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PubMed2005

Development of the WAIS-III general ability index estimate (GAI-E).

The WAIS-III General Ability Index (GAI; Tulsky, Saklofske, Wilkins, & Weiss, 2001) is a recently developed, 6-subtest measure of global intellectual functioning. However, clinical use of the GAI is currently limited by the absence of a method to estimate premorbid functioning as measured by this index. The purpose of this study was to develop regression equations to estimate GAI scores from demographic variables and WAIS-III subtest performance. Participants consisted of those subjects in the WAIS-III standardization sample that has complete demographic data (N=2,401) and were randomly divided into two groups. The first group (n=1,200) was used to develop the formulas (i.e., Development group) and the second (n=1,201) group was used to validate the prediction algorithms (i.e., Validation group). Demographic variables included age, education, ethnicity, gender and region of country. Subtest variables included vocabulary, information, picture completion, and matrix reasoning raw scores. Ten regression algorithms were generated designed to estimate GAI. The GAI-Estimate (GAI-E) algorithms accounted for 58% to 82% of the variance. The standard error of estimate ranged from 6.44 to 9.57. The correlations between actual and estimated GAI ranged from r=.76 to r=.90. These algorithms provided accurate estimates of GAI in the WAIS-III standardization sample. Implications for estimating GAI in patients with known or suspected neurological dysfunction is discussed and future research is proposed.

Abstracting and Indexing↗
PubMed1994

Physician response to a prediction rule for the triage of emergency department patients with chest pain.

OBJECTIVE: To determine the response of physicians to a noncoercive prediction rule for the triage of emergency department patients with chest pain. DESIGN: Prospective time-series intervention study. SETTING: A university hospital emergency department. PARTICIPANTS/PATIENTS: 68 physicians, all of whom were responsible for the triage of at least one of 252 patients presenting to the emergency department with a chief complaint of acute chest pain. INTERVENTION: A previously validated algorithmic prediction rule that was attached to the back of patient data forms in the emergency department. MEASUREMENTS: Patients' clinical data were recorded by the examining physician in the emergency department or by a research nurse blinded to patient outcome. The physicians recorded their own estimates of the risk of acute myocardial infarction and their reactions to the prediction rule in a self-administered questionnaire completed at the time of triage. MAIN RESULTS AND CONCLUSIONS: The physicians reported that they looked at the prediction rule during the triage of 115 (46%) of the 252 patients. The likelihood of using the prediction rule decreased significantly with increasing level of physician training. The most common reasons given for disregarding the prediction rule were confidence in unaided decision making and lack of time. The physicians reported that of the 115 cases for which the prediction rule was used, only one triage decision (1%) was changed by it. Future research should explore how prediction rules can be designed and implemented to surmount the barriers highlighted by these data.

Algorithms↗
PubMed2000

Identification of related proteins on family, superfamily and fold level.

Proteins might have considerable structural similarities even when no evolutionary relationship of their sequences can be detected. This property is often referred to as the proteins sharing only a "fold". Of course, there are also sequences of common origin in each fold, called a "superfamily", and in them groups of sequences with clear similarities, designated "family". Developing algorithms to reliably identify proteins related at any level is one of the most important challenges in the fast growing field of bioinformatics today. However, it is not at all certain that a method proficient at finding sequence similarities performs well at the other levels, or vice versa.Here, we have compared the performance of various search methods on these different levels of similarity. As expected, we show that it becomes much harder to detect proteins as their sequences diverge. For family related sequences the best method gets 75% of the top hits correct. When the sequences differ but the proteins belong to the same superfamily this drops to 29%, and in the case of proteins with only fold similarity it is as low as 15%. We have made a more complete analysis of the performance of different algorithms than earlier studies, also including threading methods in the comparison. Using this method a more detailed picture emerges, showing multiple sequence information to improve detection on the two closer levels of relationship. We have also compared the different methods of including this information in prediction algorithms. For lower specificities, the best scheme to use is a linking method connecting proteins through an intermediate hit. For higher specificities, better performance is obtained by PSI-BLAST and some procedures using hidden Markov models. We also show that a threading method, THREADER, performs significantly better than any other method at fold recognition.

