The Upcoming Lectures in Computational Biophysics

Date	Speaker	Lecture Title	Related Papers
25-July-2017	Rebecca Alford (Jeff Gray Lab)	A deep-dive into the Rosetta all-atom energy function	p1
8-August-2017	Dr. Ozge Yoluk (UMaryland)	Elucidating the Gating Mechanism of Cys-Loop Receptors	p1
22-August-2017
5-September-2017
19-September-2017
3-October-2017
17-October-2017
31-October-2017
14-November-2017
28-November-2017
12-December-2017

Lecture 22: Dr. Özge Yoluk (Alex MacKerell Lab)

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Ozge Yoluk is a postdoctoral researcher at Alex MacKerell’s lab at University of Maryland, School of Pharmacy. She obtained her bachelor's degree in Molecular Biology and Genetics from Istanbul University. She then moved to Uppsala and obtained her master’s degree in Applied Biotechnology. During her master studies, she transitioned to computational studies and decided to build her career in this field. She had learned and applied computational methods to understand the gating mechanism of ion channels during her graduate studies in Erik Lindahl’s Lab at KTH. She is currently working with proteins involved in base-excision repair pathway and force field development. 

Lecture 21: Rebecca Alford (Jeff Gray Lab)

A deep-dive into the Rosetta all-atom energy function

Over the past decade, the Rosetta biomolecular modeling suite has informed various biological questions and engineering challenges ranging from folding and docking to the interpretation of low-resolution structural data, design of nanomaterials, and vaccines. Central to Rosetta’s success is the energy function: a set of mathematical models used to approximate the free energy of a macromolecule. In this presentation, Rebecca will describe the concepts and calculations that underlie the Rosetta energy function. I will present the mathematics and origin of the major energy terms (van der Waals, implicit solvation, electrostatics, and hydrogen bonding, and design reference energies), and Rebecca will explain where numerical stability and efficiency limitations have led to modifications of these functions. Applying these concepts, she will explain how to use a Rosetta energy calculation to select and analyze the features of output models. Finally, we discuss the latest advances in the energy function that extend capabilities from soluble proteins to also include membranes, DNA, RNA, and other macromolecules.

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Rebecca Alford is a Chemical and Biomolecular Engineering Ph.D. Student in Jeff Gray’s Lab at Johns Hopkins University. Her overall goal is to develop computational tools to investigate biology and disease at the molecular level. As an undergraduate, she created RosettaMP: a suite of tools to investigate membrane protein structures. Currently, she is developing computational models of cell membrane environments toward improving energy functions for structure prediction and design. Rebecca is funded by a Hertz Foundation Fellowship and a National Science Foundation Graduate Research Fellowship.

Lecture 20: Dr. Osman Yogurtcu (Margaret Johnson Lab)

Membrane Recruitment Enables Weak Binding Endocytic Proteins to Form Stable Complexes Membrane targeting and assembly of proteins is required for vesicle trafficking and receptor mediated signaling, but it is not known to what extent the proteins recruited to these events may have evolved to exploit the 2D surface for assembly, versus pre-assembling in solution. We show that the phospholipid targeting proteins of clathrin-mediated endocytosis dramatically enhance their effective binding strength and subsequent complex formation to one another after surface recruitment in yeast and metazoans. For proteins such as clathrin that do not directly bind lipids, the enhancement is still achieved by using three distinct binding sites to stabilize the clathrin to peripheral membrane proteins on the surface. We derive simple formulas that quantify the degree of binding enhancement as a function of the protein and lipid concentrations, binding constants, and critically, the ratio of volume to membrane surface area. Our results thus apply to any cell type or geometries, including in vitro systems and the targeting of internal organelles from the cytoplasm. With a sufficient concentration of lipid recruiters, such as PIP2, we show that the effective binding strength is enhanced by orders of magnitude and becomes, surprisingly, independent of the protein-protein binding strength. We quantify how this effect varies for proteins involved in later stages of vesicle trafficking and cell division in yeast. Coupled with detailed spatially and structurally resolved simulations, we have further measured the effect of membrane recruitment on controlling the speed of assembly, and influences of crowding and diffusion on this process.

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Osman N. Yogurtcu began his career in science as an undergraduate at Koc University, Turkey using computational polymer models to study protein-drug interactions. He received his M.Sc. in computational science and engineering from the same institution. During his Ph.D. in the Mechanical Engineering Department at the Johns Hopkins University, his research focused on mechanical properties of biofilaments, such as actin, that have crucial importance on cell viability. After graduation, he joined Prof. Margaret Johnson's lab in Johns Hopkins biophysics department where they worked on computational modelling of receptor mediated endocytosis. 

Lecture 19: Jennifer Lu (Steven Salzberg Lab)

KrakEN and Bracken.

Metagenomics is a rapidly growing field of study, driven in part by our ability to generate enormous amounts of DNA sequence rapidly and inexpensively. Since the human genome was first published in 2001 (The International Human Genome Sequencing Consortium, 2001; Venter et al., 2001), sequencing technology has become approximately one million times faster and cheaper, making it possible for individual labs to generate as much sequence data as the entire Human Genome Project in just a few days. In the context of metagenomics experiments, this makes it possible to sample a complex mixture of microbes by “shotgun” sequencing, which involves simply isolating DNA, preparing the DNA for sequencing, and sequencing the mixture as deeply as possible. Shotgun sequencing is relatively unbiased compared to targeted sequencing methods (Venter et al., 2004), including widely-used 16S ribosomal RNA sequencing, and it has the additional advantage that it captures any species with a DNA-based genome, including eukaryotes that lack a 16S rRNA gene. Because it is unbiased, shotgun sequencing can also be used to estimate the abundance of each taxon (species, genus, phylum, etc.) in the original sample, by counting the number of reads belonging to each taxon. 

