Structure Prediction Modeling Research Articles

DAMBE6: New Tools for Microbial Genomics, Phylogenetics, and Molecular Evolution.

DAMBE is a comprehensive software workbench for data analysis in molecular biology, phylogenetics, and evolution. Several important new functions have been added since version 5 of DAMBE: 1) comprehensive genomic profiling of translation initiation efficiency of different genes in different prokaryotic species, 2) a new index of translation elongation (ITE) that takes into account both tRNA-mediated selection and background mutation on codon–anticodon adaptation, 3) a new and accurate phylogenetic approach based on pairwise alignment only, which is useful for highly divergent sequences from which a reliable multiple sequence alignment is difficult to obtain. Many other functions have been updated and improved including PWM for motif characterization, Gibbs sampler for de novo motif discovery, hidden Markov models for protein secondary structure prediction, self-organizing map for nonlinear clustering of transcriptomic data, comprehensive sequence alignment, and phylogenetic functions. DAMBE features a graphic, user-friendly and intuitive interface, and is freely available from http://dambe.bio.uottawa.ca.

Theory and Modeling of RNA Structure and Interactions with Metal Ions and Small Molecules.

In addition to continuous rapid progress in RNA structure determination, probing, and biophysical studies, the past decade has seen remarkable advances in the development of a new generation of RNA folding theories and models. In this article, we review RNA structure prediction models and models for ion-RNA and ligand-RNA interactions. These new models are becoming increasingly important for a mechanistic understanding of RNA function and quantitative design of RNA nanotechnology. We focus on new methods for physics-based, knowledge-based, and experimental data-directed modeling for RNA structures and explore the new theories for the predictions of metal ion and ligand binding sites and metal ion-dependent RNA stabilities. The integration of these new methods with theories about the cellular environment effects in RNA folding, such as molecular crowding and cotranscriptional kinetic effects, may ultimately lead to an all-encompassing RNA folding model.

SEQUENCE SEGMENTATION USING JOINT RNN AND STRUCTURED PREDICTION MODELS.

We describe and analyze a simple and effective algorithm for sequence segmentation applied to speech processing tasks. We propose a neural architecture that is composed of two modules trained jointly: a recurrent neural network (RNN) module and a structured prediction model. The RNN outputs are considered as feature functions to the structured model. The overall model is trained with a structured loss function which can be designed to the given segmentation task. We demonstrate the effectiveness of our method by applying it to two simple tasks commonly used in phonetic studies: word segmentation and voice onset time segmentation. Results suggest the proposed model is superior to previous methods, obtaining state-of-the-art results on the tested datasets.

A Predictive Structural Model of the Primate Connectome

Anatomical connectivity imposes strong constraints on brain function, but there is no general agreement about principles that govern its organization. Based on extensive quantitative data, we tested the power of three factors to predict connections of the primate cerebral cortex: architectonic similarity (structural model), spatial proximity (distance model) and thickness similarity (thickness model). Architectonic similarity showed the strongest and most consistent influence on connection features. This parameter was strongly associated with the presence or absence of inter-areal connections and when integrated with spatial distance, the factor allowed predicting the existence of projections with very high accuracy. Moreover, architectonic similarity was strongly related to the laminar pattern of projection origins, and the absolute number of cortical connections of an area. By contrast, cortical thickness similarity and distance were not systematically related to connection features. These findings suggest that cortical architecture provides a general organizing principle for connections in the primate brain, providing further support for the well-corroborated structural model.

A Statistical Mechanical Model for Coiled-Coil Protein Structure Prediction

A Statistical Mechanical Model for Coiled-Coil Protein Structure Prediction

Max-Margin Heterogeneous Information Machine for RGB-D Action Recognition

We propose a novel approach, max-margin heterogeneous information machine (MMHIM), for human action recognition from RGB-D videos. MMHIM fuses heterogeneous RGB visual features and depth features, and learns effective action classifiers using the fused features. Rich heterogeneous visual and depth data are effectively compressed and projected to a learned shared space and independent private spaces, in order to reduce noise and capture useful information for recognition. Knowledge from various sources can then be shared with others in the learned space to learn cross-modal features. This guides the discovery of valuable information for recognition. To capture complex spatiotemporal structural relationships in visual and depth features, we represent both RGB and depth data in a matrix form. We formulate the recognition task as a low-rank bilinear model composed of row and column parameter matrices. The rank of the model parameter is minimized to build a low-rank classifier, which is beneficial for improving the generalization power. We also extend MMHIM to a structured prediction model that is capable of making structured outputs. Extensive experiments on a new RGB-D action dataset and two other public RGB-D action datasets show that our approaches achieve state-of-the-art results. Promising results are also shown if RGB or depth data are missing in training or testing procedure.