Algorithms↗
PubMed1984

Rhodopsin's protein and carbohydrate structure: selected aspects.

A topographic model for rhodopsin has been constructed based upon evaluation of rhodopsin's sequence by a secondary structure prediction algorithm as well as chemical and enzymatic modification of rhodopsin in the membrane [Hargrave et al. (1983) Biophys. Struct. Mech. 9, 235-244]. The non-uniform distribution of several amino acids in the primary structure and within the topographic model is discussed. The seven predicted helices were evaluated and each helix was found to have one surface which is much more hydrophobic than the other. Stereoscopic views of a three dimensional model with a functional color-coding scheme incorporating these features are presented. The amino acid sequence of rhodopsin has been compared to other proteins in the Dayhoff Protein Data Bank. No obvious relationship to any other protein sequenced was found. High resolution proton magnetic resonance spectroscopy was used to reinvestigate the structure and relative proportions of rhodopsin's major and minor oligosaccharide chains. One major (Man3GlcNAc3) and two minor (Man4GlcNAc3 and Man5GlcNAc3) were observed.

Amino Acid Sequence↗
PubMed2003

GeneViTo: visualizing gene-product functional and structural features in genomic datasets.

BACKGROUND: The availability of increasing amounts of sequence data from completely sequenced genomes boosts the development of new computational methods for automated genome annotation and comparative genomics. Therefore, there is a need for tools that facilitate the visualization of raw data and results produced by bioinformatics analysis, providing new means for interactive genome exploration. Visual inspection can be used as a basis to assess the quality of various analysis algorithms and to aid in-depth genomic studies. RESULTS: GeneViTo is a JAVA-based computer application that serves as a workbench for genome-wide analysis through visual interaction. The application deals with various experimental information concerning both DNA and protein sequences (derived from public sequence databases or proprietary data sources) and meta-data obtained by various prediction algorithms, classification schemes or user-defined features. Interaction with a Graphical User Interface (GUI) allows easy extraction of genomic and proteomic data referring to the sequence itself, sequence features, or general structural and functional features. Emphasis is laid on the potential comparison between annotation and prediction data in order to offer a supplement to the provided information, especially in cases of "poor" annotation, or an evaluation of available predictions. Moreover, desired information can be output in high quality JPEG image files for further elaboration and scientific use. A compilation of properly formatted GeneViTo input data for demonstration is available to interested readers for two completely sequenced prokaryotes, Chlamydia trachomatis and Methanococcus jannaschii. CONCLUSIONS: GeneViTo offers an inspectional view of genomic functional elements, concerning data stemming both from database annotation and analysis tools for an overall analysis of existing genomes. The application is compatible with Linux or Windows ME-2000-XP operating systems, provided that the appropriate Java Runtime Environment is already installed in the system.

Bacterial Proton-Translocating ATPases↗
PubMed2006

SCARNA: fast and accurate structural alignment of RNA sequences by matching fixed-length stem fragments.

MOTIVATION: The functions of non-coding RNAs are strongly related to their secondary structures, but it is known that a secondary structure prediction of a single sequence is not reliable. Therefore, we have to collect similar RNA sequences with a common secondary structure for the analyses of a new non-coding RNA without knowing the exact secondary structure itself. Therefore, the sequence comparison in searching similar RNAs should consider not only their sequence similarities but also their potential secondary structures. Sankoff's algorithm predicts the common secondary structures of the sequences, but it is computationally too expensive to apply to large-scale analyses. Because we often want to compare a large number of cDNA sequences or to search similar RNAs in the whole genome sequences, much faster algorithms are required. RESULTS: We propose a new method of comparing RNA sequences based on the structural alignments of the fixed-length fragments of the stem candidates. The implemented software, SCARNA (Stem Candidate Aligner for RNAs), is fast enough to apply to the long sequences in the large-scale analyses. The accuracy of the alignments is better or comparable with the much slower existing algorithms. AVAILABILITY: The web server of SCARNA with graphical structural alignment viewer is available at http://www.scarna.org/.

