In silico prediction of drug resistance due to S247R mutation of Influenza H1N1 neuraminidase protein

Designing new drug candidates as inhibitors against wild and mutant type neuraminidases: molecular docking, molecular dynamics and binding free energy calculations

Influenza virus is the cause of the death of millions of people with about 3-4 pandemics every hundred years in history. It also turns into a seasonal disease, bringing about approximately 5-15% of the population to be infected and 290,000-650,000 people to die every year. These numbers reveal that it is necessary to be on the alert to work towards influenza in order to protect public health. There are FDA-approved antiviral drugs such as oseltamivir and zanamivir recommended by the World Center for Disease Prevention. However, after the recent outbreaks such as bird flu and swine flu, increasing studies have shown that the flu virus has gained resistance to these drugs. So, there is an urgent need to find new drugs effective against this virus. This study aims to investigate new drug candidates targeting neuraminidase (NA) for the treatment of influenza by using computer aided drug design approaches. They involve virtual scanning, de novo design, rational design, docking, MD, MMGB/PBSA. The investigation includes H1N1, H5N1, H2N2 and H3N2 neuraminidase proteins and their mutant variants possessing resistance to FDA-approved drugs. Virtual screening consists of approximately 30 thousand molecules while de novo and rational designs produced over a hundred molecules. These approaches produced three lead molecules with binding energies for both non-mutant (-34.84, -59.99 and -60.66 kcal/mol) and mutant (-40.40, -58.93, -76.19 kcal/mol) H2N2 NA calculated by MM-PBSA compared with those of oseltamivir -25.64 and -18.40 respectively. The results offer new drug candidates against influenza infection.Communicated by Ramaswamy H. Sarma.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

In silico analyses of acetylcholinesterase (AChE) and its genetic variants in interaction with the anti-Alzheimer drug Rivastigmine

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Despite causing great social and economic impact, there is currently no cure for AD. The most effective therapy to manage AD symptoms is based on acetylcholinesterase inhibitors (AChEi), from which rivastigmine presented numerous benefits. However, mutations in AChE, which affect approximately 5% of the population, can modify protein structure and function, changing the individual response to Alzheimer's treatment. In this study, we performed computer simulations of AChE wild type and variants R34Q, P135A, V333E, and H353N, identified by one or more genome-wide association studies, to evaluate their effects on protein structure and interaction with rivastigmine. The functional effects of AChE variants were predicted using eight machine learning algorithms, while the evolutionary conservation of AChE residues was analyzed using the ConSurf server. Autodock4.2.6 was used to predict the binding modes for the hAChE-rivastigmine complex, which is still unknown. Molecular dynamics (MD) simulations were performed in triplicates for the AChE wild type and mutants using the GROMACS packages. Among the analyzed variants, P135A was classified as deleterious by all the functional prediction algorithms, in addition to occurring at highly conserved positions, which may have harmful consequences on protein function. The molecular docking results suggested that rivastigmine interacts with hAChE at the upper active-site gorge, which was further confirmed by MD simulations. Our MD findings also suggested that the complex hAChE-rivastigmine remains stable over time. The essential dynamics revealed flexibility alterations at the active-site gorge upon mutations P135A, V333E, and H353N, which may lead to strong and nonintuitive consequences to hAChE binding. Nonetheless, similar binding affinities were registered in the MMPBSA analysis for the hAChE wild type and variants when complexed to rivastigmine. Finally, our findings indicated that the rivastigmine binding to hAChE is an energetically favorable process mainly driven by negatively charged amino acids.

In silico characterization of the impact of mutation (LEU112PRO) on the structure and function of carotenoid cleavage dioxygenase 8 in Oryza sativa

Mutation (p.LEU112PRO) in "carotenoid cleavage dioxygenase 8" (CCD8) protein increases tiller formation in rice plants by cross-talking with auxin and cytokinins. However, owing to the nonexistence of a "three-dimension" structure of CCD8, detail information about its structure and function remain elusive until date. Hence, in the present study, computational approaches were adopted to predict "three-dimensional" (3D) structure of CCD8 protein through comparative modeling techniques and to study the effect of mutation (p.LEU112PRO) on its function as well as architecture through "molecular dynamics" simulation studies. The obtained result reveals that wild-type CCD8 protein is made up of 10 α-helix and 25 β-strands while mutant CCD8 is made up of 11 α-helix and 24 β-strands. Further, molecular docking studies reveals that the wild-type has a better binding affinity with auxin and cytokinin in comparison to mutant. Subsequent molecular dynamics simulation of these four complexes, separately, reveals that the movement of both wild-type as well as mutant CCD8 get reduced after binding with auxin, which in turn prevent auxin transport out of the bud and increases tiller number. However, when cytokinin binds with wild-type and mutant CCD8, it inhibits and enhance CCD8 activity, respectively. As cytokinin positively regulates tiller number formation, enhance activity of mutant CCD8 after binding with cytokinin might be the main reason for more tiller number in mutant than wild-type plant. In the near future, mutant CCD8 along with auxin and cytokinin may be utilized for increasing grain yield in rice plants.

