Insight into neutral and disease-associated human genetic variants through interpretable predictors.

A variety of methods that predict human nonsynonymous single nucleotide polymorphisms (SNPs) to be neutral or disease-associated have been developed over the last decade. These methods are used for pinpointing disease-associated variants in the many variants obtained with next-generation sequencing technologies. The high performances of current sequence-based predictors indicate that sequence data contains valuable information about a variant being neutral or disease-associated.

Protein redesign by learning from data.

Protein redesign methods aim to improve a desired property by carefully selecting mutations in relevant regions guided by protein structure. However, often protein structural requirements underlying biological characteristics are not well understood. Here, we introduce a methodology that learns relevant mutations from a set of proteins that have the desired property and demonstrate it by successfully improving production levels of two enzymes by Aspergillus niger, a relevant host organism for industrial enzyme production.

The dorsal raphe nucleus is integral to negative prediction errors in Pavlovian fear.

Prediction errors are central to modern learning theories. While brain regions contributing to reward prediction errors have been uncovered, the sources of aversive prediction errors remain largely unknown. Here we used probabilistic and deterministic reinforcement procedures, followed by extinction, to examine the contribution of the dorsal raphe nucleus to negative, aversive prediction errors in Pavlovian fear.

SPiCE: a web-based tool for sequence-based protein classification and exploration.

Background: Amino acid sequences and features extracted from such sequences have been used to predict many protein properties, such as subcellular localization or solubility, using classifier algorithms. Although software tools are available for both feature extraction and classifier construction, their application is not straightforward, requiring users to install various packages and to convert data into different formats. This lack of easily accessible software hampers quick, explorative use of sequence-based classification techniques by biologists.

Neural estimates of imagined outcomes in the orbitofrontal cortex drive behavior and learning.

Imagination, defined as the ability to interpret reality in ways that diverge from past experience, is fundamental to adaptive behavior. This can be seen at a simple level in our capacity to predict novel outcomes in new situations. The ability to anticipate outcomes never before received can also influence learning if those imagined outcomes are not received.

Key factors in work engagement and job motivation of teaching faculty at a university medical centre.

This study reports about teacher motivation and work engagement in a Dutch University Medical Centre (UMC). We examined factors affecting the motivation for teaching in a UMC, the engagement of UMC Utrecht teaching faculty in their work, and their engagement in teaching compared with engagement in patient care and research. Based on a pilot study within various departments at the UMCU, a survey on teaching motivation and work engagement was developed and sent to over 600 UMCU teachers.

Disruption of model-based behavior and learning by cocaine self-administration in rats.

Rationale: Addiction is characterized by maladaptive decision-making, in which individuals seem unable to use adverse outcomes to modify their behavior. Adverse outcomes are often infrequent, delayed, and even rare events, especially when compared to the reliable rewarding drug-associated outcomes. As a result, recognizing and using information about their occurrence put a premium on the operation of so-called model-based systems of behavioral control, which allow one to mentally simulate outcomes of different courses of action based on knowledge of the underlying associative structure of the environment.

Exploring sequence characteristics related to high-level production of secreted proteins in Aspergillus niger.

Protein sequence features are explored in relation to the production of over-expressed extracellular proteins by fungi. Knowledge on features influencing protein production and secretion could be employed to improve enzyme production levels in industrial bioprocesses via protein engineering. A large set, over 600 homologous and nearly 2,000 heterologous fungal genes, were overexpressed in Aspergillus niger using a standardized expression cassette and scored for high versus no production.

Vector boson mass generation without new fields.

Previously a model of only vector fields with a local U(1)⊗SU(2) symmetry was introduced for which one finds a massless U(1) photon and a massive SU(2) vector boson in the lattice regularization. Here it is shown that quantization of its classical continuum action leads to perturbative renormalization difficulties. But, nonperturbative Monte Carlo calculations favor the existence of a quantum continuum limit.

Application of Biased Metropolis Algorithms: From protons to proteins.

We show that sampling with a biased Metropolis scheme is essentially equivalent to using the heatbath algorithm. However, the biased Metropolis method can also be applied when an efficient heatbath algorithm does not exist. This is first illustrated with an example from high energy physics (lattice gauge theory simulations).

Thermodynamics of two lattice ice models in three dimensions.

In a recent paper we introduced two Potts-like models in three dimensions, which share the following properties: (A) One of the ice rules is always fulfilled (in particular also at infinite temperature, beta=0 ). (B) Both ice rules hold for ground-state configurations. This allowed for an efficient calculation of the residual entropy of ice I (ordinary ice) by means of multicanonical simulations.

A hybrid recursion method to robustly ensure convergence efficiencies in the simulated scaling based free energy simulations.

Recently, we developed an efficient free energy simulation technique, the simulated scaling (SS) method [H. Li et al., J.

