EMMAs: Implementation and Assessment of a Suite of Cross-Disciplinary, Case-Based High School Activities to Explore Three-Dimensional Molecular Structure, Noncovalent Interactions, and Molecular Dynamics.

Students frequently develop misconceptions about noncovalent interactions that make it challenging for them to appropriately interpret aspects of molecular structure and interactions critical to myriad applications. We hypothesized that computational molecular modeling and visualization could provide a valuable approach to help address these core misconceptions when students are first exposed to these concepts in secondary school chemistry courses. Here, we present a series of activities exploring biomolecular drug-target interactions using molecular visualization software and an introduction to molecular dynamics methods that were implemented in secondary school classrooms. A pre- and postsurvey approach that incorporated Likert response type, written free response, and drawing-based items demonstrated that students gained an enhanced conceptualization of intermolecular interactions, particularly related to aspects of shape complementarity, after completing the activities. Students also expressed increased comfort with and facility in utilizing different three-dimensional representations of molecules in their postsurvey responses. The activities led to an increased appreciation of interdisciplinary connections of chemistry with mathematics and physics. Overall, the modular activities presented provide a relatively time-efficient and accessible manner to help promote an understanding of a traditionally challenging topic for beginning chemistry students while introducing them to contemporary research tools.

Electrostatics-Based Computational Design and Experimental Analysis of Buforin II Antimicrobial Peptide Variants with Increased DNA Affinities.

Antimicrobial peptides (AMPs) are promising alternatives to traditional antibiotics in the treatment of bacterial infections in part due to their targeting of generic bacterial structures that make it more difficult to develop drug resistance. In this study, we introduce and implement a design workflow to develop more potent AMPs by improving their electrostatic interactions with DNA, which is a putative intracellular target. Using the existing membrane-translocating AMP buforin II (BF2) as a starting point, we use a computational workflow that integrates electrostatic charge optimization, continuum electrostatics, and molecular dynamics simulations to suggest peptide positions at which a neutral BF2 residue could be substituted with arginine to increase DNA-binding affinity either significantly or minimally, with the latter choice done to determine whether AMP binding affinity depends on charge distribution and not just overall monopole. Our analyses predicted that T1R and L8R BF2 variants would yield substantial and minimal increases in DNA-binding affinity, respectively. These predictions were validated with experimental peptide-DNA binding assays with additional computational analyses providing structural insights. Additionally, experimental measurements of antimicrobial potency showed that a design to increase DNA binding can also yield greater potency. As a whole, this study takes initial steps to support the idea that (i) a design strategy aimed to increase AMP binding affinity to DNA by focusing only on electrostatic interactions can improve AMP potency and (ii) the effect on DNA binding of increasing the overall peptide monopole via arginine substitution depends on the position of the substitution. More broadly, this design strategy is a novel way to increase the potency of other membrane-translocating AMPs that target nucleic acids.

A fully integrated undergraduate introductory biology and chemistry course with a community-based focus I: Vision, design, implementation, and development.

We describe a first-semester, integrated, introductory biology and chemistry course for undergraduates at Wellesley College in Wellesley, MA, USA. Our vision was to create a supportive learning community in which students could comfortably make connections between scientific disciplines as they learned necessary content for subsequent courses, further developed problem solving, communication, and laboratory skills, and meaningfully connected with other students and with faculty during their first semester in college. Through highlighting five guiding principles that are central to the course, we describe the integrated course structure and content as well as our efforts to build community, provide support, and engage students in building skills crucial to scientists. We also highlight features of this course and institutional policies that facilitated its logistical and collaborative implementation that can be adapted to fit the needs, goals, and constraints of a diverse range of institutions. A companion article describes an assessment of our course in achieving academic and community building goals.

Macromolecular crowding effects on electrostatic binding affinity: Fundamental insights from theoretical, idealized models.

