Abdallah Sayyed-Ahmad

The Role of Conserved Waters in Conformational Transitions of Q61H K-ras

To investigate the stability and functional role of long-residence water molecules in the Q61H variant of the signaling protein K-ras, we analyzed all available Ras crystal structures and conformers derived from a series of independent explicit solvent molecular dynamics (MD) simulations totaling 1.76 µs. We show that the protein samples a different region of phase space in the presence and absence of several crystallographically conserved and buried water molecules. The dynamics of these waters is coupled with the local as well as the global motions of the protein, in contrast to less buried waters whose exchange with bulk is only loosely coupled with the motion of loops in their vicinity. Aided by two novel reaction coordinates involving the distance (d) between the Cα atoms of G60 at switch 2 and G10 at the P-loop and the N-Cα-C-O dihedral (ξ) of G60, we further show that three water molecules located in lobe1, at the interface between the lobes and at lobe2, are involved in the relative motion of residues at the two lobes of Q61H K-ras. Moreover, a d/ξ plot classifies the available Ras x-ray structures and MD-derived K-ras conformers into active GTP-, intermediate GTP-, inactive GDP-bound, and nucleotide-free conformational states. The population of these states and the transition between them is modulated by water-mediated correlated motions involving the functionally critical switch 2, P-loop and helix 3. These results suggest that water molecules act as allosteric ligands to induce a population shift among distinct switch 2 conformations that differ in effector recognition.

Poisson-Nernst-Planck Models of Nonequilibrium Ion Electrodiffusion through a Protegrin Transmembrane Pore

Protegrin peptides are potent antimicrobial agents believed to act against a variety of pathogens by forming nonselective transmembrane pores in the bacterial cell membrane. We have employed 3D Poisson-Nernst-Planck (PNP) calculations to determine the steady-state ion conduction characteristics of such pores at applied voltages in the range of −100 to +100 mV in 0.1 M KCl bath solutions. We have tested a variety of pore structures extracted from molecular dynamics (MD) simulations based on an experimentally proposed octomeric pore structure. The computed single-channel conductance values were in the range of 290–680 pS. Better agreement with the experimental range of 40–360 pS was obtained using structures from the last 40 ns of the MD simulation, where conductance values range from 280 to 430 pS. We observed no significant variation of the conductance with applied voltage in any of the structures that we tested, suggesting that the voltage dependence observed experimentally is a result of voltage-dependent channel formation rather than an inherent feature of the open pore structure. We have found the pore to be highly selective for anions, with anionic to cationic current ratios (ICl−/IK+) on the order of 103. This is consistent with the highly cationic nature of the pore but surprisingly in disagreement with the experimental finding of only slight anionic selectivity. We have additionally tested the sensitivity of our PNP model to several parameters and found the ion diffusion coefficients to have a significant influence on conductance characteristics. The best agreement with experimental data was obtained using a diffusion coefficient for each ion set to 10% of the bulk literature value everywhere inside the channel, a scaling used by several other studies employing PNP calculations. Overall, this work presents a useful link between previous work focused on the structure of protegrin pores and experimental efforts aimed at investigating their conductance characteristics.

Transcriptional regulatory network refinement and quantification through kinetic modeling, gene expression microarray data and information theory.

BackgroundGene expression microarray and other multiplex data hold promise for addressing the challenges of cellular complexity, refined diagnoses and the discovery of well-targeted treatments. A new approach to the construction and quantification of transcriptional regulatory networks (TRNs) is presented that integrates gene expression microarray data and cell modeling through information theory. Given a partial TRN and time series data, a probability density is constructed that is a functional of the time course of transcription factor (TF) thermodynamic activities at the site of gene control, and is a function of mRNA degradation and transcription rate coefficients, and equilibrium constants for TF/gene binding.ResultsOur approach yields more physicochemical information that compliments the results of network structure delineation methods, and thereby can serve as an element of a comprehensive TRN discovery/quantification system. The most probable TF time courses and values of the aforementioned parameters are obtained by maximizing the probability obtained through entropy maximization. Observed time delays between mRNA expression and activity are accounted for implicitly since the time course of the activity of a TF is coupled by probability functional maximization, and is not assumed to be proportional to expression level of the mRNA type that translates into the TF. This allows one to investigate post-translational and TF activation mechanisms of gene regulation. Accuracy and robustness of the method are evaluated. A kinetic formulation is used to facilitate the analysis of phenomena with a strongly dynamical character while a physically-motivated regularization of the TF time course is found to overcome difficulties due to omnipresent noise and data sparsity that plague other methods of gene expression data analysis. An application to Escherichia coli is presented.ConclusionMultiplex time series data can be used for the construction of the network of cellular processes and the calibration of the associated physicochemical parameters. We have demonstrated these concepts in the context of gene regulation understood through the analysis of gene expression microarray time series data. Casting the approach in a probabilistic framework has allowed us to address the uncertainties in gene expression microarray data. Our approach was found to be robust to error in the gene expression microarray data and mistakes in a proposed TRN.

