HLA Class I Supertype Classification Based on Structural Similarity.

HLA class I proteins, a critical component in adaptive immunity, bind and present intracellular Ags to CD8+ T cells. The extreme polymorphism of HLA genes and associated peptide binding specificities leads to challenges in various endeavors, including neoantigen vaccine development, disease association studies, and HLA typing. Supertype classification, defined by clustering functionally similar HLA alleles, has proven helpful in reducing the complexity of distinguishing alleles. However, determining supertypes via experiments is impractical, and current in silico classification methods exhibit limitations in stability and functional relevance. In this study, by incorporating three-dimensional structures we present a method for classifying HLA class I molecules with improved breadth, accuracy, stability, and flexibility. Critical for these advances is our finding that structural similarity highly correlates with peptide binding specificity. The new classification should be broadly useful in peptide-based vaccine development and HLA-disease association studies.

Atomistic Simulations of Polydisperse Lignin Melts Using Simple Polydisperse Residue Input Generator.

Understanding the physics of lignin will help rationalize its function in plant cell walls as well as aiding practical applications such as deriving biofuels and bioproducts. Here, we present SPRIG (Simple Polydisperse Residue Input Generator), a program for generating atomic-detail models of random polydisperse lignin copolymer melts i.e., the state most commonly found in nature. Using these models, we use all-atom molecular dynamics (MD) simulations to investigate the conformational and dynamic properties of polydisperse melts representative of switchgrass (Panicum virgatum L.) lignin. Polydispersity, branching and monolignol sequence are found to not affect the calculated glass transition temperature, Tg. The Flory-Huggins scaling parameter for the segmental radius of gyration is 0.42 ± 0.02, indicating that the chains exhibit statistics that lie between a globular chain and an ideal Gaussian chain. Below Tg the atomic mean squared displacements are independent of molecular weight. In contrast, above Tg, they decrease with increasing molecular weight. Therefore, a monodisperse lignin melt is a good approximation to this polydisperse lignin when only static properties are probed, whereas the molecular weight distribution needs to be considered while analyzing lignin dynamics.

Water molecule ordering on the surface of an intrinsically disordered protein.

The dynamics and local structure of the hydration water on surfaces of folded proteins have been extensively investigated. However, our knowledge of the hydration of intrinsically disordered proteins (IDPs) is more limited. Here, we compare the local structure of water molecules hydrating a globular protein, lysozyme, and the intrinsically disordered N-terminal of c-Src kinase (SH4UD) using molecular dynamics simulation. The radial distributions from the protein surface of the first and the second hydration shells are similar for the folded protein and the IDP. However, water molecules in the first hydration shell of both the folded protein and the IDP are perturbed from the bulk. This perturbation involves a loss of tetrahedrality, which is, however, significantly more marked for the folded protein than the IDP. This difference arises from an increase in the first hydration shell of the IDP of the fraction of hydration water molecules interacting with oxygen. The water ordering is independent of the compactness of the IDP. In contrast, the lifetimes of water molecules in the first hydration shell increase with IDP compactness, indicating a significant impact of IDP configuration on water surface pocket kinetics, which here is linked to differential pocket volumes and polarities.

HLA-Clus: HLA class I clustering based on 3D structure.

BACKGROUND: In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. RESULTS: The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. CONCLUSIONS: The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository ( https://github.com/yshen25/HLA-Clus ).

Disordered Domain Shifts the Conformational Ensemble of the Folded Regulatory Domain of the Multidomain Oncoprotein c-Src.

c-Src kinase is a multidomain non-receptor tyrosine kinase that aberrantly phosphorylates several signaling proteins in cancers. Although the structural properties of the regulatory domains (SH3-SH2) and the catalytic kinase domain have been extensively characterized, there is less knowledge about the N-terminal disordered region (SH4UD) and its interactions with the other c-Src domains. Here, we used domain-selective isotopic labeling combined with the small-angle neutron scattering contrast matching technique to study SH4UD interactions with SH3-SH2. Our results show that in the presence of SH4UD, the radius of gyration (Rg) of SH3-SH2 increases, indicating that it has a more extended conformation. Hamiltonian replica exchange molecular dynamics simulations provide a detailed molecular description of the structural changes in SH4UD-SH3-SH2 and show that the regulatory loops of SH3 undergo significant conformational changes in the presence of SH4UD, while SH2 remains largely unchanged. Overall, this study highlights how a disordered region can drive a folded region of a multidomain protein to become flexible, which may be important for allosteric interactions with binding partners. This may help in the design of therapeutic interventions that target the regulatory domains of this important family of kinases.

