Free-energy simulations reveal that both hydrophobic and polar interactions are important for influenza hemagglutinin antibody binding

In silico design of high-affinity antigenic peptides for HLA-B44

Cancer cell-killing by CD8+ T cells demands effective tumor antigen presentation by human leukocyte antigen class I (HLA-I) molecules. Screening and designing highly immunogenic neoantigens require quantitative computations to reliably predict HLA-peptide binding affinities. Here, with all-atom molecular dynamics (MD) simulations and free energy perturbation (FEP) methods, we design a collection of antigenic peptide candidates through in silico mutagenesis studies on immunogenic neoantigens, yielding enhanced binding affinities to HLA-B*44:02. In-depth structural dissection shows that introducing positively charged residues such as arginine to position 6 or lysine to position 7 of the candidates triggers conformational shifts in both peptides and the antigen-binding groove of the HLA, following the "induced-fit" mechanism. Enhancement in binding affinities compared to the wild-type was found in three out of five mutated candidates. The HLA pocket, capable of accommodating positively charged residues in positions from 5 to 7, is designated as the "dynamic pocket". Taken together, we showcase an effective structure-based binding affinity optimization framework for antigenic peptides of HLA-B*44:02 and underscore the importance of dynamic nature of the antigen-binding groove in concert with the anchoring motifs. This work provides structural insights for rational design of favorable HLA-peptide bindings and future developments in neoantigen-based therapeutics.

Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

Mutagenesis and structural studies reveal the basis for the specific binding of SARS-CoV-2 SL3 RNA element with human TIA1 protein

Viral RNA-host protein interactions are indispensable during RNA virus transcription and replication, but their detailed structural and dynamical features remain largely elusive. Here, we characterize the binding interface for the SARS-CoV-2 stem-loop 3 (SL3) cis-acting element to human TIA1 protein with a combined theoretical and experimental approaches. The highly structured SARS-CoV-2 SL3 has a high binding affinity to TIA1 protein, in which the aromatic stacking, hydrogen bonds, and hydrophobic interactions collectively direct this specific binding. Further mutagenesis studies validate our proposed 3D binding model and reveal two SL3 variants have enhanced binding affinities to TIA1. And disruptions of the identified RNA-protein interactions with designed antisense oligonucleotides dramatically reduce SARS-CoV-2 infection in cells. Finally, TIA1 protein could interact with conserved SL3 RNA elements within other betacoronavirus lineages. These findings open an avenue to explore the viral RNA-host protein interactions and provide a pioneering structural basis for RNA-targeting antiviral drug design.

Binding Affinity Calculations of Gluten Peptides to HLA Risk Modifiers: DQ2.5 versus DQ7.5

Free energy perturbation (FEP) calculations can predict relative binding affinities of an antigen and its point mutants to the same human leukocyte antigen (HLA) with high accuracy (e.g., within 1.0 kcal/mol to experiment); however, a more challenging task is to compare binding affinities of wholly different antigens binding to completely different HLAs using FEP. Researchers have used a variety of different FEP schemes to compute and compare absolute binding affinities, with varied success. Here, we propose and assess a unifying scheme to compute the relative binding affinities of different antigens binding to completely different HLAs using absolute binding affinity FEP calculations. We apply our affinity calculation technique to HLA-antigen-T-cell receptor (TCR) systems relevant to celiac disease (CeD) by investigating binding affinity differences between HLA-DQ2.5 (enhanced CeD risk) and HLA-DQ7.5 (CeD protective) in the binary (HLA-gliadin) and ternary (HLA-gliadin-TCR) binding complexes for three gliadin derived epitopes: glia-α1, glia-α2, and glia-ω1. Based on FEP calculations with our carefully designed thermodynamic cycles, we demonstrate that HLA-DQ2.5 has higher binding affinity than HLA-DQ7.5 for gliadin and enhanced binding affinity with a common TCR, agreeing with known results that the HLA-DQ2.5 serotype exhibits increased risk for CeD. Our findings reveal that our proposed absolute binding affinity FEP method is appropriate for predicting HLA binding for disparate antigens with different genotypes. We also discuss atomic-level details of HLA genotypes interacting with gluten peptides and TCRs in regard to the pathogenesis of CeD.

