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PubMed2001

Using subsite coupling to predict signal peptides.

Given a nascent protein sequence, how can one predict its signal peptide or "Zipcode" sequence? This is a first important problem for scientists to use signal peptides as a vehicle to find new drugs or to reprogram cells for gene therapy. Based on a model that takes into account the coupling effect among some key subsites, the so-called [-3, -1, +1] coupling model, a new prediction algorithm is developed. The overall rate of correct prediction for 1939 secretory proteins and 1440 non-secretary proteins was over 92%. It has not escaped our attention that the new method may also serve as a useful tool for helping investigate further many unclear details regarding the molecular mechanism of the ZIP code protein-sorting system in cells.

Algorithms↗
PubMed1998

An algorithm for preoperative prediction of reoperation risk after internal fixation of femoral neck fractures.

An algorithm was designed for preoperative prediction of the risk for reoperation, and the mortality risk, after internal fixation of femoral neck fractures. Out of 51 reviewed studies of femoral neck fractures, eight met specified inclusion criteria such as low dropout rates, a minimum of ten surgeons performing the surgery, a minimum of 2 years follow-up, and a standard age, sex, and Garden class distribution. Five of these studies were used for the construction of the algorithm, and the remaining three for testing the specificity and sensitivity of the algorithm. A separate analysis of the influence of age on the reoperation rate was also performed. In the analysis of 399 reviewed cases of femoral neck fractures, the specificity for the algorithm in predicting the risk for reoperation was 96%, and the sensitivity was 51%. The positive predictive value for the algorithm in predicting the risk for reoperation was 77%, which was three times higher compared to the commonly used predictors age and Garden class (positive predictive value 25%). For prediction of the mortality risk the positive predictive value for-the algorithm was 57%.

Aged↗
PubMed2003

3D-Jury: a simple approach to improve protein structure predictions.

MOTIVATION: Consensus structure prediction methods (meta-predictors) have higher accuracy than individual structure prediction algorithms (their components). The goal for the development of the 3D-Jury system is to create a simple but powerful procedure for generating meta-predictions using variable sets of models obtained from diverse sources. The resulting protocol should help to improve the quality of structural annotations of novel proteins. RESULTS: The 3D-Jury system generates meta-predictions from sets of models created using variable methods. It is not necessary to know prior characteristics of the methods. The system is able to utilize immediately new components (additional prediction providers). The accuracy of the system is comparable with other well-tuned prediction servers. The algorithm resembles methods of selecting models generated using ab initio folding simulations. It is simple and offers a portable solution to improve the accuracy of other protein structure prediction protocols. AVAILABILITY: The 3D-Jury system is available via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/) to the academic community. SUPPLEMENTARY INFORMATION: 3D-Jury is coupled to the continuous online server evaluation program, LiveBench (http://BioInfo.PL/LiveBench/)

Algorithms↗
PubMed2006

Identifying patients at risk of nursing home admission: The Leeds Elderly Assessment Dependency Screening tool (LEADS).

BACKGROUND: Discharge from hospital to a nursing home represents a major event in the life of an older person and should only follow a comprehensive functional and medical assessment. A previous study identified 3 dependency scales able to discriminate across outcomes for older people admitted to an acute setting. We wished to determine if a single dependency scale derived from the 3 scales could be created. In addition could this new scale with other predictors be used as a comprehensive tool to identify patients at risk of nursing home admission. METHODS: Items from the 3 scales were combined and analysed using Rasch Analysis. Sensitivity and specificity analysis and ROC curves were applied to identify the most appropriate cut score. Binary logistic regression using this cut-off, and other predictive variables, were used to create a predictive algorithm score. Sensitivity, specificity and likelihood ratio scores of the algorithm scores were used to identify the best predictive score for risk of nursing home placement. RESULTS: A 17-item (LEADS) scale was derived, which together with four other indicators, had a sensitivity of 88% for patients at risk of nursing home placement, and a specificity of 85% for not needing a nursing home placement, within 2 weeks of admission. CONCLUSION: A combined short 17-item scale of dependency plus other predictive variables can assess the risk of nursing home placement for older people in an acute care setting within 2 weeks of admission. This gives an opportunity for either early discharge planning, or therapeutic intervention to offset the risk of placement.

Aftercare↗
PubMed2000

The hepatitis B virus pregenome: prediction of RNA structure and implications for the emergence of deletions.