Algorithms↗
PubMed1993

Using a neural network to identify potential HLA-DR1 binding sites within proteins.

The presentation by antigen-presenting cells of immunodominant peptide segments in association with major histocompatibility complex (MHC) encoded proteins is fundamental to the efficacy of a specific immune response. One approach used to identify immunodominant segments within proteins has involved the development of predictive algorithms which utilize amino acid sequence data to identify structural characteristics or motifs associated with in vivo antigenicity. The parallel-computing technique termed 'neural networking' has recently been shown to be remarkably efficient at addressing the problem of pattern recognition and can be applied to predict protein secondary structure attributes directly from amino acid sequence data. In order to examine the potential of a neural network to generalize peptide structural features related to binding within class II MHC-encoded proteins, we have trained a neural network to determine whether or not any given amino acid of a protein is part of a peptide segment capable of binding to HLA-DR1. We report that a neural network trained on a data base consisting of peptide segments known to bind to HLA-DR1 is able to generalize features relating to HLA-DR1-binding capacity (r = 0.17 and p = 0.0001).

Amino Acid Sequence↗
PubMed2006

On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins.

In this study, we investigate the extent to which techniques for homology modeling that were developed for water-soluble proteins are appropriate for membrane proteins as well. To this end we present an assessment of current strategies for homology modeling of membrane proteins and introduce a benchmark data set of homologous membrane protein structures, called HOMEP. First, we use HOMEP to reveal the relationship between sequence identity and structural similarity in membrane proteins. This analysis indicates that homology modeling is at least as applicable to membrane proteins as it is to water-soluble proteins and that acceptable models (with C alpha-RMSD values to the native of 2 A or less in the transmembrane regions) may be obtained for template sequence identities of 30% or higher if an accurate alignment of the sequences is used. Second, we show that secondary-structure prediction algorithms that were developed for water-soluble proteins perform approximately as well for membrane proteins. Third, we provide a comparison of a set of commonly used sequence alignment algorithms as applied to membrane proteins. We find that high-accuracy alignments of membrane protein sequences can be obtained using state-of-the-art profile-to-profile methods that were developed for water-soluble proteins. Improvements are observed when weights derived from the secondary structure of the query and the template are used in the scoring of the alignment, a result which relies on the accuracy of the secondary-structure prediction of the query sequence. The most accurate alignments were obtained using template profiles constructed with the aid of structural alignments. In contrast, a simple sequence-to-sequence alignment algorithm, using a membrane protein-specific substitution matrix, shows no improvement in alignment accuracy. We suggest that profile-to-profile alignment methods should be adopted to maximize the accuracy of homology models of membrane proteins.

Algorithms↗
PubMed2002

An evaluation of beta-turn prediction methods.

MOTIVATION: beta-turn is an important element of protein structure. In the past three decades, numerous beta-turn prediction methods have been developed based on various strategies. For a detailed discussion about the importance of beta-turns and a systematic introduction of the existing prediction algorithms for beta-turns and their types, please see a recent review (Chou, Analytical Biochemistry, 286, 1-16, 2000). However at present, it is still difficult to say which method is better than the other. This is because of the fact that these methods were developed on different sets of data. Thus, it is important to evaluate the performance of beta-turn prediction methods. RESULTS: We have evaluated the performance of six methods of beta-turn prediction. All the methods have been tested on a set of 426 non-homologous protein chains. It has been observed that the performance of the neural network based method, BTPRED, is significantly better than the statistical methods. One of the reasons for its better performance is that it utilizes the predicted secondary structure information. We have also trained, tested and evaluated the performance of all methods except BTPRED and GORBTURN, on new data set using a 7-fold cross-validation technique. There is a significant improvement in performance of all the methods when secondary structure information is incorporated. Moreover, after incorporating secondary structure information, the Sequence Coupled Model has yielded better results in predicting beta-turns as compared with other methods. In this study, both threshold dependent and independent (ROC) measures have been used for evaluation.

