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PubMed2001

TM Finder: a prediction program for transmembrane protein segments using a combination of hydrophobicity and nonpolar phase helicity scales.

Based on the principle of dual prediction by segment hydrophobicity and nonpolar phase helicity, in concert with imposed threshold values of these two parameters, we developed the automated prediction program TM Finder that can successfully locate most transmembrane (TM) segments in proteins. The program uses the results of experiments on a series of host-guest TM segment mimic peptides of prototypic sequence KK AAAXAAAAAXAAWAAXAAAKKKK-amide (where X = each of the 20 commonly occurring amino acids) through which an HPLC-derived hydropathy scale, a hydrophobicity threshold for spontaneous membrane insertion, and a nonpolar phase helical propensity scale were determined. Using these scales, the optimized prediction algorithm of TM Finder defines TM segments by first searching for competent core segments using the combination of hydrophobicity and helicity scales, and then performs a gap-joining operation, which minimizes prediction bias caused by local hydrophilic residues and/or the choice of window size. In addition, the hydrophobicity threshold requirement enables TM Finder to distinguish reliably between membrane proteins and globular proteins, thereby adding an important dimension to the program. A full web version of the TM Finder program can be accessed at http://www.bioinformatics-canada.org/TM/.

Animals↗
PubMed1997

A comparison of a computer-based orthognathic surgery prediction system to postsurgical results.

This retrospective study analyzes the accuracy of a prediction algorithm from Quick Ceph Image. Pre- and postsurgical lateral cephalograms of 35 adult patients were computer imaged and specific landmarks digitized. Digitization error was assessed from duplicate digitizations. Sixteen measurements of the predicted and actual postsurgical hard and soft tissue landmarks were compared using Student's t test, one- and two-way analysis of variance, and a modified Bradley-Blackwood test. Results showed a good correlation between repeated digitization of all variables except soft tissue point B and E-plane. Comparison of the predicted and actual changes found that all differences were less than 1.8 mm or 3.1 degrees. Student's t test showed 10 of the 16 measurements did not differ significantly, and interclass correlation demonstrated moderate to good correlations for 13 of 16 variables. Overall the results of this study suggest that the magnitude of the differences were within clinically acceptable limits.

Adult↗
PubMed1996

Cleavage site analysis in picornaviral polyproteins: discovering cellular targets by neural networks.

Picornaviral proteinases are responsible for maturation cleavages of the viral polyprotein, but also catalyze the degradation of cellular targets. Using graphical visualization techniques and neural network algorithms, we have investigated the sequence specificity of the two proteinases 2Apro and 3Cpro. The cleavage of VP0 (giving rise to VP2 and VP4), which is carried out by a so-far unknown proteinase, was also examined. In combination with a novel surface exposure prediction algorithm, our neural network approach successfully distinguishes known cleavage sites from noncleavage sites and yields a more consistent definition of features common to these sites. The method is able to predict experimentally determined cleavage sites in cellular proteins. We present a list of mammalian and other proteins that are predicted to be possible targets for the viral proteinases. Whether these proteins are indeed cleaved awaits experimental verification. Additionally, we report several errors detected in the protein databases. A computer server for prediction of cleavage sites by picornaviral proteinases is publicly available at the e-mail address NetPicoRNA@cbs.dtu.dk or via WWW at http:@www.cbs.dtu.dk/services/NetPicoRNA/.

Amino Acid Sequence↗
PubMed1999

Protein folding: from the levinthal paradox to structure prediction.

This article is a personal perspective on the developments in the field of protein folding over approximately the last 40 years. In addition to its historical aspects, the article presents a view of the principles of protein folding with particular emphasis on the relationship of these principles to the problem of protein structure prediction. It is argued that despite much that is new, the essential elements of our current understanding of protein folding were anticipated by researchers many years ago. These elements include the recognition of the central importance of the polypeptide backbone as a determinant of protein conformation, hierarchical protein folding, and multiple folding pathways. Important areas of progress include a detailed characterization of the folding pathways of a number of proteins and a fundamental understanding of the physical chemical forces that determine protein stability. Despite these developments, fold prediction algorithms still encounter difficulties in identifying the correct fold for a given sequence. This may be due to the possibility that the free energy differences between at least a few alternate conformations of many proteins are not large. Significant progress in protein structure prediction has been due primarily to the explosive growth of sequence and structural databases. However, further progress is likely to depend in part on the ability to combine information available from databases with principles and algorithms derived from physical chemical studies of protein folding. An approach to the integration of the two areas is outlined with specific reference to the PrISM program that is a fully integrated sequence/structural-analysis/fold-recognition/homology model building software system.

