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PubMed1997

The perception of motion and structure in structure-from-motion: comparisons of affine and Euclidean formulations.

I investigated the discrimination of rigid from nonrigid structure and the perception of affine stretches along the line of sight [Norman & Todd (1993). Perception and Psychophysics, 53, pp. 279-291]. Investigations of performance at discriminating rigid from nonrigid structure showed that performance improved when number of views and amount of simulated three-dimensional nonrigidity increased. Investigations of rotations about the vertical which include affine stretches along the line of sight compared Euclidean interpretations of affine-stretching stimuli to human perception. These Euclidean interpretations were obtained from a simple algorithm which recovered structure and motion from this limited class of stimuli under the assumption that distances to the axis of rotation did not change. The algorithm predicted that stretches along the line of sight would be perceived as nearly rigid and have variable angular velocity. These predictions were supported by subjects' reports of occurrences of nonrigidity and minima of angular velocity. The Euclidean algorithm also provided measures of nonrigidity and motion coherence, and experimental results were consistent with a prediction of when perception of nonrigidity would be independent of perception of coherence. The results are discussed relative to the advantages and shortcomings of both the affine and Euclidean approaches to structure-from-motion.

Algorithms↗
PubMed2004

[Discussion of naive Bayesian algorithm in prognosis prediction of primary liver cancer].

OBJECTIVE: To apply naive Bayesian algorithm in prognosis prediction of primary liver cancer and to predict the survival expectation of patients after transcatheter arterial chemoembolization (TACE). METHOD: Naive Bayesian algorithm was applied. Using correlation analysis to sift data-attributes. Whereas the missing data were assumed to follow the same distribution as that of the known. RESULT: The same-distribution assumption of the missing data reduces the error rate from 71.9% to 9.4%. Twelve attributes were sifted from 39 attributes by the correlation analysis, which were more effective to the final classification, and had a relatively low error rate of 3.1%. CONCLUSION: The proposed method effectively increases the accuracy of classification. Successful application of the naive Bayesian algorithm in prognostic problem of primary liver cancer indicates a bright future of this method in medical field.

Algorithms↗
PubMed1981

Prediction of the secondary structure of myelin basic protein.

An investigation into the probable secondary structure of the myelin basic protein was carried out by the application of three procedures currently in use to predict the secondary structures of proteins from knowledge of their amino acid sequences. In order to increase the accuracy of the predictions, the amino acid substitutions that occur in the basic protein from different species were incorporated into the predictive algorithms. It was possible to locate regions of probable alpha-helix, beta-structure, beta-turn, and unordered conformation (coil) in the protein. One of the predictive methods introduces a bias into the algorithm to maximize or minimize the amounts of alpha-helix and/or beta-structure present; this made it possible to assess how conditions such as pH and protein concentration or the presence of anionic amphiphilic molecules could influence the protein's secondary structure. The predictions made by the three methods were in reasonably good agreement with one another. They were consistent with experimental data, provided that the stabilizing or destabilizing effects of the environment were taken into account. According to the predictions, the extent of possible alpha-helix and beta-structure formation in the protein s severely restricted by the low frequency and extensive scattering of hydrophobic residues, along with a high frequency and extensive scattering of residues that favor the formation of beta-turns and coils. Neither prolyl residues nor cationic residues per se are responsible for the low content of alpha-helix predicted in the protein. The principal ordered conformation predicted is the beta-turn. Many of the predicted beta-turns overlap extensively, involving in some cases up to 10 residues. In some of these structures it is possible for the peptide backbone to oscillate in a sinusoidal manner, generating a flat, pleated sheetlike structure. Cationic residues located in these structures would appear to be ideally oriented for interaction with lipid phosphate groups located at the cytoplasmic surface of the myelin membrane. An analysis of possible and probable conformations that the triproline sequence could assume questions the popular notion that this sequence produces a hairpin turn in the basic protein.

Amino Acid Sequence↗
PubMed1999

MEARA sequence repeat of human CstF-64 polyadenylation factor is helical in solution. A spectroscopic and calorimetric study.

