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PubMed2004

Ultrarapid mixing experiments shed new light on the characteristics of the initial conformational ensemble during the folding of ribonuclease A.

The earliest folding events in single-tryptophan mutants of RNase A were investigated by fluorescence measurements by using a combination of stopped-flow and continuous-flow mixing experiments covering the time range from 70 micros to 10 s. An ultrarapid double-jump mixing protocol was used to study refolding from an unfolded ensemble containing only native proline isomers. The continuous-flow measurements revealed a series of kinetic events on the submillisecond time scale that account for the burst-phase signal observed in previous stopped-flow experiments. An initial increase in fluorescence within the 70-micros dead time of the continuous-flow experiment is consistent with a relatively nonspecific collapse of the polypeptide chain whereas a subsequent decrease in fluorescence with a time constant of approximately 80 micros is indicative of a more specific structural event. These rapid conformational changes are not observed if RNase A is allowed to equilibrate under denaturing conditions, resulting in formation of nonnative proline isomers. Thus, contrary to previous expectations, the isomerization state of proline peptide bonds can have a major impact on the structural events during early stages of folding.

Kinetics↗

PubMed2005

Assembly of the bacteriophage T4 primosome: single-molecule and ensemble studies.

Within replisomes for DNA replication, the primosome is responsible for unwinding double-stranded DNA and synthesizing RNA primers. Assembly of the bacteriophage T4 primosome on individual molecules of ssDNA or forked DNA (fDNA) has been studied by using FRET microscopy. On either DNA substrate, an ordered process of assembly begins with tight 1:1 binding of ssDNA-binding protein (gp32) and helicase-loading protein (gp59) to the DNA. Magnesium adenosine 5'-O-(3-thiotriphosphate) (MgATPgammaS) mediates the weak binding of helicase (gp41) to DNA coated with gp32 and gp59, whereas MgATP induces gp32 and gp59 to dissociate, leaving gp41 bound to the DNA. Finally, primase (gp61) binds to the gp41.DNA complex. Ensemble studies were used to determine protein stoichiometries and binding constants. These single-molecule studies provide an unambiguous description of the pathway for assembly of the primosome on the lagging strand of DNA at a replication fork.

Adenosine Triphosphate↗

PubMed2002

Interaction of dihydrofolate reductase with methotrexate: ensemble and single-molecule kinetics.

The thermodynamics and kinetics of the interaction of dihydrofolate reductase (DHFR) with methotrexate have been studied by using fluorescence, stopped-flow, and single-molecule methods. DHFR was modified to permit the covalent addition of a fluorescent molecule, Alexa 488, and a biotin at the N terminus of the molecule. The fluorescent molecule was placed on a protein loop that closes over methotrexate when binding occurs, thus causing a quenching of the fluorescence. The biotin was used to attach the enzyme in an active form to a glass surface for single-molecule studies. The equilibrium dissociation constant for the binding of methotrexate to the enzyme is 9.5 nM. The stopped-flow studies revealed that methotrexate binds to two different conformations of the enzyme, and the association and dissociation rate constants were determined. The single-molecule investigation revealed a conformational change in the enzyme-methotrexate complex that was not observed in the stopped-flow studies. The ensemble averaged rate constants for this conformation change in both directions is about 2-4 s(-1) and is attributed to the opening and closing of the enzyme loop over the bound methotrexate. Thus the mechanism of methotrexate binding to DHFR involves multiple steps and protein conformational changes.

Base Sequence↗

PubMed1997

Ensemble quantum computing by NMR spectroscopy.

