Graphene and other two-dimensional materials offer a new class of ultrathin membranes that can have atomically defined nanopores with diameters approaching those of hydrated ions. These nanopores have the smallest possible pore volumes of any ion channel, which, due to ionic dehydration and electrokinetic effects, places them in a novel transport regime and allows membranes to be created that combine selective ionic transport with ultimate permeance and could lead to separations and sensing applications. However, experimental characterization and understanding of sub-continuum ionic transport in nanopores below 2 nm is limited. Here we show that isolated sub-2 nm pores in graphene exhibit, in contrast to larger pores, diverse transport behaviours consistent with ion transport over a free-energy barrier arising from ion dehydration and electrostatic interactions. Current-voltage measurements reveal that the conductance of graphene nanopores spans three orders of magnitude and that they display distinct linear, voltage-activated or rectified current-voltage characteristics and different cation-selectivity profiles. In rare cases, rapid, voltage-dependent stochastic switching is observed, consistent with the presence of a dissociable group in the pore vicinity. A modified Nernst-Planck model incorporating ion hydration and electrostatic effects quantitatively matches the observed behaviours.

Is it by design or by default that water molecules are observed at the interfaces of some protein-DNA complexes? Both experimental and theoretical studies on the thermodynamics of protein-DNA binding overwhelmingly support the extended hydrophobic view that water release from interfaces favors binding. Structural and energy analyses indicate that the waters that remain at the interfaces of protein-DNA complexes ensure liquid-state packing densities, screen the electrostatic repulsions between like charges (which seems to be by design), and in a few cases act as linkers between complementary charges on the biomolecules (which may well be by default). This review presents a survey of the current literature on water in protein-DNA complexes and a critique of various interpretations of the data in the context of the role of water in protein-DNA binding and principles of protein-DNA recognition in general.

We report here a computationally fast protocol for predicting binding affinities of non-metallo protein-ligand complexes. The protocol builds in an all atom energy based empirical scoring function comprising electrostatics, van der Waals, hydrophobicity and loss of conformational entropy of protein side chains upon ligand binding. The method is designed to ensure transferability across diverse systems and has been validated on a heterogenous dataset of 161 complexes consisting of 55 unique protein targets. The scoring function trained on a dataset of 61 complexes yielded a correlation of r = 0.92 for the predicted binding free energies against the experimental binding affinities. Model validation and parameter analysis studies ensure the predictive ability of the scoring function. When tested on the remaining 100 protein-ligand complexes a correlation of r = 0.92 was recovered. The high correlation obtained underscores the potential applicability of the methodology in drug design endeavors. The scoring function has been web enabled at www.scfbio-iitd.res.in/software/drugdesign/bappl.jsp as binding affinity prediction of protein-ligand (BAPPL) server.

Solid-state nanopores have emerged as versatile single-molecule sensors for applications including DNA sequencing, protein unfolding, micro-RNA detection, label-free detection of single nucleotide polymorphisms, and mapping of DNA-binding proteins involved in homologous recombination. While machining nanopores in dielectric membranes provides nanometer-scale precision, the rigid silicon support for the membrane contributes capacitive noise and limits integration with microfluidic networks for sample pre-processing. Herein, we demonstrate a technique to directly transfer solid-state nanopores machined in dielectric membranes from a silicon support into a microfluidic network. The resulting microfluidic-addressable nanopores can sense single DNA molecules at high bandwidths and with low noise, owing to significant reductions in membrane capacitance. This strategy will enable large-scale integration of solid-state nanopores with microfluidic upstream and downstream processing and permit new functions with nanopores such as complex manipulations for multidimensional analysis and parallel sensing in two and three-dimensional architectures.