Models, Molecular↗
PubMed1988

Synthesis, antiviral activity, and conformational characterization of mouse-human alpha-interferon hybrids.

Reciprocal hybrids were constructed between human and mouse interferons (IFNs), and their antiviral activity was examined on different target cells and compared to the activity of the parental molecules. In addition, we used a number of predictive algorithms on a data base of the available alpha-interferon sequences to propose a working model for the overall conformation of the alpha-interferon molecule that is consistent with the structural predictions. Remarkable conservation within the predicted alpha-helical segments of the interferon molecule was observed. We propose that the observed changes in the activity and specificity of the hybrids obtained are largely due to the sequences present in the loops at the ends of the major helical structures; these are less conserved, contain beta-bends, and are generally hydrophilic and flexible. The data on the constructed mouse-human hybrids have shown that the activity on human cells is contributed by determinants present in the N-terminal 122 amino acids of human IFN, thus implicating one or more loops within this region (e.g. loops 1-12, 25-38, 70-74, and 103-113). The activity on bovine cells appears to be localized mainly in sequence 60-121, implicating the role of loops 70-74 and/or 103-113 of the human IFN molecule. The specificity of mouse IFN for mouse cells is in some or all of the loops (70-74, 103-113, 134-139, and 163-166) in the C-terminal sequence. The proposed working model should provide guidelines for the study of the specificity of action in molecular terms.

Amino Acid Sequence↗
PubMed2005

MEDock: a web server for efficient prediction of ligand binding sites based on a novel optimization algorithm.

The prediction of ligand binding sites is an essential part of the drug discovery process. Knowing the location of binding sites greatly facilitates the search for hits, the lead optimization process, the design of site-directed mutagenesis experiments and the hunt for structural features that influence the selectivity of binding in order to minimize the drug's adverse effects. However, docking is still the rate-limiting step for such predictions; consequently, much more efficient algorithms are required. In this article, the design of the MEDock web server is described. The goal of this sever is to provide an efficient utility for predicting ligand binding sites. The MEDock web server incorporates a global search strategy that exploits the maximum entropy property of the Gaussian probability distribution in the context of information theory. As a result of the global search strategy, the optimization algorithm incorporated in MEDock is significantly superior when dealing with very rugged energy landscapes, which usually have insurmountable barriers. This article describes four different benchmark cases that span a diverse set of different types of ligand binding interactions. These benchmarks were compared with the use of the Lamarckian genetic algorithm (LGA), which is the major workhorse of the well-known AutoDock program. These results demonstrate that MEDock consistently converged to the correct binding modes with significantly smaller numbers of energy evaluations than the LGA required. When judged by a threshold of the number of energy evaluations consumed in the docking simulation, MEDock also greatly elevates the rate of accurate predictions for all benchmark cases. MEDock is available at http://medock.csie.ntu.edu.tw/ and http://bioinfo.mc.ntu.edu.tw/medock/.

Algorithms↗
PubMed2003

Compositional features of eukaryotic genomes for checking predicted genes.

Gene prediction relies on the identification of characteristic features of coding sequences that distinguish them from non-coding DNA. The recent large-scale sequencing of entire genomes from higher eukaryotes, in conjunction with currently used gene prediction algorithms, has provided an abundance of putative genes that can now be analysed for their compositional properties. Strong, systematic differences still exist, in several species, between the compositional properties of sets of ex novo predicted genes and genes that have been experimentally detected and/or verified. This is particularly evident in the estimated gene set (>45,000 genes) of the recently sequenced rice genome, where roughly half the predicted genes are compositionally unusual and have no known orthologues in the dicot Arabidopsis. In a few cases such differences might suggest a bias in experimental gene-finding protocols, but the quasi-random nature of the compositionally aberrant predicted genes is a strong indication that many, if not most, of them are false positives. It therefore appears that some important features of coding regions have not yet been taken into account in existing gene prediction programs. Statistical base compositional properties of curated gene data sets from vertebrates, which we briefly review here, should therefore provide a useful benchmark for fine-tuning probabilistic gene models and model parameters that are currently in use.

Animals↗
PubMed2026

Deciphering the Role of LNX2 as a Potential Contributor to Neurodevelopmental Disorders.

