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PubMed1995

Evaluation of equations for unbound serum concentration prediction of carbamazepine and carbamazepine-10,11-epoxide in polytherapy pediatric patients with epilepsy.

We retrospectively evaluated the ability of equations with in vivo population binding parameters of our previous study (Method 1) or an average unbound fraction of 0.25 of Pynnönen (Method 2) to predict the unbound serum carbamazepine (CBZ) concentration in 50 serum samples from 28 polytherapy pediatric patients with epilepsy. In 12 serum samples from 10 patients, the ability of equations for unbound serum carbamazepine-10,11-epoxide (CBZ-E) concentration prediction was also determined in predictive performance with in vivo population binding parameters of our previous study (Method A) or an average unbound fraction of 0.5 of Pynnönen (Method B). Mean prediction error, mean absolute prediction error (MAE), and root mean squared error (RMSE) were calculated for each method, and these values served as a measure of prediction bias and precision. Method 1 shows a bias to underpredict unbound serum CBZ. The MAE and RMSE were lower in Method 2 (MAE = 0.696 microM, RMSE = 0.912 microM) than in Method 1 (MAE = 0.946 microM, RMSE = 1.138 microM). Method 2 is superior to Method 1 in accuracy and precision. The effects of antiepileptic co-medications on predictive performance of Method 1 are relatively larger in a co-medicated group of serum samples with valproic acid (n = 33, MAE = 0.994 microM, RMSE = 1.211 microM) than in a group of serum samples without valproic acid co-medication (n = 17, MAE = 0.853 microM, RMSE = 0.979 microM).(ABSTRACT TRUNCATED AT 250 WORDS)

Adolescent↗
PubMed1997

Short-term levodopa test assessed by movement time accurately predicts dopaminergic responsiveness in Parkinson's disease.

Short-term challenges with dopaminergic agents are used in patients with idiopathic Parkinson's disease (PD) to predict the therapeutic effect of sustained levodopa treatment, but false-negative results often occur. We prospectively evaluated 74 patients with clinically diagnosed IPD and compared the predictive value of a short-term levodopa test assessed by movement time (MT) with the predictive value obtained by the evaluation with the motor examination part of the Unified Parkinson's Disease Rating Scale (UPDRS-ME). The response to long-term levodopa was accurately predicted in 96% of patients by assessing the response to the short-term test with MT and in 80% of cases with UPDRS-ME. Similar predictive values were obtained by separately analyzing 21 de novo patients. The short-term test also accurately predicted the magnitude of improvement with long-term treatment. We conclude that the predictive value for long-term dopaminergic responsiveness may be further enhanced by evaluating the short-term pharmacologic challenges with MT analysis. This is particularly useful to select de novo patients for drug trials with dopaminergic agents.

Aged↗
PubMed2002

Perceived benefits of word prediction intervention on written productivity in children with spina bifida and hydrocephalus.

Word prediction has been commonly used as a tool to enhance written productivity. However, the effectiveness of word prediction as a strategy to meet this targeted outcome has not been established. Using a single-subject alternating treatments design, this study evaluated the effect of word prediction on written productivity from the users' perspectives. Three girls and one boy aged 10-12 with spina bifida and hydrocephalus participated in the study over a period of 20 days. The Canadian Occupational Performance Measure (COPM) was used to measure changes in perception of written productivity. Analysis of individual participant data showed that participants perceived word prediction to have the potential to influence written productivity on some writing tasks. Quantitative analysis using a randomization test did not reveal any significant changes in COPM scores after using word prediction. The varied abilities of the participants in the study and the small sample size may be the reasons why statistical analysis did not show any changes. The limitations of this study included use of a copy task, lack of a supporting measure to COPM and limited generalizability. Further studies with a larger sample are necessary to explore the skills required for successful use of word prediction and the impact of word prediction on specific tasks.

Child↗
PubMed2005

Effect of training datasets on support vector machine prediction of protein-protein interactions.

