Statistical learning leads to persistent memory: evidence for one-year consolidation

Statistical learning is a robust mechanism of the brain that enables the extraction of environmental patterns, which is crucial in perceptual and cognitive domains. However, the dynamical change of processes underlying long-term statistical memory formation has not been tested in an appropriately controlled design. Here we show that a memory trace acquired by statistical learning is resistant to inference as well as to forgetting after one year. Participants performed a statistical learning task and were retested one year later without further practice. The acquired statistical knowledge was resistant to interference, since after one year, participants showed similar memory performance on the previously practiced statistical structure after being tested with a new statistical structure. These results could be key to understand the stability of long-term statistical knowledge

Flexible mapping of homology onto structure with Homolmapper

<p>Abstract</p> <p>Background</p> <p>Over the past decade, a number of tools have emerged for the examination of homology relationships among protein sequences in a structural context. Most recent software implementations for such analysis are tied to specific molecular viewing programs, which can be problematic for collaborations involving multiple viewing environments. Incorporation into larger packages also adds complications for users interested in adding their own scoring schemes or in analyzing proteins incorporating unusual amino acid residues such as selenocysteine.</p> <p>Results</p> <p>We describe homolmapper, a command-line application for mapping information from a multiple protein sequence alignment onto a protein structure for analysis in the viewing software of the user's choice. Homolmapper is small (under 250 K for the application itself) and is written in Python to ensure portability. It is released for non-commercial use under a modified University of California BSD license. Homolmapper permits facile import of additional scoring schemes and can incorporate arbitrary additional amino acids to allow handling of residues such as selenocysteine or pyrrolysine. Homolmapper also provides tools for defining and analyzing subfamilies relative to a larger alignment, for mutual information analysis, and for rapidly visualizing the locations of mutations and multi-residue motifs.</p> <p>Conclusion</p> <p>Homolmapper is a useful tool for analysis of homology relationships among proteins in a structural context. There is also extensive, example-driven documentation available. More information about homolmapper is available at <url>http://www.mcb.ucdavis.edu/faculty-labs/lagarias/homolmapper_home/homolmapper%20web%20page.htm</url>.</p

Modeling Signal Propagation Mechanisms and Ligand-Based Conformational Dynamics of the Hsp90 Molecular Chaperone Full-Length Dimer

Hsp90 is a molecular chaperone essential for protein folding and activation in normal homeostasis and stress response. ATP binding and hydrolysis facilitate Hsp90 conformational changes required for client activation. Hsp90 plays an important role in disease states, particularly in cancer, where chaperoning of the mutated and overexpressed oncoproteins is important for function. Recent studies have illuminated mechanisms related to the chaperone function. However, an atomic resolution view of Hsp90 conformational dynamics, determined by the presence of different binding partners, is critical to define communication pathways between remote residues in different domains intimately affecting the chaperone cycle. Here, we present a computational analysis of signal propagation and long-range communication pathways in Hsp90. We carried out molecular dynamics simulations of the full-length Hsp90 dimer, combined with essential dynamics, correlation analysis, and a signal propagation model. All-atom MD simulations with timescales of 70 ns have been performed for complexes with the natural substrates ATP and ADP and for the unliganded dimer. We elucidate the mechanisms of signal propagation and determine “hot spots” involved in interdomain communication pathways from the nucleotide-binding site to the C-terminal domain interface. A comprehensive computational analysis of the Hsp90 communication pathways and dynamics at atomic resolution has revealed the role of the nucleotide in effecting conformational changes, elucidating the mechanisms of signal propagation. Functionally important residues and secondary structure elements emerge as effective mediators of communication between the nucleotide-binding site and the C-terminal interface. Furthermore, we show that specific interdomain signal propagation pathways may be activated as a function of the ligand. Our results support a “conformational selection model” of the Hsp90 mechanism, whereby the protein may exist in a dynamic equilibrium between different conformational states available on the energy landscape and binding of a specific partner can bias the equilibrium toward functionally relevant complexes

Does personality affect premating isolation between locally-adapted populations?

Background: One aspect of premating isolation between diverging, locally-adapted population pairs is female mate choice for resident over alien male phenotypes. Mating preferences often show considerable individual variation, and whether or not certain individuals are more likely to contribute to population interbreeding remains to be studied. In the Poecilia mexicana-species complex different ecotypes have adapted to hydrogen sulfide (H2S)-toxic springs, and females from adjacent non-sulfidic habitats prefer resident over sulfide-adapted males. We asked if consistent individual differences in behavioral tendencies (animal personality) predict the strength and direction of the mate choice component of premating isolation in this system. Results: We characterized focal females for their personality and found behavioral measures of ‘novel object exploration’, ‘boldness’ and ‘activity in an unknown area’ to be highly repeatable. Furthermore, the interaction term between our measures of exploration and boldness affected focal females’ strength of preference (SOP) for the resident male phenotype in dichotomous association preference tests. High exploration tendencies were coupled with stronger SOPs for resident over alien mating partners in bold, but not shy, females. Shy and/or little explorative females had an increased likelihood of preferring the non-resident phenotype and thus, are more likely to contribute to rare population hybridization. When we offered large vs. small conspecific stimulus males instead, less explorative females showed stronger preferences for large male body size. However, this effect disappeared when the size difference between the stimulus males was small. Conclusions: Our results suggest that personality affects female mate choice in a very nuanced fashion. Hence, population differences in the distribution of personality types could be facilitating or impeding reproductive isolation between diverging populations depending on the study system and the male trait(s) upon which females base their mating decisions, respectively

Sequence and Structure Signatures of Cancer Mutation Hotspots in Protein Kinases

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis

The Energy Landscape Analysis of Cancer Mutations in Protein Kinases

The growing interest in quantifying the molecular basis of protein kinase activation and allosteric regulation by cancer mutations has fueled computational studies of allosteric signaling in protein kinases. In the present study, we combined computer simulations and the energy landscape analysis of protein kinases to characterize the interplay between oncogenic mutations and locally frustrated sites as important catalysts of allostetric kinase activation. While structurally rigid kinase core constitutes a minimally frustrated hub of the catalytic domain, locally frustrated residue clusters, whose interaction networks are not energetically optimized, are prone to dynamic modulation and could enable allosteric conformational transitions. The results of this study have shown that the energy landscape effect of oncogenic mutations may be allosteric eliciting global changes in the spatial distribution of highly frustrated residues. We have found that mutation-induced allosteric signaling may involve a dynamic coupling between structurally rigid (minimally frustrated) and plastic (locally frustrated) clusters of residues. The presented study has demonstrated that activation cancer mutations may affect the thermodynamic equilibrium between kinase states by allosterically altering the distribution of locally frustrated sites and increasing the local frustration in the inactive form, while eliminating locally frustrated sites and restoring structural rigidity of the active form. The energy landsape analysis of protein kinases and the proposed role of locally frustrated sites in activation mechanisms may have useful implications for bioinformatics-based screening and detection of functional sites critical for allosteric regulation in complex biomolecular systems