Comparative Assessment of Pose Prediction Accuracy in RNA-Ligand Docking.

Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein-ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA-small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand-target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ∼27%. Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand-protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein-target docking illustrates a need for further methods refinement.

Design of Broadly Cross-Reactive M Protein-Based Group A Streptococcal Vaccines.

Group A streptococcal infections are a significant cause of global morbidity and mortality. A leading vaccine candidate is the surface M protein, a major virulence determinant and protective Ag. One obstacle to the development of M protein-based vaccines is the >200 different M types defined by the N-terminal sequences that contain protective epitopes. Despite sequence variability, M proteins share coiled-coil structural motifs that bind host proteins required for virulence. In this study, we exploit this potential Achilles heel of conserved structure to predict cross-reactive M peptides that could serve as broadly protective vaccine Ags. Combining sequences with structural predictions, six heterologous M peptides in a sequence-related cluster were predicted to elicit cross-reactive Abs with the remaining five nonvaccine M types in the cluster. The six-valent vaccine elicited Abs in rabbits that reacted with all 11 M peptides in the cluster and functional opsonic Abs against vaccine and nonvaccine M types in the cluster. We next immunized mice with four sequence-unrelated M peptides predicted to contain different coiled-coil propensities and tested the antisera for cross-reactivity against 41 heterologous M peptides. Based on these results, we developed an improved algorithm to select cross-reactive peptide pairs using additional parameters of coiled-coil length and propensity. The revised algorithm accurately predicted cross-reactive Ab binding, improving the Matthews correlation coefficient from 0.42 to 0.74. These results form the basis for selecting the minimum number of N-terminal M peptides to include in potentially broadly efficacious multivalent vaccines that could impact the overall global burden of group A streptococcal diseases.

Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning.

Knowledge of the physical properties of ionic liquids (ILs), such as the surface tension and speed of sound, is important for both industrial and research applications. Unfortunately, technical challenges and costs limit exhaustive experimental screening efforts of ILs for these critical properties. Previous work has demonstrated that the use of quantum-mechanics-based thermochemical property prediction tools, such as the conductor-like screening model for real solvents, when combined with machine learning (ML) approaches, may provide an alternative pathway to guide the rapid screening and design of ILs for desired physiochemical properties. However, the question of which machine-learning approaches are most appropriate remains. In the present study, we examine how different ML architectures, ranging from tree-based approaches to feed-forward artificial neural networks, perform in generating nonlinear multivariate quantitative structure-property relationship models for the prediction of the temperature- and pressure-dependent surface tension of and speed of sound in ILs over a wide range of surface tensions (16.9-76.2 mN/m) and speeds of sound (1009.7-1992 m/s). The ML models are further interrogated using the powerful interpretation method, shapley additive explanations. We find that several different ML models provide high accuracy, according to traditional statistical metrics. The decision tree-based approaches appear to be the most accurate and precise, with extreme gradient-boosting trees and gradient-boosting trees being the best performers. However, our results also indicate that the promise of using machine-learning to gain deep insights into the underlying physics driving structure-property relationships in ILs may still be somewhat premature.

Deconstruction of biomass enabled by local demixing of cosolvents at cellulose and lignin surfaces.

A particularly promising approach to deconstructing and fractionating lignocellulosic biomass to produce green renewable fuels and high-value chemicals pretreats the biomass with organic solvents in aqueous solution. Here, neutron scattering and molecular-dynamics simulations reveal the temperature-dependent morphological changes in poplar wood biomass during tetrahydrofuran (THF):water pretreatment and provide a mechanism by which the solvent components drive efficient biomass breakdown. Whereas lignin dissociates over a wide temperature range (>25 °C) cellulose disruption occurs only above 150 °C. Neutron scattering with contrast variation provides direct evidence for the formation of THF-rich nanoclusters (Rg ∼ 0.5 nm) on the nonpolar cellulose surfaces and on hydrophobic lignin, and equivalent water-rich nanoclusters on polar cellulose surfaces. The disassembly of the amphiphilic biomass is thus enabled through the local demixing of highly functional cosolvents, THF and water, which preferentially solvate specific biomass surfaces so as to match the local solute polarity. A multiscale description of the efficiency of THF:water pretreatment is provided: matching polarity at the atomic scale prevents lignin aggregation and disrupts cellulose, leading to improvements in deconstruction at the macroscopic scale.