Knowledge of protein-protein interaction is useful for elucidating protein function via the concept of 'guilt-by-association'. A statistical learning method, Support Vector Machine (SVM), has recently been explored for the prediction of protein-protein interactions using artificial shuffled sequences as hypothetical noninteracting proteins and it has shown promising results (Bock, J. R., Gough, D. A., Bioinformatics 2001, 17, 455-460). It remains unclear however, how the prediction accuracy is affected if real protein sequences are used to represent noninteracting proteins. In this work, this effect is assessed by comparison of the results derived from the use of real protein sequences with that derived from the use of shuffled sequences. The real protein sequences of hypothetical noninteracting proteins are generated from an exclusion analysis in combination with subcellular localization information of interacting proteins found in the Database of Interacting Proteins. Prediction accuracy using real protein sequences is 76.9% compared to 94.1% using artificial shuffled sequences. The discrepancy likely arises from the expected higher level of difficulty for separating two sets of real protein sequences than that for separating a set of real protein sequences from a set of artificial sequences. The use of real protein sequences for training a SVM classification system is expected to give better prediction results in practical cases. This is tested by using both SVM systems for predicting putative protein partners of a set of thioredoxin related proteins. The prediction results are consistent with observations, suggesting that real sequence is more practically useful in development of SVM classification system for facilitating protein-protein interaction prediction.

Algorithms↗
PubMed2002

Exploratory studies of ab initio protein structure prediction: multiple copy simulated annealing, AMBER energy functions, and a generalized born/solvent accessibility solvation model.

A theoretical and computational approach to ab initio structure prediction for polypeptides in water is described and applied to selected amino acid sequences for testing and preliminary validation. The method builds systematically on the extensive efforts applied to parameterization of molecular dynamics (MD) force fields, employs an empirically well-validated continuum dielectric model for solvation, and an eminently parallelizable approach to conformational search. The effective free energy of polypeptide chains is estimated from AMBER united atom potential functions, with internal degrees of freedom for both backbone and amino acid side chains explicitly treated. The hydration free energy of each structure is determined using the Generalized Born/Solvent Accessibility (GBSA) method, modified and reparameterized to include atom types consistent with the AMBER force field. The conformational search procedure employs a multiple copy, Monte Carlo simulated annealing (MCSA) protocol in full torsion angle space, applied iteratively on sets of structures of progressively lower free energy until a prediction of a structure with lowest effective free energy is obtained. Calibration tests for the effective energy function and search algorithm are performed on the alanine dipeptide, selected protein crystal structures, and united atom decoys on barnase, crambin, and six examples from the Rosetta set. Specific demonstration cases of the method are provided for the 8-mer sequence of Ala residues, a 12-residue peptide with longer side chains QLLKKLLQQLKQ, a de novo designed 16 residue peptide of sequence (AAQAA)3Y, a 15-residue sequence with a beta sheet motif, GEWTWDATKTFTVTE, and a 36 residue small protein, Villin headpiece. The Ala 8-mer readily formed an alpha-helix. An alpha-helix structure was predicted for the 16-mer, consistent with observed results from IR and CD spectroscopy and with the pattern in psi/straight phi angles of known protein structures. The predicted structure for the 12-mer, composed of a mix of helix and less regular elements of secondary structure, lies 2.65 A RMS from the observed crystal structure. Structure prediction for the 8-mer beta-motif resulted in form 4.50 A RMS from the crystal geometry. For Villin, the predicted native form is very close to the crystal structure, RMS values of 3.5 A (including sidechains), and 1.01 A (main chain only). The methodology permits a detailed analysis of the molecular forces which dominate various segments of the predicted folding trajectory. Analysis of the results in terms of internal torsional, electrostatic and van der Waals and the electrostatic and non-electrostatic contributions to hydration, including the hydrophobic effect, is presented.

Alanine↗
PubMed2002

Predicting the disulfide bonding state of cysteines using protein descriptors.

Knowledge of the disulfide bonding state of the cysteines of proteins is of major interest in designing numerous molecular biology experiments, or in predicting their three-dimensional structure. Previous methods using the information gained from aligned sets of sequences have reached up to 82% of success in predicting the oxidation state of cysteines. In the present study, we assess the relative efficiency of different descriptors in predicting the cysteine disulfide bonding states. Our results suggest that the information on the residues flanking the cysteines is less informative about the disulfide bonding state than about the amino acid content of the whole protein. Using a combination of logistic functions learned with subsets of proteins homogeneous in terms of their amino acid content, we propose a simple prediction approach, starting from a single sequence, that reaches success rates close to 84%. This score can be improved by avoiding predictions regarding cysteines for which the decision is not well marked. For example, we obtain a score close to 87% correct prediction when we exclude predicting 10% of the cysteines.

