literature domain and the production of a unique framework for performing metasystem governance functions that is analytically generalizable. Management cybernetics, governance, and systems theory are expanded through a testable tool for meta-level organizational and system governance theories. Enterprise architecture is advanced with a multi-disciplinary framework that coherently presents and facilitates new use for architecture at the metasystem level. Methodological implications of this research include using grounded theory approach for systems theory research, where it is atypical. Although a non-traditional method, it provides an example for conducting fruitful research that can contribute knowledge. Practical implications of this research include a useable framework for complex system governance which has never before existed and a living structure adaptable to evolutionary change coming from any related domain or future practical application feedback

Proceedings of the Sixth International Conference Formal Approaches to South Slavic and Balkan languages

Proceedings of the Sixth International Conference Formal Approaches to South Slavic and Balkan Languages publishes 22 papers that were presented at the conference organised in Dubrovnik, Croatia, 25-28 Septembre 2008