A quantum computer (QC) can operate in parallel on all its possible inputs at once, but the amount of information that can be extracted from the result is limited by the phenomenon of wave function collapse. We present a new computational model, which differs from a QC only in that the result of a measurement is the expectation value of the observable, rather than a random eigenvalue thereof. Such an expectation value QC can solve nondeterministic polynomial-time complete problems in polynomial time. This observation is significant precisely because the computational model can be realized, to a certain extent, by NMR spectroscopy on macroscopic ensembles of quantum spins, namely molecules in a test tube. This is made possible by identifying a manifold of statistical spin states, called pseudo-pure states, the mathematical description of which is isomorphic to that of an isolated spin system. The result is a novel NMR computer that can be programmed much like a QC, but in other respects more closely resembles a DNA computer. Most notably, when applied to intractable combinatorial problems, an NMR computer can use an amount of sample, rather than time, which grows exponentially with the size of the problem. Although NMR computers will be limited by current technology to exhaustive searches over only 15 to 20 bits, searches over as much as 50 bits are in principle possible, and more advanced algorithms could greatly extend the range of applicability of such machines.

Chemical Phenomena↗

PubMed1989

Thermal function of a clothing ensemble during work: dependency on inner clothing layer fit.

A tight-fitting crewneck undergarment (U) and a loose-fitting shirt (S) were studied as part of a commonly used clothing ensemble (Itot = 0.22 m2 K W-1). Ten clothed male subjects performed standardized packing work (VO2 = 0.761 min-1) at three climatic conditions, 20 degrees C and Va = 0.45 m s-1 (0-30 min), at 5 degrees C and Va = 0.39 m s-1 (30-60 min) and at 5 degrees C and Va = 1.23 m s-1 (76-90 min). From 60-75 min the subjects rested at 20 degrees C. The physiological and subjective responses varied with the environment from slightly warm to cool. U resulted in warmer responses than S: torso and upper arm skin temperatures were higher at both 5 degrees C and 20 degrees C, evaporation rate was higher at 20 degrees C, mean skin temperature was higher during work at 20 degrees C, sweating tended to begin earlier and skin wettedness to be higher with U than with S. No differences were observed in core temperature, heart rates, and subjective thermal evaluations. It was concluded that a tight-fitting inner layer (U) compared to a loose-fitting one (S) allows for less cooling of the skin in both a cool and a slightly warm environment.

Adult↗

PubMed2000

Systemic properties of ensembles of metabolic networks: application of graphical and statistical methods to simple unbranched pathways.

MOTIVATION: Mathematical models are the only realistic method for representing the integrated dynamic behavior of complex biochemical networks. However, it is difficult to obtain a consistent set of values for the parameters that characterize such a model. Even when a set of parameter values exists, the accuracy of the individual values is questionable. Therefore, we were motivated to explore statistical techniques for analyzing the properties of a given model when knowledge of the actual parameter values is lacking. RESULTS: The graphical and statistical methods presented in the previous paper are applied here to simple unbranched biosynthetic pathways subject to control by feedback inhibition. We represent these pathways within a canonical nonlinear formalism that provides a regular structure that is convenient for randomly sampling the parameter space. After constructing a large ensemble of randomly generated sets of parameter values, the structural and behavioral properties of the model with these parameter sets are examined statistically and classified. The results of our analysis demonstrate that certain properties of these systems are strongly correlated, thereby revealing aspects of organization that are highly probable independent of selection. Finally, we show how specification of a given behavior affects the distribution of acceptable parameter values.

Amino Acids↗

PubMed2025

Characterizing the regulatory logic of transcriptional control at the DNA sequence level by ensembles of thermodynamic models.

MOTIVATION: Understanding how the genome encodes the regulatory logic of transcription is a main challenge of the post-genomic era, and can be overcome with the aid of customized computational tools. RESULTS: We report an automated framework for analyzing an ensemble of fits to data of a thermodynamics-based sequence-level model for transcriptional regulation. The fits are clustered accordingly with their intrinsic regulatory logic. A multiscale analysis enables visualization of quantitative features resulting from the deconvolution of the regulatory profile provided by multiple transcription factors interacting with the locus of a gene. Quantitative experimental data on reporters driven by the whole locus of the even-skipped gene in the blastoderm of Drosophila embryos was used for validating our approach. A few clusters of highly active DNA binding sites within the enhancers collectively modulate even-skipped gene transcription. Analysis of variable enhancers' length shows the importance of bound protein-protein interactions for transcriptional regulation. The interplay between activation and quenching enables function conservation of enhancers despite length variations. AVAILABILITY AND IMPLEMENTATION: The transcription factor level data used for performing the reported study is accessible in the input files in Zenodo and GitHub as well the full code. Additional data from formerly FlyEx database will be available under request.

