Quantitative predictions of binding free energy changes in drug-resistant influenza neuraminidase

Design of new chemical entities targeting both native and H275Y mutant influenza a virus by deep reinforcement learning

Influenza virus remains a major public health challenge due to its high morbidity and mortality and seasonal surge. Although antiviral drugs against the influenza virus are widely used as a first-line defense, the virus undergoes rapid genetic changes, resulting in the emergence of drug-resistant strains. Thus, new antiviral drugs that can outwit resistant strains are of significant importance. Herein, we used deep reinforcement learning (RL) algorithm to design new chemical entities (NCEs) that are able to bind to the native and H275Y mutant (oseltamivir-resistant) neuraminidases (NAs) of influenza A virus with better binding energy than oseltamivir. We generated more than 66211 NCEs, which were prioritized based on the filtering rules, structural alerts, and synthetic accessibility. Then, 18 NCEs with better MM/PBSA scores than oseltamivir were further analyzed in molecular dynamics (MD) simulations conducted for 100 ns. The MD experiments showed that 8 NCEs formed very stable complexes with the binding pocket of both native and H275Y mutant NAs of H1N1. Furthermore, most NCEs demonstrated much better binding affinity to group 2 (N2, N3, and N9) and influenza B virus NAs than oseltamivir. Although all 8 NCEs have non-sialic acid-like structures, they showed a similar binding mode as oseltamivir, indicating that it is possible to find new scaffolds with better binding and antiviral properties than sialic acid-like inhibitors. In conclusion, we have designed potential compounds as antiviral candidates for further synthesis and testing against wild and mutant influenza virus.Communicated by Ramaswamy H. Sarma.

Dynamic residue interaction network analysis of the oseltamivir binding site of N1 neuraminidase and its H274Y mutation site conferring drug resistance in influenza A virus

Background

Oseltamivir (OTV)-resistant influenza virus exhibits His-to-Tyr mutation at residue 274 (H274Y) in N1 neuraminidase (NA). However, the molecular mechanisms by which the H274Y mutation in NA reduces its binding affinity to OTV have not been fully elucidated.

Methods

In this study, we used dynamic residue interaction network (dRIN) analysis based on molecular dynamics simulation to investigate the correlation between the OTV binding site of NA and its H274Y mutation site.

Results

dRIN analysis revealed that the OTV binding site and H274Y mutation site of NA interact via the three interface residues connecting them. H274Y mutation significantly enhanced the interaction between residue 274 and the three interface residues in NA, thereby significantly decreasing the interaction between OTV and its surrounding loop 150 residues. Thus, we concluded that such changes in residue interactions could reduce the binding affinity of OTV to NA, resulting in drug resistant influenza viruses. Using dRIN analysis, we succeeded in understanding the characteristic changes in residue interactions due to H274Y mutation, which can elucidate the molecular mechanism of reduction in OTV binding affinity to influenza NA. Finally, the dRIN analysis used in this study can be widely applied to various systems such as individual proteins, protein-ligand complexes, and protein-protein complexes, to characterize the dynamic aspects of the interactions.

Novel oseltamivir-resistant mutations distant from the active site of influenza B neuraminidase

We performed a neuraminidase sequence analysis of thirty-two pediatric patients with influenza B who visited Teikyo University Hospital from January 2016 to March 2017, and found oseltamivir-resistant samples belonging to the Yamagata and Victoria lineages. Comparison with the neuraminidase sequence of oseltamivir-susceptible B/Brisbane/60/2008 revealed 5 common amino acid substitutions in many of these samples. According to the binding free energy calculation, the N340D and E358K substitutions reduced the affinity of oseltamivir to neuraminidase. Unexpectedly, these substitutions were located distant from the oseltamivir-binding site in neuraminidase. According to the molecular dynamics simulations, the N340D substitution rearranged complicated hydrogen bond networks in an extensive surface region of neuraminidase. The E358K substitution extensively altered the electrostatic potential map of the overall neuraminidase structure. Through these novel mechanisms, the N340D and E358K substitutions indirectly influenced the affinity reduction. These results may be useful for designing drugs for the treatment of oseltamivir-resistant virus infections.Communicated by Ramaswamy H. Sarma.