BACKGROUND/OBJECTIVES: Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition characterized by a complex and multifactorial genetic architecture. In this study, we report a male patient, born to non-consanguineous healthy parents, presenting with ADHD and oppositional defiant disorder (ODD). METHODS: Trio-based whole-exome sequencing (WES) was performed in the proband and both parents. Variant classification was performed according to American College of Medical Genetics and Genomics (ACMG) guidelines, and the potential pathogenicity of the identified variant was further assessed through multiple in silico prediction algorithms and protein structural analyses. RESULTS: WES identified a homozygous variant in the LNX2 gene (NM_153371.4: c.1165G>A, p.Ala389Thr), classified as a variant of uncertain significance (VUS) and supported by multiple in silico predictions. LNX2 is expressed during brain development and encodes an E3 ubiquitin ligase involved in neuronal differentiation and synaptic function. The identified variant is located within the PDZ2 domain, a functionally relevant region involved in protein-protein interactions. Although the variant is reported in population databases (gnomAD ID: rs148429804), it has not been associated with any clinical phenotype, and its presence in the homozygous state has been reported only once, remaining extremely rare and lacking clinical annotation. Structural modelling predicted localized rearrangement of the hydrogen-bonding network within the PDZ2 domain without major conformational changes. Integrative transcriptomic, and single-cell analyses further supported the biological relevance of LNX2 in neurodevelopment, highlighting its preferential association with neuronal projection-cell networks, synaptic vesicle trafficking pathways, and neuron-specific regulatory programs. CONCLUSION: Although the identified LNX2 variant cannot be considered causative for the patient's phenotype and a definitive disease-gene relationship cannot be established based on a single individual, the complementary genetic, structural, and transcriptomic findings support the biological plausibility of LNX2 as a candidate gene for neurodevelopmental disorders. Additional independent patients and functional studies will be required to clarify its contribution to human disease.

Child↗
PubMed2006

Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae.

Combinatorial regulation by transcription factor complexes is an important feature of eukaryotic gene regulation. Here, we propose a new method for identification of interactions between transcription factors (TFs) that relies on the relationship of their binding sites, and we test it using Saccharomyces cerevisiae as a model system. The algorithm predicts interacting TF pairs based on the co-occurrence of their binding motifs and the distance between the motifs in promoter sequences. This allows investigation of interactions between TFs without known binding motifs or expression data. With this approach, 300 significant interactions involving 77 TFs were identified. These included more than 70% of the known protein-protein interactions. Approximately half of the detected interacting motif pairs showed strong preferences for particular distances and orientations in the promoter sequences. These one dimensional features may reflect constraints on allowable spatial arrangements for protein-protein interactions. Evidence for biological relevance of the observed characteristic distances is provided by the finding that target genes with the same characteristic distances show significantly higher co-expression than those without preferred distances. Furthermore, the observed interactions were dynamic: most of the TF pairs were not constitutively active, but rather showed variable activity depending on the physiological condition of the cells. Interestingly, some TF pairs active in multiple conditions showed preferences for different distances and orientations depending on the condition. Our prediction and characterization of TF interactions may help to understand the transcriptional regulatory networks in eukaryotic systems.

Algorithms↗
PubMed2006

SHARP2: protein-protein interaction predictions using patch analysis.

UNLABELLED: SHARP2 is a flexible web-based bioinformatics tool for predicting potential protein-protein interaction sites on protein structures. It implements a predictive algorithm that calculates multiple parameters for overlapping patches of residues on the surface of a protein. Six parameters are calculated: solvation potential, hydrophobicity, accessible surface area, residue interface propensity, planarity and protrusion (SHARP2). Parameter scores for each patch are combined, and the patch with the highest combined score is predicted as a potential interaction site. SHARP2 enables users to upload 3D protein structure files in PDB format, to obtain information on potential interaction sites as downloadable HTML tables and to view the location of the sites on the 3D structure using Jmol. The server allows for the input of multiple structures and multiple combinations of parameters. Therefore predictions can be made for complete datasets, as well as individual structures. AVAILABILITY: http://www.bioinformatics.sussex.ac.uk/SHARP2.

Algorithms↗
PubMed2006

Validation of administrative data used for the diagnosis of upper gastrointestinal events following nonsteroidal anti-inflammatory drug prescription.

