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 similar to 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.

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.

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 similar 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.

We apply the dead-end elimination (DEE) strategy from protein design as a heuristic for the max-flow/min-cut formulation of the image segmentation problem. DEE combines aspects of constraint propagation and branch-and-bound to eliminate solutions incompatible with global optimization of the objective function. Though DEE can be used for segmentation into an arbitrary number of regions, in this paper we evaluate only the case of binary segmentation. We provide a runtime analysis and evaluation of DEE applied to two min-cut algorithms. Preliminary results show that DEE consistently reduces the search space for the Edmonds-Karp algorithm; tuning DEE as a heuristic for Boykov-Kolmogorov and other algorithms is future work.

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.

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.

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.

Electrostatic interactions between biological molecules are crucially influenced by their aqueous environment, with efficient and accurate models of solvent effects required for robust molecular design strategies. Continuum electrostatic models provide a reasonable balance between computational efficiency and accurate system representation. In this article, I review two specific molecular design strategies, charge optimization and combinatorial design, paying particular attention to how the continuum framework (also briefly described herein) successfully enables both theoretical insights and molecular designs and presents a challenge in design applications due to what I call "the isostericity constraint." Efforts to work around the isostericity constraint and other challenges are discussed. Additionally, particular emphasis is placed on using such models in the rational design of particularly tight, specific, or promiscuous interactions, in keeping with the increased sophistication of current molecular design applications.