Dynamics-Based Peptide-MHC Binding Optimization by a Convolutional Variational Autoencoder: A Use-Case Model for CASTELO

An unsolved challenge in the development of antigen-specific immunotherapies is determining the optimal antigens to target. Comprehension of antigen-major histocompatibility complex (MHC) binding is paramount toward achieving this goal. Here, we apply CASTELO, a combined machine learning-molecular dynamics (ML-MD) approach, to identify per-residue antigen binding contributions and then design novel antigens of increased MHC-II binding affinity for a type 1 diabetes-implicated system. We build upon a small-molecule lead optimization algorithm by training a convolutional variational autoencoder (CVAE) on MD trajectories of 48 different systems across four antigens and four HLA serotypes. We develop several new machine learning metrics including a structure-based anchor residue classification model as well as cluster comparison scores. ML-MD predictions agree well with experimental binding results and free energy perturbation-predicted binding affinities. Moreover, ML-MD metrics are independent of traditional MD stability metrics such as contact area and root-mean-square fluctuations (RMSF), which do not reflect binding affinity data. Our work supports the role of structure-based deep learning techniques in antigen-specific immunotherapy design.

Molecular mechanism of secreted amyloid-β precursor protein in binding and modulating GABABR1a

A recent phenomenal study discovered that the extension domain of secreted amyloid-β precursor protein (sAPP) can bind to the intrinsically disordered sushi 1 domain of the γ-aminobutyric acid type B receptor subunit 1a (GABA_BR1a) and modulate its synaptic transmission. The work provided an important structural foundation for the modulation of GABA_BR1a; however, the detailed molecular interaction mechanism, crucial for future drug design, remains elusive. Here, we further investigated the dynamical interactions between sAPP peptides and the natively unstructured sushi 1 domain using all-atom molecular dynamics simulations, for both the 17-residue sAPP peptide (APP 17-mer) and its minimally active 9 residue segment (APP 9-mer). We then explored mutations of the APP 9-mer with rigorous free energy perturbation (FEP) calculations. Our in silico mutagenesis studies revealed key residues (D4, W6, and W7) responsible for the binding with the sushi 1 domain. More importantly, one double mutation based on different vertebrate APP sequences from evolution exhibited a stronger binding (ΔΔG = −1.91 ± 0.66 kcal mol⁻¹), indicating a potentially enhanced GABA_BR1a modulator. These large-scale simulations may provide new insights into the binding mechanism between sAPP and the sushi 1 domain, which could open new avenues in the development of future GABA_BR1a-specific therapeutics.

A recent phenomenal study discovered that the extension domain of secreted amyloid-β precursor protein (sAPP) can bind to the intrinsically disordered sushi 1 domain of the γ-aminobutyric acid type B receptor subunit 1a (GABA_BR1a) and modulate its synaptic transmission.

Parameterization of Molybdenum Disulfide Interacting with Water Using the Free Energy Perturbation Method

Water contact angles (WCA) are often used to parametrize force field parameters of novel 2D nanomaterials, such as molybdenum disulfide (MoS₂), which has emerged as a promising nanomaterial in many biomedical applications due to its unique and impressive properties. However, there is a wide range of water-MoS₂ contact angles in the literature depending on the aging process on the surface of a MoS₂ nanosheet and/or substrate material. In this study, we revisit and optimize existing parameters for the basal plane of MoS₂ with two popular water models, TIP3P and SPC/E, using the wide range of WCAs from various experiments. We develop and deploy the free energy perturbation method for parametrizing MoS₂ with experimentally determined WCAs for both fresh and aged surfaces. Energy decomposition analysis on the simulation trajectories reveals that MoS₂-water interaction is dominated by van der Waals interaction, which mainly comes from the top layer of MoS₂. We conclude that to describe both fresh and aged MoS₂ surfaces it is convenient to only adjust the Lennard-Jones parameter ε_S (the depth of the potential well of a sulfur atom), which displays a surprisingly linear correlation with WCAs.

Effect of different-sizes of hydroxyapatite on the water resistance of magnesium oxychloride cement for bone repair

Magnesium oxychloride cement (MOC) has recently attracted significant attention due to its excellent mechanical properties and biological behavior.

