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PubMed2005

A new algorithm for predicting triplet-triplet energy-transfer activated complex coordinate in terms of accurate potential-energy surfaces.

The new algorithm presented here allows, for the first time, the determination of the optimal geometrical distortions that an acceptor molecule in the triplet-triplet energy-transfer process undergoes, as well as the dependence of the activation energy of the process on the triplet energy difference of donor and acceptor molecules. This algorithm makes use of the complete potential-energy surfaces (singlet and triplet states), and contrasts with the first-order approximation already published [L. M. Frutos, O. Castano, J. L. Andres, M. Merchan, and A. U. Acuna, J. Chem. Phys. 120, 1208 (2004)] in which an expansion of the potential-energy surfaces was used. This algorithm is gradient based and finds the best trajectory for the acceptor molecule, starting from S(0) ground-state equilibrium geometry, to achieve the maximum variation of the singlet-triplet energy gap with the minimum energy of activation on S(0). Therefore, the algorithm allows the determination of a "reaction path" for the triplet-triplet energy-transfer processes. Also, the algorithm could also serve eventually to find minimum-energy crossing (singlet-triplet) points on the potential-energy surface, which can play an important role in the intersystem crossing process for the acceptor molecules to recover their initial capacity as acceptors. Also addressed is the misleading use of minimum-energy paths in T(1) to describe the energy-transfer process by comparing these results with those obtained using the new algorithm. The implementation of the algorithm is illustrated with different potential-energy surface models and it is discussed in the frame of nonvertical behavior.

Journal Article↗
PubMed2006

P-wave morphology in focal atrial tachycardia: development of an algorithm to predict the anatomic site of origin.

OBJECTIVES: The purpose of this study was to perform a detailed analysis of the P-wave morphology (PWM) in focal atrial tachycardia (AT) and construct and prospectively evaluate an algorithm for identification of the anatomic site of origin. BACKGROUND: Although smaller studies have described the PWM from particular anatomic locations, a detailed algorithm characterizing the likely location of a tachycardia associated with a P-wave of unknown origin has been lacking. METHODS: The PWMs for 126 consecutive patients undergoing successful radiofrequency ablation of 130 ATs are reported. P waves were included only when the onset was preceded by a discernible isoelectric segment. P waves were classified as positive (+), negative (-), isoelectric, or biphasic. Sensitivity, specificity, and predictive values were calculated. On the basis of these results, an algorithm was constructed and prospectively evaluated in 30 new consecutive ATs. RESULTS: The distribution of ATs was right atrial (RA) in 82 of 130 (63%) and left atrial (LA) in 48 of 130 (37%). Right atrial sites included crista (n = 28), tricuspid annulus (n = 29), coronary sinus (CS) ostium (n = 14), perinodal (n = 7), right septum (n = 1), and RA appendage (n= 3). Left atrial sites included pulmonary veins (n = 32), mitral annulus (n = 8), CS body (n= 3), left septum (n = 3), and LA appendage (n = 2). In electrocardiographic lead V1, a negative or +/- P-wave demonstrated a specificity of 100% for a RA focus, and a + or -/+ P-wave demonstrated a sensitivity of 100% for a LA focus. A characteristic PWM was associated with high sensitivity and specificity at common atrial sites for tachycardia foci. A P-wave algorithm correctly identified the focus in 93%. CONCLUSIONS: Characteristic PWMs corresponding to known anatomic sites for focal AT are associated with high specificity and sensitivity. A P-wave algorithm correctly identified the site of tachycardia origin in 93%.

Adolescent↗
PubMed1998

Combined multiple sequence reduced protein model approach to predict the tertiary structure of small proteins.