Amino Acids↗
PubMed2001

EVA: large-scale analysis of secondary structure prediction.

EVA is a web-based server that evaluates automatic structure prediction servers continuously and objectively. Since June 2000, EVA collected more than 20,000 secondary structure predictions. The EVA sets sufficed to conclude that the field of secondary structure prediction has advanced again. Accuracy increased substantially in the 1990s through using evolutionary information taken from the divergence of proteins in the same structural family. Recently, the evolutionary information resulting from improved searches and larger databases has again boosted prediction accuracy by more than 4% to its current height around 76% of all residues predicted correctly in one of the three states: helix, strand, or other. The best current methods solved most of the problems raised at earlier CASP meetings: All good methods now get segments right and perform well on strands. Is the recent increase in accuracy significant enough to make predictions even more useful? We believe the answer is affirmative. What is the limit of prediction accuracy? We shall see. All data are available through the EVA web site at [cubic.bioc.columbia.edu/eva/]. The raw data for the results presented are available at [eva]/sec/bup_common/2001_02_22/.

Databases, Protein↗
PubMed2002

Triage protein fold prediction.

We have constructed, in a completely automated fashion, a new structure template library for threading that represents 358 distinct SCOP folds where each model is mathematically represented as a Hidden Markov model (HMM). Because the large number of models in the library can potentially dilute the prediction measure, a new triage method for fold prediction is employed. In the first step of the triage method, the most probable structural class is predicted using a set of manually constructed, high-level, generalized structural HMMs that represent seven general protein structural classes: all-alpha, all-beta, alpha/beta, alpha+beta, irregular small metal-binding, transmembrane beta-barrel, and transmembrane alpha-helical. In the second step, only those fold models belonging to the determined structural class are selected for the final fold prediction. This triage method gave more predictions as well as more correct predictions compared with a simple prediction method that lacks the initial classification step. Two different schemes of assigning Bayesian model priors are presented and discussed.

Animals↗
PubMed2003

Benchmarking secondary structure prediction for fold recognition.

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the "usefulness" and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

Models, Molecular↗
PubMed2003

RDOCK: refinement of rigid-body protein docking predictions.

We present a simple and effective algorithm RDOCK for refining unbound predictions generated by a rigid-body docking algorithm ZDOCK, which has been developed earlier by our group. The main component of RDOCK is a three-stage energy minimization scheme, followed by the evaluation of electrostatic and desolvation energies. Ionic side chains are kept neutral in the first two stages of minimization, and reverted to their full charge states in the last stage of brief minimization. Without side chain conformational search or filtering/clustering of resulting structures, RDOCK represents the simplest approach toward refining unbound docking predictions. Despite its simplicity, RDOCK makes substantial improvement upon the top predictions by ZDOCK with all three scoring functions and the improvement is observed across all three categories of test cases in a large benchmark of 49 non-redundant unbound test cases. RDOCK makes the most powerful combination with ZDOCK2.1, which uses pairwise shape complementarity as the scoring function. Collectively, they rank a near-native structure as the number-one prediction for 18 test cases (37% of the benchmark), and within the top 4 predictions for 24 test cases (49% of the benchmark). To various degrees, funnel-like energy landscapes are observed for these 24 test cases. To the best of our knowledge, this is the first report of binding funnels starting from global searches for a broad range of test cases. These results are particularly exciting, given that we have not used any biological information that is specific to individual test cases and the whole process is entirely automated. Among three categories of test cases, the best results are seen for enzyme/inhibitor, with a near-native structure ranked as the number-one prediction for 48% test cases, and within the top 10 predictions for 78% test cases. RDOCK is freely available to academic users at http://zlab.bu.edu/ approximately rong/dock.

Algorithms↗
PubMed2003

Predicting intrinsic disorder from amino acid sequence.

