Rational proteomics II: electrostatic nature of cofactor preference in the short-chain oxidoreductase (SCOR) enzyme family

The dominant role of long-range electrostatic interatomic interactions in nicotinamide adenine dinucleotide/nicotinamide adenine dinucleotide phosphate (NAD/NADP) cofactor recognition has been shown for enzymes of the short-chain oxidoreductase (SCOR) family. An estimation of cofactor preference based only on the contribution of the electrostatic energy term to the total energy of enzyme-cofactor interaction has been tested for approximately 40 known three-dimensional (3D) crystal complexes and approximately 330 SCOR enzymes, with cofactor preference predicted by the presence of Asp or Arg recognition residues at specific 3D positions in the beta2alpha3 loop (Duax et al., Proteins 2003;53:931-943). The results obtained were found to be consistent with approximately 90% reliable cofactor assignments for those subsets. The procedure was then applied to approximately 170 SCOR enzymes with completely uncertain NAD/NADP dependence, due to the lack of Asp and Arg marker residues. The proposed 3D electrostatic approach for cofactor assignment ("3D_DeltaE(el)") has been implemented in an automatic screening procedure, and together with the use of marker residues proposed earlier (Duax et al., Proteins 2003;53:931-943), increases the level of reliable predictions for the putative SCORs from approximately 70% to approximately 90%. It is expected to be applicable for any NAD/NADP-dependent enzyme subset having at least 25-30% sequence identity, with at least one enzyme of known 3D crystal structure.     

Extracting Information from the Power Spectrum of Synaptic Noise

In cortical neurons, synaptic "noise" is caused by the nearly random release of thousands of synapses. Few methods are presently available to analyze synaptic noise and deduce properties of the underlying synaptic inputs. We focus here on the power spectral density (PSD) of several models of synaptic noise. We examine different classes of analytically solvable kinetic models for synaptic currents, such as the "delta kinetic models," which use Dirac delta functions to represent the activation of the ion channel. We first show that, for this class of kinetic models, one can obtain an analytic expression for the PSD of the total synaptic conductance and derive equivalent stochastic models with only a few variables. This yields a method for constraining models of synaptic currents by analyzing voltage-clamp recordings of synaptic noise. Second, we show that a similar approach can be followed for the PSD of the the membrane potential (Vm) through an effective-leak approximation. Third, we show that this approach is also valid for inputs distributed in dendrites. In this case, the frequency scaling of the Vm PSD is preserved, suggesting that this approach may be applied to intracellular recordings of real neurons. In conclusion, using simple mathematical tools, we show that Vm recordings can be used to constrain kinetic models of synaptic currents, as well as to estimate equivalent stochastic models. This approach, therefore, provides a direct link between intracellular recordings in vivo and the design of models consistent with the dynamics and spectral structure of synaptic noise.     

In silico protein design by combinatorial assembly of protein building blocks

Utilizing concepts of protein building blocks, we propose a de novo computational algorithm that is similar to combinatorial shuffling experiments. Our goal is to engineer new naturally occurring folds with low homology to existing proteins. A selected protein is first partitioned into its building blocks based on their compactness, degree of isolation from the rest of the structure, and hydrophobicity. Next, the protein building blocks are substituted by fragments taken from other proteins with overall low sequence identity, but with a similar hydrophobic/hydrophilic pattern and a high structural similarity. These criteria ensure that the designed protein has a similar fold, low sequence identity, and a good hydrophobic core compared with its native counterpart. Here, we have selected two proteins for engineering, protein G B1 domain and ubiquitin. The two engineered proteins share approximately 20% and approximately 25% amino acid sequence identities with their native counterparts, respectively. The stabilities of the engineered proteins are tested by explicit water molecular dynamics simulations. The algorithm implements a strategy of designing a protein using relatively stable fragments, with a high population time. Here, we have selected the fragments by searching for local minima along the polypeptide chain using the protein building block model. Such an approach provides a new method for engineering new proteins with similar folds and low homology.     

