Domain Prediction Methods Research Articles

Channel Prediction in High-Mobility Massive MIMO: From Spatio-Temporal Autoregression to Deep Learning

While massive multiple-input multiple-output (MIMO) has achieved tremendous success in both theory and practice, it faces a crisis of sharp performance degradation in moderate or high-mobility scenarios (e.g., 30 km/h), due to the breach of uplink-downlink channel duality. Such a “curse of mobility” has spurred the research on channel prediction in high-mobility scenarios. Instead of predicting channel response matrix in the space-frequency domain, we investigate it in the angle-delay domain by utilizing the high angle-delay resolution of wideband massive MIMO systems. Specifically, we study the general angle-delay domain channel characterization and obtain that: 1) the correlations between the angle-delay domain channel response matrix (ADCRM) elements are decoupled significantly; 2) when the number of antennas and bandwidth are limited, the decoupling is insufficient and residual correlations between the neighboring ADCRM elements exist. Then focusing on the ADCRM, we propose two channel prediction methods: a spatio-temporal autoregressive (ST-AR) model-driven unsupervised-learning method and a deep learning (DL) based data-driven supervised-learning method. While the model-driven method provides a principled way for channel prediction, the data-driven method is generalizable to various channel scenarios. In particular, ST-AR exploits the residual spatio-temporal correlations of the channel element with its most neighboring elements, and DL realizes element-wise angle-delay domain channel prediction utilizing a complex-valued neural network (CVNN). Simulation results under the 3GPP non-line-of-sight (NLOS) scenarios indicate that, compared to the state-of-the-art Prony-based angular-delay domain (PAD) prediction method, both the proposed ST-AR and the CVNN-based channel prediction methods can enhance the channel prediction accuracy.

Time-domain analysis of vortex-induced vibration of a flexible mining riser transporting flow with various velocities and densities

Vortex-induced vibration (VIV) can be inevitably encountered in deep ocean mining. As the internal flow is transported axially in the flexible riser, complicated VIV dynamics occurs undergoing both the internal and external flows. Therefore, the object here is to explore the effect of the internal flow with different velocities and densities on VIV response under various uniform current. In this study, a semi-empirical time domain prediction method for VIV dynamics of flexible risers considering both internal and external flows is introduced and adopted. The governing equations are discretized and solved by using finite element method. Firstly, validations are made for VIV without internal flow based on the numerical results and experimental data. Comparisons prove that the simulation could reproduce the VIV dynamics of a flexible riser. Then with the increase of the internal flow velocities and densities, the effect of the internal flow on VIV response is examined. It is found that the dominating frequency and the root mean square (RMS) displacement in both in-line (IL) and cross-flow (CF) directions are notably influenced by the internal flow velocity and density. Besides, the drag coefficient and IL mean deflection are detected magnified while the internal flow velocity and density are increased under different external flow velocities. It should be noted that the change of the internal flow velocity and density could trigger new mode response of the flexible riser, leading to mode transition for the IL and CF dominating modes. In addition, VIV dynamics shows a similar changing trend with the increase of the internal flow velocity and density when the flexible fluid-conveying riser is subjected to different external flow velocities.

A time domain prediction method for the vortex-induced vibrations of a flexible riser

In this paper, a time domain prediction method from experimental data is proposed for vortex-induced vibration (VIV) of flexible risers. The nonlinear factors, couplings among axial tension, VIV response in cross flow (CF) direction and the hydrodynamic force, have been taken into account in this method, with a simplified tension variation model and empirical hydrodynamic force model. The hydrodynamic force, including the excitation force in the excitation region and the damping force in the damping region are the function of excitation coefficients, non-dimensional VIV amplitude and frequency based on vibration experiment data. Iterations are performed to achieve balances between the hydrodynamic forces and the VIV responses of a riser. Moreover, a new added mass coefficient of 2.0 from model tests of flexible pipes is applied, where the predicted VIV response frequencies reveal higher accuracy. Comparison between the predicted results and the experimental results under uniform flow of 2.8 m/s and shear flow of 2.0 m/s are conducted, which verifies the feasibility and reliability of the proposed method. In addition, by comparing the prediction results with and without coupling between axial tension and VIV responses, it is found that this coupling effect is of importance to VIV prediction and can improve VIV prediction accuracy, especially under the case of high flow velocity and high vibration mode.