Along with the technological advances, the number of finished and draft genomes has also grown exponentially over the past decade. At present, there are thousands of complete bacterial genomes, 20,000 draft bacterial genomes, and 80,000 full or partial virus genomes in the public GenBank archive (Benson et al., 2015). This rich resource of sequenced genomes now makes it possible to sequence uncultured, unprocessed microbial DNA from almost any environment, ranging from soil to the deep ocean to the human body, and use computational sequence comparisons to identify many of the formerly hidden species in these environments (Riesenfeld, Schloss & Handelsman, 2004). Several accurate methods have appeared that can align a sequence “read” to a database of microbial genomes rapidly and accurately (see below), but this step alone is not sufficient to estimate how much of a species is present. Complications arise when closely related species are present in the same sample–a situation that arises quite frequently–because many reads align equally well to more than one species. This requires a separate abundance estimation algorithm to resolve. In their recent article, Jennifer Lu and Steven Salzberg from Johns Hopkins University and their colleagues describe a new method, Bracken, that goes beyond simply classifying individual reads and computes the abundance of species, genera, or other taxonomic categories from the DNA sequences collected in a metagenomics experiment.

Number of reads within the Mycobacterium genus as assigned by Kraken (blue), estimated by Bracken (purple) and compared to the true read counts (green)[1].

Bracken (Bayesian Reestimation of Abundance after Classification with KrakEN) uses the taxonomic assignments made by Kraken, a very fast read-level classifier, along with information about the genomes themselves to estimate abundance at the species level, the genus level, or above. The authors of the study demonstrate that Bracken can produce accurate species- and genus-level abundance estimates even when a sample contains multiple near-identical species.

References:

[1] Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: Estimating species abundance in metagenomics data. PeerJ Computer Science. 2017 Jan 2;3:e104.

[2] Wood DE, Salzberg SL. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome biology. 2014 Mar 3;15(3):R46.

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Jennifer Lu is a Biomedical Engineering Ph.D. Candidate in Professor Steven Salzberg's lab at the Center for Computational Biology at Johns Hopkins University. With a background in Chemical and Biomolecular Engineering and Computer Science, Jennifer began her Ph.D. with the intent of applying her knowledge in Computer Science to Biomedical research. Currently, her research is focused on computational genomics and the usage of next-generation sequencing for diagnosing bacterial, fungal, or viral infections relating to human health and diseases. As part of this research, she develops and uses various computational methods for quantifying DNA sequence similarities and analyzing the genomes of human pathogens.

Lecture 18: Jeliazko Jeliazkov (Jeff Gray Lab)

Computational Modeling and Docking of Antibody Structures.

The vertebrate adaptive immune system is capable of promoting cells to degranulate or phagocytose nearly any foreign pathogen by producing immunoglobulin G (IgG) proteins (antibodies) that recognize a specific region (epitope) of a pathogenic molecule (antigen). The ability to bind diverse antigens requires a diverse population of antibodies, which is achieved through complex processes in bone marrow and lymphatic tissues, namely V(D)J recombination and somatic hypermutation. The diversity of antibodies is astonishing; the size of the theoretical naive antibody repertoire is estimated to be >1e13 in humans. In addition to their biological importance, antibodies are routinely used in biotechnology as probes and diagnostics, and dozens of antibodies have been approved as therapeutics.

Therapeutic monoclonal antibodies are a genre of biopharmaceuticals which has benefitted healthcare in various fields from oncology to immune and inflammatory disorders. Development of successful novel therapeutic antibodies requires an understanding of drug and disease mechanisms and the ability to stabilize, affinity mature, and humanize antibodies. Antibody structures can help overcome these challenges by providing atomic-level insights into structure–function relationships and the antibody–antigen interaction [e.g. see refs. (1–4)]. However, experimental techniques for obtaining antibody structures, like X-ray crystallography and nuclear magnetic resonance, are laborious, time-consuming and costly. Computational antibody structure prediction provides a fast and inexpensive route to obtain structures, including those which are not obtainable otherwise.

A schematic[1] of the modeling protocols (full flowcharts
for Rosetta Antibody and Rosetta SnugDock
are available in the original publications).

In their recent Nature Protocols article, Jeliazko Jeliazkov and Jeff Gray from Johns Hopkins Department of Chemical and Biomolecular Engineering and their international collaborators describe Rosetta-based computational protocols for predicting the 3D structure of an antibody from the sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally determined structures, as well as offering (i) energetic calculations to minimize loops, (ii) docking methodology to refine the VL–VH relative orientation and (iii) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully automated via the ROSIE web server or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers. In the figure, the structure on the left shows the FV antibody domains predicted by homology modeling (heavy chain in dark blue with CDR H1 and H2 loops in orange and CDR H3 loop in red; light chain in yellow with its CDR loops in light blue). The structure on the right depicts an antibody–antigen structure output by docking (antigen in green).

References:

[1]Weitzner BD, Jeliazkov JR, Lyskov S, Marze N, Kuroda D, Frick R, Adolf-Bryfogle J, Biswas N, Dunbrack Jr RL, Gray JJ. Modeling and docking of antibody structures with Rosetta. Nature Protocols. 2017 Feb 1;12(2):401-16.