Efficient solution technique for solving the Poisson-Boltzmann equation.

The Poisson–Boltzmann (PB) equation has been extensively used to analyze the energetics and structure of proteins and other significant biomolecules immersed in electrolyte media. A new highly efficient approach for solving PB‐type equations that allows for the modeling of many‐atoms structures such as encountered in cell biology, virology, and nanotechnology is presented. We accomplish these efficiencies by reformulating the elliptic PB equation as the long‐time solution of an advection‐diffusion equation. An efficient modified, memory optimized, alternating direction implicit scheme is used to integrate the reformulated PB equation. Our approach is demonstrated on protein composites (a polio virus capsid protomer and a pentamer). The approach has great potential for the analysis of supramillion atoms immersed in a host electrolyte.

Structure and Dynamics of Cholic Acid and Dodecylphosphocholine-Cholic Acid Aggregates

Bile acids are powerful detergents that emulsify and solubilize lipids, vitamins, cholesterol and other molecules in the biliary tract and intestines. It has long been known that bile acids form soluble mixed micelles with lipids. However, the detailed thermodynamic and structural properties of these micelles are not fully understood. This study sheds light on this issue based on results from multiple molecular dynamics simulations of cholic acid (CA) and dodecylphosphocholine (DPC) mixed micelles. We found that CA molecules form aggregates of up to 12 monomers with a mean size of 5-6. In agreement with several previous simulations and earlier predictions, the overall shape of these CA clusters is oblate disk-like such that the methyl groups point toward the core of the aggregate and the hydroxyl groups point away from it. The self-aggregation behavior of the CA clusters in the DPC-CA mixture is similar to the pure CA. Furthermore, the sizes and aggregation numbers of the DPC-CA mixed micelles are linearly dependent on CA molarity. In agreement with the radial shell model of Nichols and Ozarowski [Nichols, J. W.; Ozarowski, J. Biochemistry 1990, 29, 4600], our results demonstrate that CA molecules form a wedge between the DPC molecules with their hydroxyl and carboxyl groups facing the aqueous phase while their methyl groups are buried in and face the hydrocarbon core of the DPC micelle. The DPC-CA micelles simulated here tend to be spherical to prolate in shape, with the deviation from spherical geometry significantly increasing with increasing CA:DPC ratio.

Aggregation behavior of ibuprofen, cholic acid and dodecylphosphocholine micelles.

Non-steroidal anti-inflammatory drugs (NSAIDs) are frequently used to treat chronic pain and inflammation. However, prolonged use of NSAIDs has been known to result in Gastrointestinal (GI) ulceration/bleeding, with a bile-mediated mechanism underlying their toxicity to the lower gut. Bile acids (BAs) and phosphatidylcholines (PCs), the major components of bile, form mixed micelles to reduce the membrane disruptive actions of monomeric BAs and simple BA micelles. NSAIDs are suspected to alter the BA/PC balance in the bile, but the molecular interactions of NSAID-BA or NSAID-BA-PC remain undetermined. In this work, we used a series of all-atom molecular dynamics simulations of cholic acid (CA), ibuprofen (IBU) and dodecylphosphocholine (DPC) mixtures to study the spontaneous aggregation of CA and IBU as well as their adsorption on a DPC micelle. We found that the size of CA-IBU mixed micelles varies with their molar ratio in a non-linear manner, and that micelles of different sizes adopt similar shapes but differ in composition and internal interactions. These observations are supported by NMR chemical shift changes, NMR ROESY crosspeaks between IBU and CA, and dynamic light scattering experiments. Smaller CA-IBU aggregates were formed in the presence of a DPC micelle due to the segregation of CA and IBU away from each other by the DPC micelle. While the larger CA-IBU aggregates arising from higher IBU concentrations might be responsible for NSAID-induced intestinal toxicity, the absence of larger CA-IBU aggregates in the presence of DPC micelles may explain the observed attenuation of NSAID toxicity by PCs.

Determining the orientation of protegrin-1 in DLPC bilayers using an implicit solvent-membrane model.

Continuum models that describe the effects of solvent and biological membrane molecules on the structure and behavior of antimicrobial peptides, holds a promise to improve our understanding of the mechanisms of antimicrobial action of these peptides. In such methods, a lipid bilayer model membrane is implicitly represented by multiple layers of relatively low dielectric constant embedded in a high dielectric aqueous solvent, while an antimicrobial peptide is accounted for by a dielectric cavity with fixed partial charge at the center of each one of its atoms. In the present work, we investigate the ability of continuum approaches to predict the most probable orientation of the β-hairpin antimicrobial peptide Protegrin-1 (PG-1) in DLPC lipid bilayers by calculating the difference in the transfer free energy from an aqueous environment to a membrane-water environment for multiple orientations. The transfer free energy is computed as a sum of two terms; polar/electrostatic and non-polar. They both include energetic and entropic contributions to the free energy. We numerically solve the Poisson-Boltzmann equation to calculate the electrostatic contribution to the transfer free energy, while the non-polar contribution to the free energy is approximated using a linear solvent accessible surface area relationships. The most probable orientation of PG-1 is that with the lowest relative transfer free energy. Our simulation results indicate that PG-1 assumes an oblique orientation in DLPC lipid bilayers. The predicted most favorable orientation was with a tilt angle of 19°, which is in qualitative agreement with the experimentally observed orientations derived from solid-state NMR data.