DRPPP: A machine learning based tool for prediction of disease resistance proteins in plants

Plant disease outbreak is increasing rapidly around the globe and is a major cause for crop loss worldwide. Plants, in turn, have developed diverse defense mechanisms to identify and evade different pathogenic microorganisms. Early identification of plant disease resistance genes (R genes) can be exploited for crop improvement programs. The present prediction methods are either based on sequence similarity/domain-based methods or electronically annotated sequences, which might miss existing unrecognized proteins or low similarity proteins. Therefore, there is an urgent need to devise a novel machine learning technique to address this problem.In the current study, a SVM-based tool was developed for prediction of disease resistance proteins in plants. All known disease resistance (R) proteins (112) were taken as a positive set, whereas manually curated negative dataset consisted of 119 non-R proteins. Feature extraction generated 10,270 features using 16 different methods. The ten-fold cross validation was performed to optimize SVM parameters using radial basis function. The model was derived using libSVM and achieved an overall accuracy of 91.11% on the test dataset. The tool was found to be robust and can be used for high-throughput datasets. The current study provides instant identification of R proteins using machine learning approach, in addition to the similarity or domain prediction methods.

Time domain method for the prediction of pressure fluctuation induced by propeller sheet cavitation: Numerical simulations and experimental validation

This paper addresses the pressure fluctuation induced by a propeller sheet cavitation. This study applies the acoustic theory proposed by Ffowcs Williams and Hawkings to the prediction of the pressure fluctuation caused by the volume variations of the propeller cavitation. There are two objectives of this study. The first objective is to clarify and analyze the mechanism of the pressure fluctuation induced by the propeller sheet cavitation. The second objective is the evaluation of the developed numerical prediction method. Various factors that affect the pressure fluctuation are numerically simulated and analyzed based on the developed governing equation. The developed time domain prediction method is combined with the vortex lattice method, which solves for the unsteady sheet cavitation on the propeller blades. The numerical prediction results of the newly developed method are compared with the results of a potential-based numerical prediction method and the experimental results from the MOERI medium size cavitation tunnel tests for various operation conditions and propellers. As a result of this study, the pressure fluctuation induced by a propeller sheet cavitation is not simply proportional to the second derivative of the cavitation volume variation and inversely proportional to the distance. The fluctuation is represented by the combined result of the far-field term and the near-field term. Furthermore, various simulation results show that an elaborate prediction requires the overall consideration of the near-field term, the effect of the relative motion of the sources and the retarded time for the measurement position. The developed time domain prediction method provides reasonable results, and these results are in good agreement with the experimental results. In some cases, this method will provide much better results than the potential-based prediction method, especially for the prediction of the location where the maximum amplitude blade rate and the pressure amplitude of higher harmonics.

SECOM: A Novel Hash Seed and Community Detection Based-Approach for Genome-Scale Protein Domain Identification