Structural patterns in class 1 major histocompatibility complex-restricted nonamer peptide binding to T-cell receptors.

The startling diversity in αß T-cell receptor (TCR) sequences and structures complicates molecular-level analyses of the specificity and sensitivity determining T-cell immunogenicity. A number of three-dimensional (3D) structures are now available of ternary complexes between TCRs and peptides: major histocompatibility complexes (pMHC). Here, to glean molecular-level insights we analyze structures of TCRs bound to human class I nonamer peptide-MHC complexes. Residues at peptide positions 4-8 are found to be particularly important for TCR binding. About 90% of the TCRs hydrogen bond with one or both of the peptide residues at positions 4 and 8 presented by MHC allele HLA-A2, and this number is still ~79% for peptides presented by other MHC alleles. Residue 8, which lies outside the previously-identified central peptide region, is crucial for TCR recognition of class I MHC-presented nonamer peptides. The statistics of the interactions also sheds light on the MHC residues important for TCR binding. The present analysis will aid in the structural modeling of TCR:pMHC complexes and has implications for the rational design of peptide-based vaccines and T-cell-based immunotherapies.

Identification of Small-Molecule Inhibitors of Fibroblast Growth Factor 23 Signaling via In Silico Hot Spot Prediction and Molecular Docking to α-Klotho.

Fibroblast growth factor 23 (FGF23) is a therapeutic target for treating hereditary and acquired hypophosphatemic disorders, such as X-linked hypophosphatemic (XLH) rickets and tumor-induced osteomalacia (TIO), respectively. FGF23-induced hypophosphatemia is mediated by signaling through a ternary complex formed by FGF23, the FGF receptor (FGFR), and α-Klotho. Currently, disorders of excess FGF23 are treated with an FGF23-blocking antibody, burosumab. Small-molecule drugs that disrupt protein/protein interactions necessary for the ternary complex formation offer an alternative to disrupting FGF23 signaling. In this study, the FGF23:α-Klotho interface was targeted to identify small-molecule protein/protein interaction inhibitors since it was computationally predicted to have a large fraction of hot spots and two druggable residues on α-Klotho. We further identified Tyr433 on the KL1 domain of α-Klotho as a promising hot spot and α-Klotho as an appropriate drug-binding target at this interface. Subsequently, we performed in silico docking of ∼5.5 million compounds from the ZINC database to the interface region of α-Klotho from the ternary crystal structure. Following docking, 24 and 20 compounds were in the final list based on the lowest binding free energies to α-Klotho and the largest number of contacts with Tyr433, respectively. Five compounds were assessed experimentally by their FGF23-mediated extracellular signal-regulated kinase (ERK) activities in vitro, and two of these reduced activities significantly. Both these compounds were predicted to have favorable binding affinities to α-Klotho but not have a large number of contacts with the hot spot Tyr433. ZINC12409120 was found experimentally to disrupt FGF23:α-Klotho interaction to reduce FGF23-mediated ERK activities by 70% and have a half maximal inhibitory concentration (IC50) of 5.0 ± 0.23 µM. Molecular dynamics (MD) simulations of the ZINC12409120:α-Klotho complex starting from in silico docking poses reveal that the ligand exhibits contacts with residues on the KL1 domain, the KL1-KL2 linker, and the KL2 domain of α-Klotho simultaneously, thereby possibly disrupting the regular function of α-Klotho and impeding FGF23:α-Klotho interaction. ZINC12409120 is a candidate for lead optimization.

Combining Three-Dimensional Modeling with Artificial Intelligence to Increase Specificity and Precision in Peptide-MHC Binding Predictions.