Algorithms↗
PubMed2005

Low-frequency Fourier spectrum for predicting membrane protein types.

Cell membranes are vitally important to living cells. Although the infrastructure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Knowledge of membrane protein types often provides crucial hints toward determining the function of an uncharacterized membrane protein. With the avalanche of new protein sequences generated in the post-genomic era, it is highly demanded to develop a high throughput tool in identifying the type of newly found membrane proteins according to their primary sequences, so as to timely annotate them for reference usage in both basic research and drug discovery. To realize this, the key is to establish a powerful identifier that can catch their characteristic sequence patterns for different membrane protein types. However, it is not easy because they are buried in a pile of long and complicated sequences. In this paper, based on the concept of the pseudo-amino acid composition [K.C. Chou, PROTEINS: Struct., Funct., Genet. 43 (2001) 246-255], the low-frequency Fourier spectrum analysis is introduced. The merits by doing so are that the sequence pattern information can be more effectively incorporated into a set of discrete components, and that all the existing prediction algorithms can be straightforwardly used on such a formulation for protein samples. High success rates were observed by the re-substitution test, jackknife test, and independent dataset test, indicating that the low-frequency Fourier spectrum approach may become a very useful tool for membrane protein type prediction. The novel approach also holds a high potential for predicting many other attributes of proteins.

Fourier Analysis↗
PubMed2006

Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer.

PURPOSE: We developed a multigene predictor of pathologic complete response (pCR) to preoperative weekly paclitaxel and fluorouracil-doxorubicin-cyclophosphamide (T/FAC) chemotherapy and assessed its predictive accuracy on independent cases. PATIENTS AND METHODS: One hundred thirty-three patients with stage I-III breast cancer were included. Pretreatment gene expression profiling was performed with oligonecleotide microarrays on fine-needle aspiration specimens. We developed predictors of pCR from 82 cases and assessed accuracy on 51 independent cases. RESULTS: Overall pCR rate was 26% in both cohorts. In the training set, 56 probes were identified as differentially expressed between pCR versus residual disease, at a false discovery rate of 1%. We examined the performance of 780 distinct classifiers (set of genes + prediction algorithm) in full cross-validation. Many predictors performed equally well. A nominally best 30-probe set Diagonal Linear Discriminant Analysis classifier was selected for independent validation. It showed significantly higher sensitivity (92% v 61%) than a clinical predictor including age, grade, and estrogen receptor status. The negative predictive value (96% v 86%) and area under the curve (0.877 v 0.811) were nominally better but not statistically significant. The combination of genomic and clinical information yielded a predictor not significantly different from the genomic predictor alone. In 31 samples, RNA was hybridized in replicate with resulting predictions that were 97% concordant. CONCLUSION: A 30-probe set pharmacogenomic predictor predicted pCR to T/FAC chemotherapy with high sensitivity and negative predictive value. This test correctly identified all but one of the patients who achieved pCR (12 of 13 patients) and all but one of those who were predicted to have residual disease had residual cancer (27 of 28 patients).

Adult↗
PubMed2006

PReMod: a database of genome-wide mammalian cis-regulatory module predictions.

We describe PReMod, a new database of genome-wide cis-regulatory module (CRM) predictions for both the human and the mouse genomes. The prediction algorithm, described previously in Blanchette et al. (2006) Genome Res., 16, 656-668, exploits the fact that many known CRMs are made of clusters of phylogenetically conserved and repeated transcription factors (TF) binding sites. Contrary to other existing databases, PReMod is not restricted to modules located proximal to genes, but in fact mostly contains distal predicted CRMs (pCRMs). Through its web interface, PReMod allows users to (i) identify pCRMs around a gene of interest; (ii) identify pCRMs that have binding sites for a given TF (or a set of TFs) or (iii) download the entire dataset for local analyses. Queries can also be refined by filtering for specific chromosomal regions, for specific regions relative to genes or for the presence of CpG islands. The output includes information about the binding sites predicted within the selected pCRMs, and a graphical display of their distribution within the pCRMs. It also provides a visual depiction of the chromosomal context of the selected pCRMs in terms of neighboring pCRMs and genes, all of which are linked to the UCSC Genome Browser and the NCBI. PReMod: http://genomequebec.mcgill.ca/PReMod.