The primary structure of the human CstF-64 polyadenylation factor contains 12 nearly identical repeats of a consensus motif of five amino acid residues with the sequence MEAR(A/G). No known function has yet been ascribed to this motif; however, according to secondary structure prediction algorithms, it should form a helical structure in solution. To validate this theoretical prediction, we synthesized a 31 amino acid residue peptide (MEARA(6)) containing six repeats of the MEARA sequence and characterized its structure and stability by circular dichroism (CD) spectroscopy and differential scanning calorimetry (DSC). No effects of concentration on the CD or DSC properties of MEARA(6) were observed, indicating that the peptide is monomeric in solution at concentrations up to 2 mM. The far UV-CD spectra of MEARA(6) indicates that at a low temperature (1 degrees C) the MEARA(6) peptide has a relatively high helical content (76% at pH 2.0 and 65% at pH 7.0). The effects of pH and ionic strength on the CD spectrum of MEARA(6) suggest that a number of electrostatic interactions (e.g., i, i + 3 Arg/Glu ion pair, charge-dipole interactions) contribute to the stability of the helical structure in this peptide. DSC profiles show that the melting of MEARA(6) helix is accompanied by positive change in the enthalpy. To determine thermodynamic parameters of helix-coil transition from DSC profiles for this peptide, we developed a new, semiempirical procedure based on the calculated function for the heat capacity of the coiled state for a broad temperature range. The application of this approach to the partial molar heat capacity function for MEARA(6) provides the enthalpy change for helix formation calculated per amino acid residue as 3.5 kJ/mol.

Amino Acid Sequence↗
PubMed1998

Mapping the T cell epitopes of the Babesia bovis antigen 12D3: implications for vaccine design.

The Babesia bovis antigen 12D3 was analysed to identify potential T-cell epitopes. Two predictive algorithms identified 13 possible sites but there was minimal agreement between the different predictive methods. Experimental determination of the T-cell epitopes recognized by nine cattle was achieved using a panel of overlapping peptides which identified seven different epitopes, five of which were clustered together around residues 210-320 of the molecule. No T cell epitopes were located within the tightly disulphide bonded core of 12D3. Using a series of truncated peptides, the location of two of the epitopes was mapped to residues 35-43 and 266-275. The sequences of these two epitopes was compared with a database of previously described binding motifs for MHC II alleles and each epitope was found to contain three sequence motifs recognized by HLA-DR alleles. The BoLA-DRB3 alleles occurring in these cattle were determined by a sequence specific oligonucleotide hybridization assay. Within those cattle whose T cells proliferated in response to 12D3, there was a consistent pattern of epitope recognition and presence of particular DRB3 alleles. The implications for effective vaccine design are discussed.

Alleles↗
PubMed1999

Modeling of a roller-compaction process using neural networks and genetic algorithms.

In this study, roller-compaction of acetaminophene was studied to model the effect of binder type (hydroxypropyl methyl cellulose (HPMC), polyethylene glycol (PEG), Carbopol), binder concentration (5, 10 and 20%), number of roller-compaction passes (one or two), and extragranular microcrystalline cellulose addition on the properties of compressed tablets. Forty-two batches resulted from the experimental design. The artificial neural network methodology (ANN) along with genetic algorithms were used for data analysis and optimization. ANN and genetic models provided R2 values between 0.3593 and 0.9991 for measured responses. When a set of validation experiments was analyzed, genetic algorithm predictions of tablet characteristics were much better than the ANN. Optimization based on genetic algorithm showed that using HPMC at 20%, with two roller-compaction passes would produce mechanically acceptable acetaminophene tablets. PEG and carbopol would also produce acceptable tablets perhaps more suitable for sustained release applications. Using PEG as a binder had the additional advantage of not requiring an external lubricant during tablet manufacturing.

Acetaminophen↗
PubMed2005

Practical lessons from protein structure prediction.

