Lecture 7: Chris Bohrer (Elijah Roberts & Jie Xiao Labs)

The Chromosomes Fight Against Disorder in E. coli

Transcription in Escherichia coli generates positive supercoiling in the DNA, which is relieved by the enzymatic activity of gyrase. Recently published experimental evidence suggests that transcription initiation and elongation are inhibited by the buildup of positive supercoiling. It has therefore been proposed that intermittent binding of gyrase plays a role in transcriptional bursting. Considering that transcription is one of the most fundamental cellular processes, it is desirable to be able to account for the buildup and release of positive supercoiling in models of transcription.

Positive Supercoiling (PCOIL) is
produced when mRNA is transcribed
(from Bohrer&Roberts,BMC,2016).

In their recent paper, Chris Bohrer and Elijah Roberts from Johns Hopkins Biophysics present a detailed biophysical model of gene expression that incorporates the effects of supercoiling due to transcription. By directly linking the amount of positive supercoiling to the rate of transcription, the model predicts that highly transcribed genes’ mRNA distributions should substantially deviate from Poisson distributions, with enhanced density at low mRNA copy numbers. Additionally, the model predicts a high degree of correlation between expression levels of genes inside the same supercoiling domain.

The model, incorporating the supercoiling state of the gene, makes specific predictions that differ from previous models of gene expression. Genes in the same supercoiling domain influence the expression level of neighboring genes. Such structurally dependent regulation predicts correlations between genes in the same supercoiling domain. The topology of the chromosome therefore creates a higher level of gene regulation, which has broad implications for understanding the evolution and organization of bacterial genomes.

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Chris graduated from The Kent State University with a degree in physics and education. He is interested in studying the dynamics of gene regulatory circuits, and so decided to join both Dr. Elijah Robert’s lab as well as Dr. Jie Xiao’s lab at Johns Hopkins University Biophysics, in order to be proficient in both theory and experiments. Chris plays the guitar, runs, and makes sushi when not in the lab.

Lecture 6: Henry Lessen (Karen Fleming Lab)

Surprising results from a recent Beta-barrel membrane protein elasticity

OMPs have a common beta-barrel architecture. Image
from: Plummer, A. M., & Fleming, K. G. (2016). From
Chaperones to the Membrane with a BAM!. TiBS.

Outer membrane proteins (OMPs) play a central role in the integrity of the outer membrane of Gram-negative bacteria. The outermost membrane of Gram-negative bacteria is the ultimate protective barrier of the cell, serving as the first line of defense that guards against extracellular threats. Composed of both lipids and thousands of OMPs, biogenesis of outer membrane (OM) components and consequent OM integrity is essential for cell viability. Targeting these processes is a promising route for directed drug design against bacterial pathogens. Understanding of the OMP assembly machinery in bacteria has grown immensely owing to recent discoveries using several orthogonal techniques that include the publications of the crystal structures of key proteins recently.

OMPs have a common (beta-barrel) architecture, but they come in different sizes and functions (e.g. structural, adhesive, enzymatic and transport). OMPs are devoid of traditional cellular energy sources which point to the fact that the physical properties of the system are important. Lateral pressure applied onto the OMPs through the membrane is a key physics to study. Based on these observations, Henrey Lessen and his colleagues, using molecular dynamics simulations and an in-house developed biophysics model, obtain the elastic modulus and time-dependent forces acting on the barrel structure.

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Henry Lessen is from Alexandria, Louisiana. He completed his Bachelors of Science in Microbiology at Texas A&M University in College Station, TX where his undergraduate research was focused on improving the alkaline tolerance of cyanide degrading enzymes from yeast for possible use in bioremediation of metal-mining waste.

Currently, Henry is in Karen Fleming's lab in the Jenkins Department of Biophysics at Johns Hopkins University. His thesis research involves using experimental and computational methods to study the energetics of transmembrane backbone hydrogen bonds in the outer membrane proteins of gram-negative bacteria.