The crowded cellular environment can affect biomolecular binding energetics, with specific effects depending on the properties of the binding partners and the local environment. Often, crowding effects on binding are studied on particular complexes, which provide system-specific insights but may not provide comprehensive trends or a generalized framework to better understand how crowding affects energetics involved in molecular recognition. Here, we use theoretical, idealized molecules whose physical properties can be systematically varied along with samplings of crowder placements to understand how electrostatic binding energetics are altered through crowding and how these effects depend on the charge distribution, shape, and size of the binding partners or crowders. We focus on electrostatic binding energetics using a continuum electrostatic framework to understand effects due to depletion of a polar, aqueous solvent in a crowded environment. We find that crowding effects can depend predictably on a system's charge distribution, with coupling between the crowder size and the geometry of the partners' binding interface in determining crowder effects. We also explore the effect of crowder charge on binding interactions as a function of the monopoles of the system components. Finally, we find that modeling crowding via a lowered solvent dielectric constant cannot account for certain electrostatic crowding effects due to the finite size, shape, or placement of system components. This study, which comprehensively examines solvent depletion effects due to crowding, complements work focusing on other crowding aspects to help build a holistic understanding of environmental impacts on molecular recognition.

A fully integrated undergraduate introductory biology and chemistry course with a community-based focus II: Assessment of course effectiveness.

In the Fall of 2016, we created an integrated introductory biology/chemistry course for first-semester students at Wellesley College. This course was designed to meaningfully integrate chemistry and molecular cell biology while also building community and fostering mentorship both inside and outside the classroom. Here, we describe the assessment of this integrated course through a combination of multivariate analyses of student transcript data and student focus group discussions. Our assessment found that the integrated course provided a strongly collaborative working environment for students that provided them with skills that promoted success in future courses. Along with a rigorous consideration of the interplay between biology and chemistry, these skills appeared to support positive longer-term student outcomes. In particular, we observed significant impacts on student persistence into and performance in intermediate and advanced courses. Students from the integrated course were also significantly more likely to declare a major in biochemistry than students who took one of the traditional introductory courses. In addition, our assessment also noted the importance of a cohesive instructional team and broad faculty participation in the success and sustainability of the course.

Computationally Modeling Electrostatic Binding Energetics in a Crowded, Dynamic Environment: Physical Insights from a Peptide-DNA System.

The cell is a crowded place, and it may be crucial at times to account for the local environment when studying determinants of molecular recognition. In this work, we use continuum electrostatics calculations on snapshots extracted from molecular dynamics simulations to understand how various aspects of a crowded environment affect electrostatic binding energies between the antimicrobial peptide buforin II and DNA. By comparing multiple models for representing crowding, sequentially introducing layers of model complexity for maximum control, we explore how electrostatic binding energetics depend on crowder physical properties, the sampling of the binding partners and crowder molecules, and the treatment of bulk solvent. We show that physical characteristics can combine to create an interplay of competing effects in this highly charged system. For example, increased ionic strength screening due to crowding partially cancels out the reduced solvent screening due to water depletion. We also quantify the effect of crowders' charge distributions on binding energetics. While we focus on electrostatic effects of crowding on binding, we begin to consider nonpolar components as well, and we implement a thermodynamic cycle accounting for both bound and unbound states to show the necessity of adequate crowder sampling in future studies. The insights developed here provide a rich starting point for experiments to further explore these competing effects and, ultimately, to rationally modulate molecular recognition in the complex cellular environment.

The Quantum Mechanics of a Rolling Molecular "Nanocar".

We formulate a mathematical model of a rolling "molecular wheelbarrow"-a two-wheeled nanoscale molecular machine-informed by experiments on molecular machines recently synthesized in labs. The model is a nonholonomic system (briefly, a system with non-integrable velocity constraints), for which no general quantization procedure exists. Nonetheless, we successfully embed the system in a Hamiltonian one and then quantize the result using geometric quantization and other tools; we extract from the result the quantum mechanics of the molecular wheelbarrow, and derive explicit formulae for the quantized energy spectrum. We also study a few variants of our model, some of which ignore the model's nonholonomic constraints. We show that these variants have different quantum energy spectra, indicating that in such systems one should not ignore the nonholonomic constraints, since they alter in a non-trivial way the energy spectrum of the molecule.

Investigating the nucleic acid interactions of histone-derived antimicrobial peptides.

