494 research outputs found
Latent Evolutionary Signatures: A General Framework for Analyzing Music and Cultural Evolution
No full text<p><strong>Latent Evolutionary Signatures: A General Framework for Analyzing Music and Cultural Evolution</strong></p>
<p>© 2024 </p>
<p>Jonathan Warrell<sup>a,b,1</sup>, Leonidas Salichos<sup>a,b,e,1</sup>, Michael Gancz<sup>c,1</sup>, Mark B. Gerstein<sup>a,b,d</sup></p>
<p><sup> 1 </sup>equal contribution</p>
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<p><sup>a</sup> Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. </p>
<p><sup>b</sup> Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA. </p>
<p><sup>c</sup> Department of Music, Yale University, New Haven, CT 06520, USA. </p>
<p><sup>d</sup> Department of Computer Science, Yale University, New Haven, CT 06520, USA.</p>
<p><sup>e</sup> Department of Biological and Chemical Sciences, New York Institute of Technology, New York, NY 10023, USA.</p>
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<p>This repo contains optimal harmony and form+harmony based models for analyzing popular music as an evolutionary structure. Our model architecture is based on a traditional VAE, but with an energy-based prior that penalizes a measure of ‘evolutionary distance’, in this case informed by temporal distance across song release dates (see schematic below), in the latent space. The key output of each model is a set of latent ‘evolutionary signatures’, or characteristic distributions of chord/form k-<em>mers </em>that can be used to predict the date and genre of each song. We use the McGill Billboard corpus of popular song annotations as our database.</p>
<p>The subdirectory ‘harmonic_formal’ contains the code for training the model based both on chord progressions and formal features. The subdirectory ‘km4’ contains the code for training the model based on chord progressions of length 4.</p>
<p>In addition to the optimal models, we include some other configurations that we tested, including variants on the coarse-graining of formal units (models suffixed with ‘_A’, ‘_B’, and ‘_None’, referring to projection matrices A and B, where A retains formal categories that comprise the first 99% of the data, and collapses all others into a category of ‘other’; and B bins together semantically similar formal units), and different means of normalizing formal feature vector ‘X_struct,’ which in its raw form contains the counts of each formal category (models suffixed with ‘_binarized’ or ‘_zscore’ utilize these normalizations. These are contained within the ‘models’ folder of each subdirectory. Additional items of interest, including code for processing the McGill Billboard dataset, are included in the ‘supplemental’ folder. The ‘McGill-Billboard’ folder contains all raw data.</p>
<p>All code is currently written for MatLab.</p>
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Integrated Assessment of Genomic Correlates of Protein Evolutionary Rate
No full textRates of evolution differ widely among proteins, but the causes and consequences of such differences remain under debate. With the advent of high-throughput functional genomics, it is now possible to rigorously assess the genomic correlates of protein evolutionary rate. However, dissecting the correlations among evolutionary rate and these genomic features remains a major challenge. Here, we use an integrated probabilistic modeling approach to study genomic correlates of protein evolutionary rate in Saccharomyces cerevisiae. We measure and rank degrees of association between (i) an approximate measure of protein evolutionary rate with high genome coverage, and (ii) a diverse list of protein properties (sequence, structural, functional, network, and phenotypic). We observe, among many statistically significant correlations, that slowly evolving proteins tend to be regulated by more transcription factors, deficient in predicted structural disorder, involved in characteristic biological functions (such as translation), biased in amino acid composition, and are generally more abundant, more essential, and enriched for interaction partners. Many of these results are in agreement with recent studies. In addition, we assess information contribution of different subsets of these protein properties in the task of predicting slowly evolving proteins. We employ a logistic regression model on binned data that is able to account for intercorrelation, non-linearity, and heterogeneity within features. Our model considers features both individually and in natural ensembles ("meta-features") in order to assess joint information contribution and degree of contribution independence. Meta-features based on protein abundance and amino acid composition make strong, partially independent contributions to the task of predicting slowly evolving proteins; other meta-features make additional minor contributions. The combination of all meta-features yields predictions comparable to those based on paired species comparisons, and approaching the predictive limit of optimal lineage-insensitive features. Our integrated assessment framework can be readily extended to other correlational analyses at the genome scale.
Author Summary
Proteins encoded within a given genome are known to evolve at drastically different rates. Through recent large-scale studies, researchers have measured a wide variety of properties for all proteins in yeast. We are interested to know how these properties relate to one another and to what extent they explain evolutionary rate variation. Protein properties are a heterogeneous mix, a factor which complicates research in this area. For example, some properties (e.g., protein abundance) are numerical, while others (e.g., protein function) are descriptive; protein properties may also suffer from noise and hidden redundancies. We have addressed these issues within a flexible and robust statistical framework. We first ranked a large list of protein properties by the strength of their relationships with evolutionary rate; this confirms many known evolutionary relationships and also highlights several new ones. Similar protein properties were then grouped and applied to predict slowly evolving proteins. Some of these groups were as effective as paired species comparison in making correct predictions, although in both cases a great deal of evolutionary rate variation remained to be explained. Our work has helped to refine the set of protein properties that researchers should consider as they investigate the mechanisms underlying protein evolution.PhRMA Foundation; National Science Foundation (DGE-0654108); National Institues of Health; Williams professorship fun
The effects of an intensive vocational evaluation involving work samples on career indecision, self-esteem, and state anxiety in rehabilitation clients
No full textThe work sample approach to vocational evaluation attained prominence in rehabilitation settings largely as a result of dissatisfaction with traditional evaluation methods. Although the predictive validity of work sampling is assumed superior to paper and pencil testing, it is the career development functions that makes work sampling particularly attractive. Frequently writers have extolled the career and self exploration components of work samples.