With rapid advances in the development of DNA sequencing technologies, a plethora of high-throughput genome and proteome data from a diverse spectrum of organisms have been generated. The functional annotation and evolutionary history of proteins are usually inferred from domains predicted from the genome sequences. Traditional database-based domain prediction methods cannot identify novel domains, however, and alignment-based methods, which look for recurring segments in the proteome, are computationally demanding. Here, we propose a novel genome-wide domain prediction method, SECOM. Instead of conducting all-against-all sequence alignment, SECOM first indexes all the proteins in the genome by using a hash seed function. Local similarity can thus be detected and encoded into a graph structure, in which each node represents a protein sequence and each edge weight represents the shared hash seeds between the two nodes. SECOM then formulates the domain prediction problem as an overlapping community-finding problem in this graph. A backward graph percolation algorithm that efficiently identifies the domains is proposed. We tested SECOM on five recently sequenced genomes of aquatic animals. Our tests demonstrated that SECOM was able to identify most of the known domains identified by InterProScan. When compared with the alignment-based method, SECOM showed higher sensitivity in detecting putative novel domains, while it was also three orders of magnitude faster. For example, SECOM was able to predict a novel sponge-specific domain in nucleoside-triphosphatase (NTPases). Furthermore, SECOM discovered two novel domains, likely of bacterial origin, that are taxonomically restricted to sea anemone and hydra. SECOM is an open-source program and available at http://sfb.kaust.edu.sa/Pages/Software.aspx.

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

Development of a time domain prediction method for hull pressure fluctuation induced by marine propeller sheet cavitation with consideration of scattering effects

Using a time domain acoustic analogy, we develop a method to predict the sound field and hull pressure fluctuation generated by unsteady sheet cavitation on marine propellers. Formulation 1A of Farassat is introduced to enhance theoretical understanding of this work, and it is applied to modeling the scattered sound field created by the fuselage boundary. To express the direct sound field resulting from sheet cavitation, a new solution is studied which considers the Doppler effect and also separately expresses the near and far fields. A small cube model is used to verify the method. Computed acoustic field pressures around the cube are compared with the boundary element method, and the numerical results show good agreement. Finally, the pressure fluctuation on a ship stern model is calculated.

Random noise attenuation using f-x regularized nonstationary autoregression

We have developed a novel method for random noise attenuation in seismic data by applying regularized nonstationary autoregression (RNA) in the frequency-space ([Formula: see text]) domain. The method adaptively predicts the signal with spatial changes in dip or amplitude using [Formula: see text] RNA. The key idea is to overcome the assumption of linearity and stationarity of the signal in conventional [Formula: see text] domain prediction technique. The conventional [Formula: see text] domain prediction technique uses short temporal and spatial analysis windows to cope with the nonstationary of the seismic data. The new method does not require windowing strategies in spatial direction. We implement the algorithm by an iterated scheme using the conjugate-gradient method. We constrain the coefficients of nonstationary autoregression (NA) to be smooth along space and frequency in the [Formula: see text] domain. The shaping regularization in least-square inversion controls the smoothness of the coefficients of [Formula: see text] RNA. There are two key parameters in the proposed method: filter length and radius of shaping operator. Tests on synthetic and field data examples showed that, compared with [Formula: see text] domain and time-space domain prediction methods, [Formula: see text] RNA can be more effective in suppressing random noise and preserving the signals, especially for complex geological structure.

Protein structural domains: definition and prediction.

Recognition and prediction of structural domains in proteins is an important part of structure and function prediction. This unit lists the range of tools available for domain prediction, and describes sequence and structural analysis tools that complement domain prediction methods. Also detailed are the basic domain prediction steps, along with suggested strategies for different protein sequences and potential pitfalls in domain boundary prediction. The difficult problem of domain orientation prediction is also discussed. All the resources necessary for domain boundary prediction are accessible via publicly available Web servers and databases and do not require computational expertise.

INTRODUCTION — The First IEEE Conference on Healthcare Informatics, Imaging, and Systems biology HISB'11

