Computational Models of Classical Conditioning guest editors’ introduction

In the present special issue, the performance of current computational models of classical conditioning was evaluated under three requirements: (1) Models were to be tested against a list of previously agreed-upon phenomena; (2) the parameters were fixed across simulations; and (3) the simulations used to test the models had to be made available. These requirements resulted in three major products: (a) a list of fundamental classical-conditioning results for which there is a consensus about their reliability; (b) the necessary information to evaluate each of the models on the basis of its ordinal successes in accounting for the experimental data; and (c) a repository of computational models ready to generate simulations. We believe that the contents of this issue represent the 2012 state of the art in computational modeling of classical conditioning and provide a way to find promising avenues for future model development

Application of the PM6 semi-empirical method to modeling proteins enhances docking accuracy of AutoDock

<p>Abstract</p> <p>Background</p> <p>Molecular docking methods are commonly used for predicting binding modes and energies of ligands to proteins. For accurate complex geometry and binding energy estimation, an appropriate method for calculating partial charges is essential. AutoDockTools software, the interface for preparing input files for one of the most widely used docking programs AutoDock 4, utilizes the Gasteiger partial charge calculation method for both protein and ligand charge calculation. However, it has already been shown that more accurate partial charge calculation - and as a consequence, more accurate docking- can be achieved by using quantum chemical methods. For docking calculations quantum chemical partial charge calculation as a routine was only used for ligands so far. The newly developed Mozyme function of MOPAC2009 allows fast partial charge calculation of proteins by quantum mechanical semi-empirical methods. Thus, in the current study, the effect of semi-empirical quantum-mechanical partial charge calculation on docking accuracy could be investigated.</p> <p>Results</p> <p>The docking accuracy of AutoDock 4 using the original AutoDock scoring function was investigated on a set of 53 protein ligand complexes using Gasteiger and PM6 partial charge calculation methods. This has enabled us to compare the effect of the partial charge calculation method on docking accuracy utilizing AutoDock 4 software. Our results showed that the docking accuracy in regard to complex geometry (docking result defined as accurate when the RMSD of the first rank docking result complex is within 2 Å of the experimentally determined X-ray structure) significantly increased when partial charges of the ligands and proteins were calculated with the semi-empirical PM6 method.</p> <p>Out of the 53 complexes analyzed in the course of our study, the geometry of 42 complexes were accurately calculated using PM6 partial charges, while the use of Gasteiger charges resulted in only 28 accurate geometries. The binding affinity estimation was not influenced by the partial charge calculation method - for more accurate binding affinity prediction development of a new scoring function for AutoDock is needed.</p> <p>Conclusion</p> <p>Our results demonstrate that the accuracy of determination of complex geometry using AutoDock 4 for docking calculation greatly increases with the use of quantum chemical partial charge calculation on both the ligands and proteins.</p