Lecture 5: Dr. Julia Köhler Leman (Jeff Gray Lab)

Computational membrane protein structure prediction

Membrane proteins are critical functional molecules in the human body, constituting more than 30% of open reading frames in the human genome. As we also learned in Lecture 4, the determination of membrane protein (MP) structures has always trailed that of soluble proteins due to difficulties in their overexpression, reconstitution into membrane mimetics, and subsequent structure determination. The percentage of MP structures in the protein databank (PDB) has been at a constant 1–2% for the last decade. In contrast, over half of all drugs targetMPs, only highlighting how little we understand about drug-specific effects in the human body. To reduce this gap, researchers have attempted to predict structural features of MPs even before the first structure was experimentally elucidated.

Computational tools are instrumental to membrane protein structure prediction, consequently increasing our understanding of membrane protein function and their role in disease. In their recent article, Dr. Julia Kohler Leman and her colleagues describe a general framework facilitating membrane protein modeling and design that combines the scientific principles for membrane protein modeling with the flexible software architecture of Rosetta3. This new framework, called RosettaMP, provides a general membrane representation that interfaces with scoring, conformational sampling, and mutation routines that can be easily combined to create new protocols. To demonstrate the capabilities of this implementation, the researchers developed four proof-of-concept applications for (1) prediction of free energy changes upon mutation; (2) high-resolution structural refinement; (3) protein-protein docking; and (4) assembly of symmetric protein complexes, all in the membrane environment. The preliminary results show that these algorithms can produce meaningful scores and structures. The data also suggest needed improvements to both sampling routines and score functions. Importantly, the applications collectively demonstrate the potential of combining the flexible nature of RosettaMP with the power of Rosetta algorithms to facilitate membrane protein modeling and design.

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Dr. Julia Koehler Leman received her Bachelors and Masters of Science degrees in physics from the University of Leipzig, Germany. Her doctoral research revolved around membrane protein structure determination using NMR spectroscopy and computational techniques and she received her Ph.D. in Chemical and Physical Biology in 2012 while working with Drs. Jens Meiler and Charles Sanders at Vanderbilt University. After graduation, she worked as a postdoctoral fellow under the supervision of Dr. Jeff Gray at Hopkins on the projects that involve development and application of Rosetta software, focusing on creating and improving applications for structure prediction, docking, and design. Very recently, she started working as a research fellow at Simons Foundation. 

Dr. Koehler Leman has published 7 first-author papers and co-authored 6 papers in peer-reviewed international journals. Her current research interests lie in computational methods development in modeling membrane proteins with and without restraints from NMR spectroscopy.

Lecture 4: Dr. Sirish Kaushik Lakkaraju (Alex MacKerell Lab at UMB)

In search for a better drug against asthma (... and many others)

The seven-transmembrane α-helix structure
of a G protein–coupled receptor (wikipedia).

Occluded ligand binding pockets (LBP) in proteins with minimal or no accessibility to the surrounding environment represent a significant, yet challenging opportunity for structure-based and computer-aided drug design approaches. LBPs of more than half of all clinical drug targets, including the G-protein coupled receptors (GPCR) and nuclear receptors (NR) are either partially or fully occluded. As the efficacies of ligands of both GPCRs and NRs are known to be coupled to small conformational changes in their binding sites, accurate modeling of these sites is critical for future development of therapeutic agents.

To determine free energy maps of functional groups of LBPs, Dr. Kaushik Lakkaraju and his colleagues from the University of Maryland Baltimore combined a Grand-Canonical Monte Carlo/Molecular Dynamics (GCMC/MD) strategy [1] with the Site Identification by Ligand Competitive Saturation (SILCS) methodology. In this method, the inclusion of protein flexibility helps identify regions of the binding pockets not accessible in crystal conformations and allows for better quantitative estimates of relative ligand binding affinities in all the proteins tested. The research group’s approach successfully recapitulates locations of functional groups across diverse classes of ligands in the LBPs of many important NR and GPCR proteins. In their study, the differences in functional group requirements of the active and inactive states of the β2AR LBP were used in virtual drug screening to identify high-efficacy agonists targeting β2AR in Airway Smooth Muscle (ASM) cells which are very important drug targets in patients with severe asthma. The experimental tests showed that 7 of the 15 selected ligands effect ASM relaxation, representing a very high hit rate (46%) [2].