By incorporating predicted secondary and tertiary restraints into ab initio folding simulations, low resolution tertiary structures of a test set of 20 nonhomologous proteins have been predicted. These proteins, which represent all secondary structural classes, contain from 37 to 100 residues. Secondary structural restraints are provided by the PHD secondary structure prediction algorithm that incorporates multiple sequence information. Predicted tertiary restraints are obtained from multiple sequence alignments via a two-step process: First, "seed" side chain contacts are identified from a correlated mutation analysis, and then, the seed contacts are "expanded" by an inverse folding algorithm. These predicted restraints are then incorporated into a lattice based, reduced protein model. Depending upon fold complexity, the resulting nativelike topologies exhibit a coordinate root-mean-square deviation, cRMSD, from native between 3.1 and 6.7 A. Overall, this study suggests that the use of restraints derived from multiple sequence alignments combined with a fold assembly algorithm is a promising approach to the prediction of the global topology of small proteins.

Algorithms↗
PubMed2026

Toward personalized interventions for preventing depression in primary care: Qualitative and quantitative findings from the e-predictD pilot study.

BACKGROUND: The predictD intervention, delivered by family physicians (FPs), has demonstrated effectiveness and cost-efficiency in preventing depression and anxiety. The e-predictD study aims to design, develop, and evaluate a novel personalized intervention for depression prevention by integrating information and communication technologies (ICTs), risk prediction algorithms, and decision support systems (DSS) for both patients and FPs. OBJECTIVE: To evaluate the satisfaction, usability, and acceptability, of a beta version of the e-predictD intervention in primary care settings. METHODS: The e-predictD intervention follows a biopsychosocial approach, including an initial patient-FP interview, specific FP training, and an app. A β-version was tested in a pilot study without a control group over three months. The app integrates a validated depression risk prediction algorithm, decision algorithms, and a monitoring system supporting the DSS. The DSS generates a personalized prevention plan (PPP) from eight intervention modules: physical exercise, social relationships, problem-solving, communication skills, decision-making, assertiveness, sleep improvement, and cognitive restructuring. Patients and FPs discussed the PPP in a 15-minute baseline interview, selecting modules for implementation over three months. Semi-structured interviews gathered feedback. Assessments included depression (PHQ-9), anxiety (GAD-7), quality of life (SF-12), and major depression risk (predictD algorithm). RESULTS: Six FPs from six Spanish cities enrolled 56 non-depressed patients at moderate-to-high risk of depression; 47 (84%) completed follow-up. The app was used for a median of six days (interquartile range: 1-30). Both FPs and patients expressed satisfaction, leading to incorporated improvements. After three months, significant reductions in major depression risk and anxiety symptoms were observed, alongside improved mental quality of life. However, no significant changes were found in depressive symptoms or physical quality of life. CONCLUSION: This pilot study supports the feasibility and acceptability of the e-predictD β-version, despite lower-than-expected app usability. Health improvements were observed, warranting confirmation in a randomized controlled trial. TRIAL REGISTRATION: ClinicalTrials.gov NCT03990792.

Adult↗
PubMed1995

A new algorithm for predicting splice site sequence based on an improvement of categorical discriminant analysis.

The splicing signals which govern the excision of introns are not yet well explained, since actual splice site sequences are to some extent different from the generally accepted consensus sequences. While a quantification method (categorical discriminant analysis: CDA) has been proposed to analyze splice site signals, sample sequences lying in the overlapping region of sample scores are not discriminated well by CDA, thus limiting the predictive ability of this analytical technique. In this paper, we propose a method to improve the performance of CDA, which is applicable to the analysis of 5'-splice site signals in various mammalian genes. The proposed algorithm has revealed a distinct enhancement in the predicting ability compared to that of traditional CDA and could explain point mutations in the 5'-splice site region of rabbit beta-globin gene fairly well.

Algorithms↗
PubMed2001

Virtual interaction profiles of proteins.

We have developed a new method for the prediction of peptide sequences that bind to a protein, given a three-dimensional structure of the protein in complex with a peptide. By applying a recently developed sequence prediction algorithm and a novel ensemble averaging calculation, we generate a diverse collection of peptide sequences that are predicted to have significant affinity for the protein. Using output from the simulations, we create position-specific scoring matrices, or virtual interaction profiles (VIPs). Comparison of VIPs for a collection of binding motifs to sequences determined experimentally indicates that the prediction algorithm is accurate and applicable to a diverse range of structures. With these VIPs, one can scan protein sequence databases rapidly to seek binding partners of potential biological significance. Overall, this method can significantly enhance the information contained within a protein- peptide crystal structure, and enrich the data obtained by experimental selection methods such as phage display.