Finite volume Kolmogorov-Johnson-Mehl-Avrami theory.

We study the Kolmogorov-Johnson-Mehl-Avrami theory of phase conversion in finite volumes. For the conversion time we find the relationship tau(con)=tau(nu)[1+f(d)(q)]. Here d is the space dimension, tau(nu) the nucleation time in the volume V, and f(d)(q) a scaling function.

Numerical calculation of the combinatorial entropy of partially ordered ice.

Using a one-parameter case as an example, we demonstrate that multicanonical simulations allow for accurate estimates of the residual combinatorial entropy of partially ordered ice. For the considered case, corrections to an (approximate) analytical formula are found to be small, never exceeding 0.5%.

Wang-Landau multibondic cluster simulations for second-order phase transitions.

For a second-order phase transition the critical energy range of interest is larger than the energy range covered by a canonical Monte Carlo simulation at the critical temperature. Such an extended energy range can be covered by performing a Wang-Landau recursion for the spectral density followed by a multicanonical simulation with fixed weights. But in the conventional approach one loses the advantage due to cluster algorithms.

Finite reservoir replica exchange to enhance canonical sampling in rugged energy surfaces.

A "finite reservoir" replica exchange method is presented to further enhance sampling upon the regular replica exchange method (REM) in a rugged energy surface. The present method can facilitate important sampling more efficiently by exchanging structures with configurations randomly selected from a finite-sized reservoir; this finite reservoir is pregenerated and updated by a mechanism of replica exchange with neighboring "temperature" simulations. In practice, this proposal revises exchange schedule in REM simulations in order to make productive exchange for conformational "tunneling" more frequent.

Rugged Metropolis sampling with simultaneous updating of two dynamical variables.

The rugged Metropolis (RM) algorithm is a biased updating scheme which aims at directly hitting the most likely configurations in a rugged free-energy landscape. Details of the one-variable ( RM1 ) implementation of this algorithm are presented. This is followed by an extension to simultaneous updating of two dynamical variables ( RM2 ).

Metropolis simulations of Met-Enkephalin with solvent-accessible area parametrizations.

We investigate the solvent-accessible area method by means of Metropolis simulations of the brain peptide Met-Enkephalin at 300 K. For the energy function ECEPP/2 nine atomic solvation parameter (ASP) sets are studied. The simulations are compared with one another, with simulations with a distance dependent electrostatic permittivity epsilon(r), and with vacuum simulations (epsilon=2).

Multioverlap simulations for transitions between reference configurations.

We introduce a procedure to construct weight factors, which flatten the probability density of the overlap with respect to some predefined reference configuration. This allows one to overcome free-energy barriers in the overlap variable. Subsequently, we generalize the approach to deal with the overlaps with respect to two reference configurations so that transitions between them are induced.

Metropolis importance sampling for rugged dynamical variables.

A funnel transformation is introduced, which acts recursively from higher towards lower temperatures. It biases the a priori probabilities of a canonical or generalized ensemble Metropolis simulation, so that they zoom in on the global energy minimum, if a funnel exists indeed. A first, crude approximation to the full transformation, called rugged Metropolis one (RM1), is tested for Met-Enkephalin.

Overlap distribution of the three-dimensional Ising model.

We study the Parisi overlap probability density P(L)(q) for the three-dimensional Ising ferromagnet by means of Monte Carlo (MC) simulations. At the critical point, P(L)(q) is peaked around q=0 in contrast with the double peaked magnetic probability density. We give particular attention to the tails of the overlap distribution at the critical point, which we control over up to 500 orders of magnitude by using the multioverlap MC algorithm.

Efficiency of the multicanonical simulation method as applied to peptides of increasing size: the heptapeptide deltorphin.

The advantage of the multicanonical (MUCA) simulation method of Berg and coworkers over the conventional Metropolis method is in its ability to move a system effectively across energy barriers thereby providing results for a wide range of temperatures. However, a MUCA simulation is based on weights (related to the density of states) that should be determined prior to a production run and their calculation is not straightforward. To overcome this difficulty a procedure has been developed by Berg that calculates the MUCA weights automatically.

Functional form of the Parisi overlap distribution for the three-dimensional Edwards-Anderson Ising spin glass.

Recently, it has been conjectured that the statistics of extremes is of relevance for a large class of correlated systems. For certain probability densities this predicts the characteristic large x falloff behavior f(x) approximately exp(-ae(x)), a>0. Using a multicanonical Monte Carlo technique, we have measured the Parisi overlap distribution P(q) for the three-dimensional Edward-Anderson Ising spin glass at and below the critical temperature We find that a probability distribution related to extreme-order statistics gives an excellent description of P(q) over about 80 orders of magnitude.