D2P2: database of disordered protein predictions

We present the Database of Disordered Protein Prediction (D2P2), available at http://d2p2.pro (including website source code). A battery of disorder predictors and their variants, VL-XT, VSL2b, PrDOS, PV2, Espritz and IUPred, were run on all protein sequences from 1765 complete proteomes (to be updated as more genomes are completed). Integrated with these results are all of the predicted (mostly structured) SCOP domains using the SUPERFAMILY predictor. These disorder/structure annotations together enable comparison of the disorder predictors with each other and examination of the overlap between disordered predictions and SCOP domains on a large scale. D2P2 will increase our understanding of the interplay between disorder and structure, the genomic distribution of disorder, and its evolutionary history. The parsed data are made available in a unified format for download as flat files or SQL tables either by genome, by predictor, or for the complete set. An interactive website provides a graphical view of each protein annotated with the SCOP domains and disordered regions from all predictors overlaid (or shown as a consensus). There are statistics and tools for browsing and comparing genomes and their disorder within the context of their position on the tree of life. © The Author(s) 2012. Published by Oxford University Press

Depression, Relationship Quality, and Couples’ Demand/Withdraw and Demand/Submit Sequential Interactions

This study investigated the associations among depression, relationship quality, and demand/withdraw and demand/submit behavior in couples’ conflict interactions. Two 10-min conflict interactions were coded for each couple (N = 97) using Structural Analysis of Social Behavior (SASB; Benjamin, 1979a, 1987, 2000a). Depression was assessed categorically (via the presence of depressive disorders) and dimensionally (via symptom reports). Results revealed that relationship quality was negatively associated with demanding behavior, as well as receiving submissive or withdrawing behavior from one’s partner. Relationship quality was positively associated with withdrawal. Demanding behavior was positively associated with women’s depression symptoms but negatively associated with men’s depression symptoms. Sequential analysis revealed couples’ behavior was highly stable across time. Initiation of demand/withdraw and demand/submit sequences were negatively associated with partners’ relationship adjustment. Female demand/male withdraw was positively associated with men’s depression diagnosis. Results underscore the importance of sequential analysis when investigating associations among depression, relationship quality, and couples’ interpersonal behavior

Robot Models of Mental Disorders

Alongside technological tools to support wellbeing and treatment of mental disorders, models of these disorders can also be invaluable tools to understand, support and improve these conditions. Robots can provide ecologically valid models that take into account embodiment-, interaction-, and context-related elements. Focusing on Obsessive-Compulsive spectrum disorders, in this paper we discuss some of the potential contributions of robot models and relate them to other models used in psychology and psychiatry, particularly animal models. We also present some initial recommendations for their meaningful design and rigorous use.Final Accepted Versio

SCREENING INTERACTIONS BETWEEN PROTEINS AND DISORDERED PEPTIDES BY A NOVEL COMPUTATIONAL METHOD

Concerted interactions between proteins in cells form the basis of most biological processes. Biophysicists study protein–protein association by measuring thermodynamic and kinetic properties. Naively, strong binding affinity should be preferred in protein–protein binding to conduct certain biological functions. However, evidence shows that regulatory interactions, such as those between adapter proteins and intrinsically disordered proteins, communicate via low affinity but high complementarity interactions. PDZ domains are one class of adapters that bind linear disordered peptides, which play key roles in signaling pathways. The misregulation of these signals has been implicated in the progression of human cancers. To understand the underlying mechanism of protein-peptide binding interactions and to predict new interactions, in this thesis I have developed: (a) a unique biophysical-derived model to estimate their binding free energy; (b) a novel semi-flexible structure-based method to dock disordered peptides to PDZ domains; (c) predictions of the peptide binding landscape; and, (d) an automated algorithm and web-interface to predict the likelihood that a given linear sequence of amino acids binds to a specific PDZ domain. The docking method, PepDock, takes a peptide sequence and a PDZ protein structure as input, and outputs docked conformations and their corresponding binding affinity estimation, including their optimal free energy pathway. We have applied PepDock to screen several PDZ protein domains. The results not only validated the capabilities of PepDock to accurately discriminate interactions, but also explored the underlying binding mechanism. Specifically, I showed that interactions followed downhill free energy pathways, reconciling a relatively fast association mechanism of intrinsically disordered peptides. The pathways are such that initially the peptide’s C-terminal motif binds non-specifically, forming a weak intermediate, whereas specific binding is achieved only by a subsequent network of contacts (7–9 residues in total). This mechanism allows peptides to quickly probe PDZ domains, rapidly releasing those that do not attain sufficient affinity during binding. Further kinetic analysis indicates that disorder enhanced the specificity of promiscuous interactions between proteins and peptides, while achieving association rates comparable to interactions between ordered proteins

