Contact Propensities Research Articles

Predicting residue–residue contacts using random forest models

Protein residue-residue contact prediction can be useful in predicting protein 3D structures. Current algorithms for such a purpose leave room for improvement. We develop ProC_S3, a set of Random Forest algorithm-based models, for predicting residue-residue contact maps. The models are constructed based on a collection of 1490 non-redundant, high-resolution protein structures using >1280 sequence-based features. A new amino acid residue contact propensity matrix and a new set of seven amino acid groups based on contact preference are developed and used in ProC_S3. ProC_S3 delivers a 3-fold cross-validated accuracy of 26.9% with coverage of 4.7% for top L/5 predictions (L is the number of residues in a protein) of long-range contacts (sequence separation ≥24). Further benchmark tests deliver an accuracy of 29.7% and coverage of 5.6% for an independent set of 329 proteins. In the recently completed Ninth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9), ProC_S3 is ranked as No. 1, No. 3, and No. 2 accuracies in the top L/5, L/10 and best 5 predictions of long-range contacts, respectively, among 18 automatic prediction servers. http://www.abl.ku.edu/proc/proc_s3.html. jwfang@ku.edu Supplementary data are available at Bioinformatics online.

Explaining Cross-Country Differences in Survey Contact Rates: Application of Decomposition Methods

SummarySurvey contact rates differ across countries. However, to date the reasons for these cross-country differences have not been identified. Differences can be due to differences in survey characteristics, differences in population characteristics and differences in processes, i.e. in the association between the first and second reasons and contact propensity. The paper investigates correlates of contact by decomposing cross-country differences in predicted mean contact propensities into these three components. The findings explain which fieldwork strategies and population characteristics are related to cross-country differences in contact rates. The analyses distinguish factors explaining within-country contact propensity from factors explaining cross-country differences. We demonstrate the suitability of decomposition methods for analysing group differences in survey outcomes.

Micro-, nano- and hierarchical structures for superhydrophobicity, self-cleaning and low adhesion

Superhydrophobic surfaces exhibit extreme water-repellent properties. These surfaces with high contact angle and low contact angle hysteresis also exhibit a self-cleaning effect and low drag for fluid flow. Certain plant leaves, such as lotus leaves, are known to be superhydrophobic and self-cleaning due to the hierarchical roughness of their leaf surfaces. The self-cleaning phenomenon is widely known as the 'lotus effect'. Superhydrophobic and self-cleaning surfaces can be produced by using roughness combined with hydrophobic coatings. In this paper, the effect of micro- and nanopatterned polymers on hydrophobicity is reviewed. Silicon surfaces patterned with pillars and deposited with a hydrophobic coating were studied to demonstrate how the effects of pitch value, droplet size and impact velocity influence the transition from a composite state to a wetted state. In order to fabricate hierarchical structures, a low-cost and flexible technique that involves replication of microstructures and self-assembly of hydrophobic waxes is described. The influence of micro-, nano- and hierarchical structures on superhydrophobicity is discussed by the investigation of static contact angle, contact angle hysteresis, droplet evaporation and propensity for air pocket formation. In addition, their influence on adhesive force as well as efficiency of self-cleaning is discussed.

Multiple Contact Network Is a Key Determinant to Protein Folding Rates

Understanding the relationship between amino acid sequences and folding rates of proteins is an important task in computational and molecular biology. It has been shown that topological parameters, contact order, long-range order, and total contact distance relate well with protein folding rates. In this work, we have systematically analyzed the influence of amino acid residues that form multiple contacts in protein structures to folding rates of proteins. We observed an inverse relationship between the number of residues with multiple contacts and protein folding rates. Further analysis indicates that multiple contacts are influenced by hydrophobic residues, whereas the role is minimal between the residues that are capable of forming hydrogen bonds. The propensity of multiple contacts forming residues showed that aromatic and hydrophobic residues are dominant in two-state proteins, whereas the polar residues Ser and Thr are also preferred ones in three-state proteins. In addition, multiple contact forming residues are interconnected with each other through contact networks in protein structures. The comparison between slow and fast folding proteins demonstrated the presence of more multiple contact forming residues in slow folding proteins with a limit of 4-6 contacts/residue. These results have been reflected in amino acid sequences in the form of short-, medium-, and long-range contacts, which could discriminate slow and fast folding proteins with an accuracy of 96% using a 5-fold cross-validation method.