Blind predictions of intrinsic order and disorder were made on 42 proteins subsequently revealed to contain 9,044 ordered residues, 284 disordered residues in 26 segments of length 30 residues or less, and 281 disordered residues in 2 disordered segments of length greater than 30 residues. The accuracies of the six predictors used in this experiment ranged from 77% to 91% for the ordered regions and from 56% to 78% for the disordered segments. The average of the order and disorder predictions ranged from 73% to 77%. The prediction of disorder in the shorter segments was poor, from 25% to 66% correct, while the prediction of disorder in the longer segments was better, from 75% to 95% correct. Four of the predictors were composed of ensembles of neural networks. This enabled them to deal more efficiently with the large asymmetry in the training data through diversified sampling from the significantly larger ordered set and achieve better accuracy on ordered and long disordered regions. The exclusive use of long disordered regions for predictor training likely contributed to the disparity of the predictions on long versus short disordered regions, while averaging the output values over 61-residue windows to eliminate short predictions of order or disorder probably contributed to the even greater disparity for three of the predictors. This experiment supports the predictability of intrinsic disorder from amino acid sequence.

Amino Acids↗
PubMed2004

Strand-loop-strand motifs: prediction of hairpins and diverging turns in proteins.

Beta-sheet proteins have been particularly challenging for de novo structure prediction methods, which tend to pair adjacent beta-strands into beta-hairpins and produce overly local topologies. To remedy this problem and facilitate de novo prediction of beta-sheet protein structures, we have developed a neural network that classifies strand-loop-strand motifs by local hairpins and nonlocal diverging turns by using the amino acid sequence as input. The neural network is trained with a representative subset of the Protein Data Bank and achieves a prediction accuracy of 75.9 +/- 4.4% compared to a baseline prediction rate of 59.1%. Hairpins are predicted with an accuracy of 77.3 +/- 6.1%, diverging turns with an accuracy of 73.9 +/- 6.0%. Incorporation of the beta-hairpin/diverging turn classification into the ROSETTA de novo structure prediction method led to higher contact order models and somewhat improved tertiary structure predictions for a test set of 11 all-beta-proteins and 3 alphabeta-proteins. The beta-hairpin/diverging turn classification from amino acid sequences is available online for academic use (Meiler and Kuhn, 2003; www.jens-meiler.de/turnpred.html).

Algorithms↗
PubMed2004

Efficient methods for filtering and ranking fragments for the prediction of structurally variable regions in proteins.

The prediction of protein 3D structures close to insertions and deletions or, more generally, loop prediction, is still one of the major challenges in homology modeling projects. In this article, we developed ranking criteria and selection filters to improve knowledge-based loop predictions. These criteria were developed and optimized for a test data set containing 678 insertions and deletions. The examples are, in principle, predictable from the used loop database with an RMSD < 1 A and represent realistic modeling situations. Four noncorrelated criteria for the selection of fragments are evaluated. A fast prefilter compares the distance between the anchor groups in the template protein with the stems of the fragments. The RMSD of the anchor groups is used for fitting and ranking of the selected loop candidates. After fitting, repulsive close contacts of loop candidates with the template protein are used for filtering, and fragments with backbone torsion angles, which are unfavorable according to a knowledge-based potential, are eliminated. By the combined application of these filter criteria to the test set, it was possible to increase the percentage of predictions with a global RMSD < 1 A to over 50% among the first five ranks, with average global RMSD values for the first rank candidate that are between 1.3 and 2.2 A for different loop lengths. Compared to other examples described in the literature, our large numbers of test cases are not self-predictions, where loops are placed in a protein after a peptide loop has been cut out, but are attempts to predict structural changes that occur in evolution when a protein is affected by insertions and deletions.

Algorithms↗
PubMed2005

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures.

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.

Algorithms↗
PubMed2005

Protein flexibility and rigidity predicted from sequence.

Structural flexibility has been associated with various biological processes such as molecular recognition and catalytic activity. In silico studies of protein flexibility have attempted to characterize and predict flexible regions based on simple principles. B-values derived from experimental data are widely used to measure residue flexibility. Here, we present the most comprehensive large-scale analysis of B-values. We used this analysis to develop a neural network-based method that predicts flexible-rigid residues from amino acid sequence. The system uses both global and local information (i.e., features from the entire protein such as secondary structure composition, protein length, and fraction of surface residues, and features from a local window of sequence-consecutive residues). The most important local feature was the evolutionary exchange profile reflecting sequence conservation in a family of related proteins. To illustrate its potential, we applied our method to 4 different case studies, each of which related our predictions to aspects of function. The first 2 were the prediction of regions that undergo conformational switches upon environmental changes (switch II region in Ras) and the prediction of surface regions, the rigidity of which is crucial for their function (tunnel in propeller folds). Both were correctly captured by our method. The third study established that residues in active sites of enzymes are predicted by our method to have unexpectedly low B-values. The final study demonstrated how well our predictions correlated with NMR order parameters to reflect motion. Our method had not been set up to address any of the tasks in those 4 case studies. Therefore, we expect that this method will assist in many attempts at inferring aspects of function.