While many antimicrobial peptides (AMPs) disrupt bacterial membranes, some translocate into bacteria and interfere with intracellular processes. Buforin II and DesHDAP1 are thought to kill bacteria by interacting with nucleic acids. Here, molecular modeling and experimental measurements are used to show that neither nucleic acid binding peptide selectively binds DNA sequences. Simulations and experiments also show that changing lysines to arginines enhances DNA binding, suggesting that including additional guanidinium groups is a potential strategy to engineer more potent AMPs. Moreover, the lack of binding specificity may make it more difficult for bacteria to evolve resistance to these and other similar AMPs.

Effect of the Spiroiminodihydantoin Lesion on Nucleosome Stability and Positioning.

DNA is constantly under attack by oxidants, generating a variety of potentially mutagenic covalently modified species, including oxidized guanine base products. One such product is spiroiminodihydantoin (Sp), a chiral, propeller-shaped lesion that strongly destabilizes the DNA helix in its vicinity. Despite its unusual shape and thermodynamic effect on double-stranded DNA structure, DNA duplexes containing the Sp lesion form stable nucleosomes upon being incubated with histone octamers. Indeed, among six different combinations of lesion location and stereochemistry, only two duplexes display a diminished ability to form nucleosomes, and these only by ∼25%; the other four are statistically indistinguishable from the control. Nonetheless, kinetic factors also play a role: when the histone proteins have less time during assembly of the core particle to sample both lesion-containing and normal DNA strands, they are more likely to bind the Sp lesion DNA than during slower assembly processes that better approximate thermodynamic equilibrium. Using DNase I footprinting and molecular modeling, we discovered that the Sp lesion causes only a small perturbation (±1-2 bp) on the translational position of the DNA within the nucleosome. Each diastereomeric pair of lesions has the same effect on nucleosome positioning, but lesions placed at different locations behave differently, illustrating that the location of the lesion and not its shape serves as the primary determinant of the most stable DNA orientation.

Molecular mechanisms and design principles for promiscuous inhibitors to avoid drug resistance: lessons learned from HIV-1 protease inhibition.

Molecular recognition is central to biology and ranges from highly selective to broadly promiscuous. The ability to modulate specificity at will is particularly important for drug development, and discovery of mechanisms contributing to binding specificity is crucial for our basic understanding of biology and for applications in health care. In this study, we used computational molecular design to create a large dataset of diverse small molecules with a range of binding specificities. We then performed structural, energetic, and statistical analysis on the dataset to study molecular mechanisms of achieving specificity goals. The work was done in the context of HIV-1 protease inhibition and the molecular designs targeted a panel of wild-type and drug-resistant mutant HIV-1 protease structures. The analysis focused on mechanisms for promiscuous binding to bind robustly even to resistance mutants. Broadly binding inhibitors tended to be smaller in size, more flexible in chemical structure, and more hydrophobic in nature compared to highly selective ones. Furthermore, structural and energetic analyses illustrated mechanisms by which flexible inhibitors achieved binding; we found ligand conformational adaptation near mutation sites and structural plasticity in targets through torsional flips of asymmetric functional groups to form alternative, compensatory packing interactions or hydrogen bonds. As no inhibitor bound to all variants, we designed small cocktails of inhibitors to do so and discovered that they often jointly covered the target set through mechanistic complementarity. Furthermore, using structural plasticity observed in experiments, and potentially in simulations, is suggested to be a viable means of designing adaptive inhibitors that are promiscuous binders.

The effect of macromolecular crowding on the electrostatic component of barnase-barstar binding: a computational, implicit solvent-based study.

Macromolecular crowding within the cell can impact both protein folding and binding. Earlier models of cellular crowding focused on the excluded volume, entropic effect of crowding agents, which generally favors compact protein states. Recently, other effects of crowding have been explored, including enthalpically-related crowder-protein interactions and changes in solvation properties. In this work, we explore the effects of macromolecular crowding on the electrostatic desolvation and solvent-screened interaction components of protein-protein binding. Our simple model enables us to focus exclusively on the electrostatic effects of water depletion on protein binding due to crowding, providing us with the ability to systematically analyze and quantify these potentially intuitive effects. We use the barnase-barstar complex as a model system and randomly placed, uncharged spheres within implicit solvent to model crowding in an aqueous environment. On average, we find that the desolvation free energy penalties incurred by partners upon binding are lowered in a crowded environment and solvent-screened interactions are amplified. At a constant crowder density (fraction of total available volume occupied by crowders), this effect generally increases as the radius of model crowders decreases, but the strength and nature of this trend can depend on the water probe radius used to generate the molecular surface in the continuum model. In general, there is huge variation in desolvation penalties as a function of the random crowder positions. Results with explicit model crowders can be qualitatively similar to those using a lowered "effective" solvent dielectric to account for crowding, although the "best" effective dielectric constant will likely depend on multiple system properties. Taken together, this work systematically demonstrates, quantifies, and analyzes qualitative intuition-based insights into the effects of water depletion due to crowding on the electrostatic component of protein binding, and it provides an initial framework for future analyses.