Molecular mechanism of phosphoinositides' specificity for the inwardly rectifying potassium channel Kir2.2

We investigated the binding mechanism of PI(4,5)P₂ and variants on the inwardly rectifying potassium channel, Kir2.2. Our results not only demonstrated the molecular origin for their binding specificity, but also revealed the major driving forces.

Phosphoinositides are essential signaling lipids that play a critical role in regulating ion channels, and their dysregulation often results in fatal diseases including cardiac arrhythmia and paralysis. Despite decades of intensive research, the underlying molecular mechanism of lipid agonism and specificity remains largely unknown. Here, we present a systematic study of the binding mechanism and specificity of a native agonist, phosphatidylinositol 4,5-bisphosphate (PI(4,5)P₂) and two of its variants, PI(3,4)P₂ and PI(3,4,5)P₃, on inwardly rectifying potassium channel Kir2.2, using molecular dynamics simulations and free energy perturbations (FEPs). Our results demonstrate that the major driving force for the PI(4,5)P₂ specificity on Kir2.2 comes from the highly organized salt-bridge network formed between the charged inositol head and phosphodiester linker of PI(4,5)P₂. The unsaturated arachidonic chain is also shown to contribute to the stable binding through hydrophobic interactions with nearby Kir2.2 hydrophobic residues. Consistent with previous experimental findings, our FEP results confirmed that non-native ligands, PI(3,4)P₂ and PI(3,4,5)P₃, show significant loss in binding affinity as a result of the substantial shift from the native binding mode and unfavorable local solvation environment. However, surprisingly, the underlying molecular pictures for the unfavorable binding of both ligands are quite distinctive: for PI(3,4)P₂, it is due to a direct destabilization in the bound state, whereas for PI(3,4,5)P₃, it is due to a relative stabilization in its free state. Our findings not only provide a theoretical basis for the ligand specificity, but also generate new insights into the allosteric modulation of ligand-gated ion channels.

Binding Specificity Determines the Cytochrome P450 3A4 Mediated Enantioselective Metabolism of Metconazole

Cytochrome P450 3A4 (CYP3A4) is a promiscuous enzyme, mediating the biotransformations of ∼50% of clinically used drugs, many of which are chiral molecules. Probing the interactions between CYP3A4 and chiral chemicals is thus essential for the elucidation of molecular mechanisms of enantioselective metabolism. We developed a stepwise-restrained-molecular-dynamics (MD) method to model human CYP3A4 in a complex with cis-metconazole (MEZ) isomers and performed conventional MD simulations with a total simulation time of 2.2 μs to probe the molecular interactions. Our current study, which employs a combined experimental and theoretical approach, reports for the first time on the distinct conformational changes of CYP3A4 that are induced by the enantioselective binding of cis-MEZ enantiomers. CYP3A4 preferably metabolizes cis-RS MEZ over the cis-SR isomer, with the resultant enantiomer fraction for cis-MEZ increasing rapidly from 0.5 to 0.82. cis-RS MEZ adopts a more extended structure in the active pocket with its Cl atom exposed to the solvent, whereas cis-SR MEZ sits within the hydrophobic core of the active pocket. Free-energy-perturbation calculations indicate that unfavorable van der Waals interactions between the cis-MEZ isomers and the CYP3A4 binding pocket predominantly contribute to their binding-affinity differences. These results demonstrate that binding specificity determines the cytochrome P450 3A4 mediated enantioselective metabolism of cis-MEZ.

Longitudinal Surveillance of Porcine Rotavirus B Strains from the United States and Canada and In Silico Identification of Antigenically Important Sites

Rotavirus B (RVB) is an important swine pathogen, but control and prevention strategies are limited without an available vaccine. To develop a subunit RVB vaccine with maximal effect, we characterized the amino acid sequence variability and predicted antigenicity of RVB viral protein 7 (VP7), a major neutralizing antibody target, from clinically infected pigs in the United States and Canada. We identified genotype-specific antigenic sites that may be antibody neutralization targets. While some antigenic sites had high amino acid functional group diversity, nine antigenic sites were completely conserved. Analysis of nucleotide substitution rates at amino acid sites (dN/dS) suggested that negative selection appeared to be playing a larger role in the evolution of the identified antigenic sites when compared to positive selection, and was identified in six of the nine conserved antigenic sites. These results identified important characteristics of RVB VP7 variability and evolution and suggest antigenic residues on RVB VP7 that are negatively selected and highly conserved may be good candidate regions to include in a subunit vaccine design due to their tendency to remain stable.

