Novel method for probing the specificity binding profile of ligands: applications to HIV protease

Reduction of anoxia-induced bioenergetic disturbance in astrocytes by methanol fruit extract of Tetrapleura tetraptera and in silico evaluation of the effect of its antioxidative constituents on excitotoxicity

Oxidative stress and excitotoxicity are some of the pathophysiological abnormalities in hypoxia-induced brain injury. This study evaluated the intrinsic antioxidant property of methanol fruit extract of Tetrapleura tetraptera (TT), traditionally used for managing brain diseases such as cerebral infarction in West Africa, and its ability to protect primary astrocytes from anoxia-induced cell death. The effect of the phytochemicals present in TT on excitotoxicity was assessed in silico, through docking with human glutamate synthetase (hGS). Chromatographic and spectrophotometric analyses of TT were performed. Primary astrocytes derived from neural stem cells were treated with TT and its effect on astrocyte viability was assessed. TT-treated astrocytes were then subjected to anoxic insult and, cell viability and mitochondrial membrane potential were evaluated. Molecular docking of hGS with detected phytochemicals in TT (aridanin, naringenin, ferulic acid, and scopoletin) was performed and the number of interactions with the lead compounds, aridanin, analyzed. HPLC-DAD analysis of TT revealed the presence of various bioactive phytochemicals. TT demonstrated notable antioxidant and radical scavenging activities. TT also protected astrocytes from anoxic insult by restoring cell viability and preventing alteration to mitochondrial membrane integrity. Aridanin, naringenin, ferulic acid, and scopoletin demonstrated good binding affinities with hGS indicating that Tetrapleura tetraptera is a potential source of new plant-based bioactives relevant in the therapy of neurodegenerative diseases.

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. Proteins 2015; 83:351–372. © 2014 Wiley Periodicals, Inc.

Herpes B virus gD interaction with its human receptor--an in silico analysis approach

Background

The glycoprotein D (gD) is essential for Herpes B virus (BV) entry into mammalian cells. Nectin-1, an HSV-1 gD receptor, is found to be the receptor which mediated BV induced cell-cell fusion, while HVEM does not mediate fusion by BV glycoprotein. However, the specific sequence and structural requirements of the BV gD for the recognition of and binding to Nectin-1 are unknown. Moreover, the 3D structures of BV gD and the BV gD-receptor complex have not been determined. In this study, we propose a reliable model of the interaction of the BV gD with receptor using bioinformatics tools.

Results

The three-dimensional structures of two BV gD-receptor complexes were constructed using homology modelling and docking strategy. Based on the models of these complexes, the BV gD receptor interaction patterns were calculated. The results showed that the interface between the BV gD and nectin-1 molecule is not geometrically complementary. The computed molecular interactions indicated that two terminal extensions were the main region of BV gD that binds to nectin-1 and that hydrophobic contacts between the two molecules play key roles in their recognition and binding. The constructed BV gD-HVEM complex model showed that this complex had a lower shape complementarity value and a smaller interface area compared with the HSV-1 gD-HVEM complex, and the number of intermolecular interactions between BV gD-HVEM were fewer than that of HSV-1 gD-HVEM complex. These results could explain why HVEM does not function as a receptor for BV gD.

Conclusion

In this study, we present structural model for the BV gD in a complex with its receptor. Some features predicted by this model can explain previously reported experimental data. This complex model may lead to a better understanding of the function of BV gD and its interaction with receptor and will improve our understanding of the activation of the BV fusion and entry process.

In silico prediction of mutant HIV-1 proteases cleaving a target sequence

HIV-1 protease represents an appealing system for directed enzyme re-design, since it has various different endogenous targets, a relatively simple structure and it is well studied. Recently Chaudhury and Gray (Structure (2009) 17: 1636–1648) published a computational algorithm to discern the specificity determining residues of HIV-1 protease. In this paper we present two computational tools aimed at re-designing HIV-1 protease, derived from the algorithm of Chaudhuri and Gray. First, we present an energy-only based methodology to discriminate cleavable and non cleavable peptides for HIV-1 proteases, both wild type and mutant. Secondly, we show an algorithm we developed to predict mutant HIV-1 proteases capable of cleaving a new target substrate peptide, different from the natural targets of HIV-1 protease. The obtained in silico mutant enzymes were analyzed in terms of cleavability and specificity towards the target peptide using the energy-only methodology. We found two mutant proteases as best candidates for specificity and cleavability towards the target sequence.