Tempel: time-series mutation prediction of influenza A viruses via attention-based recurrent neural networks

Motivation

Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. The goal of this work is to predict whether mutations are likely to occur in the next flu season using historical glycoprotein hemagglutinin sequence data. One of the major challenges is to model the temporality and dimensionality of sequential influenza strains and to interpret the prediction results.

Results

In this article, we propose an efficient and robust time-series mutation prediction model (Tempel) for the mutation prediction of influenza A viruses. We first construct the sequential training samples with splittings and embeddings. By employing recurrent neural networks with attention mechanisms, Tempel is capable of considering the historical residue information. Attention mechanisms are being increasingly used to improve the performance of mutation prediction by selectively focusing on the parts of the residues. A framework is established based on Tempel that enables us to predict the mutations at any specific residue site. Experimental results on three influenza datasets show that Tempel can significantly enhance the predictive performance compared with widely used approaches and provide novel insights into the dynamics of viral mutation and evolution.

Availability and implementation

The datasets, source code and supplementary documents are available at: https://drive.google.com/drive/folders/15WULR5__6k47iRotRPl3H7ghi3RpeNXH.

Supplementary information

Supplementary data are available at Bioinformatics online.

Peramivir binding affinity with influenza A neuraminidase and research on its mutations using an induced-fit docking approach

Influenza A virus (IAV) has caused epidemic infections worldwide, with many strains resistant to inhibitors of a surface protein, neuraminidase (NA), due to point mutations on its structure. A novel NA inhibitor named peramivir was recently approved, but no exhaustive computational research regarding its binding affinity with wild-type and mutant NA has been conducted. In this study, a thorough investigation of IAV-NA PDB entries of 9 subtypes is described, providing a list of residues constituting the protein-ligand binding sites. The results of induced-fit docking approach point out key residues of wild-type NA participating in hydrogen bonds and/or ionic interactions with peramivir, among which Arg 368 is responsible for a peramivir-NA ionic interaction. Mutations on this residue greatly reduced the binding affinity of peramivir with NA, with 3 mutations R378Q, R378K and R378L (NA6) capable of deteriorating the docking performance of peramivir by over 50%. 200 compounds from 6-scaffolds were docked into these 3 mutant versions, revealing 18 compounds giving the most promising results. Among them, CMC-2012-7-1527-56 (benzoic acid scaffold, IC50 = 32 nM in inhibitory assays with IAV) is deemed the most potential inhibitor of mutant NA resisting both peramivir and zanamivir, and should be further investigated.

Source of oseltamivir resistance due to single E119D and double E119D/H274Y mutations in pdm09H1N1 influenza neuraminidase

Influenza epidemics are responsible for an average of 3-5 millions of severe cases and up to 500,000 deaths around the world. One of flu pandemic types is influenza A(H1N1)pdm09 virus (pdm09H1N1). Oseltamivir is the antiviral drug used to treat influenza targeting at neuraminidase (NA) located on the viral surface. Influenza virus undergoes high mutation rates and leads to drug resistance, and thus the development of more efficient drugs is required. In the present study, all-atom molecular dynamics simulations were applied to understand the oseltamivir resistance caused by the single E119D and double E119D/H274Y mutations on NA. The obtained results in terms of binding free energy and intermolecular interactions in the ligand-protein interface showed that the oseltamivir could not be well accommodated in the binding pocket of both NA mutants and the 150-loop moves out from oseltamivir as an "open" state. A greater number of water molecules accessible to the binding pocket could disrupt the oseltamivir binding with NA target as seen be high mobility of oseltamivir at the active site. Additionally, our finding could guide to the design and development of novel NA inhibitor drugs.

Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning

Thrombin is a key component for chemotherapeutic and antithrombotic therapy development. As the physiologic and pathologic roles of the light chain still remain vague, here, we continue previous efforts to understand the impacts of the disease-associated single deletion of LYS9 in the light chain. By combining supervised and unsupervised machine learning methodologies and more traditional structural analyses on data from 10 μs molecular dynamics simulations, we show that the conformational ensemble of the ΔK9 mutant is significantly perturbed. Our analyses consistently indicate that LYS9 deletion destabilizes both the catalytic cleft and regulatory functional regions and result in some conformational changes that occur in tens to hundreds of nanosecond scaled motions. We also reveal that the two forms of thrombin each prefer a distinct binding mode of a Na+ ion. We expand our understanding of previous experimental observations and shed light on the mechanisms of the LYS9 deletion associated bleeding disorder by providing consistent but more quantitative and detailed structural analyses than early studies in literature. With a novel application of supervised learning, i.e. the decision tree learning on the hydrogen bonding features in the wild-type and ΔK9 mutant forms of thrombin, we predict that seven pairs of critical hydrogen bonding interactions are significant for establishing distinct behaviors of wild-type thrombin and its ΔK9 mutant form. Our calculations indicate the LYS9 in the light chain has both localized and long-range allosteric effects on thrombin, supporting the opinion that light chain has an important role as an allosteric effector.