Free Energy Perturbation Calculation of Relative Binding Free Energy between Broadly Neutralizing Antibodies and the gp120 Glycoprotein of HIV-1

Direct calculation of relative binding affinities between antibodies and antigens is a long-sought goal. However, despite substantial efforts, no generally applicable computational method has been described. Here, we describe a systematic free energy perturbation (FEP) protocol and calculate the binding affinities between the gp120 envelope glycoprotein of HIV-1 and three broadly neutralizing antibodies (bNAbs) of the VRC01 class. The protocol has been adapted from successful studies of small molecules to address the challenges associated with modeling protein-protein interactions. Specifically, we built homology models of the three antibody-gp120 complexes, extended the sampling times for large bulky residues, incorporated the modeling of glycans on the surface of gp120, and utilized continuum solvent-based loop prediction protocols to improve sampling. We present three experimental surface plasmon resonance data sets, in which antibody residues in the antibody/gp120 interface were systematically mutated to alanine. The RMS error in the large set (55 total cases) of FEP tests as compared to these experiments, 0.68kcal/mol, is near experimental accuracy, and it compares favorably with the results obtained from a simpler, empirical methodology. The correlation coefficient for the combined data set including residues with glycan contacts, R2=0.49, should be sufficient to guide the choice of residues for antibody optimization projects, assuming that this level of accuracy can be realized in prospective prediction. More generally, these results are encouraging with regard to the possibility of using an FEP approach to calculate the magnitude of protein-protein binding affinities.

Investigating Substitutions in Antibody-Antigen Complexes Using Molecular Dynamics: A Case Study with Broad-spectrum, Influenza A Antibodies

In studying the binding of host antibodies to the surface antigens of pathogens, the structural and functional characterization of antibody–antigen complexes by X-ray crystallography and binding assay is important. However, the characterization requires experiments that are typically time consuming and expensive: thus, many antibody–antigen complexes are under-characterized. For vaccine development and disease surveillance, it is often vital to assess the impact of amino acid substitutions on antibody binding. For example, are there antibody substitutions capable of improving binding without a loss of breadth, or antigen substitutions that lead to antigenic escape? The questions cannot be answered reliably from sequence variation alone, exhaustive substitution assays are usually impractical, and alanine scans provide at best an incomplete identification of the critical residue–residue interactions. Here, we show that, given an initial structure of an antibody bound to an antigen, molecular dynamics simulations using the energy method molecular mechanics with Generalized Born surface area (MM/GBSA) can model the impact of single amino acid substitutions on antibody–antigen binding energy. We apply the technique to three broad-spectrum antibodies to influenza A hemagglutinin and examine both previously characterized and novel variant strains observed in the human population that may give rise to antigenic escape. We find that in some cases the impact of a substitution is local, while in others it causes a reorientation of the antibody with wide-ranging impact on residue–residue interactions: this explains, in part, why the change in chemical properties of a residue can be, on its own, a poor predictor of overall change in binding energy. Our estimates are in good agreement with experimental results—indeed, they approximate the degree of agreement between different experimental techniques. Simulations were performed on commodity computer hardware; hence, this approach has the potential to be widely adopted by those undertaking infectious disease research. Novel aspects of this research include the application of MM/GBSA to investigate binding between broadly binding antibodies and a viral glycoprotein; the development of an approach for visualizing substrate–ligand interactions; and the use of experimental assay data to rescale our predictions, allowing us to make inferences about absolute, as well as relative, changes in binding energy.