Charge Optimization Theory for Induced-Fit Ligands

The design of ligands with high affinity and specificity remains a fundamental challenge in understanding molecular recognition and developing therapeutic interventions. Charge optimization theory addresses this problem by determining ligand charge distributions that produce the most favorable electrostatic contribution to the binding free energy. The theory has been applied to the design of binding specificity as well. However, the formulations described only treat a rigid ligand-one that does not change conformation upon binding. Here, we extend the theory to treat induced-fit ligands for which the unbound ligand conformation may differ from the bound conformation. We develop a thermodynamic pathway analysis for binding contributions relevant to the theory, and we illustrate application of the theory using HIV-1 protease with our previously designed and validated subnanomolar inhibitor. Direct application of rigid charge optimization approaches to nonrigid cases leads to very favorable intramolecular electrostatic interactions that are physically unreasonable, and analysis shows the ligand charge distribution massively stabilizes the preconformed (bound) conformation over the unbound. After analyzing this case, we provide a treatment for the induced-fit ligand charge optimization problem that produces physically realistic results. The key factor is introducing the constraint that the free energy of the unbound ligand conformation be lower or equal to that of the preconformed ligand structure, which corresponds to the notion that the unbound structure is the ground unbound state. Results not only demonstrate the applicability of this methodology to discovering optimized charge distributions in an induced-fit model, but also provide some insights into the energetic consequences of ligand conformational change on binding. Specifically, the results show that, from an electrostatic perspective, induced-fit binding is not an adaptation designed to enhance binding affinity; at best, it can only achieve the same affinity as optimized rigid binding.

Prediction of HIV-1 protease/inhibitor affinity using RosettaLigand

Predicting HIV-1 protease/inhibitor binding affinity as the difference between the free energy of the inhibitor bound and unbound state remains difficult as the unbound state exists as an ensemble of conformations with various degrees of flap opening. We improve computational prediction of protease/inhibitor affinity by invoking the hypothesis that the free energy of the unbound state while difficult to predict is less sensitive to mutation. Thereby the HIV-1 protease/inhibitor binding affinity can be approximated with the free energy of the bound state alone. Bound state free energy can be predicted from comparative models of HIV-1 protease mutant/inhibitor complexes. Absolute binding energies are predicted with R = 0.71 and SE = 5.91 kJ/mol. Changes in binding free energy upon mutation can be predicted with R = 0.85 and SE = 4.49 kJ/mol. Resistance mutations that lower inhibitor binding affinity can thereby be recognized early in HIV-1 protease inhibitor development.

Thermodynamic analysis of water molecules at the surface of proteins and applications to binding site prediction and characterization

Water plays an essential role in determining the structure and function of all biological systems. Recent methodological advances allow for an accurate and efficient estimation of the thermodynamic properties of water molecules at the surface of proteins. In this work, we characterize these thermodynamic properties and relate them to various structural and functional characteristics of the protein. We find that high-energy hydration sites often exist near protein motifs typically characterized as hydrophilic, such as backbone amide groups. We also find that waters around alpha helices and beta sheets tend to be less stable than waters around loops. Furthermore, we find no significant correlation between the hydration site-free energy and the solvent accessible surface area of the site. In addition, we find that the distribution of high-energy hydration sites on the protein surface can be used to identify the location of binding sites and that binding sites of druggable targets tend to have a greater density of thermodynamically unstable hydration sites. Using this information, we characterize the FKBP12 protein and show good agreement between fragment screening hit rates from NMR spectroscopy and hydration site energetics. Finally, we show that water molecules observed in crystal structures are less stable on average than bulk water as a consequence of the high degree of spatial localization, thereby resulting in a significant loss in entropy. These findings should help to better understand the characteristics of waters at the surface of proteins and are expected to lead to insights that can guide structure-based drug design efforts.

Toward an evolutionary containment of evolving pathogen-receptors by using an ensemble of multiple mutant ligands: from the viewpoint of fitness landscape in sequence space

It is known that even if a ligand peptide is designed to bind to a target receptor on the surface of a pathogen such as viruses, bacteria or cancer cells, it is likely that some receptors are subject to random mutation and thus the ligand has a reduced ability to bind to these receptors. This issue is known as drug-resistant or escape mutants. In this paper, we present an idea to inhibit the evolving receptors by using an ensemble of all possible single- or double-point mutant sequences of the ligand peptide. Several mutant ligands in the ensemble are expected to bind to the mutant receptors, and then the ensemble may create a defensive wall surrounding the target receptors in receptor-sequence space. We examined the effectiveness of this "evolutionary containment" of the evolving receptors through eight peptide-protein complex systems, which were retrieved from the Protein Data Bank (PDB). As a result, we obtained a suggestion that the original (or parent) ligand sequence should be designed to have as high fitness as possible but to be not local optima, in order to maximize the rate of the evolutionary containment. This may be a strategy of the drug-design against evolving pathogens.