Analysis of fast boundary-integral approximations for modeling electrostatic contributions of molecular binding.

We analyze and suggest improvements to a recently developed approximate continuum-electrostatic model for proteins. The model, called BIBEE/I (boundary-integral based electrostatics estimation with interpolation), was able to estimate electrostatic solvation free energies to within a mean unsigned error of 4% on a test set of more than 600 proteins-a significant improvement over previous BIBEE models. In this work, we tested the BIBEE/I model for its capability to predict residue-by-residue interactions in protein-protein binding, using the widely studied model system of trypsin and bovine pancreatic trypsin inhibitor (BPTI). Finding that the BIBEE/I model performs surprisingly less well in this task than simpler BIBEE models, we seek to explain this behavior in terms of the models' differing spectral approximations of the exact boundary-integral operator. Calculations of analytically solvable systems (spheres and tri-axial ellipsoids) suggest two possibilities for improvement. The first is a modified BIBEE/I approach that captures the asymptotic eigenvalue limit correctly, and the second involves the dipole and quadrupole modes for ellipsoidal approximations of protein geometries. Our analysis suggests that fast, rigorous approximate models derived from reduced-basis approximation of boundary-integral equations might reach unprecedented accuracy, if the dipole and quadrupole modes can be captured quickly for general shapes.

Multiple drugs and multiple targets: an analysis of the electrostatic determinants of binding between non-nucleoside HIV-1 reverse transcriptase inhibitors and variants of HIV-1 RT.

We present a systematic, computational analysis of the electrostatic component of binding of three HIV-1 RT inhibitors-nevirapine (NVP), efavirenz (EFV), and the recently approved rilpivirine (RPV)-to wild-type (WT) and mutant variants of RT. Electrostatic charge optimization was applied to determine how suited each molecule's charge distribution is for binding WT and individual mutants of HIV-1 RT. Although the charge distributions of NVP and EFV are rather far from being optimal for tight binding, RPVs charge distribution is close to the theoretical, optimal charge distribution for binding WT HIV-1 RT, although slight changes in charge can dramatically impact binding energetics. Moreover, toward the L100I/K103N double mutant, RPVs charge distribution is quite far from optimal. We also determine the contributions of chemical moieties on each molecule toward the electrostatic component of binding and show that different regions of a drug molecule may be used for recognition by different RT variants. The electrostatic contributions of certain RT residues toward drug binding are also computed to highlight critical residues for each interaction. Finally, the charge distribution of RPV is optimized to promiscuously bind to three RT variants rather than to each one in turn, with the resulting charge distribution being a compromise between the optimal charge distributions to each individual variant. Taken together, this work demonstrates that even in a binding site considered quite hydrophobic, electrostatics play a subtle yet varying role that must be considered in designing next-generation molecules that recognize rapidly mutating targets.

A multifaceted analysis of HIV-1 protease multidrug resistance phenotypes.