Molecular dynamics analysis of antibody recognition and escape by human H1N1 influenza hemagglutinin

The antibody immunoglobulin (Ig) 2D1 is effective against the 1918 hemagglutinin (HA) and also known to cross-neutralize the 2009 pandemic H1N1 influenza HA through a similar epitope. However, the detailed mechanism of neutralization remains unclear. We conducted molecular dynamics (MD) simulations to study the interactions between Ig-2D1 and the HAs from the 1918 pandemic flu (A/South Carolina/1/1918, 18HA), the 2009 pandemic flu (A/California/04/2009, 09HA), a 2009 pandemic flu mutant (A/California/04/2009, 09HA_mut), and the 2006 seasonal flu (A/Solomon Islands/3/2006, 06HA). MM-PBSA analyses suggest the approximate free energy of binding (ΔG) between Ig-2D1 and 18HA is -74.4 kcal/mol. In comparison with 18HA, 09HA and 06HA bind Ig-2D1 ∼6 kcal/mol (ΔΔG) weaker, and the 09HA_mut bind Ig-2D1 only half as strong. We also analyzed the contributions of individual epitope residues using the free-energy decomposition method. Two important salt bridges are found between the HAs and Ig-2D1. In 09HA, a serine-to-asparagine mutation coincided with a salt bridge destabilization, hydrogen bond losses, and a water pocket formation between 09HA and Ig-2D1. In 09HA_mut, a lysine-to-glutamic-acid mutation leads to the loss of both salt bridges and destabilizes interactions with Ig-2D1. Even though 06HA has a similar ΔG to 09HA, it is not recognized by Ig-2D1 in vivo. Because 06HA contains two potential glycosylation sites that could mask the epitope, our results suggest that Ig-2D1 may be active against 06HA only in the absence of glycosylation. Overall, our simulation results are in good agreement with observations from biological experiments and offer novel mechanistic insights, to our knowledge, into the immune escape of the influenza virus.

Advances in Antibody Design

The use of monoclonal antibodies as therapeutics requires optimizing several of their key attributes. These include binding affinity and specificity, folding stability, solubility, pharmacokinetics, effector functions, and compatibility with the attachment of additional antibody domains (bispecific antibodies) and cytotoxic drugs (antibody-drug conjugates). Addressing these and other challenges requires the use of systematic design methods that complement powerful immunization and in vitro screening methods. We review advances in designing the binding loops, scaffolds, domain interfaces, constant regions, post-translational and chemical modifications, and bispecific architectures of antibodies and fragments thereof to improve their bioactivity. We also highlight unmet challenges in antibody design that must be overcome to generate potent antibody therapeutics.

Selecting vaccine strains for H3N2 human influenza A virus

H3N2 human influenza A virus causes epidemics of influenza mainly in the winter season in temperate regions. Since the antigenicity of this virus evolves rapidly, several attempts have been made to predict the major amino acid sequence of hemagglutinin 1 (HA1) in the target season of vaccination. However, the usefulness of predicted sequence was unclear because its relationship to the antigenicity was unknown. Here the antigenic model for estimating the degree of antigenic difference (antigenic distance) between amino acid sequences of HA1 was integrated into the process of selecting vaccine strains for H3N2 human influenza A virus. When the effectiveness of a potential vaccine strain for a target season was evaluated retrospectively using the average antigenic distance between the strain and the epidemic viruses sampled in the target season, the most effective vaccine strain was identified mostly in the season one year before the target season (pre-target season). Effectiveness of actual vaccines appeared to be lower than that of the strains randomly chosen in the pre-target season on average. It was recommended to replace the vaccine strain for every target season with the strain having the smallest average antigenic distance to the others in the pre-target season. The procedure of selecting vaccine strains for future epidemic seasons described in the present study was implemented in the influenza virus forecasting system (INFLUCAST) (http://www.nsc.nagoya-cu.ac.jp/~yossuzuk/influcast.html).

Bio-mimicking of proline-rich motif applied to carbon nanotube reveals unexpected subtleties underlying nanoparticle functionalization

Here, we report computational studies of the SH3 protein domain interacting with various single-walled carbon nanotubes (SWCNT) either bare or functionalized by mimicking the proline-rich motif (PRM) ligand (PPPVPPRR) and compare it to the SH3-PRM complex binding. With prolines or a single arginine attached, the SWCNT gained slightly on specificity when compared with the bare control, whereas with multi-arginine systems the specificity dropped dramatically to our surprise. Although the electrostatic interaction provided by arginines is crucial in the recognition between PRM and SH3 domain, our results suggest that attaching multiple arginines to the SWCNT has a detrimental effect on the binding affinity. Detailed analysis of the MD trajectories found two main factors that modulate the specificity of the binding: the existence of competing acidic patches at the surface of SH3 that leads to "trapping and clamping" by the arginines, and the rigidity of the SWCNT introducing entropic penalties in the proper binding. Further investigation revealed that the same "clamping" phenomenon exits in the PRM-SH3 system, which has not been reported in previous literature. The competing effects between nanoparticle and its functionalization components revealed by our model system should be of value to current and future nanomedicine designs.