Structural basis for drug and substrate specificity exhibited by FIV encoding a chimeric FIV/HIV protease

A chimeric feline immunodeficiency virus (FIV) protease (PR) has been engineered that supports infectivity but confers sensitivity to the human immunodeficiency virus (HIV) PR inhibitors darunavir (DRV) and lopinavir (LPV). The 6s-98S PR has five replacements mimicking homologous residues in HIV PR and a sixth which mutated from Pro to Ser during selection. Crystal structures of the 6s-98S FIV PR chimera with DRV and LPV bound have been determined at 1.7 and 1.8 Å resolution, respectively. The structures reveal the role of a flexible 90s loop and residue 98 in supporting Gag processing and infectivity and the roles of residue 37 in the active site and residues 55, 57 and 59 in the flap in conferring the ability to specifically recognize HIV PR drugs. Specifically, Ile37Val preserves tertiary structure but prevents steric clashes with DRV and LPV. Asn55Met and Val59Ile induce a distinct kink in the flap and a new hydrogen bond to DRV. Ile98Pro→Ser and Pro100Asn increase 90s loop flexibility, Gln99Val contributes hydrophobic contacts to DRV and LPV, and Pro100Asn forms compensatory hydrogen bonds. The chimeric PR exhibits a comparable number of hydrogen bonds, electrostatic interactions and hydrophobic contacts with DRV and LPV as in the corresponding HIV PR complexes, consistent with IC(50) values in the nanomolar range.

Analysis of HIV protease binding pockets based on 3D shape and electrostatic potential descriptors

Using a shape analysis technique, a Dataset of 79 (19 wild-type and 60 mutated) HIV-1 protease crystal structures was analyzed for the shape changes in their binding pockets. The structures are reported with different bound inhibitors and consist of a variety of mutations. Several 3D-shape descriptors based on the volumetric shape function are calculated, in addition to the electrostatic potential (EP)-based descriptors. A cluster analysis is performed using the calculated descriptors of the binding pocket of the proteins to investigate the effect of mutations or bound inhibitors on the shape of the binding pocket of proteins. We observed that mutations and/or bound ligands influence the 3D shape of the binding pocket of these HIV proteases. This study shows that the shape changes in the binding pocket caused by mutations or bound ligands can be quantitatively captured using 3D shape- and EP-based descriptors.

A comparative study of HIV-1 and HTLV-I protease structure and dynamics reveals a conserved residue interaction network

The two retroviruses human T-lymphotropic virus type I (HTLV-I) and human immunodeficiency virus type 1 (HIV-1) are the causative agents of severe and fatal diseases including adult T-cell leukemia and the acquired immune deficiency syndrome (AIDS). Both viruses code for a protease that is essential for replication and therefore represents a key target for drugs interfering with viral infection. The retroviral proteases from HIV-1 and HTLV-I share 31% sequence identity and high structural similarities. Yet, their substrate specificities and inhibition profiles differ substantially. In this study, we performed all-atom molecular dynamics (MD) simulations for both enzymes in their ligand-free states and in complex with model substrates in order to compare their dynamic behaviors and enhance our understanding of the correlation between sequence, structure, and dynamics in this protein family. We found extensive similarities in both local and overall protein dynamics, as well as in the energetics of their interactions with model substrates. Interestingly, those residues that are important for strong ligand binding are frequently not conserved in sequence, thereby offering an explanation for the differences in binding specificity. Moreover, we identified an interaction network of contacts between conserved residues that interconnects secondary structure elements and serves as a scaffold for the protein fold. This interaction network is conformationally stable over time and may provide an explanation for the highly similar dynamic behavior of the two retroviral proteases, even in the light of their rather low overall sequence identity.

Molecular Basis for Drug Resistance in HIV-1 Protease

HIV-1 protease is one of the major antiviral targets in the treatment of patients infected with HIV-1. The nine FDA approved HIV-1 protease inhibitors were developed with extensive use of structure-based drug design, thus the atomic details of how the inhibitors bind are well characterized. From this structural understanding the molecular basis for drug resistance in HIV-1 protease can be elucidated. Selected mutations in response to therapy and diversity between clades in HIV-1 protease have altered the shape of the active site, potentially altered the dynamics and even altered the sequence of the cleavage sites in the Gag polyprotein. All of these interdependent changes act in synergy to confer drug resistance while simultaneously maintaining the fitness of the virus. New strategies, such as incorporation of the substrate envelope constraint to design robust inhibitors that incorporate details of HIV-1 protease’s function and decrease the probability of drug resistance, are necessary to continue to effectively target this key protein in HIV-1 life cycle.

The subspace iteration method in protein normal mode analysis

Normal mode analysis plays an important role in relating the conformational dynamics of proteins to their biological function. The subspace iteration method is a numerical procedure for normal mode analysis that has enjoyed widespread success in the structural mechanics community due to its numerical stability and computational efficiency in calculating the lowest normal modes of large systems. Here, we apply the subspace iteration method to proteins to demonstrate its advantageous properties in this area of computational protein science. An effective algorithm for choosing the number of iteration vectors in the method is established, offering a considerable improvement over the original implementation. In the present application, computational time scales linearly with the number of normal modes computed. Additionally, the method lends itself naturally to normal mode analyses of multiple neighboring macromolecular conformations, as demonstrated in a conformational change pathway analysis of adenylate kinase. These properties, together with its computational robustness and intrinsic scalability to multiple processors, render the subspace iteration method an effective and reliable computational approach to protein normal mode analysis.