AIMS: To validate veterans affairs (VA) administrative data for the diagnosis of nonsteroidal anti-inflammatory drug (NSAID)-related upper gastrointestinal events (UGIE) and to develop a diagnostic algorithm. METHODS: A retrospective study of veterans prescribed an NSAID as identified from the national pharmacy database merged with in-patient and out-patient data, followed by primary chart abstraction. Contingency tables were constructed to allow comparison with a random sample of patients prescribed an NSAID, but without UGIE. Multivariable logistic regression analysis was used to derive a predictive algorithm. Once derived, the algorithm was validated in a separate cohort of veterans. RESULTS: Of 906 patients, 606 had a diagnostic code for UGIE; 300 were a random subsample of 11 744 patients (control). Only 161 had a confirmed UGIE. The positive predictive value (PPV) of diagnostic codes was poor, but improved from 27% to 51% with the addition of endoscopic procedural codes. The strongest predictors of UGIE were an in-patient ICD-9 code for gastric ulcer, duodenal ulcer and haemorrhage combined with upper endoscopy. This algorithm had a PPV of 73% when limited to patients >or=65 years (c-statistic 0.79). Validation of the algorithm revealed a PPV of 80% among patients with an overlapping NSAID prescription. CONCLUSIONS: NSAID-related UGIE can be assessed using VA administrative data. The optimal algorithm includes an in-patient ICD-9 code for gastric or duodenal ulcer and gastrointestinal bleeding combined with a procedural code for upper endoscopy.

Algorithms↗
PubMed2005

Human cardiac potassium channel DNA polymorphism modulates access to drug-binding site and causes drug resistance.

Expression of voltage-gated K channel, shaker-related subfamily, member 5 (KCNA5) underlies the human atrial ultra-rapid delayed rectifier K current (I(Kur)). The KCNA5 polymorphism resulting in P532L in the C terminus generates I(Kur) that is indistinguishable from wild type at baseline but strikingly resistant to drug block. In the present study, truncating the C terminus of KCNA5 generated a channel with wild-type drug sensitivity, which indicated that P532 is not a drug-binding site. Secondary structure prediction algorithms identified a probable alpha-helix in P532L that is absent in wild-type channels. We therefore assessed drug sensitivity of I(Kur) generated in vitro in CHO and HEK cells by channels predicted to exhibit or lack this C-terminal alpha-helix. All constructs displayed near-identical I(Kur) in the absence of drug challenge. However, those predicted to lack the C-terminal alpha-helix generated quinidine-sensitive currents (43-51% block by 10 microM quinidine), while the currents generated by those constructs predicted to generate a C-terminal alpha-helix were inhibited less than 12%. Circular dichroism spectroscopy revealed an alpha-helical signature with peptides derived from drug-resistant channels and no organized structure in those associated with wild-type drug sensitivity. In conclusion, we found that this secondary structure in the KCNA5 C terminus, absent in wild-type channels but generated by a naturally occurring DNA polymorphism, does not alter baseline currents but renders the channel drug resistant. Our data support a model in which this structure impairs access of the drug to a pore-binding site.

Amino Acid Substitution↗
PubMed1988

Validation of the use of Bayesian analysis in the optimization of gentamicin therapy from the commencement of dosing.

A computer program based on the statistical technique of Bayesian analysis has been adapted to run on several microcomputers. The clinical application of this method for gentamicin has been validated in 13 patients with varying degrees of renal function by a comparison of the accuracy of this method to a predictive algorithm method and one using standard pharmacokinetic principles. Blood samples for serum gentamicin analysis were taken after the administration of an intravenous loading dose of gentamicin. The results produced by each method were used to predict the peak and trough values measured on day 3 of therapy. Of the three methods studied, Bayesian analysis, using a serum gentamicin concentration drawn four hours after the initial dose, was the least biased and the most precise method for predicting the observed levels. The mean prediction error of the Bayesian analysis method, using the four-hour sample, was -0.03 mg/L for the peak serum concentration and -0.07 mg/L for the trough level on day 3. Using this method the corresponding root mean squared prediction error was 0.60 mg/L and 0.36 mg/L for the peak and trough levels, respectively.

Aged↗
PubMed2002

Prediction of proteasome cleavage motifs by neural networks.