Despite recent efforts to develop automated protein structure determination protocols, structural genomics projects are slow in generating fold assignments for complete proteomes, and spatial structures remain unknown for many protein families. Alternative cheap and fast methods to assign folds using prediction algorithms continue to provide valuable structural information for many proteins. The development of high-quality prediction methods has been boosted in the last years by objective community-wide assessment experiments. This paper gives an overview of the currently available practical approaches to protein structure prediction capable of generating accurate fold assignment. Recent advances in assessment of the prediction quality are also discussed.

Algorithms↗
PubMed2003

Prediction of single-pool Kt/v based on clinical and hemodialysis variables using multilinear regression, tree-based modeling, and artificial neural networks.

The impact of clinical and other variables on single-pool Kt/V (spKt/V) is unclear. The goal of this study was to identify clinical and hemodialysis treatment related predictors of spKt/V and use multilinear regression (LM), tree-based modeling (TBM), and artificial neural networks (ANN) to predict actual spKt/V. When 602 hemodialysis records were analyzed, spKt/V correlated with urea reduction ratio (URR) (r=0.91) and weakly with other variables. When URR was excluded, both LM and TBM identified normalized protein equivalent of total nitrogen appearance (nPNA), prehemodialysis (HD) and post-HD weights, blood flow rate, and dialyzer surface area as predictors of spKt/V. LM identified sex, height, dialyzer ultrafiltration coefficient (Kuf), and duration of dialysis, while TBM identified the dialysis nurse code. Prediction algorithms were developed from a "training" dataset, and validated on a separate ("testing") dataset. Correlation coefficients of predicted spKt/V with measured spKt/V with and without nPNA respectively were 0.745 and 0.679 for LM, 0.6 and 0.512 for TBM, and 0.634 for ANN, which performed better without using nPNA.

Algorithms↗
PubMed2000

Computational design of D-peptide inhibitors of hepatitis delta antigen dimerization.

Hepatitis delta virus (HDV) encodes a single polypeptide called hepatitis delta antigen (DAg). Dimerization of DAg is required for viral replication. The structure of the dimerization region, residues 12 to 60, consists of an anti-parallel coiled coil [Zuccola et al., Structure, 6(1998)821]. Multiple Copy Simultaneous Searches (MCSS) of the hydrophobic core region formed by the bend in the helix of one monomer of this structure were carried out for many diverse functional groups. Six critical interaction sites were identified. The Protein Data Bank was searched for backbone templates to use in the subsequent design process by matching to these sites. A 14 residue helix expected to bind to the D-isomer of the target structure was selected as the template. Over 200,000 mutant sequences of this peptide were generated based on the MCSS results. A secondary structure prediction algorithm was used to screen all sequences. and in general only those that were predicted to be highly helical were retained. Approximately 100 of these 14-mers were model built as D-peptides and docked with the L-isomer of the target monomer. Based on calculated interaction energies, predicted helicity, and intrahelical salt bridge patterns, a small number of peptides were selected as the most promising candidates. The ligand design approach presented here is the computational analogue of mirror image phage display. The results have been used to characterize the interactions responsible for formation of this model anti-parallel coiled coil and to suggest potential ligands to disrupt it.

Algorithms↗
PubMed1999

Predicting allosteric switches in myosins.

The sequences of several members of the myosin family of molecular motors are evaluated using ASP (Ambivalent Structure Predictor), a new computational method. ASP predicts structurally ambivalent sequence elements by analyzing the output from a secondary structure prediction algorithm. These ambivalent sequence elements form secondary structures that are hypothesized to function as switches by undergoing conformational rearrangement. For chicken skeletal muscle myosin, 13 discrete structurally ambivalent sequence elements are identified. All 13 are located in the heavy chain motor domain. When these sequence elements are mapped into the myosin tertiary structure, they form two compact regions that connect the actin binding site to the adenosine 5'-triphosphate (ATP) site, and the ATP site to the fulcrum site for the force-producing bending of the motor domain. These regions, predicted by the new algorithm to undergo conformational rearrangements, include the published known and putative switches of the myosin motor domain, and they form plausible allosteric connections between the three main functional sites of myosin. The sequences of several other members of the myosin I and II families are also analyzed.