Recurrent, Robust and Scalable Patterns Underlie Human Approach and Avoidance

BACKGROUND. Approach and avoidance behavior provide a means for assessing the rewarding or aversive value of stimuli, and can be quantified by a keypress procedure whereby subjects work to increase (approach), decrease (avoid), or do nothing about time of exposure to a rewarding/aversive stimulus. To investigate whether approach/avoidance behavior might be governed by quantitative principles that meet engineering criteria for lawfulness and that encode known features of reward/aversion function, we evaluated whether keypress responses toward pictures with potential motivational value produced any regular patterns, such as a trade-off between approach and avoidance, or recurrent lawful patterns as observed with prospect theory. METHODOLOGY/PRINCIPAL FINDINGS. Three sets of experiments employed this task with beautiful face images, a standardized set of affective photographs, and pictures of food during controlled states of hunger and satiety. An iterative modeling approach to data identified multiple law-like patterns, based on variables grounded in the individual. These patterns were consistent across stimulus types, robust to noise, describable by a simple power law, and scalable between individuals and groups. Patterns included: (i) a preference trade-off counterbalancing approach and avoidance, (ii) a value function linking preference intensity to uncertainty about preference, and (iii) a saturation function linking preference intensity to its standard deviation, thereby setting limits to both. CONCLUSIONS/SIGNIFICANCE. These law-like patterns were compatible with critical features of prospect theory, the matching law, and alliesthesia. Furthermore, they appeared consistent with both mean-variance and expected utility approaches to the assessment of risk. Ordering of responses across categories of stimuli demonstrated three properties thought to be relevant for preference-based choice, suggesting these patterns might be grouped together as a relative preference theory. Since variables in these patterns have been associated with reward circuitry structure and function, they may provide a method for quantitative phenotyping of normative and pathological function (e.g., psychiatric illness).National Institute on Drug Abuse (14118, 026002, 026104, DABK39-03-0098, DABK39-03-C-0098); The MGH Phenotype Genotype Project in Addiction and Mood Disorder from the Office of National Drug Control Policy - Counterdrug Technology Assessment Center; MGH Department of Radiology; the National Center for Research Resources (P41RR14075); National Institute of Neurological Disorders and Stroke (34189, 05236

Entry of Herpes Simplex Virus Type 1 (HSV-1) into the Distal Axons of Trigeminal Neurons Favors the Onset of Nonproductive, Silent Infection

Following productive, lytic infection in epithelia, herpes simplex virus type 1 (HSV-1) establishes a lifelong latent infection in sensory neurons that is interrupted by episodes of reactivation. In order to better understand what triggers this lytic/latent decision in neurons, we set up an organotypic model based on chicken embryonic trigeminal ganglia explants (TGEs) in a double chamber system. Adding HSV-1 to the ganglion compartment (GC) resulted in a productive infection in the explants. By contrast, selective application of the virus to distal axons led to a largely nonproductive infection that was characterized by the poor expression of lytic genes and the presence of high levels of the 2.0-kb major latency-associated transcript (LAT) RNA. Treatment of the explants with the immediate-early (IE) gene transcriptional inducer hexamethylene bisacetamide, and simultaneous co-infection of the GC with HSV-1, herpes simplex virus type 2 (HSV-2) or pseudorabies virus (PrV) helper virus significantly enhanced the ability of HSV-1 to productively infect sensory neurons upon axonal entry. Helper-virus-induced transactivation of HSV-1 IE gene expression in axonally-infected TGEs in the absence of de novo protein synthesis was dependent on the presence of functional tegument protein VP16 in HSV-1 helper virus particles. After the establishment of a LAT-positive silent infection in TGEs, HSV-1 was refractory to transactivation by superinfection of the GC with HSV-1 but not with HSV-2 and PrV helper virus. In conclusion, the site of entry appears to be a critical determinant in the lytic/latent decision in sensory neurons. HSV-1 entry into distal axons results in an insufficient transactivation of IE gene expression and favors the establishment of a nonproductive, silent infection in trigeminal neurons

Still the same in human causal reasoning: Evidence for a uniform similarity parameter in generalisation and summation

Wagner’s replaced elements model (REM) theory implies that generalisation and summation tests depend on a common similarity parameter, but few studies have assessed generalisation and summation within the same experimental paradigm. Three experiments adapted a methodology used in non-human animal studies to investigate this question in the case of human causal reasoning. The studies combined different amounts of training on simple discriminations (A+ vs. C−), compound discriminations (AB+ vs. CD−), and irrelevant novel cue discriminations (A n+ vs. C n−) with testing on single cues, compound cues, and compounds of cues with novel cues. The results were compared with predictions of Pearce’s configural model and Wagner’s REM elemental model. They also were examined to determine whether generalisation and summation could be accounted for using a single value for the similarity between stimulus compounds and the separable constituent cues of which the compounds were composed. The findings indicated that each theory needed to include common cues to account for the data. </jats:p