Fabrication and characterization of the hierarchical structure for superhydrophobicity and self-cleaning

Hierarchical roughness is beneficial for the superhydrophobicity. Surfaces with microstructure, nanostructure and hierarchical structure were fabricated by replication of micropattern and self-assembly of hydrophobic alkanes. The fabrication technique used is a low-cost two-step process, which provides flexibility in fabrication of variety of hierarchical structures. Fabricated structures and surface chemistry mimic the hierarchical surfaces of superhydrophobic and self-cleaning plant surfaces. The influence of structure on the superhydrophobicity at different length scales is demonstrated by investigation of contact angle, contact angle hysteresis, droplet evaporation and propensity of the air pocket formation as well as adhesive forces.

Predicting helix–helix interactions from residue contacts in membrane proteins

Motivation: Helix–helix interactions play a critical role in the structure assembly, stability and function of membrane proteins. On the molecular level, the interactions are mediated by one or more residue contacts. Although previous studies focused on helix-packing patterns and sequence motifs, few of them developed methods specifically for contact prediction.Results: We present a new hierarchical framework for contact prediction, with an application in membrane proteins. The hierarchical scheme consists of two levels: in the first level, contact residues are predicted from the sequence and their pairing relationships are further predicted in the second level. Statistical analyses on contact propensities are combined with other sequence and structural information for training the support vector machine classifiers. Evaluated on 52 protein chains using leave-one-out cross validation (LOOCV) and an independent test set of 14 protein chains, the two-level approach consistently improves the conventional direct approach in prediction accuracy, with 80% reduction of input for prediction. Furthermore, the predicted contacts are then used to infer interactions between pairs of helices. When at least three predicted contacts are required for an inferred interaction, the accuracy, sensitivity and specificity are 56%, 40% and 89%, respectively. Our results demonstrate that a hierarchical framework can be applied to eliminate false positives (FP) while reducing computational complexity in predicting contacts. Together with the estimated contact propensities, this method can be used to gain insights into helix-packing in membrane proteins.Availability: http://bio-cluster.iis.sinica.edu.tw/TMhit/Contact: tsung@iis.sinica.edu.twSupplementary information:Supplementary data are available at Bioinformatics online.

Lotus-Like Biomimetic Hierarchical Structures Developed by the Self-Assembly of Tubular Plant Waxes

Hierarchical roughness is beneficial for superhydrophobic and self-cleaning surfaces. Biomimetic hierarchical surfaces were fabricated by replication of a micropatterned master surface and self-assembly of two kinds of tubular wax crystals, which naturally occur on the superhydrophobic leaves of Tropaeolum majus (L.) and Leymus arenarius (L.). These tubule forming waxes are multicomponent waxes, composed of a mixture of long chain hydrocarbons. Thermal evaporation of wax was used to cover artificial surfaces with a homogeneous wax layer and tubule formation was initiated by temperature and a solvent vapor phase. Based on this technique, various nanostructures produced by three-dimensional tubular waxes have been fabricated by changing the wax mass. Fabricated structures and surface chemistry mimic the hierarchical surfaces of superhydrophobic and self-cleaning plant surfaces. The influence of structures on superhydrophobicity at different length scales is demonstrated by investigation of contact angle, contact angle hysteresis, droplet evaporation and propensity of air pocket formation as well as adhesive forces. The optimal structural parameters for superhydrophobicity and low static contact angle hysteresis, superior to natural plant leaves including Lotus, have been identified and provide a useful guide for development of biomimtetic superhydrophobic surfaces.