The first IEEE HISB conference, organized by Tanveer Syeda-Mahmood (IBM, Conference Chair), Xue-wen Chen (University of Kansas, Conference Chair), Aziz Boxwala (University of California, San Diego, Program Chair), James Duncan (Yale University, Program Chair), and Satoru Miyano (Tokyo University, Program Chair), was held in San Jose, California, July 26–29, 2011. It brought together leading researchers in healthcare informatics, medical imaging, and systems biology to present the state-of-the-art research in their fields and to create synergies among the three communities in understanding diseases, treatments and outcomes from different but potentially complementary aspects. HISB review is double-blind with over 72 program committee members in three tracks of informatics, imaging, and systems biology. Twenty-nine oral papers (acceptance rate is less than 20%) and 23 posters were accepted for conference presentations. The participants came from all over the world, including, for example, Australia, Brazil, Canada, China, India, Portugal, Scotland, Singapore, South Korea, Spain, UK, and of course, USA. The conference program consists of both peer-reviewed oral/poster sessions and invited sessions including plenary talks, special sessions talks, tutorials, and panels. The plenary talks include Nobel Laureate, Prof. Andrew Fire (Stanford University), who presented “sequence-based tracking of biological responses to foreign information”; Prof. Eric Grimson (Chancellor, MIT), who described “progress in biomedical imaging and its impact on healthcare”; and Prof. Charles Friedman (formerly with US Department of Health and Human Services, now with University of Michigan), who spoke about “the vision of a national ‘learning health system’ ”. In addition, in systems biology track, two invited speakers, Prof. Russ Altman (Stanford University) and Prof. Seyoung Kim (Carnegie Mellon University), presented their research progress in “computational methods for functional genomics and pharmacogenomics” and “modern statistical methods for genetic association study: structured genome–transcriptome–phenome association analysis”, respectively. The four papers, featured in this special issue, were selected from the accepted oral papers reported at the conference in the systems biology track. Enzymes, typically with a few of residues actively involved in the catalytic reactions, play a significant role in cellular processes. Choi and Kim developed a sequence-based catalytic domain prediction method by integrating clustering and information theory-based

SEQUENCE-BASED ENZYME CATALYTIC DOMAIN PREDICTION USING CLUSTERING AND AGGREGATED MUTUAL INFORMATION CONTENT

Characterizing enzyme sequences and identifying their active sites is a very important task. The current experimental methods are too expensive and labor intensive to handle the rapidly accumulating protein sequences and structure data. Thus accurate, high-throughput in silico methods for identifying catalytic residues and enzyme function prediction are much needed. In this paper, we propose a novel sequence-based catalytic domain prediction method using a sequence clustering and an information-theoretic approaches. The first step is to perform the sequence clustering analysis of enzyme sequences from the same functional category (those with the same EC label). The clustering analysis is used to handle the problem of widely varying sequence similarity levels in enzyme sequences. The clustering analysis constructs a sequence graph where nodes are enzyme sequences and edges are a pair of sequences with a certain degree of sequence similarity, and uses graph properties, such as biconnected components and articulation points, to generate sequence segments common to the enzyme sequences. Then amino acid subsequences in the common shared regions are aligned and then an information theoretic approach called aggregated column related scoring scheme is performed to highlight potential active sites in enzyme sequences. The aggregated information content scoring scheme is shown to be effective to highlight residues of active sites effectively. The proposed method of combining the clustering and the aggregated information content scoring methods was successful in highlighting known catalytic sites in enzymes of Escherichia coli K12 in terms of the Catalytic Site Atlas database. Our method is shown to be not only accurate in predicting potential active sites in the enzyme sequences but also computationally efficient since the clustering approach utilizes two graph properties that can be computed in linear to the number of edges in the sequence graph and computation of mutual information does not require much time. We believe that the proposed method can be useful for identifying active sites of enzyme sequences from many genome projects.

FIEFDom: a transparent domain boundary recognition system using a fuzzy mean operator

Protein domain prediction is often the preliminary step in both experimental and computational protein research. Here we present a new method to predict the domain boundaries of a multidomain protein from its amino acid sequence using a fuzzy mean operator. Using the nr-sequence database together with a reference protein set (RPS) containing known domain boundaries, the operator is used to assign a likelihood value for each residue of the query sequence as belonging to a domain boundary. This procedure robustly identifies contiguous boundary regions. For a dataset with a maximum sequence identity of 30%, the average domain prediction accuracy of our method is 97% for one domain proteins and 58% for multidomain proteins. The presented model is capable of using new sequence/structure information without re-parameterization after each RPS update. When tested on a current database using a four year old RPS and on a database that contains different domain definitions than those used to train the models, our method consistently yielded the same accuracy while two other published methods did not. A comparison with other domain prediction methods used in the CASP7 competition indicates that our method performs better than existing sequence-based methods.