Algorithms↗
PubMed2003

Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project.

BACKGROUND: Seattle Biomedical Research Institute (SBRI) as part of the Leishmania Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces. RESULTS: Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN) into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODONUSAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence. CONCLUSION: An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.

Algorithms↗
PubMed2000

The Prognosis of Survival in Patients with Some Solid Tumors Based on Optimal Partitions of Immunological Parameter Ranges.

New logical and statistical methods were used for the analysis of relationships between survival rate of solid tumor patients and immunological variables. These methods are based on the search of the regularities (syndromes) in the multidimensional space. The syndromes are the elements of partitions of allowable areas of variables. To estimate the statistical validity of found regularities the new technique based on Monte-Carlo computer simulation was used. The broad panel of monoclonal antibodies for lymphocyte differentiation antigens was used for subpopulation analysis. The two tasks are described. The purpose of the first task was the evaluation of significance of immunological parameters for prediction of one-year metastasis-free survival in non-metastatic osteosarcoma of extremities. The second task was the construction of the predicting algorithm for prognosis of two-year survival of patients with stomach cancer. The optimal sets of parameters for prediction of survival were found for both tasks. We found out the high predictive value of HLA DR(+) cells percentage in the 1st task, and the percentage of adhesion cells (CD50(+) lymphocytes) is the most significant parameter in the 2nd task. The predictive algorithms were developed.

Journal Article↗
PubMed2002

Transmembrane helix predictions revisited.

Methods that predict membrane helices have become increasingly useful in the context of analyzing entire proteomes, as well as in everyday sequence analysis. Here, we analyzed 27 advanced and simple methods in detail. To resolve contradictions in previous works and to reevaluate transmembrane helix prediction algorithms, we introduced an analysis that distinguished between performance on redundancy-reduced high- and low-resolution data sets, established thresholds for significant differences in performance, and implemented both per-segment and per-residue analysis of membrane helix predictions. Although some of the advanced methods performed better than others, we showed in a thorough bootstrapping experiment based on various measures of accuracy that no method performed consistently best. In contrast, most simple hydrophobicity scale-based methods were significantly less accurate than any advanced method as they overpredicted membrane helices and confused membrane helices with hydrophobic regions outside of membranes. In contrast, the advanced methods usually distinguished correctly between membrane-helical and other proteins. Nonetheless, few methods reliably distinguished between signal peptides and membrane helices. We could not verify a significant difference in performance between eukaryotic and prokaryotic proteins. Surprisingly, we found that proteins with more than five helices were predicted at a significantly lower accuracy than proteins with five or fewer. The important implication is that structurally unsolved multispanning membrane proteins, which are often important drug targets, will remain problematic for transmembrane helix prediction algorithms. Overall, by establishing a standardized methodology for transmembrane helix prediction evaluation, we have resolved differences among previous works and presented novel trends that may impact the analysis of entire proteomes.

Algorithms↗
PubMed2000

Prediction whether a human cDNA sequence contains initiation codon by combining statistical information and similarity with protein sequences.

MOTIVATION: In the previous works, we developed ATGpr, a computer program for predicting the fullness of a cDNA, i.e. whether it contains an initiation codon or not. Statistical information of short nucleotide fragments was fully exploited in the prediction algorithm. However, sequence similarities to known proteins, which are becoming increasingly available due to recent rapid growth of protein database, were not used in the prediction. In this work, we present a new prediction algorithm based on both statistical and similarity information, which provides better performance in sensitivity and specificity. RESULTS: We evaluated the accuracy of ATGpr for predicting fullness of cDNA sequences from human clustered ESTs of UniGene, and we obtained specificity, sensitivity, and correlation coefficient of this prediction. Specificity and sensitivity crossed at 46% over the ATGpr score threshold of 0.33 and the maximum correlation coefficient of 0.34 was obtained at this threshold. Without ATGpr we found it effective to use alignments with known proteins for predicting the fullness of cDNA sequences. That is, specificity increased monotonously as similarity (identity of the alignments) increased. Specificity was achieved greater than 80% if identity was greater than 40%. For more effective prediction of fullness of cDNA sequences we combined the similarity (identity of query sequence) with known proteins and ATGpr score. As a result, specificity became greater than 80% if identity was greater than 20%. AVAILABILITY: The prediction program, called ATGpr_ sim, is available at http://www.hri.co.jp/atgpr/ATGpr_sim.html CONTACT: nisikawa@crl.hitachi.co.jp