The terminally redundant pregenomic RNA of human hepatitis B virus (HBV) comprises some 3,330 nucleotides and is a replicative intermediate in the production of the circular DNA genome. Deletions are known to arise in the HBV genome during the course of chronic infection and are sometimes associated with interferon therapy. These deletions are limited to small parts of the genome such as the 357-nucleotide pre-S1 region. Long RNA molecules such as the HBV pregenome have considerable structural flexibility and will undergo secondary structure shifts between energetically favourable states in a continuous and semi-random fashion. Since prediction of structure elements that are highly conserved in different forms of one RNA molecule is now feasible by computer modelling, we have analysed the whole HBV pregenome by two different RNA structure prediction algorithms and by new methods that exploit these algorithms. Significantly, the ends of pregenomic RNA were predicted to undergo both short-range and long-range interactions, which has relevance to our knowledge of the virus replicative cycle. By incorporating phylogenetic information relating to the 6 recognised genotypes of HBV, it was possible to highlight short secondary structures that may be common to all HBV strains. For example, although the pre-S1 region was predicted to undergo local folding of a loosely defined nature, most observed pre-S1 deletions mapped to all or part of an arm carrying a better-defined structure. The loss of such sequences may be mechanistically attributable to polymerase skipping during reverse transcription, and the possible advantages of such deletions are considered.

Algorithms↗
PubMed2000

Identification of a Hsp70 recognition domain within the rubisco small subunit transit peptide.

The interaction between SStp, the transit peptide of the precursor protein to the small subunit of Rubisco (prSSU) and two Hsp70 molecular chaperones, Escherichia coli DnaK and pea (Pisum sativum) CSS1, was investigated in detail. Two statistical analyses were developed and used to investigate and predict regions of SStp recognized by DnaK. Both algorithms suggested that DnaK would have high affinity for the N terminus of SStp, moderate affinity for the central region, and low affinity for the C terminus. Furthermore, both algorithms predicted this affinity pattern for >75% of the transit peptides analyzed in the chloroplast transit peptide (CHLPEP) database. In vitro association between SStp and these Hsp70s was confirmed by three independent assays: limited trypsin resistance, ATPase stimulation, and native gel shift. Finally, synthetic peptides scanning the length of SStp and C-terminal deletion mutants of SStp were used to experimentally map the region of greatest DnaK affinity to the N terminus. CSS1 displayed a similar affinity for the N terminus of SStp. The major stromal Hsp70s affinity for the N terminus of SStp and other transit peptides supports a molecular motor model in which the chaperone functions as an ATP-dependent translocase, committing chloroplast precursor proteins to unidirectional movement across the envelope.

Adenosine Triphosphatases↗
PubMed2004

Combination of finite element modeling and optimization for the study of lumbar spine biomechanics considering the 3D thorax-pelvis orientation.

A model of the lumbar spine capable of taking into account realistic loads derived from human activity would be of great benefit in studying its normal biomechanical functioning as well as its in vivo behavior in injured and surgically altered states. This paper proposes a method to analyze the mechanical response of the lumbar spine subjected to loads derived from human activity, combining a non-linear finite element model (FEM) and an optimization-based force predicting algorithm. Loads borne by the lumbar spine at the T12-L1 level (joint loads) are first predicted with the optimization algorithm and then applied to the FEM, while a boundary condition prescribing the relative L1-sacrum rotation is imposed onto the FEM to account for three-dimensional physiological thorax-pelvis orientation. The prescribed rotation is achieved through the application of moments on L1. To account for the effect of these moments on lumbar joint loads, an iteration between the optimization technique and the FEM computation has been carried out. This method provides two main benefits over previous studies: first, it allows for the application of any 3D loading condition while considering the real 3D rotation measured between the thorax and the pelvis, and second, it makes it possible to estimate the moments that must be applied on L1 in order to maintain this rotation, taking them into account when predicting joint loads. As an example application of the method, results are presented for the lumbar spine mechanical response at the time of peak T12-L1 joint force during walking.

Algorithms↗
PubMed2006

Predicting clinical image delivery time by monitoring PACS queue behavior.

