Study of the dynamics of protein folding through minimalistic models

Starting with the Levinthal paradox, a brief introduction to the protein folding problem is presented. The existing theories of protein folding, including the folding funnel scenario, are discussed. After briefly discussing different simulation studies of model proteins, we discuss our recent work on the dynamics of folding of the model HP-36 (the chicken villin headpiece) protein by using a simplified hydropathy scale. Special attention has been paid to the statics and dynamics of contact formation among the hydrophobic residues. The results obtained from this simple model appear to be surprisingly similar to several features observed in the folding of real proteins. The account concludes with a discussion of future problems.

Ternary copper(II) complexes of thiosemicarbazones and heterocyclic bases showing N3OS coordination as models for the type-2 centers of copper monooxygenases

Six ternary copper(II) complexes of general formulation [CuLB] (1-6), where L is dianionic ONS-donor thiosemicarbazones derived from the condensation of salicylaldehyde with thiosemicarbazides and B is NN-donor heterocyclic bases like 2,2'-bipyridine, 1,10-phenanthroline and 2,9-dimethyl-1,10-phenanthroline, are prepared from a reaction of copper(II) acetate hydrate with the heterocyclic base (B) and the thiosemicarbazone (H2L) in MeOH, and structurally characterized by X-ray diffraction technique. Crystal structures of the complexes display a distorted square-pyramidal (4 + 1) coordination geometry having the ONS-donor thiosemicarbazone bonded at the basal plane. The chelating heterocyclic bases exhibit axial-equatorial mode of bonding. The complexes are one-electron paramagnetic and they show axial X-band EPR spectra in DMF-toluene glass at 77 K giving g(parallel to)(A(parallel to)) and g(perpendicular to) values of similar to2.2 (175 x 10(-4) cm(-1)) and similar to2.0 indicating a {d(x2-y2)}(1) ground state. The complexes show a d-d band near 570 nm and a charge transfer band near 400 nm in DMF. The complexes are redox active and exhibit a quasireversible Cu(II)-Cu(I) couple in DMF-0.1 M tetrabutylammonium perchlorate near 0.1 V vs. SCE. They are catalytically active in the oxidation of ascorbic acid in presence of dioxygen. The complexes with a CuN3OS coordination model the ascorbate oxidation property of dopamine beta-hydroxylase and peptidylglycine a-hydroxylating monooxygeanase. (C) 2003 Elsevier Science B.V. All rights reserved.

The promise of fuel cell-based automobiles

Fuel cell-based automobiles have gained attention in the last few years due to growing public concern about urban air pollution and consequent environmental problems. From an analysis of the power and energy requirements of a modern car, it is estimated that a base sustainable power of ca. 50 kW supplemented with short bursts up to 80 kW will suffice in most driving requirements. The energy demand depends greatly on driving characteristics but under normal usage is expected to be 200 Wh/km. The advantages and disadvantages of candidate fuel-cell systems and various fuels are considered together with the issue of whether the fuel should be converted directly in the fuel cell or should be reformed to hydrogen onboard the vehicle. For fuel cell vehicles to compete successfully with conventional internal-combustion engine vehicles, it appears that direct conversion fuel cells using probably hydrogen, but possibly methanol, are the only realistic contenders for road transportation applications. Among the available fuel cell technologies, polymer-electrolyte fuel cells directly fueled with hydrogen appear to be the best option for powering fuel cell vehicles as there is every prospect that these will exceed the performance of the internal-combustion engine vehicles but for their first cost. A target cost of $ 50/kW would be mandatory to make polymer-electrolyte fuel cells competitive with the internal combustion engines and can only be achieved with design changes that would substantially reduce the quantity of materials used. At present, prominent car manufacturers are deploying important research and development efforts to develop fuel cell vehicles and are projecting to start production by 2005.

Performance Evaluation of Distance-Hop Proportionality on Geometric Graph Models of Dense Sensor Networks

Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes on a region in Euclidean space, e.g., the unit square. After deployment, the nodes self-organise into a mesh topology. In a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this paper, we analyse the performance of this approximation. We show that nodes with a certain hop distance from a fixed anchor node lie within a certain annulus with probability approach- ing unity as the number of nodes n → ∞. We take a uniform, i.i.d. deployment of n nodes on a unit square, and consider the geometric graph on these nodes with radius r(n) = c q ln n n . We show that, for a given hop distance h of a node from a fixed anchor on the unit square,the Euclidean distance lies within [(1−ǫ)(h−1)r(n), hr(n)],for ǫ > 0, with probability approaching unity as n → ∞.This result shows that it is more likely to expect a node, with hop distance h from the anchor, to lie within this an- nulus centred at the anchor location, and of width roughly r(n), rather than close to a circle whose radius is exactly proportional to h. We show that if the radius r of the ge- ometric graph is fixed, the convergence of the probability is exponentially fast. Similar results hold for a randomised lattice deployment. We provide simulation results that il- lustrate the theory, and serve to show how large n needs to be for the asymptotics to be useful.

Voltage and Temperature Scalable Standard Cell Leakage Models Based on Stacks for Statistical Leakage Characterization

With extensive use of dynamic voltage scaling (DVS) there is increasing need for voltage scalable models. Similarly, leakage being very sensitive to temperature motivates the need for a temperature scalable model as well. We characterize standard cell libraries for statistical leakage analysis based on models for transistor stacks. Modeling stacks has the advantage of using a single model across many gates there by reducing the number of models that need to be characterized. Our experiments on 15 different gates show that we needed only 23 models to predict the leakage across 126 input vector combinations. We investigate the use of neural networks for the combined PVT model, for the stacks, which can capture the effect of inter die, intra gate variations, supply voltage(0.6-1.2 V) and temperature (0 - 100degC) on leakage. Results show that neural network based stack models can predict the PDF of leakage current across supply voltage and temperature accurately with the average error in mean being less than 2% and that in standard deviation being less than 5% across a range of voltage, temperature.

Voltage and Temperature Scalable Gate Delay and Slew Models Including Intra-Gate Variations

We investigate the feasibility of developing a comprehensive gate delay and slew models which incorporates output load, input edge slew, supply voltage, temperature, global process variations and local process variations all in the same model. We find that the standard polynomial models cannot handle such a large heterogeneous set of input variables. We instead use neural networks, which are well known for their ability to approximate any arbitrary continuous function. Our initial experiments with a small subset of standard cell gates of an industrial 65 nm library show promising results with error in mean less than 1%, error in standard deviation less than 3% and maximum error less than 11% as compared to SPICE for models covering 0.9- 1.1 V of supply, -40degC to 125degC of temperature, load, slew and global and local process parameters. Enhancing the conventional libraries to be voltage and temperature scalable with similar accuracy requires on an average 4x more SPICE characterization runs.

Voltage and Temperature Scalable Gate Delay and Slew Models Including Intra-Gate Variations

We investigate the feasibility of developing a comprehensive gate delay and slew models which incorporates output load, input edge slew, supply voltage, temperature, global process variations and local process variations all in the same model. We find that the standard polynomial models cannot handle such a large heterogeneous set of input variables. We instead use neural networks, which are well known for their ability to approximate any arbitrary continuous function. Our initial experiments with a small subset of standard cell gates of an industrial 65 nm library show promising results with error in mean less than 1%, error in standard deviation less than 3% and maximum error less than 11% as compared to SPICE for models covering 0.9- 1.1 V of supply, -40degC to 125degC of temperature, load, slew and global and local process parameters. Enhancing the conventional libraries to be voltage and temperature scalable with similar accuracy requires on an average 4x more SPICE characterization runs.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.