Amino Acid Sequence↗
PubMed2000

Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles.

Specific binding of antigenic peptides to major histocompatibility complex (MHC) class I molecules is a prerequisite for their recognition by cytotoxic T-cells. Prediction of MHC-binding peptides must therefore be incorporated in any predictive algorithm attempting to identify immunodominant T-cell epitopes, based on the amino acid sequence of the protein antigen. Development of predictive algorithms based on experimental binding data requires experimental testing of a very large number of peptides. A complementary approach relies on the structural conservation observed in crystallographically solved peptide-MHC complexes. By this approach, the peptide structure in the MHC groove is used as a template upon which peptide candidates are threaded, and their compatibility to bind is evaluated by statistical pairwise potentials. Our original algorithm based on this approach used the pairwise potential table of Miyazawa and Jernigan (Miyazawa S, Jernigan RL, 1996, J Mol Biol 256:623-644) and succeeded to correctly identify good binders only for MHC molecules with hydrophobic binding pockets, probably because of the high emphasis of hydrophobic interactions in this table. A recently developed pairwise potential table by Betancourt and Thirumalai (Betancourt MR, Thirumalai D, 1999, Protein Sci 8:361-369) that is based on the Miyazawa and Jernigan table describes the hydrophilic interactions more appropriately. In this paper, we demonstrate how the use of this table, together with a new definition of MHC contact residues by which only residues that contribute exclusively to sequence specific binding are included, allows the development of an improved algorithm that can be applied to a wide range of MHC class I alleles.

Alleles↗
PubMed2006

Narrative review: Assessment of C-reactive protein in risk prediction for cardiovascular disease.

Some experts propose C-reactive protein (CRP) as a screening tool for prediction of cardiovascular disease (CVD). Many epidemiologic studies show positive associations between elevated CRP levels and incident CVD. Assessment of the value of new prognostic tests, however, must rely on understanding of test characteristics rather than on associations measured by relative risks. In the case of CRP, test characteristics must be judged in the context of currently available CVD risk prediction algorithms. In this review of literature published before January 2006, the authors describe what is known about the additional utility of CRP in risk prediction. They find no definitive evidence that, for most individuals, CRP adds substantial predictive value above that provided by risk estimation using traditional risk factors for CVD. Use of CRP may add to risk estimation in a limited subset of individuals who are at intermediate predicted risk according to the Framingham risk score. The authors propose that many questions still must be addressed before CRP is incorporated into risk prediction algorithms and before universal screening with CRP can be recommended.

Algorithms↗
PubMed2006

Seizure prediction: the long and winding road.

The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.

Algorithms↗
PubMed1994

Prediction and site-specific mutagenesis of residues in transmembrane alpha-helices of proton-pumping nicotinamide nucleotide transhydrogenases from Escherichia coli and bovine heart mitochondria.

