Dirar Homouz

Structure, function, and folding of phosphoglycerate kinase are strongly perturbed by macromolecular crowding

We combine experiment and computer simulation to show how macromolecular crowding dramatically affects the structure, function, and folding landscape of phosphoglycerate kinase (PGK). Fluorescence labeling shows that compact states of yeast PGK are populated as the amount of crowding agents (Ficoll 70) increases. Coarse-grained molecular simulations reveal three compact ensembles: C (crystal structure), CC (collapsed crystal), and Sph (spherical compact). With an adjustment for viscosity, crowded wild-type PGK and fluorescent PGK are about 15 times or more active in 200 mg/ml Ficoll than in aqueous solution. Our results suggest a previously undescribed solution to the classic problem of how the ADP and diphosphoglycerate binding sites of PGK come together to make ATP: Rather than undergoing a hinge motion, the ADP and substrate sites are already located in proximity under crowded conditions that mimic the in vivo conditions under which the enzyme actually operates. We also examine T-jump unfolding of PGK as a function of crowding experimentally. We uncover a nonmonotonic folding relaxation time vs. Ficoll concentration. Theory and modeling explain why an optimum concentration exists for fastest folding. Below the optimum, folding slows down because the unfolded state is stabilized relative to the transition state. Above the optimum, folding slows down because of increased viscosity.

Crowded, cell-like environment induces shape changes in aspherical protein

How the crowded environment inside cells affects the structures of proteins with aspherical shapes is a vital question because many proteins and protein–protein complexes in vivo adopt anisotropic shapes. Here we address this question by combining computational and experimental studies of a football-shaped protein (i.e., Borrelia burgdorferi VlsE) in crowded, cell-like conditions. The results show that macromolecular crowding affects protein-folding dynamics as well as overall protein shape. In crowded milieus, distinct conformational changes in VlsE are accompanied by secondary structure alterations that lead to exposure of a hidden antigenic region. Our work demonstrates the malleability of “native” proteins and implies that crowding-induced shape changes may be important for protein function and malfunction in vivo.

Macromolecular Crowding Modulates Folding Mechanism of α/β Protein Apoflavodoxin

Protein dynamics in cells may be different from those in dilute solutions in vitro, because the environment in cells is highly concentrated with other macromolecules. This volume exclusion because of macromolecular crowding is predicted to affect both equilibrium and kinetic processes involving protein conformational changes. To quantify macromolecular crowding effects on protein folding mechanisms, we investigated the folding energy landscape of an alpha/beta protein, apoflavodoxin, in the presence of inert macromolecular crowding agents, using in silico and in vitro approaches. By means of coarse-grained molecular simulations and topology-based potential interactions, we probed the effects of increased volume fractions of crowding agents (phi(c)) as well as of crowding agent geometry (sphere or spherocylinder) at high phi(c). Parallel kinetic folding experiments with purified Desulfovibro desulfuricans apoflavodoxin in vitro were performed in the presence of Ficoll (sphere) and Dextran (spherocylinder) synthetic crowding agents. In conclusion, we identified the in silico crowding conditions that best enhance protein stability, and discovered that upon manipulation of the crowding conditions, folding routes experiencing topological frustrations can be either enhanced or relieved. Our test-tube experiments confirmed that apoflavodoxins time-resolved folding path is modulated by crowding agent geometry. Macromolecular crowding effects may be a tool for the manipulation of protein-folding and function in living cells.

Modulation of Calmodulin Plasticity by the Effect of Macromolecular Crowding

In vitro biochemical reactions are most often studied in dilute solution, a poor mimic of the intracellular space of eukaryotic cells, which are crowded with mobile and immobile macromolecules. Such crowded conditions exert volume exclusion and other entropic forces that have the potential to impact chemical equilibria and reaction rates. In this article, we used the well-characterized and ubiquitous molecule calmodulin (CaM) and a combination of theoretical and experimental approaches to address how crowding impacts CaMs conformational plasticity. CaM is a dumbbell-shaped molecule that contains four EF hands (two in the N-lobe and two in the C-lobe) that each could bind Ca(2+), leading to stabilization of certain substates that favor interactions with other target proteins. Using coarse-grained molecular simulations, we explored the distribution of CaM conformations in the presence of crowding agents. These predictions, in which crowding effects enhance the population of compact structures, were then confirmed in experimental measurements using fluorescence resonance energy transfer techniques of donor- and acceptor-labeled CaM under normal and crowded conditions. Using protein reconstruction methods, we further explored the folding-energy landscape and examined the structural characteristics of CaM at free-energy basins. We discovered that crowding stabilizes several different compact conformations, which reflects the inherent plasticity in CaMs structure. From these results, we suggest that the EF hands in the C-lobe are flexible and can be thought of as a switch, while those in the N-lobe are stiff, analogous to a rheostat. New combinatorial signaling properties may arise from the product of the differential plasticity of the two distinct lobes of CaM in the presence of crowding. We discuss the implications of these results for modulating CaMs ability to bind Ca(2+) and target proteins.

Multiscale investigation of chemical interference in proteins.