Among the specific variables work samples are assumed to positively affect are anxiety about making a career choice, career decidedness, and self-esteem. However these career development benefits like the predictive validity of work sampling have largely been unexamined. This study is an initial exploratory investigation of these proposed career development functions. It seeks to determine if undergoing a work-sample-based evaluation is associated with (a) a reduction in anxiety connected with career decision-making, (b) lessened career indecision, and (c) enhanced self-esteem.
To accomplish the aforementioned, 60 clients of a comprehensive rehabilitation center were administered the A-State Scale of the State-Trait Anxiety Scale, the Career Decision Scale, and the Self-Esteem Inventory prior to beginning a comprehensive work-sample-based vocational evaluation, and again after the evaluation was completed. Three different handicapping conditions were studied with an equal nuber of subjects in the three groups: (a) mentally retarded, (b) learning disabled, and (c) emotionally disturbed.
Using a repeated measures multivariate analysis of variance, a change in dependent measures scores from pretesting to postesting was observed. Evaluation activities did not interact with type of handicapping condition to affect these scores. Post hoc analysis indicated positive changes occurred in anxiety associated with career decision-making and self-esteem.
Super‘s (1983) model of career maturity was employed to examine the career development effects of a work—sample-based vocational evaluation. This model cites the counterproductive effects of anxiety and low self-esteem on career planning, both of which have been validated empirically. Consequently the change in the sample's anxiety and self-esteem are assumed to enhance the probability of career planning. The time between the vocational evaluation and posttesting may have been insufficient for career indecision levels to have changed.
The limitations of the study, are addressed as are the implications of the study for future research.Ed. D
Predicting protein ligand binding motions with the Conformation Explorer
No full textBackground Knowledge of the structure of proteins bound to known or potential ligands is crucial for biological understanding and drug design. Often the 3D structure of the protein is available in some conformation, but binding the ligand of interest may involve a large scale conformational change which is difficult to predict with existing methods. Results We describe how to generate ligand binding conformations of proteins that move by hinge bending, the largest class of motions. First, we predict the location of the hinge between domains. Second, we apply an Euler rotation to one of the domains about the hinge point. Third, we compute a short-time dynamical trajectory using Molecular Dynamics to equilibrate the protein and ligand and correct unnatural atomic positions. Fourth, we score the generated structures using a novel fitness function which favors closed or holo structures. By iterating the second through fourth steps we systematically minimize the fitness function, thus predicting the conformational change required for small ligand binding for five well studied proteins. Conclusions We demonstrate that the method in most cases successfully predicts the holo conformation given only an apo structure
Average Core Structures and Variability Measures for Protein Families: Application to the Immunoglobulins
No full textFlexOracle: predicting flexible hinges by identification of stable domains-4
No full text<p><b>Copyright information:</b></p><p>Taken from "FlexOracle: predicting flexible hinges by identification of stable domains"</p><p>http://www.biomedcentral.com/1471-2105/8/215</p><p>BMC Bioinformatics 2007;8():215-215.</p><p>Published online 22 Jun 2007</p><p>PMCID:PMC1933439.</p><p></p>�120, 319–320. b. Zheng et al. identify the boundaries of the small lobe as residues 40 and 127, slightly different from HAG. The single-cut predictors had significant minima near residue 120, with more ambiguous results for the other two hinges. c. 2-cut FlexOracle primary prediction: residues 314–317. Others: 30–33, 62–65. 42–45, 82–85. 122–125. The 2-cut predictor was partially successful. The primary prediction coincides with one of the hinges, as does the fourth prediction, and one of the second predictions. There are also three false positives (62–65 and 42–45, and 82–85) among the higher predictions
FlexOracle: predicting flexible hinges by identification of stable domains
No full textAbstract Background Protein motions play an essential role in catalysis and protein-ligand interactions, but are difficult to observe directly. A substantial fraction of protein motions involve hinge bending. For these proteins, the accurate identification of flexible hinges connecting rigid domains would provide significant insight into motion. Programs such as GNM and FIRST have made global flexibility predictions available at low computational cost, but are not designed specifically for finding hinge points. Results Here we present the novel FlexOracle hinge prediction approach based on the ideas that energetic interactions are stronger within structural domains than between them, and that fragments generated by cleaving the protein at the hinge site are independently stable. We implement this as a tool within the Database of Macromolecular Motions, MolMovDB.org. For a given structure, we generate pairs of fragments based on scanning all possible cleavage points on the protein chain, compute the energy of the fragments compared with the undivided protein, and predict hinges where this quantity is minimal. We present three specific implementations of this approach. In the first, we consider only pairs of fragments generated by cutting at a single location on the protein chain and then use a standard molecular mechanics force field to calculate the enthalpies of the two fragments. In the second, we generate fragments in the same way but instead compute their free energies using a knowledge based force field. In the third, we generate fragment pairs by cutting at two points on the protein chain and then calculate their free energies. Conclusion Quantitative results demonstrate our method's ability to predict known hinges from the Database of Macromolecular Motions.</p
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