Zinc is one of the most important metal ions found in proteins performing specific functions associated with life processes. Coordination geometry of the zinc ion in the active site of the metalloprotein-ligand complexes poses a challenge in determining ligand binding affinities accurately in structure-based drug design. We report here an all atom force field based computational protocol for estimating rapidly the binding affinities of zinc containing metalloprotein-ligand complexes, considering electrostatics, van der Waals, hydrophobicity, and loss in conformational entropy of protein side chains upon ligand binding along with a nonbonded approach to model the interactions of the zinc ion with all the other atoms of the complex. We examined the sensitivity of the binding affinity predictions to the choice of Lennard-Jones parameters, partial atomic charges, and dielectric treatments adopted for system preparation and scoring. The highest correlation obtained was R 2 = 0.77 (r = 0.88) for the predicted binding affinity against the experiment on a heterogenous dataset of 90 zinc containing metalloprotein-ligand complexes consisting of five unique protein targets. Model validation and parameter analysis studies underscore the robustness and predictive ability of the scoring function. The high correlation obtained suggests the potential applicability of the methodology in designing novel ligands for zinc-metalloproteins. The scoring function has been web enabled for free access at www.scfbio-iitd.res.in/software/ drugdesign/bapplz.jsp as BAPPL-Z server (Binding Affinity Prediction of Protein-Ligand complexes containing Zinc metal ions). Proteins 2007;67:1167-1178. V V C 2007 Wiley-Liss, Inc.

The discovery of new pharmaceuticals via computer modeling is one of the key challenges in modern medicine. The advent of global networks of genomic, proteomic and metabolomic endeavors is ushering in an increasing number of novel and clinically important targets for screening. Computational methods are anticipated to play a pivotal role in exploiting the structural and functional information to understand specific molecular recognition events of the target macromolecule with candidate hits leading ultimately to the design of improved leads for the target. In this review, we sketch a system independent, comprehensive physicochemical pathway for lead molecule design focusing on the emerging in silico trends and techniques. We survey strategies for the generation of candidate molecules, docking them with the target and ranking them based on binding affinities. We present a molecular level treatment for distinguishing affinity from specificity of a ligand for a given target. We also discuss the significant aspects of drug absorption, distribution, metabolism, excretion and toxicity (ADMET) and highlight improved protocols required for higher quality and throughput of in silico methods employed at early stages of discovery. We present a realization of the various stages in the pathway proposed with select examples from the literature and from our own research to demonstrate the way in which an iterative process of computer design and validation can aid in developing potent leads. The review thus summarizes recent advances and presents a viewpoint on improvements envisioned in the years to come for automated computer aided lead molecule discovery.

Nanofluidic sensing elements have been the focus of recent experiments for numerous applications from nucleic acid fragment sizing to single-molecule DNA sequencing. These applications critically rely on high measurement fidelity, and methods to increase resolution are required. Herein, we describe fabrication and testing of a nanochannel device that enhances measurement resolution by performing multiple measurements (>100) on single DNA molecules. The enhanced measurement resolution enabled length discrimination between a mixture of λ-DNA (48.5 kbp) and T7 DNA (39.9 kbp) molecules, which were detected as transient current changes during translocation of the molecules through the nanochannel. As long DNA molecules are difficult to resolve quickly and with high fidelity with conventional electrophoresis, this approach may yield potentially portable, direct electrical sizing of DNA fragments with high sensitivity and resolution.

Cannabinoids are part of an endogenous signaling system consisting of cannabinoid receptors and endogenous cannabinoids as well as the enzymatic machinery for their synthesis and degradation. Depolarization-induced suppression of excitation (DSE) is a form of cannabinoid CB 1 receptor-mediated inhibition of synaptic transmission that involves the production of the endogenous cannabinoid 2-arachidonoyl glycerol (2-AG). Both diacylglycerol lipase a (DAGLa) and DAGLb can produce 2-AG in vitro, but evidence from knockout animals argues strongly for a predominant, even exclusive, role for DAGLa in regulation of 2-AG-mediated synaptic plasticity. What role, if any, might be played by DAGLb remains largely unknown. Cultured autaptic hippocampal neurons exhibit robust DSE. With the ability to rapidly modulate expression of DAGLa and DAGLb in these neurons with short hairpin RNA, they are well suited for a comparative study of the roles of each isoform in mediating DSE. We find that RNA interference knockdown of DAGLa substantially reduces autaptic DSE, shifting the "depolarizationresponse curve" from an ED 50 value of 1.7 seconds to 3.0 seconds. Surprisingly, DAGLb knockdown diminishes DSE as much or more (ED 50 6.4 seconds), suggesting that DAGLb is also responsible for a portion of 2-AG production in autaptic neurons. Similarly, the two DAGLs both contribute to the production of 2-AG via group I metabotropic glutamate receptors. Our results provide the first explicit evidence for a role of DAGLb in modulating neurotransmission.