Impact of tetramerization on the ligand recognition of N1 influenza neuraminidase via MMGBSA approach

Influenza virus neuraminidase (NA) is a homotetrameric surface protein that, in contrast to other non-influenza NAs, requires a quaternary assembly to exhibit enzymatic activity, suggesting that the oligomeric state significantly impacts the active site of influenza NA. Nevertheless, most structure-based drug design studies have been reported by employing the monomeric state in the closed or open-loop due to the computational cost of employing the tetrameric NA. In this work, we present MD simulations coupled to the MMGBSA approach of avian N1 type NA in its monomeric and tetrameric closed and open-loop state both with and without the inhibitor oseltamivir and its natural substrate, sialic acid. Structural and energetic analyses revealed that the tetrameric state impacts flexibility as well as the map of interactions participating in stabilizing the protein-ligand complexes with respect to the monomeric state. It was observed that the tetrameric state exerts dissimilar effects in binding affinity, characteristic of positive and negative cooperativity for oseltamivir and sialic acid, respectively. Based on our results, to perform a confident structure-based drug design, as well as to evaluate the impact of key mutations through MD simulations, it is important to consider the tetrameric state closed-loop state.

Alanine mutation of the catalytic sites of Pantothenate Synthetase causes distinct conformational changes in the ATP binding region

The enzyme Pantothenate synthetase (PS) represents a potential drug target in Mycobacterium tuberculosis. Its X-ray crystallographic structure has demonstrated the significance and importance of conserved active site residues including His44, His47, Asn69, Gln72, Lys160 and Gln164 in substrate binding and formation of pantoyl adenylate intermediate. In the current study, molecular mechanism of decreased affinity of the enzyme for ATP caused by alanine mutations was investigated using molecular dynamics (MD) simulations and free energy calculations. A total of seven systems including wild-type + ATP, H44A + ATP, H47A + ATP, N69A + ATP, Q72A + ATP, K160A + ATP and Q164A + ATP were subjected to 50 ns MD simulations. Docking score, MM-GBSA and interaction profile analysis showed weak interactions between ATP (substrate) and PS (enzyme) in H47A and H160A mutants as compared to wild-type, leading to reduced protein catalytic activity. However, principal component analysis (PCA) and free energy landscape (FEL) analysis revealed that ATP was strongly bound to the catalytic core of the wild-type, limiting its movement to form a stable complex as compared to mutants. The study will give insight about ATP binding to the PS at the atomic level and will facilitate in designing of non-reactive analogue of pantoyl adenylate which will act as a specific inhibitor for PS.

Why Do Exceptionally Dangerous Gain-of-Function Experiments in Influenza?

This chapter makes the case against performing exceptionally dangerous gain-of-function experiments that are designed to create potentially pandemic and novel strains of influenza, for example, by enhancing the airborne transmissibility in mammals of highly virulent avian influenza strains. This is a question of intense debate over the last 5 years, though the history of such experiments goes back at least to the synthesis of viable influenza A H1N1 (1918) based on material preserved from the 1918 pandemic. This chapter makes the case that experiments to create potential pandemic pathogens (PPPs) are nearly unique in that they present biosafety risks that extend well beyond the experimenter or laboratory performing them; an accidental release could, as the name suggests, lead to global spread of a virulent virus, a biosafety incident on a scale never before seen. In such cases, biosafety considerations should be uppermost in the consideration of alternative approaches to experimental objectives and design, rather than being settled after the fact, as is appropriately done for most research involving pathogens. The extensive recent discussion of the magnitude of risks from such experiments is briefly reviewed. The chapter argues that, while there are indisputably certain questions that can be answered only by gain-of-function experiments in highly pathogenic strains, these questions are narrow and unlikely to meaningfully advance public health goals such as vaccine production and pandemic prediction. Alternative approaches to experimental influenza virology and characterization of existing strains are in general completely safe, higher throughput, more generalizable, and less costly than creation of PPP in the laboratory and can thereby better inform public health. Indeed, virtually every finding of recent PPP experiments that has been cited for its public health value was predated by similar findings using safe methodologies. The chapter concludes that the unique scientific and public health value of PPP experiments is inadequate to justify the unique risks they entail and that researchers would be well-advised to turn their talents to other methodologies that will be safe and more rewarding scientifically.