The expectation of rapid image retrieval from PACS users contributes to increased information technology (IT) infrastructure investments to increase performance as well as continuing demands upon PACS administrators to respond to "slow" system performance. The ability to provide predicted delivery times to a PACS user may curb user expectations for "fastest" response especially during peak hours. This, in turn, could result in a PACS infrastructure tailored to more realistic performance demands. A PACS with a stand-alone architecture under peak load typically holds study requests in a queue until the DICOM C-Move command can take place. We investigate the contents of a stand-alone architecture PACS RetrieveSend queue and identified parameters and behaviors that enable a more accurate prediction of delivery time. A prediction algorithm for studies delayed in a stand-alone PACS queue can be extendible to other potential bottlenecks such as long-term storage archives. Implications of a queue monitor in other PACS architectures are also discussed.

Algorithms↗
PubMed2006

MolDock: a new technique for high-accuracy molecular docking.

In this article we introduce a molecular docking algorithm called MolDock. MolDock is based on a new heuristic search algorithm that combines differential evolution with a cavity prediction algorithm. The docking scoring function of MolDock is an extension of the piecewise linear potential (PLP) including new hydrogen bonding and electrostatic terms. To further improve docking accuracy, a re-ranking scoring function is introduced, which identifies the most promising docking solution from the solutions obtained by the docking algorithm. The docking accuracy of MolDock has been evaluated by docking flexible ligands to 77 protein targets. MolDock was able to identify the correct binding mode of 87% of the complexes. In comparison, the accuracy of Glide and Surflex is 82% and 75%, respectively. FlexX obtained 58% and GOLD 78% on subsets containing 76 and 55 cases, respectively.

Algorithms↗
PubMed2004

A comparison of siRNA efficacy predictors.

Short interfering RNA (siRNA) efficacy prediction algorithms aim to increase the probability of selecting target sites that are applicable for gene silencing by RNA interference. Many algorithms have been published recently, and they base their predictions on such different features as duplex stability, sequence characteristics, mRNA secondary structure, and target site uniqueness. We compare the performance of the algorithms on a collection of publicly available siRNAs. First, we show that our regularized genetic programming algorithm GPboost appears to have a higher and more stable performance than other algorithms on the collected datasets. Second, several algorithms gave close to random classification on unseen data, and only GPboost and three other algorithms have a reasonably high and stable performance on all parts of the dataset. Third, the results indicate that the siRNAs' sequence is sufficient input to siRNA efficacy algorithms, and that other features that have been suggested to be important may be indirectly captured by the sequence.

Algorithms↗
PubMed1993

Inorganic phosphate compartmentation in the normal isolated canine brain.

In vivo 31P magnetic resonance spectra of 16 isolated dog brains were studied by using a 9.4-T wide-bore superconducting magnet. The observed Pi peak had an irregular shape, which implied that it represented more than one single homogeneous pool of Pi. To evaluate our ability to discriminate between single and multiple peaks and determine peak areas, we designed studies of simulated 31Pi spectra with the signal-to-noise (S/N) ratios ranging from infinity to 4.4 with reference to the simulated Pi peak. For the analysis we used computer programs with a linear prediction algorithm (NMR-Fit) and a Marquardt-Levenberg nonlinear curve-fit algorithm (Peak-Fit). When the simulated data had very high S/N levels, both methods located the peak centers precisely; however, the Marquardt-Levenberg algorithm (M-L algorithm) was the more reliable at low S/N levels. The linear prediction method was poor at determining peak areas; at comparable S/N levels, the M-L algorithm determined all peak areas relatively accurately. Application of the M-L algorithm to the individual experimental in vivo dog brain data resolved the Pi peak into seven or more separate components. A composite spectrum obtained by averaging all spectral data from six of the brains with normal O2 utilization was fitted using the M-L algorithm. The results suggested that there were eight significant peaks with the following chemical shifts: 4.07, 4.29, 4.45, 4.62, 4.75, 4.84, 4.99, and 5.17 parts per million (ppm). Although linear prediction demonstrated the presence of only three peaks, all corresponded to values obtained using the M-L algorithm. The peak indicating a compartment at 5.17 ppm (pH 7.34) was assigned to venous pH on the basis of direct simultaneous electrode-based measurements. On the basis of earlier electrode studies of brain compartmental pH, the peaks at 4.99 ppm (pH 7.16) and 4.84 ppm (pH 7.04) were thought to represent interstitial fluid and the astrocyte cytoplasm, respectively.

Adenosine Triphosphate↗
PubMed2006

Whom to treat? The contribution of vertebral X-rays to risk-based algorithms for fracture prediction. Results from the European Prospective Osteoporosis Study.