Allosteric Regulation↗
PubMed1998

Prediction of the secondary structure contents of globular proteins based on three structural classes.

The prediction of the secondary structural contents (those of alpha-helix and beta-strand) of a globular protein is of great use in the prediction of protein structure. In this paper, a new prediction algorithm has been proposed based on Chou's database [Chou (1995), Proteins 21, 319-344]. The new algorithm is an improved multiple linear regression method, taking into account the nonlinear and coupling terms of the frequencies of different amino acids and the length of the protein. The prediction is also based on the structural classes of proteins, but instead of four classes, only three classes are considered, the alpha class, beta class, and the mixed alpha+beta and alpha/beta class or simply the alphabeta class. Thus the ambiguity that usually occurs between alpha+beta proteins and alpha/beta proteins is eliminated. A resubstitution examination for the algorithm shows that the average absolute errors are 0.040 and 0.035 for the prediction of alpha-helix content and beta-strand content, respectively. An examination of cross-validation, the jackknife analysis, shows that the average absolute errors are 0.051 and 0.045 for the prediction of alpha-helix content and beta-strand content, respectively. Both examinations indicate the self-consistency and the extrapolating effectiveness of the new algorithm. Compared with other methods, ours has the merits of simplicity and convenience for use, as well as high prediction accuracy. By incorporating the prediction of the structural classes, the only input of our method is the amino acid composition and the length of the protein to be predicted.

Algorithms↗
PubMed1988

Analysis and prediction of the different types of beta-turn in proteins.

beta-Turns have been extracted from 59 non-identical proteins (resolution 2 A) using the standard criterion that the distance between C alpha (i) and C alpha (i + 3) is less than 7 A (1 A = 0.1 nm). The beta-turns have been classified, using phi, psi angles, into seven conventional turn types (I, I', II, II', IV, VIa, VIb) and a new class of beta-turn, designated type VIII, in which the central residues (i + 1, i + 2) adopt an alpha R beta conformation. Most beta-turn types are found in various topological environments, with the exception of I' and II' beta-turns, where 83% and 50%, respectively, are found in beta-hairpins. Sufficient data have been gathered to enable, for the first time, the separate statistical analysis of type I and II beta-turns. The two turn types have been shown to be strikingly different in their sequence preferences. Type I turns favour Asp, Asn, Ser and Cys at i; Asp, Ser, Thr and Pro at i + 1; Asp, Ser, Asn and Arg at i + 2; Gly, Trp and Met at i + 3, whilst type II turns prefer Pro at i + 1; Gly and Asn at i + 2; Gln and Arg at i + 3. These preferences have been explained by the specific side-chain interactions observed within the X-ray structures. The positional trends for type I and II beta-turns have been incorporated into the simple empirical predictive algorithm originally developed by P.N. Lewis et al. The program has improved the positional prediction of beta-turns, and has enhanced and extended the method by predicting the type of beta-turn. Since the observed preferences reflect local interactions these predictions are applicable not only to proteins, but also to peptides, many of which are thought to contain beta-turns.

Amino Acids↗
PubMed2002

Prediction of promiscuous peptides that bind HLA class I molecules.

Promiscuous T-cell epitopes make ideal targets for vaccine development. We report here a computational system, MULTIPRED, for the prediction of peptide binding to the HLA-A2 supertype. It combines a novel representation of peptide/MHC interactions with a hidden Markov model as the prediction algorithm. MULTIPREDis both sensitive and specific, and demonstrates high accuracy of peptide-binding predictions for HLA-A*0201, *0204, and *0205 alleles, good accuracy for *0206 allele, and marginal accuracy for *0203 allele. MULTIPREDreplaces earlier requirements for individual prediction models for each HLA allelic variant and simplifies computational aspects of peptide-binding prediction. Preliminary testing indicates that MULTIPRED can predict peptide binding to HLA-A2 supertype molecules with high accuracy, including those allelic variants for which no experimental binding data are currently available.