The Karyote physico-chemical genomic, proteomic, metabolic cell modeling system.

Modeling approaches to the dynamics of a living cell are presented that are strongly based on its underlying physical and chemical processes and its hierarchical spatio-temporal organization. Through the inclusion of a broad spectrum of processes and a rigorous analysis of the multiple scale nature of cellular dynamics, we are attempting to advance cell modeling and its applications. The presentation focuses on our cell modeling system, which integrates data archiving and quantitative physico-chemical modeling and information theory to provide a seamless approach to the modeling/data analysis endeavor. Thereby the rapidly growing mess of genomic, proteomic, metabolic, and cell physiological data can be automatically used to develop and calibrate a predictive cell model. The discussion focuses on the Karyote cell modeling system and an introduction to the CellX and VirusX models. The Karyote software system integrates three elements: (1) a model-building and data archiving module that allows one to define a cell type to be modeled through its reaction network, structure, and transport processes as well as to choose the surrounding medium and other parameters of the phenomenon to be modeled; (2) a genomic, proteomic, metabolic cell simulator that solves the equations of metabolic reaction, transcription/translation polymerization and the exchange of molecules between parts of the cell and with the surrounding medium; and (3) an information theory module (ITM) that automates model calibration and development, and integrates a variety of data types with the cell dynamic computations. In Karyote, reactions may be fast (equilibrated) or slow (finite rate), and the special effects of enzymes and other minority species yielding steady-state cycles of arbitrary complexities are accounted for. These features of the dynamics are handled via rigorous multiple scale analysis. A user interface allows for an automated generation and solution of the equations of multiple timescale, compartmented dynamics. Karyote is based on a fixed intracellular structure. However, cell response to changes in the host medium, damage, development or transformation to abnormality can involve dramatic changes in intracellular structure. As this changes the nature of the cellular dynamics, a new model, CellX, is being developed based on the spatial distribution of concentration and other variables. This allows CellX to capture the self-organizing character of cellular behavior. The self-assembly of organelles, viruses, and other subcellular bodies is being addressed in a second new model, VirusX, that integrates molecular mechanics and continuum theory. VirusX is designed to study the influence of a host medium on viral self-assembly, structural stability, infection of a single cell, and transmission of disease.

pMD-Membrane: A Method for Ligand Binding Site Identification in Membrane-Bound Proteins

Probe-based or mixed solvent molecular dynamics simulation is a useful approach for the identification and characterization of druggable sites in drug targets. However, thus far the method has been applied only to soluble proteins. A major reason for this is the potential effect of the probe molecules on membrane structure. We have developed a technique to overcome this limitation that entails modification of force field parameters to reduce a few pairwise non-bonded interactions between selected atoms of the probe molecules and bilayer lipids. We used the resulting technique, termed pMD-membrane, to identify allosteric ligand binding sites on the G12D and G13D oncogenic mutants of the K-Ras protein bound to a negatively charged lipid bilayer. In addition, we show that differences in probe occupancy can be used to quantify changes in the accessibility of druggable sites due to conformational changes induced by membrane binding or mutation.

Computational Equilibrium Thermodynamic and Kinetic Analysis of K-Ras Dimerization through an Effector Binding Surface Suggests Limited Functional Role

Dimer formation is believed to have a substantial impact on regulating K-Ras function. However, the evidence for dimerization and the molecular details of the process are scant. In this study, we characterize a K-Ras pseudo-C2-symmetric dimerization interface involving the effector interacting β2-strand. We used structure matching and all-atom molecular dynamics (MD) simulations to predict, refine, and investigate the stability of this interface. Our MD simulation suggested that the β2-dimer is potentially stable and remains relatively close to its initial conformation due to the presence of a number of hydrogen bonds, ionic salt bridges, and other favorable interactions. We carried out potential of mean force calculations to determine the relative binding strength of the interface. The results of these calculations indicated that the β2 dimerization interface provides a weak binding free energy in solution and a dissociation constant that is close to 1 mM. Analyses of Brownian dynamics simulations suggested an association rate kon ≈ 10(5)-10(6) M(-1) s(-1). Combining these observations with available literature data, we propose that formation of auto-inhibited β2 K-Ras dimers is possible but its fraction in cells is likely very small under normal physiologic conditions.