INTRODUCTION: Vertebral fracture is a strong risk factor for future spine and hip fractures; yet recent data suggest that only 5-20% of subjects with a spine fracture are identified in primary care. We aimed to develop easily applicable algorithms predicting a high risk of future spine fracture in men and women over 50 years of age. METHODS: Data was analysed from 5,561 men and women aged 50+ years participating in the European Prospective Osteoporosis Study (EPOS). Lateral thoracic and lumbar spine radiographs were taken at baseline and at an average of 3.8 years later. These were evaluated by an experienced radiologist. The risk of a new (incident) vertebral fracture was modelled as a function of age, number of prevalent vertebral fractures, height loss, sex and other fracture history reported by the subject, including limb fractures occurring between X-rays. Receiver Operating Characteristic (ROC) curves were used to compare the predictive ability of models. RESULTS: In a negative binomial regression model without baseline X-ray data, the risk of incident vertebral fracture significantly increased with age [RR 1.74, 95% CI (1.44, 2.10) per decade], height loss [1.08 (1.04, 1.12) per cm decrease], female sex [1.48 (1.05, 2.09)], and recalled fracture history; [1.65 (1.15, 2.38) to 3.03 (1.66, 5.54)] according to fracture site. Baseline radiological assessment of prevalent vertebral fracture significantly improved the areas subtended by ROC curves from 0.71 (0.67, 0.74) to 0.74 (0.70, 0.77) P=0.013 for predicting 1+ incident fracture; and from 0.74 (0.67, 0.81) to 0.83 (0.76, 0.90) P=0.001 for 2+ incident fractures. Age, sex and height loss remained independently predictive. The relative risk of a new vertebral fracture increased with the number of prevalent vertebral fractures present from 3.08 (2.10, 4.52) for 1 fracture to 9.36 (5.72, 15.32) for 3+. At a specificity of 90%, the model including X-ray data improved the sensitivity for predicting 2+ and 1+ incident fractures by 6 and 4 fold respectively compared with random guessing. At 75% specificity the improvements were 3.2 and 2.4 fold respectively. With the modelling restricted to the subjects who had BMD measurements (n=2,409), the AUC for predicting 1+ vs. 0 incident vertebral fractures improved from 0.72 (0.66, 0.79) to 0.76 (0.71, 0.82) upon adding femoral neck BMD (P=0.010). CONCLUSION: We conclude that for those with existing vertebral fractures, an accurately read spine X-ray will form a central component in future algorithms for targeting treatment, especially to the most vulnerable. The sensitivity of this approach to identifying vertebral fracture cases requiring anti-osteoporosis treatment, even when X-rays are ordered highly selectively, exceeds by a large margin the current standard of practice as recorded anywhere in the world.

Age Factors↗
PubMed2005

Efficient recognition of folds in protein 3D structures by the improved PRIDE algorithm.

UNLABELLED: An improved version of the PRIDE (PRobaility of IDEntity) fold prediction algorithm has been developed, based on more solid statistical basis, fast search capabilities and efficient input structure processing. The new algorithm is effective in identifying protein structures at the 'H' level of the CATH hierarchy. AVAILABILITY: The new algorithm is integrated into the PRIDE2 web servers at http://pride.szbk.u-szeged.hu and http://www.icgeb.org/pride. SUPPLEMENTARY INFORMATION: Detailed documentation and performance evaluation is available in the description section of the PRIDE2 web server.

Algorithms↗
PubMed2002

Docking of small ligands to low-resolution and theoretically predicted receptor structures.

We have developed a simple docking procedure that is able to utilize low-resolution models of proteins created by structure prediction algorithms such as threading or ab initio folding to predict the conformation of receptor-small ligand complexes. In our approach, using only approximate, discretized models of both molecules, we search for the steric and quasi-chemical complementarity between a ligand and the receptor molecules. This averaging procedure allows for the compensation of numerous structural inaccuracies resulting from the theoretical predictions of the receptor structure. The best relative orientation of these two models is obtained by an exhaustive scan over the rigid body's six-dimensional translational and rotational degrees of freedom. The search method is based on a real space grid-searching algorithm, unlike docking methods based on the fast Fourier Transform algorithm. We have applied this algorithm to rebuild structures of several complexes available in the Protein Data Bank. The structures of the receptors are produced by means of our threading algorithm PROSPECTOR, subsequently refined, and then utilized in the docking experiment. In many cases, not only is the localization of the binding site on the receptor surface correctly identified, but the proper orientation of the bounded ligand is also reasonably well reproduced within the level of accuracy of the modeled receptor itself.