Computer-Assisted Protein Domain Boundary Prediction Using the Dom-Pred Server

Domain prediction from sequence is a particularly challenging task, and currently, a large variety of different methodologies are employed to tackle the task. Here we try to classify these diverse approaches into a number of broad categories. Completely automatic domain prediction from sequence alone is currently fraught with problems, but this should not be so surprising since human experts currently have significant disagreement on domain assignment even when given the structures. It can be argued that we should only test the domain prediction methods on benchmark data that human experts agree upon and this is the approach we take in this paper. Even for the data sets on which human experts agree, automatic structure-based domain assignment still cannot always agree, and so again it is still unlikely that domain prediction methods will reliably obtain correct results completely automatically. We make the argument that computer-assisted domain prediction is a more achievable goal. With this aim in mind, we present the DomPred server. This server provides the user with the results from two completely different categories of method (DPS and DomSSEA). In this paper, each method is individually benchmarked against one of the latest domain prediction benchmarks to provide information about their respective reliabilities. A variety of different benchmark scores are employed since the accuracy of a domain prediction method depends critically on what types of results one wishes to obtain (single/multi-domain classification, domain number, residue linker positions, etc.). Also both of these methods, implemented within the DomPred server, can suggest alternative domain predictions, allowing the user to make the final decision based on these results and applying their own background knowledge to the problem. The DomPred server is available from the URL:http://bioinf.cs.ucl.ac.uk/software.html.

Functional Classification Using Phylogenomic Inference

Phylogenomic inference of protein (or gene) function attempts to address the question, “What function does this protein perform?” in an evolutionary context. As originally outlined by Jonathan Eisen [1–3], phylogenomic inference of protein function is a multistep process involving selection of homologs, multiple sequence alignment (MSA), and phylogenetic tree construction; overlaying annotations on the tree topology; discriminating between orthologs and paralogs; and—finally—inferring the function of a protein based on the orthologs identified by this process and the annotations retrieved. Figure 1 shows an example of using annotated subfamily groupings to infer function, in a manner similar to [1]. One of us, while at Celera Genomics, separately came up with a similar approach for the functional classification of the human genome [4], based on the automated identification of functional subfamilies using the SCI-PHY algorithm and the use of subfamily hidden Markov models (HMMs) to classify novel sequences [5,6]. Our experiences over the past several years in developing computational pipelines for automating phylogenomic inference at the genome scale [7]—and the challenges we have faced in this effort—motivate this paper. Figure 1 Phylogenomic Analysis of Protein Function Using Subfamily Annotation In practice, phylogenomic inference of gene function is not often used. Far from it. The majority of novel sequences are assigned a putative function through the use of annotation transfer from the top hits in a database search. In our analysis of over 300,000 proteins in the UniProt database, only 3% of proteins with informative annotations (i.e., those not labelled as “hypothetical” or “unknown”) had experimental support for their annotations; 97% were annotated using electronic evidence alone. These annotations are uploaded to GenBank, where they persist even if they are eventually determined to be in error. The systematic errors associated with this annotation protocol have been pointed out by numerous investigators over the years [8–10]. The root causes of these errors are these: Gene duplication. This enables protein superfamilies to innovate novel functions on the same structural template, so that the top database hit may have a function distinct from the query. Domain shuffling. Domain fusion and fission events add an additional layer of complexity, as a query and database hit may share only a local region of homology and thus have entirely different molecular functions and structures. Propagation of existing errors in database annotations. This is particularly pernicious, as existing annotation errors are seldom detected and, even if detected, are not necessarily corrected. Evolutionary distance. Two proteins can share a common ancestor and domain structure, yet have very different functions simply due to their presence in very divergently related species. Phylogenomic analysis, properly applied, avoids these errors and provides a mechanism for detecting existing database annotation errors [3,7]. Why then is phylogenomic inference not used more widely? We believe this is due to four reasons. First, the actual frequency of annotation error is not known, so the gravity of the situation is not recognized. Second, phylogenomic inference is a much more complicated endeavor than a simple database search and requires significantly more expertise and computing resources. It is therefore not easily applied at the genome scale. Third, millions of dollars and years of effort have been poured into developing computational annotation systems that depend on annotation transfer from top database hits, perhaps overlaid with domain prediction methods such as PFAM or the NCBI CDD [11,12]. Fourth, phylogenomic approaches to protein function prediction have arisen only in the last few years, while database search methods have been available for much longer. Revolutions do not normally take place overnight. These four reasons result in phylogenomic inference being applied on a one-off basis, for a few protein superfamilies here and there. This may be about to change. A variety of software tools and algorithms enabling phylogenomic inference have been developed in recent years (see Table 1). Some of these methods have based annotation transfer on the identification of orthologs [13–15] or of functional subfamilies [6,16–21]. Other groups have used whole-tree analyses [22–24]. Still other groups employ expert knowledge to define functional subtypes and then develop statistical models to allow users to classify novel sequences [25,26]; these expert system-based approaches are unfortunately limited by the scarcity of experimental data for most protein families. Table 1 Resources for Phylogenomic Analysis It is worth examining the assumptions underlying these phylogenomic resources, and phylogenomic inference as a whole.