Amino Acid Sequence↗
PubMed2005

An assessment of the accuracy of methods for predicting hydrogen positions in protein structures.

The addition of hydrogen atoms to models or experimental structures of proteins that contain only non-hydrogen atoms is a common step in crystallographic structure refinement, in theoretical studies of proteins, and in protein structure prediction. Accurate prediction of the hydrogen positions is essential, since they constitute around half of the atoms in proteins and hence contribute significantly to their energetics. Many computational tools exist for predicting hydrogen positions, although to date no quantitative comparison has been made of their accuracy or efficiency. Here we take advantage of the recent increase in ultra-high-resolution X-ray crystal structures (< 0.9 A resolution), as well as of a number of relatively high-resolution neutron diffraction structures (< 1.8 A resolution), to compare the quality of the predictions generated by a large set of commonly used methods. These include CHARMM, CNS, GROMACS, MCCE, MolProbity, WHAT IF, and X-PLOR. The hydrogen atoms that lack a rotational degree of freedom are mostly, but not always, accurately predicted. For hydrogens with a rotational degree of freedom, all the methods give much less accurate predictions. The predictions for the hydroxyl hydrogens are analyzed in detail, particularly those buried within the protein, and some explanation is provided for the errors observed. The results provide a means to make informed decisions regarding the choice and implementation of methodologies for placing hydrogens on structures of proteins. They also point to shortcomings in current force fields and suggest the need for improved descriptions of hydrogen bonding energetics.

Amines↗
PubMed2005

Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure.

The present study is an attempt to develop a neural network-based method for predicting the real value of solvent accessibility from the sequence using evolutionary information in the form of multiple sequence alignment. In this method, two feed-forward networks with a single hidden layer have been trained with standard back-propagation as a learning algorithm. The Pearson's correlation coefficient increases from 0.53 to 0.63, and mean absolute error decreases from 18.2 to 16% when multiple-sequence alignment obtained from PSI-BLAST is used as input instead of a single sequence. The performance of the method further improves from a correlation coefficient of 0.63 to 0.67 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields a mean absolute error value of 15.2% between the experimental and predicted values, when tested on two different nonhomologous and nonredundant datasets of varying sizes. The method consists of two steps: (1) in the first step, a sequence-to-structure network is trained with the multiple alignment profiles in the form of PSI-BLAST-generated position-specific scoring matrices, and (2) in the second step, the output obtained from the first network and PSIPRED-predicted secondary structure information is used as an input to the second structure-to-structure network. Based on the present study, a server SARpred (http://www.imtech.res.in/raghava/sarpred/) has been developed that predicts the real value of solvent accessibility of residues for a given protein sequence. We have also evaluated the performance of SARpred on 47 proteins used in CASP6 and achieved a correlation coefficient of 0.68 and a MAE of 15.9% between predicted and observed values.

Amino Acids↗
PubMed2005

Assessment of fold recognition predictions in CASP6.

The Sixth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP6) held in December 2004 focused on the prediction of the structures of 90 protein domains from 64 targets. Thirty-eight of these were classified as "fold recognition," defined as being similar in fold to proteins of known structure at the time of submission of the predictions. Only the "first" predictions and those longer than 20 amino acids for each domain were assessed, resulting in 4527 predictions from 165 groups. The assessment was accomplished by the use of six structure alignment programs and three scoring measures based on these alignments. The use of a variety of measures resulted in scoring insensitive to the peculiarities of any one alignment method. The top-ranked methods in the prediction of structures that were clearly homologous to proteins in the Protein Data Bank primarily used servers and other programs based on achieving a consensus of many remote homology detection and fold recognition methods. The top-ranked methods in prediction of structures less clearly related or unrelated to proteins of known structures used fragment building methods in addition to the fold recognition meta methods.

Algorithms↗