Computational models of cells and tissues: machines, agents and fungal infection

Computational models have been of interest in biology for many years and have represented a particular approach to trying to understand biological processes and phenomena from a systems point of view. Much of the early work was rather abstract and high level and probably seemed to many to be of more philosophical than practical value. There have, however, been some advances in the development of more realistic models and the current state of computer science research provides us with new opportunities through both the emergence of models that can model seriously complex systems and also the support that modern software can give to the modelling process. This paper describes a few of the early simple models and then goes on to look at some new ideas in the area with a particular application drawn from the world of mycology. Some general principles relating to how new and emerging computational techniques can help to represent and understand extremely complex models conclude the paper.     

Do neocortical pyramidal neurons display stochastic resonance?

Neocortical pyramidal neurons in vivo are subject to an intense synaptic background activity that has a significant impact on various electrophysiological properties and dendritic integration. Using detailed biophysical models of a morphologically reconstructed neocortical pyramidal neuron, in which synaptic background activity was simulated according to recent measurements in cat parietal cortex in vivo, we show that the responsiveness of the cell to additional periodic subthreshold stimuli can be significantly enhanced through mechanisms similar to stochastic resonance. We compare several paradigms leading to stochastic resonance-like behavior, such as varying the strength or the correlation in the background activity. A new type of resonance-like behavior was obtained when the correlation was varied, in which case the responsiveness is sensitive to the statistics rather than the strength of the noise. We suggest that this type of resonance may be relevant to information processing in the cerebral cortex.     

How Spike Synchronization Among Olfactory Neurons Can Contribute to Sensory Discrimination

Recent studies in honeybees have demonstrated that, when odor-evoked action potentials in antennal lobe neurons are pharmacologically desynchronized, the bees are impaired in their ability to discriminate chemically similar odor stimuli. Using a reduced computational model of the honeybee antennal lobe, we show how changes in spike-synchronization properties alone, independent of changes in overall spike-discharge rate or differences in activity levels among responsive neurons, can produce changes in associative learning similar to those observed experimentally.     

Computational de novo design,and characterization of an A(2)B(2) diiron protein

Diiron proteins are found throughout nature and have a diverse range of functions; proteins in this class include methane monooxygenase, ribonucleotide reductase, Delta(9)-acyl carrier protein desaturase, rubrerythrin, hemerythrin, and the ferritins. Although each of these proteins has a very different overall fold, in every case the diiron active site is situated within a four-helix bundle. Additionally, nearly all of these proteins have a conserved Glu-Xxx-Xxx-His motif on two of the four helices with the Glu and His residues ligating the iron atoms. Intriguingly, subtle differences in the active site can result in a wide variety of functions. To probe the structural basis for this diversity, we designed an A(2)B(2) heterotetrameric four-helix bundle with an active site similar to those found in the naturally occurring diiron proteins. A novel computational approach was developed for the design, which considers the energy of not only the desired fold but also alternatively folded structures. Circular dichroism spectroscopy, analytical ultracentrifugation, and thermal unfolding studies indicate that the A and B peptides specifically associate to form an A(2)B(2) heterotetramer. Further, the protein binds Zn(II) and Co(II) in the expected manner and shows ferroxidase activity under single turnover conditions.     

Protein sequential resonance assignments by combinatorial enumeration using 13C alpha chemical shifts and their (i,i-1) sequential connectivities

The need for the structural characterization of proteins on a genomic scale has brought with it demands for new technology to speed the structure determination process. In NMR, one bottleneck is the sequential assignment of backbone resonances. In this paper, we explore the computational complexity of the sequential assignment problem using only ¹³C chemical shift data and C (i,i–1) sequential connectivity information, all of which can potentially be obtained from a single three-dimensional NMR spectrum. Although it is generally believed that there is too much ambiguity in such data to provide sufficient information for sequential assignment, we show that a straightforward combinatorial search algorithm can be used to find correct and unambiguous sequential assignments in a reasonable amount of CPU time for small proteins (approximately 80 residues or smaller) when there is little missing data. The deleterious effect of missing or spurious peaks and the dependence on match tolerances is also explored. This simple algorithm could be used as part of a semi-automated, interactive assignment procedure, e.g., to test partial manually determined solutions fo uniqueness and to extend these solutions.