We present a predictive method that can simulate an essential step in the antigen presentation in higher vertebrates, namely the step involving the proteasomal degradation of polypeptides into fragments which have the potential to bind to MHC Class I molecules. Proteasomal cleavage prediction algorithms published so far were trained on data from in vitro digestion experiments with constitutive proteasomes. As a result, they did not take into account the characteristics of the structurally modified proteasomes--often called immunoproteasomes--found in cells stimulated by gamma-interferon under physiological conditions. Our algorithm has been trained not only on in vitro data, but also on MHC Class I ligand data, which reflect a combination of immunoproteasome and constitutive proteasome specificity. This feature, together with the use of neural networks, a non-linear classification technique, make the prediction of MHC Class I ligand boundaries more accurate: 65% of the cleavage sites and 85% of the non-cleavage sites are correctly determined. Moreover, we show that the neural networks trained on the constitutive proteasome data learns a specificity that differs from that of the networks trained on MHC Class I ligands, i.e. the specificity of the immunoproteasome is different than the constitutive proteasome. The tools developed in this study in combination with a predictor of MHC and TAP binding capacity should give a more complete prediction of the generation and presentation of peptides on MHC Class I molecules. Here we demonstrate that such an approach produces an accurate prediction of the CTL the epitopes in HIV Nef. The method is available at www.cbs.dtu.dk/services/NetChop/.

Algorithms↗
PubMed1995

Consensus preferred hydration sites in six FKBP12-drug complexes.

A set of consensus hydration sites for the FK506-FKBP12 complex are derived by comparing six FKBP12-drug complexes. These hydration sites include a subset of the observed water molecules plus some sites that are occupied by neighboring protein atoms in the FK506-FKBP12 crystal structure. Two hydration prediction algorithms, AUTO-SOL and AQUARIUS2, showed significant increases in apparent efficacy using these consensus water sites, suggesting that our proposed set of consensus hydration sites is truly a better representation of the hydration properties of FKBP12 in solution. Predictably, the consensus hydration sites include all buried water molecules. Otherwise, the features of solvation sites included in the consensus list versus those discarded reveal no distinctive features that would allow them to be selected unambiguously without reference to multiple crystal forms. We suggest that analyses such as this one are a crucial prelude to any theoretical analysis aimed at understanding hydration properties.

Algorithms↗
PubMed1993

Penetration of industrial chemicals across the skin: a predictive model.

The recently reported dermal absorption and toxicity potential of industrial chemicals is reconsidered using an alternative physicochemically based model of skin penetration. In this model, the outermost, and least permeable, component of the skin [namely, the stratum corneum (SC)] is considered to provide only a lipoidal transport pathway into the body for chemicals that come into contact with the skin. The predictive algorithm of the model is biophysically compatible with known SC properties, and is based on experimental determinations of permeability coefficients through human skin in vitro for nearly 100 compounds of widely divergent physicochemical properties. This simpler prediction results in significantly lower estimates of maximum percutaneous penetration fluxes.

Diffusion↗
PubMed1987

Computer assisted size measurement on a digitising tablet of nucleic acid and protein molecules from gels.

A method is presented for the rapid and convenient determination of molecular weight or chain length of macromolecules from their electrophoretic mobility on gels, using a computer-controlled digitising tablet. A novel feature is accurate compensation for 'smile' or 'frown' profiles as well as for the possible splay or curvature of lanes. A family of monotonic, asymptotic, generalised quadratics is calculated to fit locally the known values in a marker track, and a weighting function is then applied to these enveloping curves so that the prediction algorithm simulates the interpolation of unknown values from a smooth graph drawn through the known bands. Results for double stranded DNA, single and double stranded RNA, and protein molecules are given. The average error of the predicted values against the known molecular sizes was 0.2% for dsDNA, 1.7% for dsRNA, 0.7% for ssRNA and 3.6% for protein molecules.

Computers↗
PubMed1995

Isolation and characterization of a Vibrio cholerae gene (tagA) that encodes a ToxR-regulated lipoprotein.

The Vibrio cholerae (Vc) gene (tagA) coding for the TagA lipoprotein has been isolated. Sequencing of tagA revealed the presence of an open reading frame (ORF) of 568 amino acids with a characteristic signal peptidase II cleavage site at the N terminus. Electrophoretic analysis of proteins synthesized by Escherichia coli (Ec) cells following T7 promoter/RNA polymerase-directed expression of tagA, revealed a closely migrating doublet of proteins corresponding to two species of TagA. Computer-generated alignment algorithms predict that a homology exists between Vc TagA and Ec K99 fimbriae biogenesis determinant FanD.

Algorithms↗