Thermodynamics↗

PubMed2026

Predicting enhancer-promoter interactions using a stacking-based ensemble strategy.

MOTIVATION: Enhancer-promoter interactions (EPIs) are essential for gene regulation and disease progression. Recent studies have shown that distal enhancers can regulate target genes through interactions with nearby promoters, providing important insights into transcriptional regulation mechanisms. Although high-throughput experimental techniques have enabled large-scale identification of EPIs, these methods are often costly and time-consuming. In addition, existing computational approaches still face challenges in effectively integrating heterogeneous feature representations from different cell lines. RESULTS: We propose a stacked ensemble framework for EPI prediction that integrates feature representations from diverse cell line datasets using multiple machine learning algorithms. The extracted complementary patterns are further combined by an XGBoost classifier to improve robustness against overfitting. Experiments on six independent datasets show that the proposed method achieves superior accuracy and generalization compared with existing EPI prediction models, with an average AUROC of 0.909 while maintaining computational efficiency. AVAILABILITY: The source code and its archived release are available at GitHub and Zenodo. The Zenodo archive provides a versioned snapshot of the repository: https://zenodo.org/records/19952998.

Promoter Regions, Genetic↗

PubMed2005

Ensemble dependence model for classification and prediction of cancer and normal gene expression data.

MOTIVATION: DNA microarray technologies make it possible to simultaneously monitor thousands of genes' expression levels. A topic of great interest is to study the different expression profiles between microarray samples from cancer patients and normal subjects, by classifying them at gene expression levels. Currently, various clustering methods have been proposed in the literature to classify cancer and normal samples based on microarray data, and they are predominantly data-driven approaches. In this paper, we propose an alternative approach, a model-driven approach, which can reveal the relationship between the global gene expression profile and the subject's health status, and thus is promising in predicting the early development of cancer. RESULTS: In this work, we propose an ensemble dependence model, aimed at exploring the group dependence relationship of gene clusters. Under the framework of hypothesis-testing, we employ genes' dependence relationship as a feature to model and classify cancer and normal samples. The proposed classification scheme is applied to several real cancer datasets, including cDNA, Affymetrix microarray and proteomic data. It is noted that the proposed method yields very promising performance. We further investigate the eigenvalue pattern of the proposed method, and we discover different patterns between cancer and normal samples. Moreover, the transition between cancer and normal patterns suggests that the eigenvalue pattern of the proposed models may have potential to predict the early stage of cancer development. In addition, we examine the effects of possible model mismatch on the proposed scheme.

Algorithms↗

PubMed2005

Proteomic mass spectra classification using decision tree based ensemble methods.

MOTIVATION: Modern mass spectrometry allows the determination of proteomic fingerprints of body fluids like serum, saliva or urine. These measurements can be used in many medical applications in order to diagnose the current state or predict the evolution of a disease. Recent developments in machine learning allow one to exploit such datasets, characterized by small numbers of very high-dimensional samples. RESULTS: We propose a systematic approach based on decision tree ensemble methods, which is used to automatically determine proteomic biomarkers and predictive models. The approach is validated on two datasets of surface-enhanced laser desorption/ionization time of flight measurements, for the diagnosis of rheumatoid arthritis and inflammatory bowel diseases. The results suggest that the methodology can handle a broad class of similar problems.

Algorithms↗

PubMed2006

An ensemble of K-local hyperplanes for predicting protein-protein interactions.

Prediction of protein-protein interaction is a difficult and important problem in biology. In this paper, we propose a new method based on an ensemble of K-local hyperplane distance nearest neighbor (HKNN) classifiers, where each HKNN is trained using a different physicochemical property of the amino acids. Moreover, we propose a new encoding technique that combines the amino acid indices together with the 2-Grams amino acid composition. A fusion of HKNN classifiers combined with the 'Sum rule' enables us to obtain an improvement over other state-of-the-art methods. The approach is demonstrated by building a learning system based on experimentally validated protein-protein interactions in human gastric bacterium Helicobacter pylori and in Human dataset.