Nanostructures for superhydrophobicity and low adhesion

Nanoroughness is beneficial for superhydrophobicity and low adhesion. Self assembly of hydrophobic alkanes has been used to modify flat and microstructured surfaces. Various nanostructures, produced by three-dimensional alkane crystals, have been fabricated by changing the crystal density. Microstructures were fabricated by replication of a micropatterned master surface and alkanes have been applied as thin hydrophobic three-dimensional crystals to create a hierarchical structure. The fabrication technique provides flexibility in fabrication of a variety of nano- and hierarchical structures. Fabricated structures and surface chemistry mimic superhydrophobic plant surfaces. The influence of nanostructures with different crystal densities on the superhydrophobicity is demonstrated by investigation of contact angle, contact angle hysteresis, droplet evaporation and propensity of air pocket formation as well as adhesive forces.

Ranking the factors that contribute to protein β‐sheet folding

The formation of beta-sheet domains in proteins involves five energetically important factors: the formation of networks of hydrogen bonds and hydrophobic faces, and the residue propensities, or preferences, to be found at the edges of the beta-sheet, to adopt the extended conformation, and to make contact with other residues. These relative energy contributions define a potential energy function. Here, we show how optimizing this potential energy function reveals the formation of hydrophobic faces as the utmost factor. The potential energy function was optimized to minimize the Z-scores of the native topologies among the exhaustive sets of over 400 different beta-sheets. These results corroborate with experimental data that showed the environment of a protein is an important modulator of beta-sheet folding. The contact propensities were found to be the least important, which could explain the poor predictive power of beta-strand alignment methods based on pair-wise contact matrices.

Modeling of smoothening effect on morphologies of annealed submicron nickel particles used for electrically conductive adhesives

Microelectronic packaging has been accelerating towards adoption of solutions that offer lower cost, higher electrical performance, and better reliability. Flip chip technology lends itself excellently to these goals. The development of anisotropic and isotropic conductive adhesives (ACA and ICA, respectively) as an alternative to solder bumps has received extensive attention in flip chip packaging as it offers an array of advantages such as finer pitch interconnects, green processes, low cost, and low temperature processing. Nickel, with its lower cost than that of silver and better thermal stability than that of copper, appears to be a viable candidate material for ICA and ACA applications. In the present study, a spiky surface morphology of nickel particles synthesized by means of a hydrothermal reduction method was clearly observed. The spiky morphology may have a detrimental effect on the conductivity of the ICA and ACA flip chip interconnect due to its smaller contact surface and propensity towards oxidation. The morphology of the nickel surface can be changed by annealing the particles between 270 and 360°C for 4–12h. A smoothening effect leading to more or less smooth spherical particles along with an increase in conductivity was observed after annealing at 360°C for up to 12h. An analytical model considering surface diffusion is established here to describe the temperature activated and energy minimization driven smoothening of the Ni particles.

Solvated docking: introducing water into the modelling of biomolecular complexes

Interfacial water, which plays an important role in mediating biomolecular interactions, has been neglected in the modelling of biomolecular complexes. We present a solvated docking approach that explicitly accounts for the presence of water in protein-protein complexes. Our solvated docking protocol is based on the concept of the first encounter complex in which a water layer is present in-between the molecules. It mimics the pathway from this initial complex towards the final assembly in which most waters have been expelled from the interface. Docking is performed from solvated biomolecules and waters are removed in a biased Monte Carlo procedure based on water-mediated contact propensities obtained from an analysis of high-resolution crystal structures. We demonstrate the feasibility of this approach for protein-protein complexes representing both 'wet' and 'dry' interfaces. Solvated docking leads to improvements both in quality and scoring. Water molecules are recovered that closely match the ones in the crystal structures. Solvated docking will be made available in the future release of HADDOCK version 2.0 (http://www.nmr.chem.uu.nl/haddock).