Algorithms↗
PubMed2002

Peptomics, identification of novel cationic Arabidopsis peptides with conserved sequence motifs.

Few plant peptides involved in intercellular communication have been experimentally isolated. Sequence analysis of the Arabidopsis thaliana genome has revealed numerous transmembrane receptors predicted to bind proteinacious ligands, emphasizing the importance of identifying peptides with signaling function. Annotation of the Arabidopsis genome sequence has made it possible to identify peptide-encoding genes. However, such annotational identification is impeded because small genes are poorly predicted by gene-prediction algorithms, thus prompting the alternative approaches described here. We initially performed a systematic analysis of short polypeptides encoded by annotated genes on two Arabidopsis chromosomes using SignalP to identify potentially secreted peptides. Subsequent homology searches with selected, putatively secreted peptides, led to the identification of a potential, large Arabidopsis family of 34 genes. The predicted peptides are characterized by a conserved C-terminal sequence motif and additional primary structure conservation in a core region. The majority of these genes had not previously been annotated. A subset of the predicted peptides show high overall sequence similarity to Rapid Alkalinization Factor (RALF), a peptide isolated from tobacco. We therefore refer to this peptide family as RALFL for RALF-Like. RT-PCR analysis confirmed that several of the Arabidopsis genes are expressed and that their expression patterns vary. The identification of a large gene family in the genome of the model organism Arabidopsis thaliana demonstrates that a combination of systematic analysis and homology searching can contribute to peptide discovery.

Algorithms↗
PubMed2006

A database of bacterial lipoproteins (DOLOP) with functional assignments to predicted lipoproteins.

Lipid modification of the N-terminal Cys residue (N-acyl-S-diacylglyceryl-Cys) has been found to be an essential, ubiquitous, and unique bacterial posttranslational modification. Such a modification allows anchoring of even highly hydrophilic proteins to the membrane which carry out a variety of functions important for bacteria, including pathogenesis. Hence, being able to identify such proteins is of great value. To this end, we have created a comprehensive database of bacterial lipoproteins, called DOLOP, which contains information and links to molecular details for about 278 distinct lipoproteins and predicted lipoproteins from 234 completely sequenced bacterial genomes. The website also features a tool that applies a predictive algorithm to identify the presence or absence of the lipoprotein signal sequence in a user-given sequence. The experimentally verified lipoproteins have been classified into different functional classes and more importantly functional domain assignments using hidden Markov models from the SUPERFAMILY database that have been provided for the predicted lipoproteins. Other features include the following: primary sequence analysis, signal sequence analysis, and search facility and information exchange facility to allow researchers to exchange results on newly characterized lipoproteins. The website, along with additional information on the biosynthetic pathway, statistics on predicted lipoproteins, and related figures, is available at http://www.mrc-lmb.cam.ac.uk/genomes/dolop/.

Bacterial Proteins↗
PubMedDate not supplied

A novel predictive technique for the MHC class II peptide-binding interaction.

Antigenic peptide is presented to a T-cell receptor through the formation of a stable complex with a Major Histocompatibility Complex (MHC) molecule. Various predictive algorithms have been developed to estimate a peptide's capacity to form a stable complex with a given MHC Class II allele, a technique integral to the strategy of vaccine design. These have previously incorporated such computational techniques as quantitative matrices and neural networks. We have developed a novel predictive technique that uses molecular modeling of predetermined crystal structures to estimate the stability of an MHC Class II peptide complex. This is the 1st structure-based technique, as previous methods have been based on binding data. ROC curves are used to quantify the accuracy of the molecular modeling technique. The novel predictive technique is found to be comparable with the best predictive software currently available.

Alleles↗
PubMed2005

Looking at enzymes from the inside out: the proximity of catalytic residues to the molecular centroid can be used for detection of active sites and enzyme-ligand interfaces.