[2]Sircar A, Kim ET, Gray JJ. RosettaAntibody: antibody variable region homology modeling server. Nucleic acids research. 2009 May 20:gkp387.

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Jeliazko Jeliazkov received his B.S. in Physics from the University of Illinois at Urbana-Champaign. Since graduating, he joined the Program in Molecular Biophysics and is pursuing a Ph.D. under the tutelage of Prof. Jeffrey Gray. His research involves the computational prediction of protein–protein interactions, focusing in particular on antibody–antigen, disordered–ordered protein domain, and crystallographic (non-biological) protein–protein interactions.

Lecture 17: Prof. Sagar Khare

A New Weapon in the Fight Against Cancer and Viral Infections: Custom-Designed Enzymes

Arising out of natural selection, the structures of proteins (and their complexes with small molecules, nucleic acids, and other proteins) display exquisitely fine-tuned molecular recognition, which is critical for life to operate. Under selection conditions, accurate molecular recognition must be robust to random perturbations such as mutations. Yet, natural proteins are also evolvable — variation in a few amino acids can lead to profound changes in function, e.g. a new enzymatic activity can arise in an “old” enzyme. In other words, these molecular interactions have the fascinating property of being simultaneously functionally robust and plastic.

Fitness scoring function [1].

Characterizing the substrate specificity of protease enzymes is critical for illuminating the molecular basis of their diverse and complex roles in a wide array of biological processes. Rapid and accurate prediction of their extended substrate specificity would also aid in the design of custom proteases capable of selectively and controllably cleaving biotechnologically or therapeutically relevant targets. However, current in silico approaches for protease specificity prediction, rely on, and are therefore limited by, machine learning of sequence patterns in known experimental data. In his talk, Prof. Sagar Khare described a general approach for predicting peptidase substrates de novo using protein structure modeling and biophysical evaluation of enzyme–substrate complexes. His research team constructed atomic resolution models of thousands of candidate substrate–enzyme complexes for each of five model proteases belonging to the four major protease mechanistic classes—serine, cysteine, aspartyl, and metallo-proteases—and develop a discriminatory scoring function using enzyme design modules from Rosetta and AMBER's MMPBSA. They ranked putative substrates based on calculated interaction energy with a modeled near-attack conformation of the enzyme active site. Their results show that the energetic patterns obtained from these simulations can be used to robustly rank and classify known cleaved and uncleaved peptides and that these structural-energetic patterns have greater discriminatory power compared to purely sequence-based statistical inference. Combining sequence and energetic patterns using machine-learning algorithms further improves classification performance, and analysis of structural models provides physical insight into the structural basis for the observed specificities.

Summary of the CPG2 circular permutations [2].

In the second part of his talk, Prof. Khare talked about spatio-temporal design of enzymes. Carboxypeptidase G2 (CPG2) is an Food and Drug Administration (FDA)-approved enzyme drug used to treat methotrexate (MTX) toxicity in cancer patients receiving MTX treatment. It has also been used in directed enzyme-prodrug chemotherapy, but this strategy has been hampered by off-site activation of the prodrug by the circulating enzyme. The development of a tumor protease activatable CPG2, which could be achieved using a circular permutation of CPG2 fused to an inactivating ‘prodomain’, would aid in these applications. The research team reported the development of a protease accessibility-based screen to identify candidate sites for circular permutation in proximity of the CPG2 active site. The resulting six circular permutants showed similar expression, structure, thermal stability, and, in four cases, activity levels compared to the wild-type enzyme. They rationalize these results based on structural models of the permutants obtained using the Rosetta software by developing a cell growth-based selection system, and demonstrated that when fused to periplasm-directing signal peptides, one of the circular permutants confers MTX resistance in Escherichiacoli with equal efficiency as the wild-type enzyme. As the permutants have similar properties to wild-type CPG2, these enzymes are promising starting points for the development of autoinhibited, protease-activatable zymogen forms of CPG2 for use in therapeutic contexts[2]. 

Related References:

[1] Pethe, M. A., Rubenstein, A. B., & Khare, S. D. (2017). Large-scale Structure-based Prediction and Identification of Novel Protease Substrates using Computational Protein Design. Journal of Molecular Biology, 429(2), 220-236

[2] Yachnin, B. J., & Khare, S. D. (2017). Engineering carboxypeptidase G2 circular permutations for the design of an autoinhibited enzyme. Protein engineering, design & selection: PEDS, 1.

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Sagar Khare is an Assistant Professor at Rutgers University. He teaches Chemistry and Chemical Biology and seeks to understand the structural determinants of enzymatic specificity and reactivity using a combination of computational protein design and experimental characterization. His research team's goal is to develop a quantitative and predictive understanding of specificity at protein-ligand and protein-peptide interfaces; which will inform various therapeutic and synthetic applications. Prior to working at Rutgers, Prof. Sagar Khare was a Postdoctoral Fellow at the University of Washington in Seattle, Washington. He was also a Software Engineer for Affymax Research Institute in Bangalore, India. 

Lecture 16: Dr. Yukun Wang (Martin Ulmschneider Lab)

Experimentally guided simulations reveal the mechanism of action of antimicrobial peptides

Since their discovery over 100 years ago, thousands of pore-forming antimicrobial peptides (AMPs) have been identified, revealing great variations in size, secondary structure and sequence composition. Despite this wealth of data no common pore-forming motif has been discovered, and the molecular basis of antimicrobial activity remains poorly understood. One of the key difficulties has been the transience of pores formed by AMPs, which has proved challenging for experimental structure determination. In the absence of experimental structures a range of theoretical models have been proposed that try to rationalize how soluble AMPs, which carry multiple charged residues, can insert into hydrophobic membranes to form pores and what these pores might look like.