Statistical potential‐based amino acid similarity matrices for aligning distantly related protein sequences

Aligning distantly related protein sequences is a long-standing problem in bioinformatics, and a key for successful protein structure prediction. Its importance is increasing recently in the context of structural genomics projects because more and more experimentally solved structures are available as templates for protein structure modeling. Toward this end, recent structure prediction methods employ profile-profile alignments, and various ways of aligning two profiles have been developed. More fundamentally, a better amino acid similarity matrix can improve a profile itself; thereby resulting in more accurate profile-profile alignments. Here we have developed novel amino acid similarity matrices from knowledge-based amino acid contact potentials. Contact potentials are used because the contact propensity to the other amino acids would be one of the most conserved features of each position of a protein structure. The derived amino acid similarity matrices are tested on benchmark alignments at three different levels, namely, the family, the superfamily, and the fold level. Compared to BLOSUM45 and the other existing matrices, the contact potential-based matrices perform comparably in the family level alignments, but clearly outperform in the fold level alignments. The contact potential-based matrices perform even better when suboptimal alignments are considered. Comparing the matrices themselves with each other revealed that the contact potential-based matrices are very different from BLOSUM45 and the other matrices, indicating that they are located in a different basin in the amino acid similarity matrix space.

Intramolecular surface contacts contain information about protein–protein interface regions

Some amino acids clearly show preferences over others in protein-protein interfaces. These preferences, or so-called interface propensities can be used for a priori interface prediction. We investigated whether the prediction accuracy could be improved by considering not single but pairs of residues in an interface. Here we present the first systematic analysis of intramolecular surface contacts in interface prediction. We show that preferences do exist for contacts within and around an interface region within one molecule: specific pairs of amino acids are more often occurring than others. Using intramolecular contact propensities in a blind test, higher average scores were assigned to interface residues than to non-interface residues. This effect persisted as small but significant when the contact propensities were corrected to eliminate the influence of single amino acid interface propensity. This indicates that intramolecular contact propensities may replace interface propensities in protein-protein interface prediction. The source code is available on request from the authors.

Survey Participation, Nonresponse Bias, Measurement Error Bias, and Total Bias

A common hypothesis about practices to reduce survey nonresponse is that those persons brought into the respondent pool through persuasive efforts may provide data filled with measurement error. Two questions flow from this hypothesis. First, does the mean square error of a statistic increase when sample persons who are less likely to be contacted or cooperate are incorporated into the respondent pool? Second, do nonresponse bias estimates made on the respondents, using survey reports instead of records, provide accurate information about nonresponse bias? Using a unique data set, the Wisconsin Divorce Study, with divorce records as the frame and questions about the frame information included in the questionnaire, this article takes a first look into these two issues. We find that the relationship between nonresponse bias, measurement error bias, and response propensity is statistic- specific and specific to the type of nonresponse. Total bias tends to be lower on estimates calculated using all respondents, compared with those with only the highest contact and cooperation propensities, and nonresponse bias analyses based on respondents yield conclusions simi- lar to those based on records. Finally, we find that error properties of statistics may differ from error properties of the individual variables used to calculate the statistics.

The Customer, Co-Worker, and Management Burnout. Distinction in Service Settings

ABSTRACT Academic research indicates that distinct types of workplace burnout exist in service settings, including management burnout (MBO), customer burnout (CUBO), and co-worker burnout (CWBO). Each type of burnout can have detrimental influences on important service outcomes. As managers attempt to improve service quality, it is important that they consider the impact of each burnout dimension. The current work extends previous work on the psychological antecedents of burnout by considering the effects of personality traits on these distinct types of burnout and by considering the extent to which each type of burnout influences customer contact behaviors and propensity to leave the service organization. The researchers present the results of an empirical study that included survey data obtained from banking employees. The results support several hypothesized relationships that relate personality traits to each burnout facet, as well as the hypothesized influence of the burnout dimensions on customer contact performance and intentions to leave the service firm. Implications of the findings and suggestions for future research are discussed.