Nicotinamide nucleotide transhydrogenase from bovine heart consists of a single polypeptide of 109 kD. The complete gene for this transhydrogenase was constructed, and the protein primary structure was determined from the cDNA. As compared to the previously published sequences of partially overlapping clones, three residues differed: Ala591 (previously Phe), Val777 (previously Glu), and Ala782 (previously Arg). The Escherichia coli transhydrogenase consists of an alpha subunit of 52 kD and a beta subunit of 48 kD. Alignment of the protein primary structure of the bovine trashydrogenase with that of the transhydrogenase from E. coli showed an identity of 52%, indicating similarly folded structures. Prediction of transmembrane-spanning alpha-helices, obtained by applying several prediction algorithms to the primary structures of the revised bovine heart and E. coli transhydrogenases, yielded a model containing 10 transmembrane alpha-helices in both transhydrogenases. In E. coli transhydrogenase, four predicted alpha-helices were located in the alpha subunit and six alpha-helices were located in the beta subunit. Various conserved amino acid residues of the E. coli transhydrogenase located in or close to predicted transmembrane alpha-helixes were replaced by site-specific mutagenesis. Conserved negatively charged residues in predicted transmembrane alpha-helices possibly participating in proton translocation were identified as beta Glu82 (Asp655 in the bovine enzyme) and beta Asp213 (asp787 in the bovine enzyme) located close to the predicted alpha-helices 7 and 9 of the beta subunit. beta Glu82 was replaced by Lys or Gln and beta Asp213 by Asn or His. However, the catalytic as well as the proton pumping activity was retained. In contrast, mutagenesis of the conserved beta His91 residue (His664 in the bovine enzyme) to Ser, Thr, and Cys gave an essentially inactive enzyme. Mutation of alpha His450 (corresponding to His481 in the bovine enzyme) to Thr greatly lowered catalytic activity without abolishing proton pumping. Since no other conserved acidic or basic residues were predicted in transmembrane alpha-helices regardless of the prediction algorithm used, proton translocation by transhydrogenase was concluded to involve a basic rather than an acidic residue. The only conserved cysteine residue, beta Cys260 (Cys834 in the bovine enzyme), located in the predicted alpha-helix 10 of the E. coli transhydrogenase, previously suggested to function as a redox-active dithiol, proved not to be essential, suggesting that redox-active dithiols do not play a role in the mechanism of transhydrogenase.

Algorithms↗
PubMed2006

An algorithm for predicting protein-protein interaction sites: Abnormally exposed amino acid residues and secondary structure elements.

Multiprotein systems mediate most regulatory processes in living organisms. Although the structures of the individual proteins are often defined, less is known of the structures of multiprotein systems. Computational methods for predicting interfaces, using evolutionary conservation and/or physicochemical data, have been developed. Here we consider the use of solvent accessibility, residue propensity, and hydrophobicity, in conjunction with secondary structure data, as prediction parameters. We analyze the influence of residue type and secondary structure on solvent accessibility and define a measure of "relative exposedness." Clustering abnormally high scoring residues provides a basis for predicting interaction sites. The analysis is extended to investigate abnormally exposed secondary structure elements, particularly beta-sheet strands. We show that surface-exposed beta-strands lacking protective features are more likely to be found at protein-protein interfaces, allowing us to create an algorithm with approximately 68% and approximately 75% accuracy in differentiating between interacting and edge strands in isolated beta-strands and beta-sheet strands, respectively. These methods of identifying abnormally exposed surface regions are combined in an algorithm, which, on a data set of 77 unbound and disjoint (single chain extracted from complex) structures, predicts 79% of the protein-protein interfaces correctly. If enzyme-inhibitor complexes, where the inhibitor mimics a nonprotein substrate, are excluded, the accuracy increases to 85%.

Algorithms↗
PubMed2005

Crystal packing effects on protein loops.

The effects of crystal packing on protein loop structures are examined by (1) a comparison of loops in proteins that have been crystallized in alternate packing arrangements, and (2) theoretical prediction of loops both with and without the inclusion of the crystal environment. Results show that in a minority of cases, loop geometries are dependent on crystal packing effects. Explicit representation of the crystal environment in a loop prediction algorithm can be used to model these effects and to reconstruct the structures, and relative energies, of a loop in alternative packing environments. By comparing prediction results with and without the inclusion of the crystal environment, the loop prediction algorithm can further be used to identify cases in which a crystal structure does not represent the most stable state of a loop in solution. We anticipate that this capability has implications for structural biology.

Algorithms↗