Predictability of antigenic evolution for H3N2 human influenza A virus

Influenza A virus continues to pose a threat to public health. Since this virus can evolve escape mutants rapidly, it is desirable to predict the antigenic evolution for developing effective vaccines. Although empirical methods have been proposed and reported to predict the antigenic evolution more or less accurately, they did not provide much insight into the effects of unobserved mutations and the mechanisms of antigenic evolution. Here a theoretical method was introduced to predict the antigenic evolution of H3N2 human influenza A virus by evaluating de novo mutations through estimating the antigenic distance. The antigenic distance defined with the hemagglutination inhibition (HI) titer was estimated with antigenic models taking into account the volume, isoelectric point, relative solvent accessibility, and distances from receptor-binding sites (RBS) and N-linked glycosylation sites (NGS) for amino acids in hemagglutinin 1 (HA1). When the best model with the optimized parameter values was used to predict the antigenic evolution for the dominant strains, the prediction accuracy was relatively low. However, there appeared to be an overall tendency that the amino acid sites with larger potential net effect on antigenicity were more likely to evolve and the amino acid changes with larger potential effect were more likely to take place, suggesting that natural selection may operate to enhance the antigenic evolution of H3N2 human influenza A virus.

Molecular recognition of metabotropic glutamate receptor type 1 (mGluR1): synergistic understanding with free energy perturbation and linear response modeling

Metabotropic glutamate receptors (mGluRs) constitute an important family of the G-protein coupled receptors. Due to their widespread distribution in the central nervous system (CNS), these receptors are attractive candidates for understanding the molecular basis of various cognitive processes as well as for designing inhibitors for relevant psychiatric and neurological disorders. Despite many studies on drugs targeting the mGluR receptors to date, the molecular level details on the ligand binding dynamics still remain unclear. In this study, we performed in silico experiments for mGluR1 with 29 different ligands including known synthetic agonists and antagonists as well as natural amino acids. The ligand-receptor binding affinities were estimated by the use of atomistic simulations combined with the mathematically rigorous, Free Energy Perturbation (FEP) method, which successfully recognized the native agonist l-glutamate among the highly favorable binders, and also accurately distinguished antagonists from agonists. Comparative contact analysis also revealed the binding mode differences between natural and non-natural amino acid-based ligands. Several factors potentially affecting the ligand binding affinity and specificity were identified including net charges, dipole moments, and the presence of aromatic rings. On the basis of these findings, linear response models (LRMs) were built for different sets of ligands that showed high correlations (R(2) > 0.95) to the corresponding FEP binding affinities. These results identify some key factors that determine ligand-mGluR1 binding and could be used for future inhibitor designs and support a role for in silico modeling for understanding receptor ligand interactions.

The complex and specific pMHC interactions with diverse HIV-1 TCR clonotypes reveal a structural basis for alterations in CTL function

Immune control of viral infections is modulated by diverse T cell receptor (TCR) clonotypes engaging peptide-MHC class I complexes on infected cells, but the relationship between TCR structure and antiviral function is unclear. Here we apply in silico molecular modeling with in vivo mutagenesis studies to investigate TCR-pMHC interactions from multiple CTL clonotypes specific for a well-defined HIV-1 epitope. Our molecular dynamics simulations of viral peptide-HLA-TCR complexes, based on two independent co-crystal structure templates, reveal that effective and ineffective clonotypes bind to the terminal portions of the peptide-MHC through similar salt bridges, but their hydrophobic side-chain packings can be very different, which accounts for the major part of the differences among these clonotypes. Non-specific hydrogen bonding to viral peptide also accommodates greater epitope variants. Furthermore, free energy perturbation calculations for point mutations on the viral peptide KK10 show excellent agreement with in vivo mutagenesis assays, with new predictions confirmed by additional experiments. These findings indicate a direct structural basis for heterogeneous CTL antiviral function.