FragMaps overlaid on the LBP of AR [2]

The conformational dynamics of a macromolecule can be modulated by a number of factors, including changes in the environment, ligand binding, and interactions with other macromolecules, among others. Therefore, there is always a need for faster extraction and comparison of functionally relevant conformational dynamics information from extended molecular simulations of large and complex macromolecular systems. Dr. Lakkaraju and his colleagues also have developed DIRECT-ID, a method that quantifies the differences in macromolecular conformational dynamics and automatically extracts the structural features responsible for these changes. Given a set of molecular dynamics (MD) simulations of a macromolecule, the method calculates the norms of the differences in covariance matrices for each pair of trajectories. A matrix of these norms thus quantifies the differences in conformational dynamics across the set of simulations. As a demonstration of its applicability to bio-macromolecular systems, DIRECT-ID was used to identify relevant ligand-modulated structural variations in the β2 -adrenergic (β2 AR) G-protein coupled receptor. Micro-second MD simulations of the β2 AR in an explicit lipid bilayer were run in the apo-state and complexed with the ligands: BI-167107 (agonist), epinephrine (agonist), salbutamol (long-acting partial agonist), or carazolol (inverse agonist). Each ligand was found to modulate the conformational dynamics of β2 AR differently and DIRECT-ID analysis of the inverse-agonist vs. agonist-modulated β2 AR identified residues known through previous studies to selectively propagate deactivation/activation information, along with some previously unidentified ligand-specific microswitches across the GPCR [3]. DIRECT-ID is accessible from here.

References:

[1] Lakkaraju, S. K.; Raman, E. P.; Yu, W.; MacKerell, A. D.Sampling of Organic Solutes in Aqueous and Heterogeneous Environments using Oscillating Excess Chemical Potentials in Grand Canonical-Like Monte Carlo-Molecular Dynamics Simulations J. Chem. Theory Comput. 2014,10, 2281– 2290

[2] Lakkaraju, Sirish Kaushik, et al. "Mapping Functional Group Free Energy Patterns at Protein Occluded Sites: Nuclear Receptors and G-Protein Coupled Receptors." Journal of chemical information and modeling 55.3 (2015): 700-708.

[3] Lakkaraju, Sirish Kaushik, et al. "DIRECT‐ID: An automated method to identify and quantify conformational variations—application to β2‐adrenergic GPCR." Journal of computational chemistry 37.4 (2016): 416-425.

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Dr. Sirish Kaushik Lakkaraju received his Bachelor of Engineering degree in electronics and instrumentation from the University of Madras at Chennai, India in 2003. He received his Master of Science degree in biomedical engineering from Texas A&M University in 2008 and his Ph.D. in December 2011 with Dr. Wonmuk Hwang. After graduation, he worked as a postdoctoral fellow under the supervision of Dr. Alex MacKerell at University of Maryland, Baltimore on the projects that involve method development and in-silico drug design. Recently, he started his second postdoc at Pfizer. 

Dr. Lakkaraju has published 8 first-author papers and co-authored 2 papers in peer-reviewed international journals. His current research interests lie in studying the role of dynamics of biological macromolecules and macromolecular assemblies on their function and also computational drug design.

Lecture 3: Dr. Jin Seob Kim

How do Keratin Intermediate Filaments self-assemble?

   Skin epidermal layers [2]

Intermediate filaments (IFs) provide structural and mechanical support vitally important to maintenance of cell and tissue integrity under stress. In vitro studies established that IFs must be crosslinked into a network in order to generate the elasticity and mechanical properties consistent with their mechanical support role in living cells. Accordingly, keratin IFs are typically organized into large bundles in epithelial surface. Skin epithelial cells express type I and type II IF genes, whose protein products copolymerize to form 10-nm-wide IFs in their cytoplasm. Experimentally, various types of keratin IFs are able to self-organize into crosslinked networks at subphysiological concentrations. For IFs comprised of keratin proteins 5 and 14 (K5, K14), found in basal keratinocytes of epidermis, bundling can be self-driven through interactions between K14's carboxy-terminal tail domain and two regions in the central α-helical rod domain of K5. 

Mechanisms of keratin filament bundling in basal keratinocytes of epidermis.
Ultrastructural examination shows that keratin filaments are abundant and show a
loosely bundled organization in basal layer keratinocytes of epidermis [3].