Spontaneous membrane insertion and intra-membrane
oligomerization of maculatin [1]

Scientists from the Johns Hopkins Institue of NanoBio technology reports[1] an experimentally guided unbiased simulation methodology that yields the mechanism of spontaneous pore assembly for the AMP maculatin at atomic resolution. Rather than a single pore, maculatin forms an ensemble of structurally diverse temporarily functional low-oligomeric pores, which mimic integral membrane protein channels in structure. These pores continuously form and dissociate in the membrane. Membrane permeabilization is dominated by hexa-, hepta- and octamers, which conduct water, ions and small dyes. Pores were shown to form by consecutive addition of individual helices to a transmembrane helix or helix bundle, in contrast to current poration models. The diversity of the pore architectures—formed by a single sequence—may be a key feature in preventing bacterial resistance and could explain why sequence–function relationships in AMPs remain elusive.

Related References:

[1] Wang, Y., Chen, C. H., Hu, D., Ulmschneider, M. B., & Ulmschneider, J. P. (2016). Spontaneous formation of structurally diverse membrane channel architectures from a single antimicrobial peptide. Nature Communications, 7.

[2] Wimley, W. C., & Hristova, K. (2011). Antimicrobial peptides: successes, challenges and unanswered questions. The Journal of membrane biology, 239(1-2), 27-34.

[3] Ulmschneider, M. B., Ulmschneider, J. P., Schiller, N., Wallace, B. A., Von Heijne, G., & White, S. H. (2014). Spontaneous transmembrane helix insertion thermodynamically mimics translocon-guided insertion. Nature communications, 5, 4863.

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Dr. Yukun Wang did his undergrad in Biology in Nanjing Agricultural University. After graduation, he did his Ph.D. in Biophysics at the Institute of Natural Sciences & School Of Life Sciences And Biotechnology, Shanghai Jiao Tong University. Currently, he is a postdoctoral fellow at the Institute for Nano-Bio-Technology in the Whiting School of Engineering at The Johns Hopkins University working with Prof. Martin Ulmschneider. Dr. Wang's research interests span from the functional mechanism of antimicrobial peptides to developing free energy calculation algorithms for rare event dynamics in the lipid-peptides systems. He enjoys computational designing of membrane-active peptides and proteins.

Lecture 15: Prof. Alex MacKerell

From Lemkul, J. A. et. al., Chemical reviews, 2016 [2].

Recent developments in polarizable force field research
Atomistic molecular dynamics (MD) simulations have become an integral component of the tool set used to examine biomolecular systems. Since the first MD simulation of a protein in 1977 [1], the time scales and sizes of computationally tractable simulations have grown by orders of magnitude. Systems may now be simulated on the microsecond or millisecond time scale and contain over a million atoms due to increased computing power and ever-improving algorithms for parallel and graphical processing unit (GPU)-based computing. Such capabilities also allow for more rigorous testing of the accuracy of the models used for such MD simulations.
Molecular mechanics force fields that explicitly account for induced polarization represent the next generation of physical models for molecular dynamics simulations. Several methods exist for modeling induced polarization, and in this seminar, Prof. MacKerell reviewed the classical Drude oscillator model, in which electronic degrees of freedom are modeled by charged particles attached to the nuclei of their core atoms by harmonic springs. He described the latest developments in Drude force field parametrization and application, primarily in the last 15 years. Emphasis was placed on the Drude-2013 polarizable force field for proteins, DNA, lipids, and carbohydrates. He discussed its parametrization protocol, development history, and recent simulations of biologically interesting systems, highlighting specific studies in which induced polarization plays a critical role in reproducing experimental observables and understanding physical behavior. As the Drude oscillator model is computationally tractable and available in a wide range of simulation packages, it is anticipated that use of these more complex physical models will lead to new and important discoveries of the physical forces driving a range of chemical and biological phenomena.
Related References:
[1] McCammon, J. A., Gelin, B. R., & Karplus, M. (1977). Dynamics of folded proteins. Nature, 267(5612), 585.
[2] Lemkul, J. A., Huang, J., Roux, B., & MacKerell Jr, A. D. (2016). An empirical polarizable force field based on the classical drude oscillator model: development history and recent applications. Chemical reviews, 116(9), 4983-5013.

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Alex MacKerell received an A.S. in biology in 1979 from Gloucester County College, Sewell, NJ, followed by a B.S. in chemistry in 1981 from the University of Hawaii, Honolulu, HI, and a Ph.D. in biochemistry in 1985 from Rutgers University, New Brunswick, NJ. Subsequent training involved postdoctoral fellowships in the Department of Medical Biophysics, Karolinska Intitutet, Stockholm, Sweden, in the area of experimental and theoretical biophysics and in the Department of Chemistry, Harvard University in theoretical chemistry. Following one year as a visiting professor at Swarthmore College, Swarthmore, PA, he assumed his faculty position in the School of Pharmacy, University of Maryland, Baltimore in 1993. MacKerell is currently the Grollman-Glick Professor of Pharmaceutical Sciences in the School of Pharmacy and the Director of the University of Maryland Computer-Aided Drug Design Center. MacKerell is also cofounder and Chief Scientific Officer of SilcsBio LLC. Research interests include the development of theoretical chemistry methods, with emphasis on empirical force field development; structure–function studies of proteins, carbohydrates, and nucleic acids; and the application of theoretical methods to drug discovery.