Algorithms↗
PubMed2006

Towards an integrated protein-protein interaction network: a relational Markov network approach.

Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.

Algorithms↗
PubMed1997

Incorporating global information into secondary structure prediction with hidden Markov models of protein folds.

Here we propose an approach to include global structural information in the secondary structure prediction procedure based on hidden Markov models (HMMs) of protein folds. We first identify the correct fold or 'topology' of a protein by means of the HMMs of topology families of proteins. Then the most likely structural model for that protein is used to modify the sequence of secondary structure states previously obtained with a prediction algorithm. Our goal is to investigate the effect on the prediction accuracy of including global structural information in the secondary structure prediction scheme, by means of the HMMs. We find that when the HMM of the predicted topology of a protein is used to adjust the secondary structure sequence predicted originally with the Quadratic-Logistic method, the cross-validated prediction accuracy (Q3) improves by 3%. The topology is correctly predicted in 68% of the cases. We conclude that this HMM based approach is a promising tool for effectively incorporating global structural information in the secondary structure prediction scheme.

Algorithms↗
PubMed2001

An HLA-directed molecular and bioinformatics approach identifies new HLA-A11 HIV-1 subtype E cytotoxic T lymphocyte epitopes in HIV-1-infected Thais.

Only limited cytotoxic T lymphocyte (CTL) epitope mapping has been done in nonsubtype B HIV-infected persons. We used molecular immunogenetic tools to determine HIV-specific CTL responses in HIV-1 Env subtype E-infected female sex workers (FSWs) from northern Thailand, where more than 50% of the population is HLA-A11 positive. EpiMatrix, a computer-based T cell epitope prediction algorithm, and a manual editing approach were used to predict 77 possible HLA-A11 CTL epitopes in HIV-1, some of which were conserved between subtypes B and E. MHC binding of these peptides was determined in an HLA-A11 stabilization assay, and binding peptides were tested for CTL recognition in eight HLA-A11-positive FSWs. Subtype E versions of known HLA-A2 subtype B HIV epitopes were also tested in four HLA-A2 positive FSWs. CTL responses were detected in all HLA-A11-positive and in three of four HLA-A2-positive persons. Among the 12 FSWs responses to peptides were found to Pol in 9 (75%), Env in 7 (58%), Nef in 5 (42%), and Gag in 5 (42%), and to conserved epitopes in 8 (67%). To identify HLA-A11 CTL epitopes in the absence of prediction tools, it would have been necessary to test almost 3000 10-mer peptides. EpiMatrix and manual predictions reduced this number to 77, of which 26 were MHC binding and 12 were CTL epitopes. Six of these HLA-A11 CTL epitopes have not been previously reported and are located in RT, gp120, and gp41. This report of CTL responses in subtype E-infected individuals defines epitopes that may be useful in HIV pathogenesis or vaccine studies.

Algorithms↗
PubMed2003

Secreted protein prediction system combining CJ-SPHMM, TMHMM, and PSORT.

To increase the coverage of secreted protein prediction, we describe a combination strategy. Instead of using a single method, we combine Hidden Markov Model (HMM)-based methods CJ-SPHMM and TMHMM with PSORT in secreted protein prediction. CJ-SPHMM is an HMM-based signal peptide prediction method, while TMHMM is an HMM-based transmembrane (TM) protein prediction algorithm. With CJ-SPHMM and TMHMM, proteins with predicted signal peptide and without predicted TM regions are taken as putative secreted proteins. This HMM-based approach predicts secreted protein with Ac (Accuracy) at 0.82 and Cc (Correlation coefficient) at 0.75, which are similar to PSORT with Ac at 0.82 and Cc at 0.76. When we further complement the HMM-based method, i.e., CJ-SPHMM + TMHMM with PSORT in secreted protein prediction, the Ac value is increased to 0.86 and the Cc value is increased to 0.81. Taking this combination strategy to search putative secreted proteins from the International Protein Index (IPI) maintained at the European Bioinformatics Institute (EBI), we constructed a putative human secretome with 5235 proteins. The prediction system described here can also be applied to predicting secreted proteins from other vertebrate proteomes.

Computational Biology↗