Crystallographic studies of the extracytoplasmic function sigma factor sigma(J) from Mycobacterium tuberculosis

Mycobacterium tuberculosis has multiple sigma factors which enable the bacterium to reprogram its transcriptional machinery under diverse environmental conditions. sigma(J), an extracytoplasmic function sigma factor, is upregulated in late stationary phase cultures and during human macrophage infection. sigma(J) governs the cellular response to hydrogen peroxide-mediated oxidative stress. sigma(J) differs from other canonical sigma factors owing to the presence of a SnoaL_2 domain at the C-terminus. sigma(J) crystals belonged to the tetragonal space group I422, with unit-cell parameters a = b = 133.85, c = 75.08 angstrom. Diffraction data were collected to 2.16 angstrom resolution on the BM14 beamline at the European Synchrotron Radiation Facility (ESRF).

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Towards an exact factorization of the molecular wave function

An exact single-product factorisation of the molecular wave function for the timedependent Schrodinger equation is investigated by using an ansatz involving a phasefactor. By using the Frenkel variational method, we obtain the Schrodinger equations for the electronic and nuclear wave functions. The concept of a potential energy surface (PES) is retained by introducing a modified Hamiltonian as suggested earlier by Cederbaum. The parameter in the phase factor is chosen such that the equations of motion retain the physically appealing Born- Oppenheimer-like form, and is therefore unique.

The putative role of some conserved water molecules in the structure and function of human transthyretin

Human transthyretin (hTTR) is a multifunctional protein that is involved in several neurodegenerative diseases. Besides the transportation of thyroxin and vitamin A, it is also involved in the proteolysis of apolipoprotein A1 and A beta peptide. Extensive analyses of 32 high-resolution X-ray and neutron diffraction structures of hTTR followed by molecular-dynamics simulation studies using a set of 15 selected structures affirmed the presence of 44 conserved water molecules in its dimeric structure. They are found to play several important roles in the structure and function of the protein. Eight water molecules stabilize the dimeric structure through an extensive hydrogen-bonding network. The absence of some of these water molecules in highly acidic conditions (pH <= 4.0) severely affects the interfacial hydrogen-bond network, which may destabilize the native tetrameric structure, leading to its dissociation. Three pairs of conserved water molecules contribute to maintaining the geometry of the ligand-binding cavities. Some other water molecules control the orientation and dynamics of different structural elements of hTTR. This systematic study of the location, absence, networking and interactions of the conserved water molecules may shed some light on various structural and functional aspects of the protein. The present study may also provide some rational clues about the conserved water-mediated architecture and stability of hTTR.

Protein Structure and Function: Looking through the Network of Side-Chain Interactions

Network theory has become an excellent method of choice through which biological data are smoothly integrated to gain insights into complex biological problems. Understanding protein structure, folding, and function has been an important problem, which is being extensively investigated by the network approach. Since the sequence uniquely determines the structure, this review focuses on the networks of non-covalently connected amino acid side chains in proteins. Questions in structural biology are addressed within the framework of such a formalism. While general applications are mentioned in this review, challenging problems which have demanded the attention of scientific community for a long time, such as allostery and protein folding, are considered in greater detail. Our aim has been to explore these important problems through the eyes of networks. Various methods of constructing protein structure networks (PSN) are consolidated. They include the methods based on geometry, edges weighted by different schemes, and also bipartite network of protein-nucleic acid complexes. A number of network metrics that elegantly capture the general features as well as specific features related to phenomena, such as allostery and protein model validation, are described. Additionally, an integration of network theory with ensembles of equilibrium structures of a single protein or that of a large number of structures from the data bank has been presented to perceive complex phenomena from network perspective. Finally, we discuss briefly the capabilities, limitations, and the scope for further explorations of protein structure networks.

Dissecting pi-helices: sequence, structure and function

A new procedure for the identification of regular secondary structures using a C-alpha trace has identified 659 pi-helices in 3582 protein chains, solved at high resolution. Taking advantage of this significantly expanded database of pi-helices, we have analysed the functional and structural roles of helices and determined the position-wise amino acid propensity within and around them. These helices range from 5 to 18 residues in length with the average twist and rise being 85.2 +/- 7.2 and 1.28 +/- 0.31 angstrom, respectively. A total of 546 (similar to 83%) out of 659 pi-helices occur in conjunction with alpha-helices, with 101 pi-helices being interspersed between two alpha-helices. The majority of interspersed pi-helices were found to be conserved across a large number of structures within a protein family and produce a significant bend in the overall helical segment as well as local distortions in the neighbouring a-helices. The presence of a pi-helical fragment leads to appropriate orientation of the constituent residues, so as to facilitate favourable interactions and also help in proper folding of the protein chain. In addition to intra helical 6 -> 1 N H center dot center dot center dot O hydrogen bonds, pi-helices are also stabilized by several other non-bonded interactions. pi-Helices show distinct positional residue preferences, which are different from those of a-helices.

Mutations in the nucleotide binding and hydrolysis domains of Helicobacter pylori MutS2 lead to altered biochemical activities and inactivation of its in vivo function

Background: Helicobacter pylori MutS2 (HpMutS2), an inhibitor of recombination during transformation is a non-specific nuclease with two catalytic sites, both of which are essential for its anti-recombinase activity. Although HpMutS2 belongs to a highly conserved family of ABC transporter ATPases, the role of its ATP binding and hydrolysis activities remains elusive. Results: To explore the putative role of ATP binding and hydrolysis activities of HpMutS2 we specifically generated point mutations in the nucleotide-binding Walker-A (HpMutS2-G338R) and hydrolysis Walker-B (HpMutS2-E413A) domains of the protein. Compared to wild-type protein, HpMutS2-G338R exhibited similar to 2.5-fold lower affinity for both ATP and ADP while ATP hydrolysis was reduced by similar to 3-fold. Nucleotide binding efficiencies of HpMutS2-E413A were not significantly altered; however the ATP hydrolysis was reduced by similar to 10-fold. Although mutations in the Walker-A and Walker-B motifs of HpMutS2 only partially reduced its ability to bind and hydrolyze ATP, we demonstrate that these mutants not only exhibited alterations in the conformation, DNA binding and nuclease activities of the protein but failed to complement the hyper-recombinant phenotype displayed by mutS2-disrupted strain of H. pylori. In addition, we show that the nucleotide cofactor modulates the conformation, DNA binding and nuclease activities of HpMutS2. Conclusions: These data describe a strong crosstalk between the ATPase, DNA binding, and nuclease activities of HpMutS2. Furthermore these data show that both, ATP binding and hydrolysis activities of HpMutS2 are essential for the in vivo anti-recombinase function of the protein.

Spatio-temporal Map Formation Based on a Potential Function

We revisit the problem of temporal self organization using activity diffusion based on the neural gas (NGAS) algorithm. Using a potential function formulation motivated by a spatio-temporal metric, we derive an adaptation rule for dynamic vector quantization of data. Simulations results show that our algorithm learns the input distribution and time correlation much faster compared to the static neural gas method over the same data sequence under similar training conditions.