Ethical alternatives to experiments with novel potential pandemic pathogens

Please see later in the article for the Editors' Summary

Applying physics-based scoring to calculate free energies of binding for single amino acid mutations in protein-protein complexes

Predicting changes in protein binding affinity due to single amino acid mutations helps us better understand the driving forces underlying protein-protein interactions and design improved biotherapeutics. Here, we use the MM-GBSA approach with the OPLS2005 force field and the VSGB2.0 solvent model to calculate differences in binding free energy between wild type and mutant proteins. Crucially, we made no changes to the scoring model as part of this work on protein-protein binding affinity--the energy model has been developed for structure prediction and has previously been validated only for calculating the energetics of small molecule binding. Here, we compare predictions to experimental data for a set of 418 single residue mutations in 21 targets and find that the MM-GBSA model, on average, performs well at scoring these single protein residue mutations. Correlation between the predicted and experimental change in binding affinity is statistically significant and the model performs well at picking "hotspots," or mutations that change binding affinity by more than 1 kcal/mol. The promising performance of this physics-based method with no tuned parameters for predicting binding energies suggests that it can be transferred to other protein engineering problems.

Accurate calculation of mutational effects on the thermodynamics of inhibitor binding to p38α MAP kinase: a combined computational and experimental study

A major current challenge for drug design efforts focused on protein kinases is the development of drug resistance caused by spontaneous mutations in the kinase catalytic domain. The ubiquity of this problem means that it would be advantageous to develop fast, effective computational methods that could be used to determine the effects of potential resistance-causing mutations before they arise in a clinical setting. With this long-term goal in mind, we have conducted a combined experimental and computational study of the thermodynamic effects of active-site mutations on a well-characterized and high-affinity interaction between a protein kinase and a small-molecule inhibitor. Specifically, we developed a fluorescence-based assay to measure the binding free energy of the small-molecule inhibitor, SB203580, to the p38α MAP kinase and used it measure the inhibitor's affinity for five different kinase mutants involving two residues (Val38 and Ala51) that contact the inhibitor in the crystal structure of the inhibitor-kinase complex. We then conducted long, explicit-solvent thermodynamic integration (TI) simulations in an attempt to reproduce the experimental relative binding affinities of the inhibitor for the five mutants; in total, a combined simulation time of 18.5 μs was obtained. Two widely used force fields - OPLS-AA/L and Amber ff99SB-ILDN - were tested in the TI simulations. Both force fields produced excellent agreement with experiment for three of the five mutants; simulations performed with the OPLS-AA/L force field, however, produced qualitatively incorrect results for the constructs that contained an A51V mutation. Interestingly, the discrepancies with the OPLS-AA/L force field could be rectified by the imposition of position restraints on the atoms of the protein backbone and the inhibitor without destroying the agreement for other mutations; the ability to reproduce experiment depended, however, upon the strength of the restraints' force constant. Imposition of position restraints in corresponding simulations that used the Amber ff99SB-ILDN force field had little effect on their ability to match experiment. Overall, the study shows that both force fields can work well for predicting the effects of active-site mutations on small molecule binding affinities and demonstrates how a direct combination of experiment and computation can be a powerful strategy for developing an understanding of protein-inhibitor interactions.

21st century natural product research and drug development and traditional medicines

Natural products and related structures are essential sources of new pharmaceuticals, because of the immense variety of functionally relevant secondary metabolites of microbial and plant species. Furthermore, the development of powerful analytical tools based upon genomics, proteomics, metabolomics, bioinformatics and other 21st century technologies are greatly expediting identification and characterization of these natural products. Here we discuss the synergistic and reciprocal benefits of linking these 'omics technologies with robust ethnobotanical and ethnomedical studies of traditional medicines, to provide critically needed improved medicines and treatments that are inexpensive, accessible, safe and reliable. However, careless application of modern technologies can challenge traditional knowledge and biodiversity that are the foundation of traditional medicines. To address such challenges while fulfilling the need for improved (and new) medicines, we encourage the development of Regional Centres of 'omics Technologies functionally linked with Regional Centres of Genetic Resources, especially in regions of the world where use of traditional medicines is prevalent and essential for health.