Protein Folding and Structure Prediction from the Ground Up II: AAWSEM for α/β Proteins.

The atomistic associative memory, water mediated, structure and energy model (AAWSEM) is an efficient coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using structural information derived from all-atom simulations of long segments of the protein. For α helical proteins, the accuracy of structure prediction using AAWSEM has been established previously. In this article, we examine the capability of AAWSEM to predict the structure of α/β proteins. We also elaborate on an iterative approach that uses the structures from a first round of AAWSEM simulation as fragment memories. This iterative scheme improves the quality of the structure prediction and makes the free energy profile more funneled toward native configurations. We explore the use of clustering analyses as a way of evaluating the confidence in various structure prediction models. Clustering using a local relative order parameter (mutual Q) of the predicted structural ensemble turns out to be optimal. The tightest cluster according to mutual Q generally has the most correctly folded structure. Since there is no bioinformatic input, AAWSEM amounts to an ab initio protein structure prediction method that combines the efficiency of coarse-grained simulations with the local structural accuracy that can be achieved from all-atom simulations.

Structural characterization of the α-mating factor prepro-peptide for secretion of recombinant proteins in Pichia pastoris

The methylotrophic yeast Pichia pastoris has been used extensively for expressing recombinant proteins because it combines the ease of genetic manipulation, the ability to provide complex posttranslational modifications and the capacity for efficient protein secretion. The most successful and commonly used secretion signal leader in Pichia pastoris has been the alpha mating factor (MATα) prepro secretion signal. However, limitations exist as some proteins cannot be secreted efficiently, leading to strategies to enhance secretion efficiency by modifying the secretion signal leader. Based on a Jpred secondary structure prediction and knob-socket modeling of tertiary structure, numerous deletions and duplications of the MATα prepro leader were engineered to evaluate the correlation between predicted secondary structure and the secretion level of the reporters horseradish peroxidase (HRP) and Candida antarctica lipase B. In addition, circular dichroism analyses were completed for the wild type and several mutant pro-peptides to evaluate actual differences in secondary structure. The results lead to a new model of MATα pro-peptide signal leader, which suggests that the N and C-termini of MATα pro-peptide need to be presented in a specific orientation for proper interaction with the cellular secretion machinery and for efficient protein secretion.

Predicting Molecular Crystal Properties from First Principles: Finite-Temperature Thermochemistry to NMR Crystallography.

Molecular crystals occur widely in pharmaceuticals, foods, explosives, organic semiconductors, and many other applications. Thanks to substantial progress in electronic structure modeling of molecular crystals, attention is now shifting from basic crystal structure prediction and lattice energy modeling toward the accurate prediction of experimentally observable properties at finite temperatures and pressures. This Account discusses how fragment-based electronic structure methods can be used to model a variety of experimentally relevant molecular crystal properties. First, it describes the coupling of fragment electronic structure models with quasi-harmonic techniques for modeling the thermal expansion of molecular crystals, and what effects this expansion has on thermochemical and mechanical properties. Excellent agreement with experiment is demonstrated for the molar volume, sublimation enthalpy, entropy, and free energy, and the bulk modulus of phase I carbon dioxide when large basis second-order Møller-Plesset perturbation theory (MP2) or coupled cluster theories (CCSD(T)) are used. In addition, physical insight is offered into how neglect of thermal expansion affects these properties. Zero-point vibrational motion leads to an appreciable expansion in the molar volume; in carbon dioxide, it accounts for around 30% of the overall volume expansion between the electronic structure energy minimum and the molar volume at the sublimation point. In addition, because thermal expansion typically weakens the intermolecular interactions, neglecting thermal expansion artificially stabilizes the solid and causes the sublimation enthalpy to be too large at higher temperatures. Thermal expansion also frequently weakens the lower-frequency lattice phonon modes; neglecting thermal expansion causes the entropy of sublimation to be overestimated. Interestingly, the sublimation free energy is less significantly affected by neglecting thermal expansion because the systematic errors in the enthalpy and entropy cancel somewhat. Second, because solid state nuclear magnetic resonance (NMR) plays an increasingly important role in molecular crystal studies, this Account discusses how fragment methods can be used to achieve higher-accuracy chemical shifts in molecular crystals. Whereas widely used plane wave density functional theory models are largely restricted to generalized gradient approximation (GGA) functionals like PBE in practice, fragment methods allow the routine use of hybrid density functionals with only modest increases in computational cost. In extensive molecular crystal benchmarks, hybrid functionals like PBE0 predict chemical shifts with 20-30% higher accuracy than GGAs, particularly for 1H, 13C, and 15N nuclei. Due to their higher sensitivity to polarization effects, 17O chemical shifts prove slightly harder to predict with fragment methods. Nevertheless, the fragment model results are still competitive with those from GIPAW. The improved accuracy achievable with fragment approaches and hybrid density functionals increases discrimination between different potential assignments of individual shifts or crystal structures, which is critical in NMR crystallography applications. This higher accuracy and greater discrimination are highlighted in application to the solid state NMR of different acetaminophen and testosterone crystal forms.