Computational approach to understand molecular mechanism involved in BPH resistance in Bt- rice plant

In silico approach was utilised to identify differentially expressed key hub genes during BPH infestation on Bt rice plant, under laboratory conditions. Re-analysis of GSE74745 data with in-house R scripts and STRING database reveals that only 5 key hub genes, namely Os05g0176100, Os06g0683200, Os07g0208500, Os07g0252400 and Os07g0424400, belonging to cellulose synthase family, are differentially expressed and have confidence score ≥0.9 among themselves. Conserve domain analysis of all proteins encoded via these 5 key hub genes reveals that they have a common cellulose synthase domain, in which "Plant-Conserved Region" (PCR) is highly conserved. After binding with other domains of cellulose synthase proteins or other accessory proteins, like sucrose synthase, PCR serves as a metabolic channel to deliver UDP-Glucose, which is the main substrate for cellulose synthesis, into the active site of cellulose synthase and initiate cellulose synthesis. Simulation study of recently solved topological model of PCR [PDB ID: 5JNP] and molecular docking studies of PCR with UDP-glucose reveals that, during BPH infestation, in nearby phloem tissue where BPH suck sap, there is an increase interaction of UDP-glucose with PCR and other accessory proteins which in turn increases both the stability of PCR and the production of cellulose, finally causing callose deposition at that site and hence causing longer nymphal developmental period and lower fertility of BPH infested on Bt rice. In near future, these differentially identified 5 hub genes could be possible targets for controlling BPH infestation in rice plant under field conditions and increasing rice yield globally.

In silico site-directed mutagenesis of neutralizing mAb 4C4 and analysis of its interaction with G-H loop of VP1 to explore its therapeutic applications against FMD

Investigating the behaviour of bio-molecules through computational mutagenesis is gaining interest to facilitate the development of new therapeutic solutions for infectious diseases. The antigenetically variant genotypes of foot and mouth disease virus (FMDV) and their subsequent infections are challenging to tackle with traditional vaccination. In such scenario, neutralizing antibodies might provide an alternate solution to manage the FMDV infection. Thus, we have analysed the interaction of the mAb 4C4 with a synthetic G-H loop of FMDV-VP1 through in silico mutagenesis and molecular modelling. Initially, a set of 25,434 mutants were designed and the mutants having better energetic stability than 4C4 were clustered based on sequence identity. The best mutant representing each cluster was selected and evaluated for its binding affinity with the antigen in terms of docking scores, interaction energy and binding energy. Six mutants have confirmed better binding affinities towards the antigen than 4C4. Further, interaction of these mutants with the natural G-H loop that is bound to mAb SD6 was also evaluated. One 4C4 variant having mutations at the positions 2034(N→L), 2096(N→C), 2098(D→Y), 2532(T→K) and 2599(A→G) has revealed better binding affinities towards both the synthetic and natural G-H loops than 4C4 and SD6, respectively. A molecular dynamic simulation for 50 ns was conducted for mutant and wild-type antibody structures which supported the pre-simulation results. Therefore, these mutations on mAb 4C4 are believed to provide a better antibody-based therapeutic option for FMD. Communicated by Ramaswamy H. Sarma.

Structural, molecular motions, and free-energy landscape of Leishmania sterol-14α-demethylase wild type and drug resistant mutant: a comparative molecular dynamics study

Sterol-14α-demethylase (CYP51) is an ergosterol pathway enzyme crucial for the survival of infectious Leishmania parasite. Recent high-throughput metabolomics and whole genome sequencing study revealed amphotericin B resistance in Leishmania is indeed due to mutation in CYP51. The residue of mutation (asparagine 176) is conserved across the kinetoplastidae and not in yeast or humans, portraying its functional significance. In order to understand the possible cause for the resistance, knowledge of structural changes due to mutation is of high importance. To shed light on the structural changes of wild and mutant CYP51, we conducted comparative molecular dynamics simulation study. The active site, substrate biding cavity, substrate channel entrance (SCE), and cavity involving the mutated site were studied based on basic parameters and large concerted molecular motions derived from essential dynamics analyses of 100 ns simulation. Results indicated that mutant CYP51 is stable and less compact than the wild type. Correspondingly, the solvent accessible surface area (SASA) of the mutant was found to be increased, especially in active site and cavities not involving the mutation site. Free-energy landscape analysis disclosed mutant to have a rich conformational diversity than wild type, with various free-energy conformations of mutant having SASA greater than wild type with SCE open. More residues were found to interact with the mutant CYP51 upon docking of substrate to both the wild and mutant CYP51. These results indicate that, relative to wild type, the N176I mutation of CYP51 in Leishmania mexicana could possibly favor increased substrate binding efficiency.