Structure of Lmaj006129AAA, a hypothetical protein fromLeishmania major

The gene product of structural genomics target Lmaj006129 from Leishmania major codes for a 164-residue protein of unknown function. When SeMet expression of the full-length gene product failed, several truncation variants were created with the aid of Ginzu, a domain-prediction method. 11 truncations were selected for expression, purification and crystallization based upon secondary-structure elements and disorder. The structure of one of these variants, Lmaj006129AAH, was solved by multiple-wavelength anomalous diffraction (MAD) using ELVES, an automatic protein crystal structure-determination system. This model was then successfully used as a molecular-replacement probe for the parent full-length target, Lmaj006129AAA. The final structure of Lmaj006129AAA was refined to an R value of 0.185 (Rfree = 0.229) at 1.60 A resolution. Structure and sequence comparisons based on Lmaj006129AAA suggest that proteins belonging to Pfam sequence families PF04543 and PF01878 may share a common ligand-binding motif.

Enhanced recognition of protein transmembrane domains with prediction-based structural profiles

Membrane domain prediction has recently been re-evaluated by several groups, suggesting that the accuracy of existing methods is still rather limited. In this work, we revisit this problem and propose novel methods for prediction of alpha-helical as well as beta-sheet transmembrane (TM) domains. The new approach is based on a compact representation of an amino acid residue and its environment, which consists of predicted solvent accessibility and secondary structure of each amino acid. A recently introduced method for solvent accessibility prediction trained on a set of soluble proteins is used here to indicate segments of residues that are predicted not to be accessible to water and, therefore, may be 'buried' in the membrane. While evolutionary profiles in the form of a multiple alignment are used to derive these simple 'structural profiles', they are not used explicitly for the membrane domain prediction and the overall number of parameters in the model is significantly reduced. This offers the possibility of a more reliable estimation of the free parameters in the model with a limited number of experimentally resolved membrane protein structures. Using cross-validated training on available sets of structurally resolved and non-redundant alpha and beta membrane proteins, we demonstrate that membrane domain prediction methods based on such a compact representation outperform approaches that utilize explicitly evolutionary profiles and multiple alignments. Moreover, using an external evaluation by the TMH Benchmark server we show that our final prediction protocol for the TM helix prediction is competitive with the state-of-the-art methods, achieving per-residue accuracy of approximately 89% and per-segment accuracy of approximately 80% on the set of high resolution structures used by the TMH Benchmark server. At the same time the observed rates of confusion with signal peptides and globular proteins are the lowest among the tested methods. The new method is available online at http://minnou.cchmc.org.