A Patient-Specific Model for Predicting Tibia Soft Tissue Insertions From Bony Outlines Using a Spatial Structure Supervised Learning Framework

Recreating the natural anatomy in ligament reconstruction is crucial to fully restore the knee joint function and reduce impingement on iatrogenic injury to adjacent structures, yet is subject to the difficulties in locating ligament and other associated soft tissues insertion sites intraoperatively and the high interperson morphological variability cross patients. In this study, we present a new quantitative analysis method capable of achieving personalized identification of cruciate ligament and soft tissue insertions. We craft patient-specific features of tibia outline that can be accurately and reliably measured from CT images. In addition, we propose a supervised structure learning and prediction model with special interdimensional and response structure regularization terms to capture relationship between the spatial arrangement of soft tissue insertions and the patient-specific features extracted from the tibia outlines. In the experiment, the proposed model outperforms baseline models and provides an accurate and accessible approach that can be used as the first and the most critical step to achieve personalized surgical planning in cruciate ligament reconstruction.

A predictive structural model for gifted students' performance: A study based on intelligence and its implicit theories

The current study aimed at identifying to what extent gifted students' academic performance differs in light of their emotional, social, analytical, creative, practical, and implicit intelligence, and to explore which of these are more effective for differentiating students' performance. This study was also an attempt to determine the direct effects of different kinds of intelligence on student performance, and to generate a structural model that could explain the relationship among different kinds of intelligence, students' implicit theories of intelligence, and student performance. The 174 participants were randomly chosen from primary school students who participated in summer enrichment programs. An emotional intelligence scale, a social intelligence scale, the analytical, creative, and practical intelligence tests of the Aurora Battery, an implicit intelligence scale, and performance assessment inventory were administered. A cluster analysis revealed that there were three profiles for students. The structural equation model confirmed that the predictor factors had positive and significant effects on performance. These predictor variables accounted for (68%) of the percent of the variance in performance. Ultimately, the factors affecting student performance were, in order of decreasing magnitude, emotional intelligence, analytical intelligence, practical intelligence, creative intelligence, implicit intelligence, and social intelligence. Furthermore, there were strong effects of implicit theory of intelligence on the different kinds of intelligence.

Folding of Protein Ions in the Gas Phase after Cation-to-Anion Proton-Transfer Reactions.