We developed a multiscale approach (MultiSCAAL) that integrates the potential of mean force obtained from all-atomistic molecular dynamics simulations with a knowledge-based energy function for coarse-grained molecular simulations in better exploring the energy landscape of a small protein under chemical interference such as chemical denaturation. An excessive amount of water molecules in all-atomistic molecular dynamics simulations often negatively impacts the sampling efficiency of some advanced sampling techniques such as the replica exchange method and it makes the investigation of chemical interferences on protein dynamics difficult. Thus, there is a need to develop an effective strategy that focuses on sampling structural changes in protein conformations rather than solvent molecule fluctuations. In this work, we address this issue by devising a multiscale simulation scheme (MultiSCAAL) that bridges the gap between all-atomistic molecular dynamics simulation and coarse-grained molecular simulation. The two key features of this scheme are the Boltzmann inversion and a protein atomistic reconstruction method we previously developed (SCAAL). Using MultiSCAAL, we were able to enhance the sampling efficiency of proteins solvated by explicit water molecules. Our method has been tested on the folding energy landscape of a small protein Trp-cage with explicit solvent under 8M urea using both the all-atomistic replica exchange molecular dynamics and MultiSCAAL. We compared computational analyses on ensemble conformations of Trp-cage with its available experimental NOE distances. The analysis demonstrated that conformations explored by MultiSCAAL better agree with the ones probed in the experiments because it can effectively capture the changes in side-chain orientations that can flip out of the hydrophobic pocket in the presence of urea and water molecules. In this regard, MultiSCAAL is a promising and effective sampling scheme for investigating chemical interference which presents a great challenge when modeling protein interactions in vivo.

Robust Hybrid Memristor-CMOS Memory: Modeling and Design

In this paper, we explore various aspects of memristor modeling and use them to propose improved access operations and design of a memristor-based memory. We study the current mathematical and SPICE modeling of memristors and compare them with known device specifications. Based on this survey of existing models, we adopt an improved mathematical model of the memristor that captures the well-established features of memristive devices. This modeling is used to analyze the time and voltage characteristics of stable read and write operations. The tradeoffs between the various design parameters such as voltage, frequency, noise margin, and area are also analyzed. Based on the device modeling, we propose a hybrid CMOS-memristor memory cell and architecture that addresses the limitations of memristor such as state drift, cell-cell interference, and refresh requirements. Memristor is used as a state element, and CMOS-based transistors are used to isolate, control, decode, and inter operate the logic. We verify our design using SPICE simulation using a 28-nm model for CMOS and a modified memristor model.

Protein recognition and selection through conformational and mutually induced fit

Significance Protein–protein interactions drive most every biological process, but in many instances the domains mediating recognition are disordered. How specificity in binding is attained in the absence of defined structure is a fascinating problem but not well understood. Calmodulin presents a unique opportunity to investigate mechanisms for selectivity given that it interacts with several hundred different potential targets. In our work, a combined experimental and theoretical approach is taken to define how target selectivity occurs at the molecular level. Our study shows that the binding interactions require mutually induced conformational changes in both calmodulin and the target protein, and broadly informs how intrinsically disordered proteins can achieve both high affinity and high specificity. Protein–protein interactions drive most every biological process, but in many instances the domains mediating recognition are disordered. How specificity in binding is attained in the absence of defined structure contrasts with well-established experimental and theoretical work describing ligand binding to protein. The signaling protein calmodulin presents a unique opportunity to investigate mechanisms for target recognition given that it interacts with several hundred different targets. By advancing coarse-grained computer simulations and experimental techniques, mechanistic insights were gained in defining the pathways leading to recognition and in how target selectivity can be achieved at the molecular level. A model requiring mutually induced conformational changes in both calmodulin and target proteins was necessary and broadly informs how proteins can achieve both high affinity and high specificity.

State of the art of metal oxide memristor devices

Abstract Memristors are one of the emerging technologies that can potentially replace state-of-the-art integrated electronic devices for advanced computing and digital and analog circuit applications including neuromorphic networks. Over the past few years, research and development mostly focused on revolutionizing the metal oxide materials, which are used as core components of the popular metal-insulator-metal memristors owing to their highly recognized resistive switching behavior. This paper outlines the recent advancements and characteristics of such memristive devices, with a special focus on (i) their established resistive switching mechanisms and (ii) the key challenges associated with their fabrication processes including the impeding criteria of material adaptation for the electrode, capping, and insulator component layers. Potential applications and an outlook into future development of metal oxide memristive devices are also outlined.

Mathematical modeling of a memristor device

The realization of the missing fourth element by Hewlett-Packard in 2008, the memristor, adds new promising technology that enables the continuing improvement of performance, power and cost of integrated circuits and keeping Moores law alive. Memristor-based technology provides much better scalability, higher utilization when used as memory, and overall lower power consumption. This paper presents a detailed study of existing memristor modeling using Matlab simulations. We studied three different models to predict the behavior of the memristor device. We developed the Matlab algorithms for all models and analyzed them for their compatibility with the experimentally established characteristics of HP memristor, as well as their viability for use in memory circuits. We discussed all the difficulties with these models and adopt a modified model that gives more realistic description of a memristor device.

Modeling and Optimization of Memristor and STT-RAM-Based Memory for Low-Power Applications

Conventional charge-based memory usage in low-power applications is facing major challenges. Some of these challenges are leakage current for static random access memory (SRAM) and dynamic random access memory (DRAM), additional refresh operation for DRAM, and high programming voltage for Flash. In this paper, two emerging resistive random access memory (ReRAM) technologies are investigated, memristor and spin-transfer torque (STT)-RAM, as potential universal memory candidates to replace traditional ones. Both of these nonvolatile memories support zero leakage and low-voltage operation during read access, which makes them ideal for devices with long sleep time. To date, high write energy for both memristor and STT-RAM is one of the major inhibitors for adopting the technologies. The primary contribution of this paper is centered on addressing the high write energy issue by trading off retention time with noise margin. In doing so, the memristor and STT-RAM power has been compared with the traditional six-transistor-SRAM-based memory power and potential application in wireless sensor nodes is explored. This paper uses 45-nm foundry process technology data for SRAM and physics-based mathematical models derived from real devices for memristor and STT-RAM. The simulations are conducted using MATLAB and the results show a potential power savings of 87% and 77% when using memristor and STT-RAM, respectively, at 1% duty cycle.