Kernel-based machine learning protocol for predicting DNA-binding proteins

DNA-binding proteins (DNA-BPs) play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Attempts have been made to identify DNA-BPs based on their sequence and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines (SVMs). Information used for classification is derived from characteristics that include surface and overall composition, overall charge and positive potential patches on the protein surface. In total 121 DNA-BPs and 238 non-binding proteins are used to build and evaluate the protocol. In self-consistency, accuracy value of 100% has been achieved. For cross-validation (CV) optimization over entire dataset, we report an accuracy of 90%. Using leave 1-pair holdout evaluation, the accuracy of 86.3% has been achieved. When we restrict the dataset to less than 20% sequence identity amongst the proteins, the holdout accuracy is achieved at 85.8%. Furthermore, seven DNA-BPs with unbounded structures are all correctly predicted. The current performances are better than results published previously. The higher accuracy value achieved here originates from two factors: the ability of the SVM to handle features that demonstrate a wide range of discriminatory power and, a different definition of the positive patch. Since our protocol does not lean on sequence or structural homology, it can be used to identify or predict proteins with DNA-binding function(s) regardless of their homology to the known ones

The MEK2-binding tumor suppressor hDlg is recruited by E-cadherin to the midbody ring

Background: The human homologue of the Drosophila Discs-large tumor suppressor protein, hDlg, is a multi-domain cytoplasmic protein that localizes to the membrane at intercellular junction sites. At both synaptic junctions and epithelia cell-cell junctions, hDlg is known to recruit several signaling proteins into macromolecular complexes. hDlg is also found at the midbody, a small microtubule-rich structure bridging the two daughter cells during cytokinesis, but its function at this site is not clear. Results: Here we describe the interaction of hDlg with the activated form of MEK2 of the canonical RAF/MEK/ERK pathway, a protein that is found at the midbody during cytokinesis. We show that both proteins localize to a sub-structure of the midbody, the midbody ring, and that the interaction between the PDZ domains of hDlg and the C-terminal portion of MEK2 is dependent on the phosphorylation of MEK2. Finally, we found that E-cadherin also localizes to the midbody and that its expression is required for the isoform-specific recruitment of hDlg, but not activated MEK2, to that structure. Conclusion: Our results suggest that like at other cell-cell junction sites, hDlg is part of a macromolecular complex of structural and signaling proteins at the midbody.Molecular and Cellular Biolog

NAPS: a residue-level nucleic acid-binding prediction server

Nucleic acid-binding proteins are involved in a great number of cellular processes. Understanding the mechanisms underlying these proteins first requires the identification of specific residues involved in nucleic acid binding. Prediction of NA-binding residues can provide practical assistance in the functional annotation of NA-binding proteins. Predictions can also be used to expedite mutagenesis experiments, guiding researchers to the correct binding residues in these proteins. Here, we present a method for the identification of amino acid residues involved in DNA- and RNA-binding using sequence-based attributes. The method used in this work combines the C4.5 algorithm with bootstrap aggregation and cost-sensitive learning. Our DNA-binding model achieved 79.1% accuracy, while the RNA-binding model reached an accuracy of 73.2%. The NAPS web server is freely available at http://proteomics.bioengr.uic.edu/NAPS

Big Bang Nucleosynthesis Constraints on Brane Cosmologies

We examine constraints from Big Bang nucleosynthesis on type II Randall-Sundrum brane cosmologies with both a dark radiation component and a quadratic term that depends on the 5-dimensional Planck mass, M_5. Using limits on the abundances of deuterium and helium-4, we calculate the allowed region in the M_5-dark radiation plane and derive the precise BBN bound on M_5 alone with no dark radiation: M_5 > 13 TeV.Comment: 3 pages, 1 figure, references added, to appear in Phys. Lett.

Activation of GTP hydrolysis in mRNA-tRNA translocation by elongation factor G

During protein synthesis, elongation of the polypeptide chain by each amino acid is followed by a translocation step in which mRNA and transfer RNA (tRNA) are advanced by one codon. This crucial step is catalyzed by elongation factor G (EF-G), a guanosine triphosphatase (GTPase), and accompanied by a rotation between the two ribosomal subunits. A mutant of EF-G, H91A, renders the factor impaired in guanosine triphosphate (GTP) hydrolysis and thereby stabilizes it on the ribosome. We use cryogenic electron microscopy (cryo-EM) at near-atomic resolution to investigate two complexes formed by EF-G H91A in its GTP state with the ribosome, distinguished by the presence or absence of the intersubunit rotation. Comparison of these two structures argues in favor of a direct role of the conserved histidine in the switch II loop of EF-G in GTPase activation, and explains why GTP hydrolysis cannot proceed with EF-G bound to the unrotated form of the ribosome

Reciprocating excitation of a flexible beam: Benchmark study

A long and slender flexible beam is set in oscillatory motion to observe its deflection. A novel application of digital image processing is employed to obtain contactless discrete measurements of the beam tip deflection. We compare the measured data to those predicted by a flexible multibody dynamics simulation (flxdyn). This study is intended as a benchmark. Moreover, the system is described in sufficient detail to enable other investigators to repeat, and build upon results herein presented for the first time

Atlas motion platform generalized kinematic model: Atlas motion platform

Conventional training simulators commonly use a hexapod configuration to provide motion cues. While widely used, studies have shown that hexapods are incapable of producing the range of motion required to achieve high fidelity simulation required in many applications. A novel alternative is the Atlas motion platform. This paper presents a new generalized kinematic model of the platform which can be applied to any spherical platform actuated by three omnidirectional wheels. In addition, conditions for slip-free and singularity-free motions are identified. Two illustrative examples are given for different omnidirectional wheel configurations

Dynamics and vibration analysis of the interface between a non-rigid sphere and omnidirectional wheel actuators

This paper presents analysis of the dynamics and vibration of an orientation motion platform utilizing a sphere actuated by omnidirectional wheels. The purpose of the analysis is to serve as a design tool for the construction of a six-degree-of-freedom motion platform with unlimited rotational motion. The equations of motion are presented taking flexibility of the system into account. The behaviour of the system is illustrated by sample configurations with a range of omnidirectional wheel types and geometries. Vibration analysis follows, and

A discrete dynamic programming approximation to the multiobjective deterministic finite horizon optimal control problem

This paper addresses the problem of finding an approximation to the minimal element set of the objective space for the class of multiobjective deterministic finite horizon optimal control problems. The objective space is assumed to be partially ordered by a pointed convex cone containing the origin. The approximation procedure consists of a two-step discretization in time and state space. Following the first-order time discretization, the dynamic programming principle is used to find the multiobjective discrete dynamic programming equation equivalent to the resulting discrete multiobjective optimal control problem. The multiobjective discrete dynamic programming equation is finally discretized in the state space. The convergence of the approximation for both discretization steps is discussed