The structure and folding of a protein in solution depends on noncovalent interactions within the protein and those with surrounding ions and molecules. Decoupling these interactions in solution is challenging, which has hindered the development of accurate physics-based models for structure prediction. Investigations of proteins in the gas phase can be used to selectively decouple factors affecting the structures of proteins. Here, we use cation-to-anion proton-transfer reactions (CAPTR) to reduce the charge states of denatured ubiquitin ions in the gas phase, and ion mobility to probe their structures. In CAPTR, a precursor charge state is selected (P) and reacted with monoanions to generate charge-reduced product ions (C). Following each CAPTR event, denatured ubiquitin ions (13+ to 6+) yield products that rapidly isomerize to structures that have smaller collision cross sections (Ω). The Ω values of CAPTR product ions depend strongly on C and very weakly on P. Pre- and post-CAPTR activation was then used to probe the potential-energy surfaces of the precursor and product ions, respectively. Post-CAPTR activation showed that ions of different P fold differently and populate different regions of the potential-energy surface of that ion. Finally, pre-CAPTR activation showed that the structures of protein ions can be indirectly investigated using ion mobility of their CAPTR product ions, even for subtle structural differences that are not apparent from ion mobility characterization of the activated precursor ions. More generally, these results show that CAPTR strongly complements existing techniques for characterizing the structures and dynamics of biological molecules in the gas phase.

Predicción de la satisfacción y el rendimiento escolar: el compromiso como mediador

Introducción. Los resultados académicos en la escuela, tanto el rendimiento como el sentimiento de satisfacción con ella, han sido estudiados desde diversas perspectivas, dadas sus implicaciones en el diseño de políticas educativas. El objetivo de este estudio es poner a prueba dos modelos estructurales predictivos, uno del rendimiento académico y el otro sobre la satisfacción con la escuela. Método. Estos modelos valoran importantes variables del contexto académico y posicionan el compromiso escolar del estudiante como un mediador de los efectos del contexto sobre las variables resultado. Para poner a prueba estos modelos se ha contado con una muestra de 1164 estudiantes de enseñanza media dominicanos. Los modelos estructurales se estimaron en EQS 5.7. Los resultados empíricos dan apoyo a ambos modelos predictivos; los productos obtenidos apuntan a que el compromiso escolar es un relevante intermediario de los efectos de las variables contextuales y del alumnado en sus logros académicos. Conclusiones. El fruto del trabajo da pie a concluir que hay que replantear el contexto escolar para primar la calidad de la instrucción, las relaciones afectivas entre los profesores y los alumnos, y un clima socioemocional que favorezca el aprendizaje orientado a la maestría. Así, se discuten los resultados obtenidos a la luz de la evidencia anterior y se sugieren también las limitaciones derivadas del carácter transversal del estudio así como las direcciones de cara a investigaciones futuras.

Addressing the Role of Conformational Diversity in Protein Structure Prediction.

Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.

The Effects of Political Cynicism and National Siege Mentality on the Internalization of an Anti-European Sentiment

<p>This paper aims to provide an insight into the political-psychological understanding of an attitudinal construct labeled anti-European sentiment. A structural equation model for prediction was developed and evaluated by using full information mximum likelihood estimates obtained from LISREL 8.52 computer program. Assumption was that both political cynicism and national siege mentality would have an effect on anti-European sentiment. The data reported here were obtained by standard survey methods on the sample of adult population in Croatia (N=533). Confirmatory factor analysis (CFA) was performed to explore factorial and construct validity of the measures used in this research. CFA yielded unidimensional construct measurements with acceptable fit indices. Structural model indicated that exogenous variables (political cynicism, national siege mentality) have significant effects on the anti-European sentiment used as an endogenous (dependent) variable. Goodness-of-fit indices suggested acceptable fit of the model (RMSEA=0.07, CFI=0.97, NNFI=0.97, SRMR=0.05). Given the amount of variance of anti-European sentiment, it was showen that political cynicism and national siege mentality have strong predictive validity for anti-European sentiment (43 percent of the variance was explained by the structural model). In order to explain the interactions among the variables investigated, the author proposed the distrust-threat model of political hostility.</p>

Structure Prediction and 3D Modeling of Single Stranded DNA from Sequence for Aptamer-Based Biosensors

Aptamers consist of short oligonucleotides that bind specific targets. They provide advantages over antibodies along different lines, including robustness, low cost, and reusability. Their relatively simple chemical structure allows the insertion of electrochemical or fluorescent reporter molecules as well as surface-binding agents in specific locations, which have been recently exploited for the development of aptamer-based biosensors and a number of direct detection strategies. Mainstream use of these devices, however, still requires significant improvements in optimization for consistency and reproducibility. DNA aptamers are more stable than their RNA counterparts for biomedical applications but have the disadvantage of lacking the wide array of computational tools for structural prediction currently available for single stranded RNA. Here, we present the first approach to predict from sequence the three-dimensional structures of single stranded DNA required for aptamer applications. The approach consists of a pipeline that integrates sequentially building ssDNA secondary structure from sequence, constructing equivalent 3D ssRNA models, transforming the 3D ssRNA models into ssDNA 3D structures, and refining the resulting ssDNA 3D structures. Through this pipeline, our approach faithfully predicts the representative structures available in the Nucleic Acid Database and Protein Data Bank databases. Our results, thus, open up a much-needed avenue for integrating DNA in the computational analysis and design of aptamer-based biosensors.