The reliable prediction of the affinity of candidate peptides for the MHC is important for predicting their potential antigenicity and thus influences medical applications, such as decisions on their inclusion in T cell-based vaccines. In this study, we present a rapid, predictive computational approach that combines a popular, sequence-based artificial neural network method, NetMHCpan 4.0, with three-dimensional structural modeling. We find that the ensembles of bound peptide conformations generated by the programs MODELLER and Rosetta FlexPepDock are less variable in geometry for strong binders than for low-affinity peptides. In tests on 1271 peptide sequences for which the experimental dissociation constants of binding to the well-characterized murine MHC allele H-2Db are known, by applying thresholds for geometric fluctuations the structure-based approach in a standalone manner drastically improves the statistical specificity, reducing the number of false positives. Furthermore, filtering candidates generated with NetMHCpan 4.0 with the structure-based predictor led to an increase in the positive predictive value (PPV) of the peptides correctly predicted to bind very strongly (i.e., K d < 100 nM) from 40 to 52% (p = 0.027). The combined method also significantly improved the PPV when tested on five human alleles, including some with limited data for training. Overall, an average increase of 10% in the PPV was found over the standalone sequence-based method. The combined method should be useful in the rapid design of effective T cell-based vaccines.

Dynamic odd-even effect in n-alkane systems: a molecular dynamics study.

Alternation in various properties of n-alkanes (CnH2n+2) as a function of carbon content (n) is termed 'odd-even effect'. Here, we report a comprehensive molecular dynamics simulation study on n-alkane systems carried out with n ranging between 3 (propane) and 8 (octane), examining the odd-even effect in melting point, density, intramolecular conformational ordering, translational and rotational motion. We observe an odd-even alternation in these properties, but with heptane (n = 7) exhibiting anomalous behavior for all except conformational ordering. Our simulations also show the presence of odd-even behavior in rotational and translational dynamics, below and above the melting point, respectively. The results highlight the role of both molecular shape and the variation in density and their interplay in the origins of the odd-even effect.

Generation of the configurational ensemble of an intrinsically disordered protein from unbiased molecular dynamics simulation.

Intrinsically disordered proteins (IDPs) are abundant in eukaryotic proteomes, play a major role in cell signaling, and are associated with human diseases. To understand IDP function it is critical to determine their configurational ensemble, i.e., the collection of 3-dimensional structures they adopt, and this remains an immense challenge in structural biology. Attempts to determine this ensemble computationally have been hitherto hampered by the necessity of reweighting molecular dynamics (MD) results or biasing simulation in order to match ensemble-averaged experimental observables, operations that reduce the precision of the generated model because different structural ensembles may yield the same experimental observable. Here, by employing enhanced sampling MD we reproduce the experimental small-angle neutron and X-ray scattering profiles and the NMR chemical shifts of the disordered N terminal (SH4UD) of c-Src kinase without reweighting or constraining the simulations. The unbiased simulation results reveal a weakly funneled and rugged free energy landscape of SH4UD, which gives rise to a heterogeneous ensemble of structures that cannot be described by simple polymer theory. SH4UD adopts transient helices, which are found away from known phosphorylation sites and could play a key role in the stabilization of structural regions necessary for phosphorylation. Our findings indicate that adequately sampled molecular simulations can be performed to provide accurate physical models of flexible biosystems, thus rationalizing their biological function.

Lpp positions peptidoglycan at the AcrA-TolC interface in the AcrAB-TolC multidrug efflux pump.

The multidrug efflux pumps of Gram-negative bacteria are a class of complexes that span the periplasm, coupling both the inner and outer membranes to expel toxic molecules. The best-characterized example of these tripartite pumps is the AcrAB-TolC complex of Escherichia coli. However, how the complex interacts with the peptidoglycan (PG) cell wall, which is anchored to the outer membrane (OM) by Braun's lipoprotein (Lpp), is still largely unknown. In this work, we present molecular dynamics simulations of a complete, atomistic model of the AcrAB-TolC complex with the inner membrane, OM, and PG layers all present. We find that the PG localizes to the junction of AcrA and TolC, in agreement with recent cryo-tomography data. Free-energy calculations reveal that the positioning of PG is determined by the length and conformation of multiple Lpp copies anchoring it to the OM. The distance between the PG and OM measured in cryo-electron microscopy images of wild-type E. coli also agrees with the simulation-derived spacing. Sequence analysis of AcrA suggests a conserved role for interactions with PG in the assembly and stabilization of efflux pumps, one that may extend to other trans-envelope complexes as well.