Lecture 14: David Holland (Margaret Johnson Lab)

Finding the right partner in a crowded world

Mammalian cells contain ∼21,000 genes encoding upwards to 100,000 protein types. 5-40% of cell volume is occupied by macromolecules, posing challenges for cell proteins to locate functional partners and increasing the risk of nonspecific (nonfunctional) interactions. Overexpressed proteins, in particular, will saturate functional partners, leaving leftovers for nonspecific binding instead. Eukaryotic cells have evolved various methods to help proteins function reliably, including compartmentalization, allostery, and structural and chemical properties of binding sites. Cells may optimize specificity as a function of their binding networks; first through concentration balance, next through network structure. Using the Gillespie algorithm, David Holland and Dr. Margaret Johnson from Johns Hopkins Biophysics simulated specific and nonspecific binding in 500 networks of 90-200 nodes with varying topological properties under equal, random, and stoichiometrically balanced protein concentrations. Binding affinities for all specific and nonspecific interactions were determined using a coarse-grained protein sequence model. The research team found out that the concentration balance significantly reduced the number of nonspecific interactions, as did local topological patterns (motifs) that allowed increased difference between specific and nonspecific binding affinities. 

Schematic illustrating (left) the binding of three proteins, where matching features indicate binding interfaces, (center) the corresponding interface-interaction network with orange and green indicating shared and non-shared binding interfaces, respectively, and (right) the protein-protein interaction network (from [1]).

To test if these motifs are selected for in real networks, they sampled possible interface-interaction networks (IINs) for the Clathrin-mediated endocytosis PPI network in yeast, using a Monte Carlo algorithm and a fitness function to select for features that allow increased specificity. The fitness function reproduced several features of the real IIN, including fragmentation, a scale-free degree distribution, the presence of square motifs and a low number of chain and triangle motifs. These features differed significantly from unbiased sampling. In conclusion, there is selective pressure on the evolution of real IINs to avoid nonfunctional interactions and that non-optimal features of the real IIN (chains, larger network components) likely serve a functional purpose.

Relevant Publications:

[1]Johnson, ME, & G Hummer (2013) “Evolutionary Pressure on the Topology of Protein Interface Interaction Networks.” J Phys Chem B 117:13098-106. 

[2]Johnson, ME, G Hummer (2013). “Interface-resolved network of protein-protein interactions.” PLoS Comput Biol 9(5):e1003065 

[3]Keil, C, E Verschueren, J Yang, & L Serrano (2013) “Integration of protein abundance and structure data reveals competition in the ErbB signaling network.” Science Signaling 6(306): ra109 [4]Johnson, ME, & G Hummer (2011) “Nonspecific binding limits the number of proteins in a cell and shapes their interaction networks.” PNAS 108(2):603-8. 

[5]Zhang, J, S Maslov, & EI Shakhnovich (2008) “Constraints imposed by non-functional protein-protein interactions on gene expression and proteome size.” Mol Sys Biol 4:210 

[6]Vavouri, T, JI Semple, R Garcia-Verdugo, & B Lehner (2009) “Intrinsic protein disorder and interaction promiscuity are widely associated with dosage sensitivity.” Cell 138: 198-208.

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David Holland received his B.Sc. in Biomedical Engineering from the University of Virginia in 2011. After graduating, he joined the department of Biomedical Engineering at Johns Hopkins. He is currently earning his Ph.D. under Margaret Johnson where he studies the effects of protein abundance and protein-protein interaction network structure on protein mis-interactions. In his spare time, David practices taekwondo and is also teaching a course on network science. 

Lecture 13: Athena Chen (Margaret Johnson Lab)

Spatial Cell Modeling Methods 

Due to the complexity of cells, it is useful to use computational tools to understand and predict mechanisms of biological processes. Ordinary differential equations (ODEs) and partial differential equations (PDEs) have proven to be successful at modeling various large systems. However, to understand certain biological processes such as bacterial cell division, high spatial resolution is necessary to discern the underlying mechanisms and interactions. ODEs and PDEs, along with the Gillespie algorithm for stochastic modeling, do not provide spatial resolution at a single-particle level; they are all concentration-based methods. MCell, the Free Propagator Reweighting Algorithm (FPR), and Smoldyn are algorithms that provide single-particle resolution, but may be computationally expensive and may not necessarily yield accurate protein dynamics.

Change in concentration of molecule A in an irreversible 3D reaction A+A -> 0. In this parameter set, the initial distances between molecules causes an increased initial reaction rate.

In her recent work, Athena Chen and her mentors Dr. Margaret Johnson and Dr. Osman Yogurtcu in the Johns Hopkins Biophysics department, analyzed the strengths and limitations of ODEs, PDEs, Gillespie, MCell, Smoldyn, and FPR through establishing and performing a set of benchmark tests. Though all modeling methods extracted the correct equilibrium concentrations for most of the tested reactions, the resulting protein dynamics were not necessarily correct. Simulations at high rates and large densities showed that despite providing single-particle resolution, Smoldyn and MCell do not pick up single-particle effects where the distance between two molecules affects the probability of binding. Furthermore, the dynamics given by Smoldyn and MCell are dependent on the time step selected. On the other hand, FPR correctly identified single-particle effects and yielded dynamics independent of the selected timestep.