Amino Acid Motifs↗
PubMed2002

Modification of sonic boom wave forms during propagation from the source to the ground.

A number of physical processes work to modify the shape of sonic boom wave forms as the wave form propagates from the aircraft to a receiver on the ground. These include frequency-dependent absorption, nonlinear steepening, and scattering by atmospheric turbulence. In the past two decades, each of these effects has been introduced into numerical prediction algorithms and results compared to experimental measurements. There is still some disagreement between measurements and prediction, but those differences are now in the range of tens of percent. The processes seem to be understood. The present understanding of sonic boom evolution will be presented along with experimental justification.

Aircraft↗
PubMed1999

Non-linear analysis of the rhythmic activity in rodent brains.

This paper discusses the employment of non-parametric non-linear prediction algorithms to investigate non-linear dynamics in the rhythmic brain activity of rats. Three algorithms (Sugihara-May Simplex, K-neighbour and Casdagli's) were tested yielding similar prediction results which--when subject to a suitable bootstrap based t-tests--revealed that the theta waves recorded in rat brains cannot have their intrinsic non-linearity dismissed at a significance of 0.05.

Algorithms↗
PubMed2000

Prediction of locoregional extension and metastatic disease in prostate cancer: a review.

Despite efforts to enhance the accuracy of prediction of extraprostatic disease, approximately 40% of the men undergoing radical prostatectomy are found at surgery to have non-organ-confined cancer. Predictive algorithms based on multivariate regression analysis and neural networks are widely available and are superior to our standard empirical methods of clinical staging. These algorithms have been validated in diverse and well-characterized patient groups. For enhancement of the predictive value, data input must be standardized and improved input variables must be incorporated. In addition to the three "classic" staging parameters, i.e., pretreatment prostate-specific antigen (PSA), biopsy pathology, and digital rectal examination, new variables now show promise in predicting disease extent and may be integrated in future predictive models. This review focuses on our present methods for prediction of locoregional spread and distant metastases in men with clinically localized prostate cancer.

Algorithms↗
PubMed2006

Computational prediction of the cross-reactive neutralizing epitope corresponding to the [corrected] monclonal [corrected] antibody b12 specific for HIV-1 gp120.

Backtracking from antibodies to their corresponding epitopes is a rational approach for vaccine design. Here we apply such a reverse immunological strategy for mapping the cross-reactive neutralizing epitope corresponding to the monoclonal antibody (mAb) b12 specific for HIV-1 gp120. b12 was used to screen a combinatorial phage display random peptide library and nineteen 12mer cysteine-looped peptides were affinity purified. These were used as input for analysis with the predictive algorithm Mapitope. Based on the input panel of peptides and the antigen's atomic structure, Mapitope predicts candidate epitopes on the surface of the antigen. Two major clusters were predicted as candidate b12 epitopes. These could be discriminated by a series of experiments, which included point mutagenesis of selected residues and binding assays. Moreover, the prediction of the b12 epitope was further strengthened by comparison with additional predictions for two competing antibodies, b6 and m14. Finally, support of our prediction was obtained in view of the fact that b12, m14, and b6 were found to compete against mAb 17b binding to gp120. The b12 epitope is predicted to consist of four peptide segments of gp120 (residues V254-T257, D368-F376, E381-Y384, and I420-I424), which lie at the periphery of the CD4 binding site.

Algorithms↗
PubMed1987

The use of folding patterns in the prediction of protein topologies.

We proved previously that the distribution of formation energies which may be associated with the predicted secondary structures (or nuclei) is specific of the folding process (Busetta, B. 1986, Biochim. Biophys. Acta 870, 327-338). We developed a new predictive algorithm for protein topologies, based on the search of standard 'folding patterns'. In another manner, the strongest predicted nuclei are used to propose a fast sequence-alignment process which is efficient for distantly related proteins.

Alcohol Dehydrogenase↗