Structure prediction of methyoxy-polybrominated diphenyl ethers (MeO-PBDEs) through GC–MS analysis of their corresponding PBDEs

It is hard to quantify the trace pollutants in the environment without the corresponding reference standards. Structure identifications of unknown organic pollutants are thus of great importance in environmental analysis. As for polybrominated diphenyl ethers (PBDE) with one substituent of methoxyl group, there are 837 congeners, but only 32 standards are commercially available. In this work, an effective method based on gas chromatography coupled with mass spectrometry (GC–MS) was proposed to predict the potential structures of methoxylated polybrominated diphenyl ethers (MeO-PBDEs). The mass fragmentation pattern using SIM mode not only provided the useful information on the substitution position of methoxyl group, the number of Br atoms, but also guaranteed the high sensitivity for trace analysis. Br distribution patterns of the unknown MeO-PBDEs were revealed by a linear regression model with dummy variables which described the retention time relationship between MeO-PBDEs and the corresponding PBDEs on different types of GC columns. This method was successfully used to identify three new MeO-PBDEs metabolites of BDE-28 as 4-MeO-BDE-22, 4′-MeO-BDE-25 and 4-MeO-BDE-31 in the pumpkins. Therefore, the newly developed structure prediction model based on GC–MS behavior is helpful in the evaluation of unknown PBDE metabolites in the environment.

Transcriptome-wide interrogation of RNA secondary structure in living cells with icSHAPE.

icSHAPE (in vivo click selective 2-hydroxyl acylation and profiling experiment) captures RNA secondary structure at a transcriptome-wide level by measuring nucleotide flexibility at base resolution. Living cells are treated with the icSHAPE chemical NAI-N3 followed by selective chemical enrichment of NAI-N3-modified RNA, which provides an improved signal-to-noise ratio compared with similar methods leveraging deep sequencing. Purified RNA is then reverse-transcribed to produce cDNA, with SHAPE-modified bases leading to truncated cDNA. After deep sequencing of cDNA, computational analysis yields flexibility scores for every base across the starting RNA population. The entire experimental procedure can be completed in ∼5 d, and the sequencing and bioinformatics data analysis take an additional 4-5 d with no extensive computational skills required. Comparing in vivo and in vitro icSHAPE measurements can reveal in vivo RNA-binding protein imprints or facilitate the dissection of RNA post-transcriptional modifications. icSHAPE reactivities can additionally be used to constrain and improve RNA secondary structure prediction models.

Monocular Road Detection Using Structured Random Forest

Road detection is a key task for autonomous land vehicles. Monocular vision-based road-detection algorithms are mostly based on machine learning approaches and are usually cast as classification problems. However, the pixel-wise classifiers are faced with the ambiguity caused by changes in road appearance, illumination and weather. An effective way to reduce the ambiguity is to model the contextual information with structured learning and prediction. Currently, the widely used structured prediction model in road detection is the Markov random field or conditional random field. However, the random field-based methods require additional complex optimization after pixel-wise classification, making them unsuitable for real-time applications. In this paper, we present a structured random forest-based road-detection algorithm which is capable of modelling the contextual information efficiently. By mapping the structured label space to a discrete label space, the test function of each split node can be trained in a similar way to that of the classical random forests. Structured random forests make use of the contextual information of image patches as well as the structural information of the labels to get more consistent results. Besides this benefit, by predicting a batch of pixels in a single classification, the structured random forest-based road detection can be much more efficient than the conventional pixel-wise random forest. Experimental results tested on the KITTI-ROAD dataset and data collected in typical unstructured environments show that structured random forest-based road detection outperforms the classical pixel-wise random forest both in accuracy and efficiency.