Algorithms↗

PubMed2005

Survival ensembles.

We propose a unified and flexible framework for ensemble learning in the presence of censoring. For right-censored data, we introduce a random forest algorithm and a generic gradient boosting algorithm for the construction of prognostic and diagnostic models. The methodology is utilized for predicting the survival time of patients suffering from acute myeloid leukemia based on clinical and genetic covariates. Furthermore, we compare the diagnostic capabilities of the proposed censored data random forest and boosting methods, applied to the recurrence-free survival time of node-positive breast cancer patients, with previously published findings.

Algorithms↗

PubMed1992

The ensemble reactions of hydroxyl radical exhibit no specificity for primary or secondary structure of DNA.

Hydroxyl radical reacts at numerous sites within nucleic acids to form a wide range of derivatives yet the conformational specificity of only one of these processes, direct strand fragmentation, has received much attention to date. Since the deleterious effects of this radical are not likely limited to strand fragmentation in vivo, this report examined the conformational specificity expressed in a more general manner. For this, modification of DNA was induced by the hydroxyl radical generating system of H2O2 and Fe-EDTA. The ensemble rate of oxidation (nucleobase + deoxyribose backbone) was determined from the overall consumption of a series of oligonucleotides that were designed to model random coils and double helixes containing complementary and noncomplementary base pairing. The resulting pseudo-first order rate constants derived from this model system were relatively unaffected by nucleotide sequence or secondary structure and varied from only 0.022 to 0.048 s-1. Consequently, the indiscriminant nature of hydroxyl radical appears to persist beyond strand fragmentation to include nucleobase oxidation as well.

Base Composition↗

PubMed2002

HOX Pro DB: the functional genomics of hox ensembles.

The HOX Pro database contains information about the organization, function and evolution of gene ensembles, notably the homeobox-containing genes. It is now clear that a subset of genes containing the homeobox motif play key roles in the orchestration of genes which control embryonic patterning, morphogenesis, cell differentiation and malignant transformation. The HOX Pro contains a broad spectrum of information including images, diagrams and animations. Currently this amounts to approximately 700 HTML pages together with 400 images which contain information on 200 groups of genes and 90 promoters, in turn linked to maps of 13 HOX clusters and nine genetic networks. There are about 700 sequences of individual hox-genes of animals classified in approximately 200 homologous or paralogous groups. Graphical representation of HOX clusters and Hox-based networks is accomplished by means of flow and 3D diagrams, JavaScript animations and Java applets. The HOX Pro now includes sections presenting data mining and data simulation issues. The DB is located at http://www.iephb.nw.ru/hoxpro.

Animals↗

PubMed2003

Ensemble and single-molecule fluorescence spectroscopic study of the binding modes of the bis-benzimidazole derivative Hoechst 33258 with DNA.

Ensemble and single-molecule fluorescence measurements of 2'-(4-hydroxyphenyl)-5-[5-(4-methylpiperazine-1-yl) benzimidazo-2-yl]-benzimidazole (H-258)- calf thymus (CT) DNA complexes at various [H-258]/[DNA bp] ratios were performed to elucidate the binding of H-258 with DNA. Upon binding to double-stranded CT DNA (CT ds DNA) at a [H-258]/[DNA bp] ratio of 0.05 the relative fluorescence quantum yield, Phi(f), of H-258 increases from 0.02 to 0.58. The fluorescence decay can be fitted almost by a mono-exponential model with a lifetime of approximately 3.6 ns. This indicates that H-258 binds almost quantitatively in the minor groove of DNA at low [H-258]/[DNA bp] ratios. With increasing [H-258]/[DNA bp] ratios, e.g. 0.15 and 0.20, the fluorescence quantum yield of H-258 decreases to 0.28 and 0.19, respectively. Fitting of the fluorescence decays measured for higher [H-258]/[DNA bp] ratios reveals the presence of additional shorter fluorescence lifetime components in the range of 0.5-2.0 ns. Our results suggest that H-258 partially intercalates in G:C sequences at higher [H-258]/[DNA bp] ratios reflected by a lifetime component of 1.5-2 ns. In addition, stacking or adsorption of H-258 molecules on DNA occurs at higher [H-258]/[DNA bp] ratios. These molecules exhibit a short fluorescence lifetime of approximately 500 ps and are more exposed to the aqueous environment. Fluorescence transients of the intensity and lifetime of single H-258 CT ds DNA demonstrate that weakly (unspecific) bound H-258 molecules exhibit a shorter fluorescence lifetime and a strongly reduced photostability.