Novel Small Molecule Fibroblast Growth Factor 23 Inhibitors Increase Serum Phosphate and Improve Skeletal Abnormalities in Hyp Mice.

Excess fibroblast growth factor (FGF) 23 causes hereditary hypophosphatemic rickets, such as X-linked hypophosphatemia (XLH) and tumor-induced osteomalacia (TIO). A small molecule that specifically binds to FGF23 to prevent activation of the fibroblast growth factor receptor/α-Klotho complex has potential advantages over the currently approved systemically administered FGF23 blocking antibody. Using structure-based drug design, we previously identified ZINC13407541 (N-[[2-(2-phenylethenyl)cyclopenten-1-yl]methylidene]hydroxylamine) as a small molecule antagonist for FGF23. Additional structure-activity studies developed a series of ZINC13407541 analogs with enhanced drug-like properties. In this study, we tested in a preclinical Hyp mouse homolog of XLH a direct connect analog [(E)-2-(4-(tert-butyl)phenyl)cyclopent-1-ene-1-carbaldehyde oxime] (8n), which exhibited the greatest stability in microsomal assays, and [(E)-2-((E)-4-methylstyryl)benzaldehyde oxime] (13a), which exhibited increased in vitro potency. Using cryo-electron microscopy structure and computational docking, we identified a key binding residue (Q156) of the FGF23 antagonists, ZINC13407541, and its analogs (8n and 13a) in the N-terminal domain of FGF23 protein. Site-directed mutagenesis and bimolecular fluorescence complementation-fluorescence resonance energy transfer assay confirmed the binding site of these three antagonists. We found that pharmacological inhibition of FGF23 with either of these compounds blocked FGF23 signaling and increased serum phosphate and 1,25-dihydroxyvitamin D [1,25(OH)2D] concentrations in Hyp mice. Long-term parenteral treatment with 8n or 13a also enhanced linear bone growth, increased mineralization of bone, and narrowed the growth plate in Hyp mice. The more potent 13a compound had greater therapeutic effects in Hyp mice. Further optimization of these FGF23 inhibitors may lead to versatile drugs to treat excess FGF23-mediated disorders. SIGNIFICANCE STATEMENT: This study used structure-based drug design and medicinal chemistry approaches to identify and optimize small molecules with different stability and potency, which antagonize excessive actions of fibroblast growth factor 23 (FGF23) in hereditary hypophosphatemic rickets. The findings confirmed that these antagonists bind to the N-terminus of FGF23 to inhibit its binding to and activation of the fibroblast growth factor receptors/α-Klotho signaling complex. Administration of these lead compounds improved phosphate homeostasis and abnormal skeletal phenotypes in a preclinical Hyp mouse model.

Structure-based group A streptococcal vaccine design: Helical wheel homology predicts antibody cross-reactivity among streptococcal M protein-derived peptides.

Group A streptococcus (Strep A) surface M protein, an α-helical coiled-coil dimer, is a vaccine target and a major determinant of streptococcal virulence. The sequence-variable N-terminal region of the M protein defines the M type and also contains epitopes that promote opsonophagocytic killing of streptococci. Recent reports have reported considerable cross-reactivity among different M types, suggesting the prospect of identifying cross-protective epitopes that would constitute a broadly protective multivalent vaccine against Strep A isolates. Here, we have used a combination of immunological assays, structural biology, and cheminformatics to construct a recombinant M protein-based vaccine that included six Strep A M peptides that were predicted to elicit antisera that would cross-react with an additional 15 nonvaccine M types of Strep A. Rabbit antisera against this recombinant vaccine cross-reacted with 10 of the 15 nonvaccine M peptides. Two of the five nonvaccine M peptides that did not cross-react shared high sequence identity (≥50%) with the vaccine peptides, implying that high sequence identity alone was insufficient for cross-reactivity among the M peptides. Additional structural analyses revealed that the sequence identity at corresponding polar helical-wheel heptad sites between vaccine and nonvaccine peptides accurately distinguishes cross-reactive from non-cross-reactive peptides. On the basis of these observations, we developed a scoring algorithm based on the sequence identity at polar heptad sites. When applied to all epidemiologically important M types, this algorithm should enable the selection of a minimal number of M peptide-based vaccine candidates that elicit broadly protective immunity against Strep A.

