The binding specificities of RNA- and DNA-binding proteins are determined from experimental data using a ‘deep learning’ approach.

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Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Artificial Organic Host Molecules for Anions

The 3 angstrom resolution crystal structure of the Escherichia coli catabolite gene activator protein (CAP) complexed with a 30-base pair DNA sequence shows that the DNA is bent by 90 degrees. This bend results almost entirely from two 40 degrees kinks that occur between TG/CA base pairs at positions 5 and 6 on each side of the dyad axis of the complex. DNA sequence discrimination by CAP derives both from sequence-dependent distortion of the DNA helix and from direct hydrogen-bonding interactions between three protein side chains and the exposed edges of three base pairs in the major groove of the DNA. The structure of this transcription factor--DNA complex provides insights into possible mechanisms of transcription activation.

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Crystal structure of a CAP-DNA complex: the DNA is bent by 90 degrees.

All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from this is the last candidate. next esc will revert to uncompleted text. he publisher. Overview Dataset shift is a challenging situation where the joint distribution of inputs and outputs differs between the training and test stages. Covariate shift is a simpler particular case of dataset shift where only the input distribution changes (covariate denotes input), while the conditional distribution of the outputs given the inputs p(y|x) remains unchanged. Dataset shift is present in most practical applications for reasons ranging from the bias introduced by experimental design, to the mere irreproducibility of the testing conditions at training time. For example, in an image classification task, training data might have been recorded under controlled laboratory conditions, whereas the test data may show different lighting conditions. In other applications, the process that generates data is in itself adaptive. Some of our authors consider the problem of spam email filtering: successful " spammers " will try to build spam in a form that differs from the spam the automatic filter has been built on. Dataset shift seems to have raised relatively little interest in the machine learning community until very recently. Indeed, many machine learning algorithms are based on the assumption that the training data is drawn from exactly the same distribution as the test data on which the model will later be evaluated. Semi-supervised learning and active learning, two problems that seem very similar to covariate shift have received much more attention. How do they differ from covariate shift? Semi-supervised learning is designed to take advantage of unlabeled data present at training time, but is not conceived to be robust against changes in the input distribution. In fact, one can easily construct examples of covariate shift for which common SSL strategies such as the " cluster assumption " will lead to disaster. In active learning the algorithm is asked to select from the available unlabeled inputs those for which obtaining the label will be most beneficial for learning. This is very relevant in contexts where labeling data is very costly, but active learning strategies 2 Contents are not specifically design for dealing with covariate shift. This book attempts to give an overview of the different recent efforts that are being …

Adaptive computation and machine learning

The three-dimensional structure of a TATA-box binding polypeptide complexed with the TATA element of the adenovirus major late promoter has been determined by X-ray crystallography at 2.25 A resolution. Binding of the saddle-shaped protein induces a conformational change in the DNA, inducing sharp kinks at either end of the sequence TATAAAAG. Between the kinks, the right-handed double helix is smoothly curved and partially unwound, presenting a widened minor groove to TBP's concave, antiparallel β-sheet. Side-chain/base interactions are restricted to the minor groove, and include hydrogen bonds, van der Waals contacts and phenylalanine–base stacking interactions.

Co-crystal structure of TBP recognizing the minor groove of a TATA element

Determinations of the salt sensitivity of enzymes extracted from the halophilic alga Dunaliella viridis revealed that pentose phosphate isomerase, ribulose diphosphate carboxylase, glucose-6-phosphate dehydrogenase, and phosphohexose isomerase were inhibited by NaCl concentrations far lower than that in the growth medium (3.75 m). The inhibition was reversible and was not prevented by preparing the extracts in the presence of salt. Potassium, lithium, and cesium chlorides were equally inhibitory. In contrast, whole cells require rather high levels of NaCl for optimal growth, whereas growth is inhibited by low levels of the other cations. The results suggest a specific mechanism for the exclusion of sodium from the interior of the cell. Images

Effects of Salts on the Halophilic Alga Dunaliella viridis

An optimized potential function for base-stacking interaction is constructed. Stacking energies between the complementary pairs of a dimer are calculated as a function of the rotational angle and separation distance. Using several different sets of atomic charges, the electrostatic component in the monopole-monopole approximation (MMA) is compared to the more refined segmented multipole–multipole representation (SMMA); the general features of the stacking minima are found to be correctly reproduced with IEHT or CNDO atomic charges. The electrostatic component is observed to control the location of stacking minima. The MMA, in general, is not a reliable approximation of the SMMA in regions away from minima; however, the MMA is reliable in predicting the location and nature of stacking minima. The attractive part of the Lennard-Jones 6–12 potential is compared to and parameterized against the expression for the second-order interaction terms composed of multipole-bond polarizability for the polarization energy and transition-dipole bond polarizabilities for approximation of the dispersion energy. The repulsive part of the Lennard-Jones potential is compared to a Kitaygorodski-type repulsive function; changing the exponent from its usual value of 12 to 11.7 gives significantly better agreement with the more refined repulsive function. Stacking minima calculated with the optimized potential method are compared with various perturbation-type treatments. The optimized potential method yields results that compare as well with melting data as do any of the more recent and expensive perturbation methods.

An optimized potential function for the calculation of nucleic acid interaction energies. I - Base stacking

The S/sub N/2 reaction between ammonia and formic acid has been studied as a model reaction for peptide bond formation using the semiempirical MNDO and ab initio molecular orbital methods. Two reaction mechanisms have been examined, i.e., a stepwise and a concerted reaction. The stationary points of each reaction including intermediate and transition states have been identified and free energies have been calculated for all geometry optimized reaction species to determine the thermodynamics and kinetics of each reaction. The stepwise mechanism was found to be more favorable than the concerted one by both MNDO and ab initio calculations. However, the ab initio method predicts both mechanisms to be fairly competitive with free energies of activation of about 50 kcal/mol. Despite excellent agreement between both methods in the calculated entropies and thermal energies, the minimum basis set character of MNDO leads to values of free energy of activation much higher than those obtained by the ab initio method. The basis set dependence and effect of correlation of the computed ab initio results and the relative effects of polarization and correlation were also investigated by using a number of basis sets up to 6-31G** and estimates of correlation energy by Moller-Plesset perturbationmore » theory up to fourth order. Correlation energy was found to ba a significant factor in the stabilization of transition states.« less

Quantum chemical studies of a model for peptide bond formation: formation of formamide and water from ammonia and formic acid

Glassy structures of water were generated by rapidly quenching configurations of 64 and 343 molecules of liquid water; and the potential energy was then expanded through quadratic order around local minima generated in this way, and the properties of the resulting harmonic system were calculated. The results obtained were used to test the degree to which the structure of liquid water is similar to that of a harmonic aqueous glass. The radial distribution functions for the glass were found to be remarkably similar to those of the liquid. Translational modes were found to be almost entirely responsible for the broadening of the oxygen-oxygen radial distribution function of the quenched configuration, and also primarily responsible for the broadening of other radial distribution functions.

Robert D. Macelroy

Papers

Effects of Salts on the Halophilic Alga Dunaliella viridis

An optimized potential function for the calculation of nucleic acid interaction energies. I - Base stacking

Quantum chemical studies of a model for peptide bond formation: formation of formamide and water from ammonia and formic acid

Comparison of the structure of harmonic aqueous glasses and liquid water.

Current concepts and future directions of CELSS.