Large domain motions in Ago protein controlled by the guide DNA-strand seed region determine the Ago-DNA-mRNA complex recognition process

The recognition mechanism and cleavage activity of argonaute (Ago), miRNA, and mRNA complexes are the core processes to the small non-coding RNA world. The 5′ nucleation at the ‘seed’ region (position 2–8) of miRNA was believed to play a significant role in guiding the recognition of target mRNAs to the given miRNA family. In this paper, we have performed all-atom molecular dynamics simulations of the related and recently revealed Ago-DNA:mRNA ternary complexes to study the dynamics of the guide-target recognition and the effect of mutations by introducing “damaging” C·C mismatches at different positions in the seed region of the DNA-RNA duplex. Our simulations show that the A-form-like helix duplex gradually distorts as the number of seed mismatches increases and the complex can survive no more than two such mismatches. Severe distortions of the guide-target heteroduplex are observed in the ruinous 4-sites mismatch mutant, which give rise to a bending motion of the PAZ domain along the L1/L2 “hinge-like” connection segment, resulting in the opening of the nucleic-acid-binding channel. These long-range interactions between the seed region and PAZ domain, moderated by the L1/L2 segments, reveal the central role of the seed region in the guide-target strands recognition: it not only determines the guide-target heteroduplex’s nucleation and propagation, but also regulates the dynamic motions of Ago domains around the nucleic-acid-binding channel.

Characterization of epitopes recognized by monoclonal antibodies: experimental approaches supported by freely accessible bioinformatic tools

Monoclonal antibodies (mAbs) have been used successfully both in research and for clinical purposes. The possible use of protective mAbs directed against different microbial pathogens is currently being considered. The fine definition of the epitope recognized by a protective mAb is an important aspect to be considered for possible development in epitope-based vaccinology. The most accurate approach to this is the X-ray resolution of mAb/antigen crystal complex. Unfortunately, this approach is not always feasible. Under this perspective, several surrogate epitope mapping strategies based on the use of bioinformatics have been developed. In this article, we review the most common, freely accessible, bioinformatic tools used for epitope characterization and provide some basic examples of molecular visualization, editing and computational analysis.

An induced pocket for the binding of potent fusion inhibitor CL-385319 with H5N1 influenza virus hemagglutinin

The influenza glycoprotein hemagglutinin (HA) plays crucial roles in the early stage of virus infection, including receptor binding and membrane fusion. Therefore, HA is a potential target for developing anti-influenza drugs. Recently, we characterized a novel inhibitor of highly pathogenic H5N1 influenza virus, CL-385319, which specifically inhibits HA-mediated viral entry. Studies presented here identified the critical binding residues for CL-385319, which clustered in the stem region of the HA trimer by site-directed mutagenesis. Extensive computational simulations, including molecular docking, molecular dynamics simulations, molecular mechanics generalized Born surface area (MM_GBSA) calculations, charge density and Laplacian calculations, have been carried out to uncover the detailed molecular mechanism that underlies the binding of CL-385319 to H5N1 influenza virus HA. It was found that the recognition and binding of CL-385319 to HA proceeds by a process of “induced fit” whereby the binding pocket is formed during their interaction. Occupation of this pocket by CL-385319 stabilizes the neutral pH structure of hemagglutinin, thus inhibiting the conformational rearrangements required for membrane fusion. This “induced fit” pocket may be a target for structure-based design of more potent influenza fusion inhibitors.

Comprehensive interrogation of natural TALE DNA-binding modules and transcriptional repressor domains

Transcription activator-like effectors (TALE) are sequence-specific DNA binding proteins that harbor modular, repetitive DNA binding domains. TALEs have enabled the creation of customizable designer transcriptional factors and sequence-specific nucleases for genome engineering. Here we report two improvements of the TALE toolbox for achieving efficient activation and repression of endogenous gene expression in mammalian cells. We show that the naturally occurring repeat variable diresidue (RVD) Asn-His (NH) has high biological activity and specificity for guanine, a highly prevalent base in mammalian genomes. We also report an effective TALE transcriptional repressor architecture for targeted inhibition of transcription in mammalian cells. These findings will improve the precision and effectiveness of genome engineering that can be achieved using TALEs.