Molecular dynamics analysis of the binding of human interleukin-6 with interleukin-6 α-receptor.

Human interleukin-6 (hIL-6) is a multifunctional cytokine that regulates immune and inflammatory responses in addition to metabolic and regenerative processes and cancer. hIL-6 binding to the IL-6 receptor (IL-6Rα) induces homodimerization and recruitment of the glycoprotein (gp130) to form a hexameric signaling complex. Anti-IL-6 and IL-6R antibodies are clinically approved inhibitors of IL-6 signaling pathway for treating rheumatoid arthritis and Castleman's disease, respectively. There is a potential to develop novel small molecule IL-6 antagonists derived from understanding the structural basis for IL-6/IL-6Rα interactions. Here, we combine homology modeling with extensive molecular dynamics (MD) simulations to examine the association of hIL-6 with IL-6Rα. A comparison with MD of apo hIL-6 reveals that the binding of hIL-6 to IL-6Rα induces structural and dynamic rearrangements in the AB loop region of hIL-6, disrupting intraprotein contacts and increasing the flexibility of residues 48 to 58 of the AB loop. In contrast, due to the involvement of residues 59 to 78 in forming contacts with the receptor, these residues of the AB loop are observed to rigidify in the presence of the receptor. The binary complex is primarily stabilized by two pairs of salt bridges, Arg181 (hIL-6)- Glu182 (IL-6Rα) and Arg184 (hIL-6)- Glu183 (IL-6Rα) as well as hydrophobic and aromatic stacking interactions mediated essentially by Phe residues in both proteins. An interplay of electrostatic, hydrophobic, hydrogen bonding, and aromatic stacking interactions facilitates the formation of the hIL-6/IL-6Rα complex.

Peptide nucleic acid Hoogsteen strand linker design for major groove recognition of DNA thymine bases.

Sequence-specific targeting of double-stranded DNA and non-coding RNA via triple-helix-forming peptide nucleic acids (PNAs) has attracted considerable attention in therapeutic, diagnostic and nanotechnological fields. An E-base (3-oxo-2,3-dihydropyridazine), attached to the polyamide backbone of a PNA Hoogsteen strand by a side-chain linker molecule, is typically used in the hydrogen bond recognition of the 4-oxo group of thymine and uracil nucleic acid bases in the major groove. We report on the application of quantum chemical computational methods, in conjunction with spatial constraints derived from the experimental structure of a homopyrimidine PNA·DNA-PNA hetero-triplex, to investigate the influence of linker flexibility on binding interactions of the E-base with thymine and uracil bases in geometry-optimised model systems. Hydrogen bond formation between the N2 E-base atom and target pyrimidine base 4-oxo groups in model systems containing a ß-alanine linker (J Am Chem Soc 119:11116, 1997) was found to incur significant internal strain energy and the potential disruption of intra-stand aromatic base stacking interactions in an oligomeric context. In geometry-optimised model systems containing a 3-trans olefin linker (Bioorg Med Chem Lett 14:1551, 2004) the E-base swung out away from the target pyrimidine bases into the solvent. These findings are in qualitative agreement with calorimetric measurements in hybridisation experiments at T-A and U-A inversion sites. In contrast, calculations on a novel 2-cis olefin linker design indicate that it could permit simultaneous E-base hydrogen bonding with the thymine 4-oxo group, circumvention and solvent screening of the thymine 5-methyl group, and maintenance of triplex intra-stand base stacking interactions.

Biological Membrane Organization and Cellular Signaling.

To execute their many vital functions, cell membranes are highly organized. Here, we review how membrane structure shapes signal transduction across membranes. Recent experimental and computational advances have shed significant light on mechanisms linking the function of membrane signaling proteins to the composition and physical properties of the membrane lateral structures in which they are embedded. We provide an overview of the structural characteristics of membranes containing heterogeneous mixtures of lipids and other molecules and summarize work on "raft" domains in model and cell membranes, as determined by microscopy, spectroscopy, neutron scattering, and computer simulations. We discuss the principles of partitioning of proteins into membranes and how the structure, dynamics, and function of membrane-embedded and peripheral proteins can be modulated by specific membrane components and physical properties of membranes and raft domains. Finally, we discuss challenges and future directions toward a molecular-level understanding of how membrane organization gives rise to various context-dependent cellular signaling.