Analysis of the distances of the exposed residues in 175 enzymes from the centroids of the molecules indicates that catalytic residues are very often found among the 5% of residues closest to the enzyme centroid. This property of catalytic residues is implemented in a new prediction algorithm (named EnSite) for locating the active sites of enzymes and in a new scheme for re-ranking enzyme-ligand docking solutions. EnSite examines only 5% of the molecular surface (represented by surface dots) that is closest to the centroid, identifying continuous surface segments and ranking them by their area size. EnSite ranks the correct prediction 1-4 in 97% of the cases in a dataset of 65 monomeric enzymes (rank 1 for 89% of the cases) and in 86% of the cases in a dataset of 176 monomeric and multimeric enzymes from all six top-level enzyme classifications (rank 1 in 74% of the cases). Importantly, identification of buried or flat active sites is straightforward because EnSite "looks" at the molecular surface from the inside out. Detailed examination of the results indicates that the proximity of the catalytic residues to the centroid is a property of the functional unit, defined as the assembly of domains or chains that form the active site (in most cases the functional unit corresponds to a single whole polypeptide chain). Using the functional unit in the prediction further improves the results. The new property of active sites is also used for re-evaluating enzyme-inhibitor unbound docking results. Sorting the docking solutions by the distance of the interface to the centroid of the enzyme improves remarkably the ranks of nearly correct solutions compared to ranks based on geometric-electrostatic-hydrophobic complementarity scores.

Algorithms↗
PubMed1996

Proposed structure of putative glucose channel in GLUT1 facilitative glucose transporter.

A family of structurally related intrinsic membrane proteins (facilitative glucose transporters) catalyzes the movement of glucose across the plasma membrane of animal cells. Evidence indicates that these proteins show a common structural motif where approximately 50% of the mass is embedded in lipid bilayer (transmembrane domain) in 12 alpha-helices (transmembrane helices; TMHs) and accommodates a water-filled channel for substrate passage (glucose channel) whose tertiary structure is currently unknown. Using recent advances in protein structure prediction algorithms we proposed here two three-dimensional structural models for the transmembrane glucose channel of GLUT1 glucose transporter. Our models emphasize the physical dimension and water accessibility of the channel, loop lengths between TMHs, the macrodipole orientation in four-helix bundle motif, and helix packing energy. Our models predict that five TMHs, either TMHs 3, 4, 7, 8, 11 (Model 1) or TMHs 2, 5, 11, 8, 7 (Model 2), line the channel, and the remaining TMHs surround these channel-lining TMHs. We discuss how our models are compatible with the experimental data obtained with this protein, and how they can be used in designing new biochemical and molecular biological experiments in elucidation of the structural basis of this important protein function.

Amino Acid Sequence↗
PubMed1996

Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites.

BACKGROUND: Molecular docking seeks to predict the geometry and affinity of the binding of a small molecule to a given protein of known structure. Rigid docking has long been used to screen databases of small molecules, because docking techniques that account for ligand flexibility have either been too slow or have required significant human intervention. Here we describe a docking algorithm, Hammerhead, which is a fast, automated tool to screen for the binding of flexible molecules to protein binding sites. RESULTS: We used Hammerhead to successfully dock a variety of positive control ligands into their cognate proteins. The empirically tuned scoring function of the algorithm predicted binding affinities within 1.3 log units of the known affinities for these ligands. Conformations and alignments close to those determined crystallographically received the highest scores. We screened 80 000 compounds for binding to streptavidin, and biotin was predicted as the top-scoring ligand, with other known ligands included among the highest-scoring dockings. The screen ran in a few days on commonly available hardware. CONCLUSIONS: Hammerhead is suitable for screening large databases of flexible molecules for binding to a protein of known structure. It correctly docks a variety of known flexible ligands, and it spends an average of only a few seconds on each compound during a screen. The approach is completely automated, from the elucidation of protein binding sites, through the docking of molecules, to the final selection of compounds for assay.

Algorithms↗