A number of fundamental questions remain about the pathway through which the keratin IF surface can promote the crosslinked network formation. In their work, Jin Seob Kim, Chang-Hun Lee and Pierre Coulombe from Johns Hopkins exploit theoretical principles and computational modeling to investigate how IF crosslinking happens. In their simple model, keratin IFs are treated as rigid rods to apply Brownian dynamics simulation. The authors' findings suggest that long-range interactions between IFs are required to initiate the formation of bundle-like configurations, while tail domain-mediated binding events act to stabilize them. The simple model explains the differences observed in the mechanical properties of wild-type versus disease-causing, defective IF networks. This effort extends the notion that the structural support function of keratin IFs necessitates a combination of intrinsic and extrinsic determinants, and makes specific predictions about the mechanisms involved in the formation of crosslinked keratin networks in vivo.

Theoretical time evolution of bundle formation [1]. 

Jin Seob's Article:

[1] Kim, Jin Seob, Chang-Hun Lee, and Pierre A. Coulombe. "Modeling the self-organization property of keratin intermediate filaments." Biophysical journal 99.9 (2010): 2748-2756.

References:

[2] Matsusaki, Michiya, et al. "Development of full‐thickness human skin equivalents with blood and lymph‐like capillary networks by cell coating technology." Journal of Biomedical Materials Research Part A 103.10 (2015): 3386-3396.

[3] Lee, Chang-Hun, and Pierre A. Coulombe. "Self-organization of keratin intermediate filaments into cross-linked networks." The Journal of cell biology 186.3 (2009): 409-421.

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Dr. Jin Seob Kim received a B.A. degree and M.S. degree in mechanical engineering from Seoul National University. After his master’s degree, he worked as a research engineer at SAMSUNG Electronics. In 2006, he received his Ph.D. degree in mechanical engineering at the Johns Hopkins University under the supervision of Dr. Gregory S. Chirikjian. After graduation, he worked as a postdoctoral fellow under the supervision of Drs. Sean Sun and Pierre Coulombe in the projects on computational cell morphology, cytoskeletal filaments, and skin homeostasis. Currently, he is an assistant research professor in the department of mechanical engineering at the Johns Hopkins University.

Dr. Kim’s research interests lie in mathematical modeling and simulation in the broad areas ranging from biology (molecular to cellular biology, systems biology and mechanobiology) and engineering (robotics and dynamics). 

Dr. Kim has published highly cited 9 first author papers and co-authored 4 papers in peer reviewed international journals. Also he has published 4 peer-reviewed papers in highly cited international conferences on robotics and computational biology.

Lecture 2: Nash Rochman (Sean Sun Lab)

To Grow is Not Enough

E. coli cell cycle duration distributions measured
at constant nutrient conditions. [1]

The mantra, “Survival of the Fittest,” coined by Spencer and popularized by Darwin himself, pervades every corner of biology. Fitness is usually defined to be the “birth-rate” or the rate at which new individuals are added to the population. Cooperative and multicellular systems may require a more complicated definition; but often even these phenomena are shown to derive from the maximization of total sustainable single cell number. In the case of non-cooperative, single cell species (e.g. bacteria at low cell density), fitness as birth-rate is accepted. For such a population during exponential growth, the number of cells in an ensemble can be well described as a function of time if we know the initial number N0, and the cell cycle duration τ, yielding N(t) = N0 exp(ln(2)t/τ). In this way the constant r = ln(2)/τ, often labeled the“growth-rate”, is used to measure fitness - the larger r and the faster an organism grows, the fitter it is.

Quantitative single cell measurements have shown that cell cycle duration (CCD, the time between cell divisions) for diverse cell types is a noisy variable. The underlying distribution of CCD is mean scalable with a universal shape for many cell types in a variety of environments. In their recent article, Nash Rochman and Sean Sun from Johns Hopkins mechanical engineering department have developed a phenomenological model for the regulation of cellular division time distributions determining both bulk growth rate and ensemble fluctuations. In this model, they propose a cellular ‘‘fitness’’ function which incorporates not only growth rate, which is maximized when fluctuations are minimized, but also ensemble response time to environmental stimulus which decreases for increasing fluctuations. Experimental single cell division data is collected on a population of isogenic cells subjected to varying environmental stimuli and compared to the model. The authors then have shown through both experiment and theory that increasing the amount of noise in the regulation of the cell cycle negatively impacts the growth rate, but positively correlates with improved cellular response to fluctuating environments. These results suggest that even non-cooperative cells in exponential growth phase do not optimize fitness through growth rate alone, but also optimize adaptability to changing conditions. In a manner similar to genetic evolution, increasing the noise in biochemical processes correlates with improved response of the system to environmental changes.