BACKGROUND: Great strides have been made in the effective treatment of HIV-1 with the development of second-generation protease inhibitors (PIs) that are effective against historically multi-PI-resistant HIV-1 variants. Nevertheless, mutation patterns that confer decreasing susceptibility to available PIs continue to arise within the population. Understanding the phenotypic and genotypic patterns responsible for multi-PI resistance is necessary for developing PIs that are active against clinically-relevant PI-resistant HIV-1 variants. RESULTS: In this work, we use globally optimal integer programming-based clustering techniques to elucidate multi-PI phenotypic resistance patterns using a data set of 398 HIV-1 protease sequences that have each been phenotyped for susceptibility toward the nine clinically-approved HIV-1 PIs. We validate the information content of the clusters by evaluating their ability to predict the level of decreased susceptibility to each of the available PIs using a cross validation procedure. We demonstrate the finding that as a result of phenotypic cross resistance, the considered clinical HIV-1 protease isolates are confined to ~6% or less of the clinically-relevant phenotypic space. Clustering and feature selection methods are used to find representative sequences and mutations for major resistance phenotypes to elucidate their genotypic signatures. We show that phenotypic similarity does not imply genotypic similarity, that different PI-resistance mutation patterns can give rise to HIV-1 isolates with similar phenotypic profiles. CONCLUSION: Rather than characterizing HIV-1 susceptibility toward each PI individually, our study offers a unique perspective on the phenomenon of PI class resistance by uncovering major multidrug-resistant phenotypic patterns and their often diverse genotypic determinants, providing a methodology that can be applied to understand clinically-relevant phenotypic patterns to aid in the design of novel inhibitors that target other rapidly evolving molecular targets as well.

Cellular level models as tools for cytokine design.

Cytokines and growth factors are critical regulators that connect intracellular and extracellular environments through binding to specific cell-surface receptors. They regulate a wide variety of immunological, growth, and inflammatory response processes. The overall signal initiated by a population of cytokine molecules over long time periods is controlled by the subtle interplay of binding, signaling, and trafficking kinetics. Building on the work of others, we abstract a simple kinetic model that captures relevant features from cytokine systems as well as related growth factor systems. We explore a large range of potential biochemical behaviors, through systematic examination of the model's parameter space. Different rates for the same reaction topology lead to a dramatic range of biochemical network properties and outcomes. Evolution might productively explore varied and different portions of parameter space to create beneficial behaviors, and effective human therapeutic intervention might be achieved through altering network kinetic properties. Quantitative analysis of the results reveals the basis for tensions among a number of different network characteristics. For example, strong binding of cytokine to receptor can increase short-term receptor activation and signal initiation but decrease long-term signaling due to internalization and degradation. Further analysis reveals the role of specific biochemical processes in modulating such tensions. For instance, the kinetics of cytokine binding and receptor activation modulate whether ligand-receptor dissociation can generally occur before signal initiation or receptor internalization. Beyond analysis, the same models and model behaviors provide an important basis for the design of more potent cytokine therapeutics by providing insight into how binding kinetics affect ligand potency.

Symmetric signaling by an asymmetric 1 erythropoietin: 2 erythropoietin receptor complex.

Via sites 1 and 2, erythropoietin binds asymmetrically to two identical receptor monomers, although it is unclear how asymmetry affects receptor activation and signaling. Here we report the design and validation of two mutant erythropoietin receptors that probe the role of individual members of the receptor dimer by selectively binding either site 1 or site 2 on erythropoietin. Ba/F3 cells expressing either mutant receptor do not respond to erythropoietin, but cells co-expressing both receptors respond to erythropoietin by proliferation and activation of the JAK2-Stat5 pathway. A truncated receptor with only one cytosolic tyrosine (Y343) is sufficient for signaling in response to erythropoietin, regardless of the monomer on which it is located. Similarly, only one receptor in the dimer needs a juxtamembrane hydrophobic L253 or W258 residue, essential for JAK2 activation. We conclude that despite asymmetry in the ligand-receptor interaction, both sides are competent for signaling, and appear to signal equally.

A dual receptor crosstalk model of G-protein-coupled signal transduction.

Macrophage cells that are stimulated by two different ligands that bind to G-protein-coupled receptors (GPCRs) usually respond as if the stimulus effects are additive, but for a minority of ligand combinations the response is synergistic. The G-protein-coupled receptor system integrates signaling cues from the environment to actuate cell morphology, gene expression, ion homeostasis, and other physiological states. We analyze the effects of the two signaling molecules complement factors 5a (C5a) and uridine diphosphate (UDP) on the intracellular second messenger calcium to elucidate the principles that govern the processing of multiple signals by GPCRs. We have developed a formal hypothesis, in the form of a kinetic model, for the mechanism of action of this GPCR signal transduction system using data obtained from RAW264.7 macrophage cells. Bayesian statistical methods are employed to represent uncertainty in both data and model parameters and formally tie the model to experimental data. When the model is also used as a tool in the design of experiments, it predicts a synergistic region in the calcium peak height dose response that results when cells are simultaneously stimulated by C5a and UDP. An analysis of the model reveals a potential mechanism for crosstalk between the Galphai-coupled C5a receptor and the Galphaq-coupled UDP receptor signaling systems that results in synergistic calcium release.

Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems.

Drug resistance is a significant obstacle in the effective treatment of diseases with rapidly mutating targets, such as AIDS, malaria, and certain forms of cancer. Such targets are remarkably efficient at exploring the space of functional mutants and at evolving to evade drug binding while still maintaining their biological role. To overcome this challenge, drug regimens must be active against potential target variants. Such a goal may be accomplished by one drug molecule that recognizes multiple variants or by a drug "cocktail"--a small collection of drug molecules that collectively binds all desired variants. Ideally, one wants the smallest cocktail possible due to the potential for increased toxicity with each additional drug. Therefore, the task of designing a regimen for multiple target variants can be framed as an optimization problem--find the smallest collection of molecules that together "covers" the relevant target variants. In this work, we formulate and apply this optimization framework to theoretical model target ensembles. These results are analyzed to develop an understanding of how the physical properties of a target ensemble relate to the properties of the optimal cocktail. We focus on electrostatic variation within target ensembles, as it is one important mechanism by which drug resistance is achieved. Using integer programming, we systematically designed optimal cocktails to cover model target ensembles. We found that certain drug molecules covered much larger regions of target space than others, a phenomenon explained by theory grounded in continuum electrostatics. Molecules within optimal cocktails were often dissimilar, such that each drug was responsible for binding variants with a certain electrostatic property in common. On average, the number of molecules in the optimal cocktails correlated with the number of variants, the differences in the variants' electrostatic properties at the binding interface, and the level of binding affinity required. We also treated cases in which a subset of target variants was to be avoided, modeling the common challenge of closely related host molecules that may be implicated in drug toxicity. Such decoys generally increased the size of the required cocktail and more often resulted in infeasible optimizations. Taken together, this work provides practical optimization methods for the design of drug cocktails and a theoretical, physics-based framework through which useful insights can be achieved.

Specificity in molecular design: a physical framework for probing the determinants of binding specificity and promiscuity in a biological environment.

Binding specificity is an important consideration in drug design. An effective drug molecule often must bind with high specificity to its intended target in the body; lower specificity implies the possibility of significant binding to unintended partners, which could instigate deleterious side effects. However, if the target is a rapidly mutating agent, a drug that is too specific will quickly lose its efficacy by not binding well to functional mutants. Therefore, in molecular design, it is crucial to tailor the binding specificity of a drug to the problem at hand. In practice, specificity is often studied on a case-by-case basis, and it is difficult to create general understanding of the determinants of specificity from the union of such available cases. In this work, we undertook a comprehensive, general study of molecular binding with emphasis on understanding the determinants of specificity from a physical standpoint. By extending a theoretical framework grounded in continuum electrostatics and creating an abstracted lattice model that captures key physical aspects of binding interactions, we systematically explored the relationship between a molecule's physical characteristics and its binding specificity toward potential partners. The theory and simulated binding interactions suggested that charged molecules are more specific binders than their hydrophobic counterparts for several reasons. First, the biological spectrum of possible binding characteristics includes more partners that bind equally well to hydrophobic ligands than to charged ligands. Also, charged ligands, whose electrostatic potentials have strong orientational dependence, are more sensitive to shape complementarity than their hydrophobic counterparts. Ligand conformational and orientational flexibility can further influence a charged molecule's ability to bind specifically. Interestingly, we found that conformational flexibility can increase the specificity of polar and charged ligands, by allowing them to greatly lower the binding free energy to a select few partners relative to others. Additionally, factors such as a molecule's size and the ionic strength of the solution were found to predictably affect binding specificity. Taken together, these results, all of which stem from a unified theoretical framework, provide valuable physical insight into the general determinants of binding specificity and promiscuity in a biological environment. The general principles discussed here could prove useful in the design of molecules with tailored specificities, leading to more effective therapeutics.