Animals↗

PubMed2004

Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling.

Current applications of microarrays focus on precise classification or discovery of biological types, for example tumor versus normal phenotypes in cancer research. Several challenging scientific tasks in the post-genomic epoch, like hunting for the genes underlying complex diseases from genome-wide gene expression profiles and thereby building the corresponding gene networks, are largely overlooked because of the lack of an efficient analysis approach. We have thus developed an innovative ensemble decision approach, which can efficiently perform multiple gene mining tasks. An application of this approach to analyze two publicly available data sets (colon data and leukemia data) identified 20 highly significant colon cancer genes and 23 highly significant molecular signatures for refining the acute leukemia phenotype, most of which have been verified either by biological experiments or by alternative analysis approaches. Furthermore, the globally optimal gene subsets identified by the novel approach have so far achieved the highest accuracy for classification of colon cancer tissue types. Establishment of this analysis strategy has offered the promise of advancing microarray technology as a means of deciphering the involved genetic complexities of complex diseases.

Acute Disease↗

PubMed1993

Recursive ensemble mutagenesis.

We have developed a generally applicable experimental procedure to find functional proteins that are many mutational steps from wild type. Optimization algorithms, which are typically used to search for solutions to certain combinatorial problems, have been adapted to the problem of searching the 'sequence space' of proteins. Many of the steps normally performed by a digital computer are embodied in this new molecular genetics technique, termed recursive ensemble mutagenesis (REM). REM uses information gained from previous iterations of combinatorial cassette mutagenesis (CCM) to search sequence space more efficiently. We have used REM to simultaneously mutate six amino acid residues in a model protein. As compared to conventional CCM, one iteration of REM yielded a 30-fold increase in the frequency of 'positive' mutants. Since a multiplicative factor of similar magnitude is expected for the mutagenesis of additional sets of six residues, performing REM on 18 sites is expected to yield an exponential (30,000-fold) increase in the throughput of positive mutants as compared to random [NN(G,C)]18 mutagenesis.

Amino Acid Sequence↗

PubMed1995

An approach to protein homology modelling based on an ensemble of NMR structures: application to the Sox-5 HMG-box protein.

A new approach has been developed to reduce multiple protein structures obtained from NMR structure analysis to a smaller number of representative structures which still reflect the structural diversity of the data sets. The method, based on the clustering of similar structures, has been tested in the homology model building of the structure of Sox-5, a sequence-specific DNA-binding protein belonging to the high mobility group (HMG) nuclear proteins family. Sox (SRY box) genes are the autosomal genes related to the sex-determining SRY, Y chromosomal gene. The Sox-5 protein, encoded by one of the SRY-related genes, displays a 29% sequence identity with the HMG1 B-box domain whose structure, determined previously by NMR, has been used in our study to predict the structure of Sox-5. Two independent ensembles of HMG1 structures, each represented by closely related coordinate sets, were used. Nine representative structures for HMG1 were subsequently selected as starting points for the modelling of Sox-5. The model of the protein shows close similarity to the HMG1 fold, with differences at the secondary structure level located mainly in alpha-helices 1 and 3. A left-handed, three residue per turn polyproline II helix, forming a conserved polyproline II/alpha-helix supersecondary motif, was identified in the N-terminal region of Sox-5 and other HMG boxes.

Algorithms↗