As an example of the effects of molecular geometry, diffusion, and stochasticity of protein dynamics, we examined a model for bacterial cell division. From oscillations in protein concentrations, the cell can identify the center of the cell to ensure identical offspring and division of genetic information. 

Written by Athena Chen

Relevant Articles:

1-Yogurtcu, Osman N., and Margaret E. Johnson. "Theory of bi-molecular association dynamics in 2D for accurate model and experimental parameterization of binding rates." The Journal of chemical physics 143.8 (2015): 084117.

2-Andrews, Steven S., et al. "Detailed simulations of cell biology with Smoldyn 2.1." PLoS Comput Biol 6.3 (2010): e1000705.

3-Johnson, Margaret E., and Gerhard Hummer. "Free-Propagator Reweighting Integrator for Single-Particle Dynamics in Reaction-Diffusion Models of Heterogeneous Protein-Protein Interaction Systems." Physical Review X 4.3 (2014): 031037.

4-Kerr, Rex A., et al. "Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces." SIAM journal on scientific computing 30.6 (2008): 3126-3149.

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Athena Chen is currently working on her Bachelors of Arts in Biophysics and Bachelors of Science in Applied Mathematics and Statistics from Johns Hopkins University. As part of Dr. Margaret Johnson’s lab in the department of Biophysics, she analyzes the accuracy of methods for modeling the dynamics of protein interactions. In her free time, she enjoys yoga and figure skating. 

Lecture 12: Andrei Kucharavy (Rong Li Lab)

Cellular Adaptation Under Stress

Diagram for a cell population adaptation.

Whether the products of human activity, or naturally occurring social, economic or biological, complex systems share the same properties. Composed of a large number of individual components, their components do not have a straightforward relation to their properties and often interact one with another in unexpected ways. Because of that, different instances of the same complex systems are built from slightly different components. Such differences give rise to heterogeneity within a population, which in turn raises significant difficulties for their study. From the biological perspective, such events have been formalized as Fisher’s geometric model, that has been formalized in the thirties of the last century and has been independently re-discovered in unrelated domains as algorithms for ergodic explorations of multi-dimensional spaces for an optimal value function point. 

Andrei Kucharavy and Dr. Rong Li from Johns Hopkins Medicine propose an enhancement of Fisher’s geometric model, allowing to explain a range of previously unexplained observations in biology. Mathematical analysis of their enhancement provides a set of rules applicable to the optimization of a large class of ergodic exploration algorithms.

Related Journal Articles:
1. H. A. Orr, The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6, 119–127 (2005) 
2. H. A. Orr, R. L. Unckless, The Population Genetics of Evolutionary Rescue. PLoS Genet. 10, e1004551 (2014). 
3. P. S. Pennings, Standing genetic variation and the evolution of drug resistance in HIV. PLoS Comput. Biol. 8 (2012).
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Andrei Kucharavy received his Engineer’s Degree in Physics, Mathematics, Programming and Bioinformatics from Ecole Polytechnique, France in 2011. After graduation, he did masters in computational biology at Ecole Polytechnique Fédérale de Lausanne, Switzerland. Currently, Andrei is a Ph.D. student under the joint direction of Dr. Rong Li from Johns Hopkins and Dr. Gilles Fischer, exploring molecular mechanisms of aneuploidy-enabled stress adaptation and drug resistance it enables in yeast and cancer. He works on developing computational methods enabling systematic analysis of molecular mechanisms underlying complex traits. The interface of biological network analysis and evolution theory is his particular interest.

Lecture 11: Dr. Ana Damjanović

Simulating with the right pH.

Schematic representation of a constant-pH simulation
with the two-dimensional EDS-HREM method.

Solution pH is one of the most important environmental factors that affects the structure and dynamics of proteins. Almost all biologically relevant properties of proteins are affected by pH: stability, folding and assembly, interactions with ligands and other biological molecules, solubility, aggregation properties, and enzymatic activity. A change in pH may induce a change in the protonation state of ionizable groups, which in turn can cause structural changes in proteins. Structural changes triggered by protonation/deprotonation can be exploited for function, such as in the case of ATP synthase, bacteriorhodopsin, cytochrome c oxidase, or the photoactive yellow protein.

Superimposed 1 ns trajectories of snake cardiotoxin
from the (A) 2D and (B) 1D constant-pH
EDS-HREM simulations at pH = 2.

Ana Damjanovic from Johns Hopkins Biophysics and her colleagues from NIH present a new method for enhanced sampling for constant-pH simulations in explicit water based on a two-dimensional (2D) replica exchange scheme. The new method is a significant extension of a previously developed constant-pH simulation method, which is based on enveloping distribution sampling (EDS) coupled with a one-dimensional (1D) Hamiltonian exchange method (HREM). EDS constructs a hybrid Hamiltonian from multiple discrete end state Hamiltonians that, in this case, represent different protonation states of the system. The ruggedness and heights of the hybrid Hamiltonian’s energy barriers can be tuned by the smoothness parameter. Within the context of the 1D EDS-HREM method, exchanges are performed between replicas with different smoothness parameters, allowing frequent protonation-state transitions and sampling of conformations that are favored by the end-state Hamiltonians. In this work, the 1D method is extended to 2D with an additional dimension, external pH. Within the context of the 2D method (2D EDS-HREM), exchanges are performed on a lattice of Hamiltonians with different pH conditions and smoothness parameters. The research team demonstrates that both the 1D and 2D methods exactly reproduce the thermodynamic properties of the semi-grand canonical (SGC) ensemble of a system at a given pH. They have tested the new 2D method on aspartic acid, glutamic acid, lysine, a four-residue peptide (sequence KAAE), and snake cardiotoxin. In all cases, the 2D method converges faster and without loss of precision; the only limitation is a loss of flexibility in how CPU time is employed. The results for snake cardiotoxin demonstrate that the 2D method enhances protonation-state transitions, samples a wider conformational space with the same amount of computational resources, and converges significantly faster overall than the original 1D method.