Armadillo: Domain Boundary Prediction by Amino Acid Composition

The identification and annotation of protein domains provides a critical step in the accurate determination of molecular function. Both computational and experimental methods of protein structure determination may be deterred by large multi-domain proteins or flexible linker regions. Knowledge of domains and their boundaries may reduce the experimental cost of protein structure determination by allowing researchers to work on a set of smaller and possibly more successful alternatives. Current domain prediction methods often rely on sequence similarity to conserved domains and as such are poorly suited to detect domain structure in poorly conserved or orphan proteins. We present here a simple computational method to identify protein domain linkers and their boundaries from sequence information alone. Our domain predictor, Armadillo (http://armadillo.blueprint.org), uses any amino acid index to convert a protein sequence to a smoothed numeric profile from which domains and domain boundaries may be predicted. We derived an amino acid index called the domain linker propensity index (DLI) from the amino acid composition of domain linkers using a non-redundant structure dataset. The index indicates that Pro and Gly show a propensity for linker residues while small hydrophobic residues do not. Armadillo predicts domain linker boundaries from Z-score distributions and obtains 35% sensitivity with DLI in a two-domain, single-linker dataset (within +/-20 residues from linker). The combination of DLI and an entropy-based amino acid index increases the overall Armadillo sensitivity to 56% for two domain proteins. Moreover, Armadillo achieves 37% sensitivity for multi-domain proteins, surpassing most other prediction methods. Armadillo provides a simple, but effective method by which prediction of domain boundaries can be obtained with reasonable sensitivity. Armadillo should prove to be a valuable tool for rapidly delineating protein domains in poorly conserved proteins or those with no sequence neighbors. As a first-line predictor, domain meta-predictors could yield improved results with Armadillo predictions.

Meta-DP: domain prediction meta-server

Meta-DP, a domain prediction meta-server provides a simple interface to predict domains in a given protein sequence using a number of domain prediction methods. The Meta-DP is a convenient resource because through accessing a single site, users automatically obtain the results of the various domain prediction methods along with a consensus prediction. The Meta-DP is currently coupled to 10 domain prediction servers and can be extended to include any number of methods. Meta-DP can thus become a centralized repository of available methods. Meta-DP was also used to evaluate the performance of 13 domain prediction methods in the context of CAFASP-DP. The Meta-DP server is freely available at http://meta-dp.bioinformatics.buffalo.edu and the CAFASP-DP evaluation results are available at http://cafasp4.bioinformatics.buffalo.edu/dp/update.html hkaur@bioinformatics.buffalo.edu Available at http://cafasp4.bioinformatics.buffalo.edu/dp/update.html.

DETERMINATION OF UNKNOWN IMPACT FORCE ACTING ON A SIMPLY SUPPORTED BEAM

This work presents a predictive model for determining the location and amplitude of an unknown impact force acting on a simply supported beam. Both time and frequency domain prediction methods are developed, respectively. The structural modal parameters can be first obtained by theoretical modal analysis (TMA) or by experimental modal analysis (EMA). The structural response at time and frequency domains due to an unknown impact force can then be measured and recorded. The predicted response can also be formulated and expressed as functions of amplitude and location of the impact force. The sum of square errors between the predicted and measured response is then defined as the objective function, while the amplitude and the location of the unknown impact force are defined as design variables. The optimisation problem is thereby constructed and can be solved for the amplitude of the impact force. The mode shape information associated to the location of the impact force can also be resolved and compared to the structural mode shapes to determine the location of the unknown impact force. Both numerical and experimental prediction results are presented. Results show that the predictive model is feasible and leads to the prediction of magnitude and location of the unknown impact force for arbitrary structures as well.