Effects of Peutz-Jeghers syndrome (PJS) causing missense mutations L67P, L182P, G242V and R297S on the structural dynamics of LKB1 (Liver kinase B1) protein

The liver kinase B1 (LKB1) is encoded by LKB1 gene. Several pathogenic mutations of LKB1 causing Peutz-Jeghers syndrome and also cancers in breast, gastric, pancreas, and colon have been reported. The present study is focused to analyze the effects on the structural dynamics of LKB1 caused by the 4 pathogenic missense mutations (L67P, L182P, G242V, and R297S), which are reported to reduce the catalytic activity. In this study, the structural changes of LKB1 in apo- and in heterotrimeric complex (LKB1-STRADα-MO25α) form with wild and mutated LKB1 are investigated using all atomistic molecular dynamic simulation. The present study reveals that these four mutations initiate local structural distortions and the solvent accessibility of the surrounding regions of ATP-binding pocket such as glycine-rich loop, αB and αC loop, activation and catalytic loops. The mutations of L67P, L182P, and G242 V induce distortions of the secondary structure of β1-β3 sheets, π - π interaction (observed between Phe204 of LKB1 and Phe243 of MO25α), and increase the helical properties (both helical twist and length) of the adjacent αH-helix, respectively. The active kinase features like the conformation of catalytic and activation loops, salt bridge and, finally, the formation of stable R- and C-hydrophobic spines are also found to be perturbed by these mutations. Hence, the observed mutation-induced structural distortions fail to coordinate the essential binding nature of LKB1 with STRADα and MO25α, which eventually affects the native function of LKB1. These observations are in line with the experimentally reported reduced kinase activity of LKB1.

Global update on the susceptibility of human influenza viruses to neuraminidase inhibitors, 2015-2016

Four World Health Organization (WHO) Collaborating Centres for Reference and Research on Influenza and one WHO Collaborating Centre for the Surveillance, Epidemiology and Control of Influenza (WHO CCs) assessed antiviral susceptibility of 14,330 influenza A and B viruses collected by WHO-recognized National Influenza Centres (NICs) between May 2015 and May 2016. Neuraminidase (NA) inhibition assay was used to determine 50% inhibitory concentration (IC₅₀) data for NA inhibitors (NAIs) oseltamivir, zanamivir, peramivir and laninamivir. Furthermore, NA sequences from 13,484 influenza viruses were retrieved from public sequence databases and screened for amino acid substitutions (AAS) associated with reduced inhibition (RI) or highly reduced inhibition (HRI) by NAIs. Of the viruses tested by WHO CCs 93% were from three WHO regions: Western Pacific, the Americas and Europe. Approximately 0.8% (n = 113) exhibited either RI or HRI by at least one of four NAIs.

As in previous seasons, the most common NA AAS was H275Y in A(H1N1)pdm09 viruses, which confers HRI by oseltamivir and peramivir. Two A(H1N1)pdm09 viruses carried a rare NA AAS, S247R, shown in this study to confer RI/HRI by the four NAIs. The overall frequency of A(H1N1)pdm09 viruses containing NA AAS associated with RI/HRI was approximately 1.8% (125/6915), which is slightly higher than in the previous 2014-15 season (0.5%). Three B/Victoria-lineage viruses contained a new AAS, NA H134N, which conferred HRI by zanamivir and laninamivir, and borderline HRI by peramivir. A single B/Victoria-lineage virus harboured NA G104E, which was associated with HRI by all four NAIs. The overall frequency of RI/HRI phenotype among type B viruses was approximately 0.6% (43/7677), which is lower than that in the previous season.

Overall, the vast majority (>99%) of the viruses tested by WHO CCs were susceptible to all four NAIs, showing normal inhibition (NI). Hence, NAIs remain the recommended antivirals for treatment of influenza virus infections. Nevertheless, our data indicate that it is prudent to continue drug susceptibility monitoring using both NAI assay and sequence analysis.

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A total of 14,330 influenza viruses were collected worldwide, May 2015–May 2016.

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Approximately 0.8% showed reduced inhibition by at least one NA inhibitor.

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The frequency of viruses with reduced inhibition was slightly higher than in 2014–15 (0.5%).

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NA inhibitors remain an appropriate choice for influenza treatment.

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Global surveillance of influenza antiviral susceptibility should be continued.