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Ana Damjanovic received her B.Sc. in Physics from Belgrade University (1995), and a Ph.D. in Physics from University of Illinois at Urbana-Champaign (2001). For her Ph.D. she worked with Prof. Klaus Schulten on QM description of energy transfer in light-harvesting complexes in various photosynthetic organisms. She did postdocs at UC Berkeley and Johns Hopkins University. At JHU she worked on understanding hydration and conformational changes in proteins through molecular dynamics simulations. She is presently an Associate Research Scientist and Lecturer in the Dept. of Biophysics at Johns Hopkins University. Her current research interests are in the area of development and applications of molecular dynamics simulations at constant pH.

Lecture 10: Collin Tokheim (Rachel Karchin Lab)

Hotspots in Cancer.

Missense mutations are perhaps the most difficult mutation type to interpret in human cancers. Truncating loss-of-function mutations and structural rearrangements generate major changes in the protein product of a gene, but a single missense mutation yields only a small change in protein chemistry. The impact of missense mutation on protein function, cellular behavior, cancer etiology, and progression may be negligible or profound, for reasons that are not yet well understood. Missense mutations are frequent in most cancer types, accounting for approximately 85% of the somatic mutations observed in solid human tumors, and the cancer genomics community has prioritized the task of identifying important missense mutations discovered in sequencing studies. Whole exome sequencing (WES) studies of cancer have created new opportunities to better understand the importance of missense mutations. This enormous collection of data now allows detection of patterns with power that was unheard of a few years ago.

Comparison of hotspot detection in the TSG FBXW7 in 1D and 3D [1].

In their recent work, Collin Tokheim from Johns Hopkins Biomedical Engineering and his colleagues used The Cancer Genome Atlas mutation data and identified 3D clusters of cancer mutations ("hotspot regions") at amino-acid-residue resolution in 91 genes, of which 56 are known cancer-associated genes. The hotspot regions identified by their method are smaller than a protein domain or protein– protein interface and in many cases can be linked precisely with functional features such as binding sites, active sites, and sites of experimentally characterized mutations. The hotspot regions are shown to be biologically relevant to cancer, and they discovered that there are characteristic differences between regions in the two types of driver genes, oncogenes and tumor suppressor genes (TSG). These differences include region size, mutational diversity, evolutionary conservation, and amino acid residue physiochemistry. For the first time, the research team quantifies why the great majority of well-known hotspot regions occur in oncogenes. Because hotspot regions in TSGs are larger, more heterogeneous than those in oncogenes, they are more difficult to detect using protein sequence alone and are likely to be underreported. The results indicate that protein structure–based 3D mutation clustering increases power to find hotspot regions, particularly in TSGs.

Good reads on the subject:

[1] Tokheim, Collin, et al. "Exome-scale discovery of hotspot mutation regions in human cancer using 3D protein structure." Cancer research (2016): canres-3190.

[2] Perdigão, Nelson, et al. "Unexpected features of the dark proteome." Proceedings of the National Academy of Sciences 112.52 (2015): 15898-15903.

[3] Kamburov, Atanas, et al. "Comprehensive assessment of cancer missense mutation clustering in protein structures." Proceedings of the National Academy of Sciences 112.40 (2015): E5486-E5495.

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Collin Tokheim got his bachelor's degree in biomedical engineering at the University of Iowa. After a short stint working in an RNA genomics lab, he came to Hopkins to work on a Ph.D. in Biomedical Engineering. He currently works in Rachel Karchin's lab doing computational research applied to cancer genomics. Collin's current research focuses on how protein structures used on an exome-scale can inform which missense mutations are likely drivers of cancer.

Lecture 9: Max C. Klein (Elijah Roberts Lab)

An epigenetic landscape
(from Epigenetics Unraveled).

Analyzing rare events in biology. 
Changes in cellular phenotype can often be tightly correlated with changes in biochemistry. The macroscopic (phenotypical) and the microscopic (biochemical) descriptions of these phenotype switching events can be unified through the theoretical framework of epigenetic landscapes (EL). In approximate terms, the EL of a cellular system is a map that describes every possible system state (expressed in terms of a count of relevant DNA, proteins, etc.) and the probability of the system being in each of these states. An EL can be used to completely describe the dynamics of its associated system, allowing for a deep level of understanding and, potentially, control of the system. It is possible to take the list of chemical species and reactions involved in a cellular process and, using theory and modeling, generate the corresponding EL. Unfortunately, the computer time required to generate an EL using the standard stochastic simulation method, called Brute Force Sampling (BFS), makes it difficult to generate the EL of even a relatively simple multi-state system.

