Distance-dependent dielectric constants and their application to double-helical DNA

Detailed studies of structures of biological macromolecules, even in simplified models, involve many costly and time-consuming calculations. Any thorough methods require sampling of an extremely large conformation and momentum space. Calculations of electrostatic interactions, which depend on many physical factors, such as the details of solvent, solvent accessibility in macromolecules, and molecular polarizability, are always developed in a compromise between more rigorous, detailed models and the need for immediate application to complicated biological systems. In this paper, a middle ground is taken between the more exact theoretical models and the simplest constant values for the dielectric constant. The effects of solvent, counterions, and molecular polarizability are incorporated through a set of adjustable parameters that should be determined from experimental conditions. Several previous forms for the dielectric function are compared with the new ones. The present methods use Langevin functions to span the region of dielectric constant between bulk solvent and cavity values. Application of such dielectric models to double-helical DNA is important because base-stacking preferences were previously demonstrated [A. Sarai, J. Mazur, R. Nussinov, and R. L. Jernigan (1988) Biochemistry, vol. 27, pp. 8498-8502] to be sensitive to the electrostatic formulation. Here we find that poly(dG).poly(dC) can be A form for high screening and B form for low screening. By contrast, poly(dA).poly(dT) can only take the B form. Base stacking is more sensitive to the form of the dielectric function than are the sugar-phosphate backbone conformations. Also in B form, the backbone conformations are not so affected by the base types as in A form.     

Identifying sequence-structure pairs undetected by sequence alignments

We examine how effectively simple potential functions previously developed can identify compatibilities between sequences and structures of proteins for database searches. The potential function consists of pairwise contact energies, repulsive packing potentials of residues for overly dense arrangement and short-range potentials for secondary structures, all of which were estimated from statistical preferences observed in known protein structures. Each potential energy term was modified to represent compatibilities between sequences and structures for globular proteins. Pairwise contact interactions in a sequence-structure alignment are evaluated in a mean field approximation on the basis of probabilities of site pairs to be aligned. Gap penalties are assumed to be proportional to the number of contacts at each residue position, and as a result gaps will be more frequently placed on protein surfaces than in cores. In addition to minimum energy alignments, we use probability alignments made by successively aligning site pairs in order by pairwise alignment probabilities. The results show that the present energy function and alignment method can detect well both folds compatible with a given sequence and, inversely, sequences compatible with a given fold, and yield mostly similar alignments for these two types of sequence and structure pairs. Probability alignments consisting of most reliable site pairs only can yield extremely small root mean square deviations, and including less reliable pairs increases the deviations. Also, it is observed that secondary structure potentials are usefully complementary to yield improved alignments with this method. Remarkably, by this method some individual sequence-structure pairs are detected having only 5-20% sequence identity.     

Potentials 'R'Us web-server for protein energy estimations with coarse-grained knowledge-based potentials

Background  

Knowledge-based potentials have been widely used in the last 20 years for fold recognition, protein structure prediction from amino acid sequence, ligand binding, protein design, and many other purposes. However generally these are not readily accessible online.     

Finite element modeling of the left atrium to facilitate the design of an endoscopic atrial retractor

With the worldwide prevalence of cardiovascular diseases, much attention has been focused on simulating the characteristics of the human heart to better understand and treat cardiac disorders. The purpose of this study is to build a finite element model of the left atrium (LA) that incorporates detailed anatomical features and realistic material characteristics to investigate the interaction of heart tissue and surgical instruments. This model is used to facilitate the design of an endoscopically deployable atrial retractor for use in minimally invasive, robotically assisted mitral valve repair. Magnetic resonance imaging (MRI) scans of a pressurized explanted porcine heart were taken to provide a 3D solid model of the heart geometry, while uniaxial tensile tests of porcine left atrial tissue were conducted to obtain realistic material properties for noncontractile cardiac tissue. A finite element model of the LA was constructed using ANSYS Release 9.0 software and the MRI data. The Mooney-Rivlin hyperelastic material model was chosen to characterize the passive left atrial tissue; material constants were derived from tensile test data. Finite element analysis (FEA) models of a CardioVations Port Access retractor and a prototype endoscopic retractor were constructed to simulate interaction between each instrument and the LA. These contact simulations were used to compare the quality of retraction between the two instruments and to optimize the design of the prototype retractor. Model accuracy was verified by comparing simulated cardiac wall deflections to those measured by MRI. FEA simulations revealed that peak forces of approximately 2.85 N and 2.46 N were required to retract the LA using the Port Access and prototype retractors, respectively. These forces varied nonlinearly with retractor blade displacement. Dilation of the atrial walls and rigid body motion of the chamber were approximately the same for both retractors. Finite element analysis is shown to be an effective tool for analyzing instrument/tissue interactions and for designing surgical instruments. The benefits of this approach to medical device design are significant when compared to the alternatives: constructing prototypes and evaluating them via animal or clinical trials.     