Nash's theory compared with eight step environmental change experiments. The experimental distributions are displayed using colors with highest probability in red and lowest probability in blue. The black lines are the model predictions for the average. [1]

Nash’s Article:

[1]: Rochman, Nash, Fangwei Si, and Sean X. Sun. "To Grow is Not Enough: Impact of Noise on Cell Environmental Response and Fitness." arXiv preprint arXiv:1603.01579 (2016).

Good reads on the subject:

P1: Hashimoto, Mikihiro, et al. "Noise-driven growth rate gain in clonal cellular populations." Proceedings of the National Academy of Sciences (2016): 201519412.

P2: Powell, E. O. "Growth rate and generation time of bacteria, with special reference to continuous culture." Microbiology 15.3 (1956): 492-511.

P3: Stukalin, Evgeny B., et al. "Age-dependent stochastic models for understanding population fluctuations in continuously cultured cells." Journal of The Royal Society Interface 10.85 (2013): 20130325.

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Nash Rochman studied chemical physics and mathematics in college (started at Bard College at Simon’s Rock and transferred to Brown) taking a particular interest in evolutionary dynamics which brought him to the ChemBE department here at Hopkins for his PhD. With his advisor Sean Sun (MechE), he has become engaged in a variety of problems motivated by exciting analytical predictions that also provide the potential for convincing experimental verification. When not in the office/lab, he likes to play and compose music – playing mostly jazz (trumpet) and writing mostly concert music.

Lecture 1: Dr. Rati Sharma (Elijah Roberts Lab)

The Switch to Mate

Two haploid yeast cells of opposite
mating types secrete pheromones,
grow projections and mate. (Wikipedia)

Many vital eukaryotic cellular functions require cells to respond to a directional gradient of a signaling molecule. The first two steps in any eukaryotic chemotactic/chemotropic pathway are gradient detection and cell polarization. Like many processes, such chemotactic and chemotropic decisions are made using a relatively small number of molecules and are thus susceptible to internal and external fluctuations during signal transduction. Large cell-to-cell variations in the magnitude and direction of a response are therefore possible and do, in fact, occur in natural systems. One very interesting and complex example to this kind of cellular response in a noisy environment is the mating of two yeast cells (Saccharomyces cerevisiae).

In their recent work, Drs. Rati Sharma and Elijah Roberts from the Johns Hopkins Biophysics Department use 3D probabilistic modeling of a simple gradient sensing pathway to study the capacity for individual cells to accurately determine the direction of an external gradient, despite fluctuations/noise. By including a stochastic external gradient in the simulations using a novel gradient boundary condition (see the diagram below), they compare and contrast three different variants of the gradient sensing pathway, one monostable and two bistable. 

Diagram of the simulated system.

The results show that an architecture combining bistability with spatial positive feedback permits the cell to both accurately detect and internally amplify an external gradient. The researchers observe strong polarization in all individual cells, but in a distribution of directions centered on the gradient. Polarization accuracy in the study was strongly dependent upon a spatial positive feedback term that allows the pathway to trade accuracy for polarization strength. Finally, it is shown that additional feedback links providing information about the gradient to multiple levels in the pathway can help the cell to refine initial inaccuracy in the polarization direction.

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Rati Sharma is a postdoctoral researcher who is interested in understanding stochasticity or noise in systems at the microscopic level. As a member of the Roberts Lab at Johns Hopkins University, she is studying noise and its effects on gene networks. In particular, she is studying the ways noise can effect and aid the cellular decision making process in gene networks, specifically, the pheromone response pathway of mating yeast cells. 

During her PhD in Indian Institute of Science, she looked at noise and its effects on polymers and polymeric systems such as melts, semiflexible and Rouse polymers. In addition, she studied work and force fluctuation theorems on the application of flow and mechanical forces to polymers. The phenomenon of subdiffusion was a recurring theme in her study of stochasticity within these systems. 

She has published 7 first author papers and co-authored 1 paper in peer reviewed international journals.