Inter-residue distances derived from fold contact propensities correlate with evolutionary substitution costs

BackgroundThe wealth of information on protein structure has led to a variety of statistical analyses of the role played by individual amino acid types in the protein fold. In particular, the contact propensities between the various amino acids can be converted into folding energies that have proved useful in structure prediction. The present study addresses the relationship of protein folding propensities to the evolutionary relationship between residues.ResultsThe contact preferences of residue types observed in a representative sample of protein structures are converted into a residue similarity matrix or inter-residue distance matrix. Remarkably, these distances correlate excellently with evolutionary substitution costs. Residue vectors are derived from the distance matrix. The residue vectors give a concrete picture of the grouping of residues into families sharing properties crucial for protein folding.ConclusionsInter-residue distances have proved useful in showing the explicit relationship between contact preferences and evolutionary substitution rates. It is proposed that the distance matrix derived from structural analysis may be useful in aligning proteins where remote homologs share structural features. Residue vectors derived from the distance matrix illustrate the spatial arrangement of residues and point to ways in which they can be grouped.

Combined use of physicochemical data and small-molecule crystallographic contact propensities to predict interactions in protein binding sites

Knowledge-based methods are a good alternative to force-field-based methods for the analysis of sites of interaction in protein binding cavities. Both the Protein Data Bank (PDB) and the Cambridge Structural Database (CSD) offer a good amount of data on non-covalent interactions. Although different from protein-derived data, small-molecule crystal data from the CSD are worth looking at as they provide a much more abundant and diverse set of intermolecular contacts. CSD data, when properly corrected by use of octanol-water pi values, can be used to predict the type of ligand chemical group most likely to occupy a given position within a protein binding site. Comparison with observed positions of ligand groups shows that the success rates of these predictions vary from 23% to 84%. Often, the group predicted to be most preferred at a given position is similar but not identical to the observed ligand group; if these are considered successes, prediction success rates range from 71% to 94%. Using PDB data, the corresponding rates are 16% to 79%, and 61% to 96%. Specificity of prediction of NH groups is somewhat better when using PDB interaction data, but results of prediction of hydrophobic groups seem worse than those obtained with CSD data. We have analysed the importance of data selection by applying different filters to eliminate unwanted interactions from our knowledge-base. The presence of certain types of interactions can be undesirable if they are unrepresentative of biological situations (contact to solvent molecules in small-molecule crystal structures, secondary crystallographic contacts) or if they are likely to add noise to the data without conveying much new information (long-distance contacts, sparsely-populated data sets). The elimination of solvent contacts was found to have no effect on the prediction of ligand groups in our test set. Both secondary-contact filtering and noise filtering were found to have a clear beneficial effect on predictive ability.

Molecular analysis of cancer. An overview.

Human cancers are generally characterized by acquisition of a series of somatic mutations. Molecular techniques, such as those described in this volume, have been used to identify a plethora of chromosomal translocations and mutations associated with carcinogenesis. The analysis and comparison of the array of genetic changes occurring in malignancy will enable a move toward a better understanding of cancer development. This will eventually lead to the development of improved therapies tailored to take into account the cytogenetic and molecular characteristics of specific human cancers.

How do potentials derived from structural databases relate to "true" potentials?

Knowledge-based potentials are used widely in protein folding and inverse folding algorithms. Two kinds of derivation methods are used. (1) The interactions in a database of known protein structures are assumed to obey a Boltzmann distribution. (2) The stability of the native folds relative to a manifold of misfolded structures is optimized. Here, a set of previously derived contact and secondary structure propensity potentials, taken as the "true" potentials, are employed to construct an artificial protein structural database from protein fragments. Then, new sets of potentials are derived to see how they are related to the true potentials. Using the Boltzmann distribution method, when the stability of the structures in the database lies within a certain range, both contact potentials and secondary structure propensities can be derived separately with remarkable accuracy. In general, the optimization method was found to be less accurate due to errors in the "excess energy" contribution. When the excess energy terms are kept as a constraint, the true potentials are recovered exactly.