Distributions of amino acids suggest that certain residue types more effectively determine protein secondary structure

Exponential growth in the number of available protein sequences is unmatched by the slower growth in the number of structures. As a result, the development of efficient and fast protein secondary structure prediction methods is essential for the broad comprehension of protein structures. Computational methods that can efficiently determine secondary structure can in turn facilitate protein tertiary structure prediction, since most methods rely initially on secondary structure predictions. Recently, we have developed a fast learning optimized prediction methodology (FLOPRED) for predicting protein secondary structure (Saraswathi et al. in JMM 18:4275, 2012). Data are generated by using knowledge-based potentials combined with structure information from the CATH database. A neural network-based extreme learning machine (ELM) and advanced particle swarm optimization (PSO) are used with this data to obtain better and faster convergence to more accurate secondary structure predicted results. A five-fold cross-validated testing accuracy of 83.8 % and a segment overlap (SOV) score of 78.3 % are obtained in this study. Secondary structure predictions and their accuracy are usually presented for three secondary structure elements: α-helix, β-strand and coil but rarely have the results been analyzed with respect to their constituent amino acids. In this paper, we use the results obtained with FLOPRED to provide detailed behaviors for different amino acid types in the secondary structure prediction. We investigate the influence of the composition, physico-chemical properties and position specific occurrence preferences of amino acids within secondary structure elements. In addition, we identify the correlation between these properties and prediction accuracy. The present detailed results suggest several important ways that secondary structure predictions can be improved in the future that might lead to improved protein design and engineering.     

A Model of the Strain-induced B-Z Transition

Abstract

The B-to-Z transition in supercoiled circular DNA is modeled as a strain-induced nonlinear excitation process. Using a model, in which DNA is regarded as a chain of units with a bistable energy function along the twisting coordinate together with a harmonic inter-unit interaction, we show that a Z region and the accompanying two B-Z junctions of finite width appear naturally as a solution of nonlinear equations, when the strain exceeds a critical value. We examine the B-Z transition behaviour as a function of twist under various situations. We also analyse available experimental results on B-Z transition in supercoiled plasmid with G-C insertions by this mechanistic model in order to estimate the magnitude of model parameters. The energy barrier of the B-Z transition is estimated to be of the order of 1 kcal/mole per base pair. The analysis shows that if the length of the insertion is less than a certain value, the entire insertion converts to Z form at a transition point, but if the insertion is much longer, the B-Z transition exhibits a different behavior, in which part of the insertion flips to Z form and the Z region expands linearly upon changing linking number.     

Genetic influences on cortical regionalization in the human brain

Animal data demonstrate that the development of distinct cortical areas is influenced by genes that exhibit highly regionalized expression patterns. In this paper, we show genetic patterning of cortical surface area derived from MRI data from 406 adult human twins. We mapped genetic correlations of areal expansion between selected seed regions and all other cortical locations, with the selection of seed points based on results from animal studies. "Marching seeds" and a data-driven, hypothesis-free, fuzzy-clustering approach provided convergent validation. The results reveal strong anterior-to-posterior graded, bilaterally symmetric patterns of?regionalization, largely consistent with patterns previously reported in nonhuman mammalian models. Broad similarities in genetic patterning between rodents and humans might suggest a conservation of cortical patterning mechanisms, whereas dissimilarities might reflect the functionalities most essential to each species.     

Molecular mechanisms of chaperonin GroEL-GroES function.

The dynamics of the GroEL-GroES complex is investigated with a coarse-grained model. This model is one in which single-residue points are connected to other such points, which are nearby, by identical springs, forming a network of interactions. The nature of the most important (slowest) normal modes reveals a wide variety of motions uniquely dependent upon the central cavity of the structure, including opposed torsional rotation of the two GroEL rings accompanied by the alternating compression and expansion of the GroES cap binding region, bending, shear, opposed radial breathing of the cis and trans rings, and stretching and contraction along the protein assembly's long axis. The intermediate domains of the subunits are bifunctional due to the presence of two hinges, which are alternatively activated or frozen by an ATP-dependent mechanism. ATP binding stabilizes a relatively open conformation (with respect to the central cavity) and hinders the motion of the hinge site connecting the intermediate and equatorial domains, while enhancing the flexibility of the second hinge that sets in motion the apical domains. The relative flexibilities of the hinges are reversed in the nucleotide-free form. Cooperative cross-correlations between subunits provide information about the mechanism of action of the protein. The mechanical motions driven by the different modes provide variable binding surfaces and variable sized cavities in the interior to enable accommodation of a broad range of protein substrates. These modes of motion could be used to manipulate the substrate's conformations.     

Predicting DNA-binding sites of proteins from amino acid sequence

Background  

Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.