The Forward Flux Sampling (FFS) method, which belongs to a larger family of Enhanced Sampling methods, was previously developed by ten Wolde and coworkers to address this computational limitation. Although FFS can speed up EL simulations by multiple orders of magnitude, there is a commensurate increase in the complexity of simulation setup. Specifically, there are a number of novel free parameters that the simulation user must specify. Each of these parameters has a significant and indirect influence on the precision of an FFS simulation’s final results. It is, therefore, desirable to develop a version of the FFS algorithm in which the relationship of parameter choice to error is made explicit.

Max Klein and Dr. Elijah Roberts from Johns Hopkins Biophysics have designed and implemented a supercomputer-compatible version of the Forward Flux Enhanced Sampling method for stochastic simulation of biochemical networks. Their version greatly simplifies parameter choice and simulation setup relative to the base Forward Flux algorithm. A user of the program needs only to specify the desired level of precision error. The program will determine a set of parameters to use that will achieve this target error level while optimizing the simulation run time. The Forward Flux implementation has been tested and verified in terms of its ability to calculate switching rate constants and epigenetic landscapes. A preview build of the code is currently available here.

Related Articles:

[1] Dickson, Alex, and Aaron R. Dinner. "Enhanced sampling of nonequilibrium steady states." Annual review of physical chemistry 61 (2010): 441-459.

[2] Allen, Rosalind J., Daan Frenkel, and Pieter Rein ten Wolde. "Forward flux sampling-type schemes for simulating rare events: Efficiency analysis." The Journal of chemical physics 124.19 (2006): 194111.

[3] Becker, Nils B., and Pieter Rein ten Wolde. "Rare switching events in non-stationary systems." The Journal of chemical physics 136.17 (2012): 174119.

[4] Gardner, Timothy S., Charles R. Cantor, and James J. Collins. "Construction of a genetic toggle switch in Escherichia coli." Nature 403.6767 (2000): 339-342.

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Max C. Klein received his B.A. in Physics, from Reed College, Oregon in 2013. After graduation, he joined the Program in Molecular Biophysics at Johns Hopkins University. Currently, with Dr. Elijah Roberts from the Biophysics Department, Max is developing new methods to simulate decision making in cells using a hybrid multi-CPU/multi-GPU computational architecture.

Lecture 8: Dr. Yasser Aboelkassem (Natalia Trayanova Lab)

The heart of the matter. 

Mesh representation of the
ventricles of the heart

Ventricular relaxation occurs as intracellular calcium drops to resting levels. Under low calcium conditions, contraction is inhibited by the troponin/tropomyosin complex. However, experimental evidence has long suggested that some degree of actin-myosin interaction is possible even in the absence of calcium. Under calcium-free conditions, as many as 5% of actin binding sites are occupied by myosin, according to some estimates made from solution studies of purified myofilament components. Despite abundant in vitro evidence for calcium-independent activation (CIA), its relevance to in vivo cardiac function is not clear. Striated muscle preparations can produce small amounts of actin-myosin-based force under low calcium conditions, especially near physiological temperatures. This suggests that residual actin-myosin cross bridges resist diastolic filling, adding to the resistance provided by other structures such as collagen and titin. However, distinguishing the contributions of these various factors is technically challenging, and cross bridge-based diastolic stiffness remains controversial.

Schematic diagram of model components and states [1].

In their recent study, Dr. Yasser Aboelkassem and his colleagues investigate CIA using computational analysis by adding a structurally motivated representation of this phenomenon to an existing myofilament model, which allowed predictions of CIA-dependent muscle behavior. The researchers found that a certain amount of CIA was essential for the model to reproduce reported effects of nonfunctional troponin C on myofilament force generation. Consequently, those data enabled estimation of ΔGCIA, the energy barrier for activating a thin filament regulatory unit in the absence of calcium. Using this estimate of ΔGCIA as a point of reference (∼7 kJ mol−1), they examined its impact on various aspects of muscle function through additional simulations. CIA decreases the Hill coefficient of steady-state force while increasing myofilament calcium sensitivity. At the same time, CIA has minimal effect on the rate of force redevelopment after slack/restretch. Simulations of twitch tension show that the presence of CIA increases peak tension while profoundly delaying relaxation. We tested the model’s ability to represent perturbations to the calcium regulatory mechanism by analyzing twitch records measured in transgenic mice expressing a cardiac troponin I mutation (R145G). The effects of the mutation on twitch dynamics were fully reproduced by a single parameter change, namely lowering ΔGCIA by 2.3 kJ mol−1 relative to its wild-type value. The analyses of Dr. Aboelkassem's and his team suggest that CIA is present in cardiac muscle under normal conditions and that its modulation by gene mutations or other factors can alter both systolic and diastolic function.

[1] Aboelkassem, Yasser, et al. "Contributions of Ca 2+-Independent Thin Filament Activation to Cardiac Muscle Function." Biophysical journal 109.10 (2015): 2101-2112.

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Dr. Yasser Aboelkassem obtained his BSc. in Aerospace Engineering from Cairo University, Egypt and received his master's degree in Mechanical Engineering from a joint program between Concordia-McGill University, Montreal, Canada. After graduation, Dr. Aboelkassem moved to Virginia Tech where he obtained a Master in Applied Mathematics degree and a Ph.D. in Engineering Science and Mechanics in 2012. He did his first postdoctoral training working in cardiac mechanics at the department of Biomedical Engineering at Yale University. Currently, he is a postdoc research associate with Natalia Trayanova at the Institute of Computational Medicine, Johns Hopkins University.

Dr. Aboelkassem has published 16 first-author papers and co-authored 6 papers in peer-reviewed international journals. His current research focus is the multiscale modelling of cardiac thin filament activation.