Parallel Process: An Empirical Investigation

The purpose of the present study was to conduct an empirical investigation of parallel process. The study used a cross-sectional design in which 30 therapy relationships and the corresponding supervision relationships were studied. The therapist assessed the behavior manifested by the patient during a targeted therapy session. Following the subsequent supervision session, the supervisor assessed the behavior manifested by the supervisee during the supervision session. In addition, each of the triad participants (patient, therapist, supervisor) rated the level of anxiety they experienced during the targeted therapy and supervision sessions. Measures of interpersonal style for each of the subjects were also obtained. Correlations were computed between each therapy relationship and the corresponding supervision relationship. The correlations were formed by pairing the therapist\u27s rating of the patient\u27s behavior during the targeted therapy session with the supervisor\u27s rating of the supervisee\u27s behavior during the targeted supervision session. In 67 percent of the triads the Pearson product-moment correlations were significant. Across all triads, 20 percent of the variation in the patient\u27s behavior during the targeted therapy session could be accounted for by the variation in the supervisee\u27s behavior during the targeted supervision session. Regression analyses were used to investigate conditions which might facilitate the occurrence of parallel process. No relationship was found between the level of anxiety experienced by the subjects during the targeted sessions and the occurrence of parallel process. The level of complementarity, as derived by the pairings in interpersonal styles between the participants in each relationship, also failed to predict the occurrence of parallel process. The results of a two-way analysis of variance with repeated measures indicated that the behavioral profile obtained by patients was similar to the profile obtained by supervisees. The finding suggested that helpees, whether patients or supervisees, tended to manifest similar behaviors. It was concluded that the occurrence of parallel process may be due to the similarity in role relationship between the patient and therapist in therapy and the supervisee and supervisor in supervision

A Theory of Natural Addiction

Economic theories of rational addiction aim to describe consumer behavior in the presence of habit-forming goods. We provide a biological foundation for this body of work by formally specifying conditions under which it is optimal to form a habit. We demonstrate the empirical validity of our thesis with an in-depth review and synthesis of the biomedical literature concerning the action of opiates in the mammalian brain and their eects on behavior. Our results lend credence to many of the unconventional behavioral assumptions employed by theories of rational addiction, including adjacent complementarity and the importance of cues, attention, and self-control in determining the behavior of addicts. We oer evidence for the special case of the opiates that "harmful" addiction is the manifestation of a mismatch between behavioral algorithms encoded in the human genome and the expanded menu of choices faced by consumers in the modern world

Computational Analysis and Prediction of Intrinsic Disorder and Intrinsic Disorder Functions in Proteins

COMPUTATIONAL ANALYSIS AND PREDICTION OF INTRINSIC DISORDER AND INTRINSIC DISORDER FUNCTIONS IN PROTEINS By Akila Imesha Katuwawala A dissertation submitted in partial fulfillment of the requirements for the degree of Engineering, Doctor of Philosophy with a concentration in Computer Science at Virginia Commonwealth University. Virginia Commonwealth University, 2021 Director: Lukasz Kurgan, Professor, Department of Computer Science Proteins, as a fundamental class of biomolecules, have been studied from various perspectives over the past two centuries. The traditional notion is that proteins require fixed and stable three-dimensional structures to carry out biological functions. However, there is mounting evidence regarding a “special” class of proteins, named intrinsically disordered proteins, which do not have fixed three-dimensional structures though they perform a number of important biological functions. Computational approaches have been a vital component to study these intrinsically disordered proteins over the past few decades. Prediction of the intrinsic disorder and functions of intrinsic disorder from protein sequences is one such important computational approach that has recently gained attention, particularly in the advent of the development of modern machine learning techniques. This dissertation runs along two basic themes, namely, prediction of the intrinsic disorder and prediction of the intrinsic disorder functions. The work related to the prediction of intrinsic disorder covers a novel approach to evaluate the predictive performance of the current computational disorder predictors. This approach evaluates the intrinsic disorder predictors at the individual protein level compared to the traditional studies that evaluate them over large protein datasets. We address several interesting aspects concerning the differences in the protein-level vs. dataset-level predictive quality, complementarity and predictive performance of the current predictors. Based on the findings from this assessment we have conceptualized, developed, tested and deployed an innovative platform called DISOselect that recommends the most suitable computational disorder predictors for a given protein, with an underlying goal to maximize the predictive performance. DISOselect provides advice on whether a given disorder predictor would provide an accurate prediction for a given protein of user’s interest, and recommends the most suitable disorder predictor together with an estimate of its expected predictive quality. The second theme, prediction of the intrinsic disorder functions, includes first-of-its-kind evaluation of the current computational disorder predictors on two functional sub-classes of the intrinsically disordered proteins. This study introduces several novel evaluation strategies to assess predictive performance of disorder prediction methods and focuses on the evaluation for disorder functions associated with interactions with partner molecules. Results of this analysis motivated us to conceptualize, design, test and deploy a new and accurate machine learning-based predictor of the disordered lipid-binding residues, DisoLipPred. We empirically show that the strong predictive performance of DisoLipPred stems from several innovative design features and that its predictions complements results produced by current disorder predictors, disorder function predictors and predictors of transmembrane regions. We deploy DisoLipPred as a convenient webserver and discuss its predictions on the yeast proteome

Rank miRNA: a web tool for identifying polymorphisms altering miRNA target sites

Abstract MicroRNAs (miRNAs) are small non-coding RNA molecules that have an important role in a wide range of biological processes, since they interact with specific mRNAs affecting the expression of the corresponding proteins. The role of miRNA can be deeply influenced by Single Nucleotide Polymorphisms (SNPs), in particular in their seed sites, since these variations may modify their affinity with particular transcripts, but they may also generate novel binding capabilities for specific miRNA binding sites or destroy them. Several computational tools for miRNA-target site predictions have been developed, but the obtained results are often not in agreement, making the study the binding sites hard, and the analysis of SNP effects even harder. For these reasons, we developed a web application called Rank miRNA, which allows to retrieve and aggregate the results of three prediction tools, but also to process and compare new input miRNA sequences, allowing the analysis of how variations impact on their function. Therefore, our tool is also able to predict the impact of SNPs (and any other kind of variations) on miRNA-mRNA binding capability and also to find the target genes of (potentially new) miRNA sequences. We evaluated the performance of Rank miRNA on specific human SNPs, which are likely to be involved in several mental disorder diseases, showing the potentiality of our tool in helping the study of miRNA-target interactions