Analogue inhibitors by modifying oseltamivir based on the crystal neuraminidase structure for treating drug-resistant H5N1 virus

Antiviral potential of natural products from marine microbes

Humans have been suffered from viral infections over the centuries, such as influenza, HSV, and HIV, which have killed millions of people worldwide. However, the availability of effective treatments for infectious diseases remains limited until now, as most of the viral pathogens resisted to many medical treatments. Marine microbes are currently one of the most copious sources of pharmacologically active natural products, which have constantly provided promising antivirus agents. To date, a large number of marine microbial secondary metabolites with antiviral activities have been widely reported. In this review, we have summarized the potential antivirus compounds from marine microorganisms over the last decade. In addition, the structures, bioactivities, and origins of these compounds were discussed as well.

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.

The hydrophobic side chain of oseltamivir influences type A subtype selectivity of neuraminidase inhibitors

Neuraminidase, which plays a critical role in the influenza virus life cycle, is a target for new therapeutic agents. The study of structure-activity relationships revealed that the C-5 position amino group of oseltamivir was pointed to 150-cavity of the neuraminidase in group 1. This cavity is important for selectivity of inhibitors against N1 versus N2 NA. A serial of influenza neuraminidase inhibitors with the oseltamivir scaffold containing lipophilic side chains at the C-5 position have been synthesized and evaluated for their influenza neuraminidase inhibitory activity and selectivity. The results indicated that compound 13o (H5N1 IC₅₀  = 0.1 ± 0.04 μm, H3N2 IC₅₀  = 0.26 ± 0.18 μm) showed better inhibitory activity and selectivity against the group 1 neuraminidase. This study may provide a clue to design of better group 1 neuraminidase inhibitors.

Exploring Strong Interactions in Proteins with Quantum Chemistry and Examples of Their Applications in Drug Design

OBJECTIVES

Three strong interactions between amino acid side chains (salt bridge, cation-π, and amide bridge) are studied that are stronger than (or comparable to) the common hydrogen bond interactions, and play important roles in protein-protein interactions.

METHODS

Quantum chemical methods MP2 and CCSD(T) are used in calculations of interaction energies and structural optimizations.

RESULTS

The energies of three types of amino acid side chain interactions in gaseous phase and in aqueous solutions are calculated using high level quantum chemical methods and basis sets. Typical examples of amino acid salt bridge, cation-π, and amide bridge interactions are analyzed, including the inhibitor design targeting neuraminidase (NA) enzyme of influenza A virus, and the ligand binding interactions in the HCV p7 ion channel. The inhibition mechanism of the M2 proton channel in the influenza A virus is analyzed based on strong amino acid interactions.

CONCLUSION

(1) The salt bridge interactions between acidic amino acids (Glu- and Asp-) and alkaline amino acids (Arg+, Lys+ and His+) are the strongest residue-residue interactions. However, this type of interaction may be weakened by solvation effects and broken by lower pH conditions. (2) The cation- interactions between protonated amino acids (Arg+, Lys+ and His+) and aromatic amino acids (Phe, Tyr, Trp and His) are 2.5 to 5-fold stronger than common hydrogen bond interactions and are less affected by the solvation environment. (3) The amide bridge interactions between the two amide-containing amino acids (Asn and Gln) are three times stronger than hydrogen bond interactions, which are less influenced by the pH of the solution. (4) Ten of the twenty natural amino acids are involved in salt bridge, or cation-, or amide bridge interactions that often play important roles in protein-protein, protein-peptide, protein-ligand, and protein-DNA interactions.

Computational assay of Zanamivir binding affinity with original and mutant influenza neuraminidase 9 using molecular docking

Based upon molecular docking, this study aimed to find notable in silico neuraminidase 9 (NA9) point mutations of the avian influenza A H7N9 virus that possess a Zanamivir resistant property and to determine the lead compound capable of inhibiting these NA9 mutations. Seven amino acids (key residues) at the binding site of neuraminidase 9 responsible for Zanamivir-NA9 direct interactions were identified and 72 commonly occurring mutant NA9 versions were created using the Sybyl-X 2.0 software. The docking scores obtained after Zanamivir was bound to all mutant molecules of NA9 revealed 3 notable mutations R292W, R118P, and R292K that could greatly reduce the binding affinity of the medicine. These 3 mutant NA9 versions were then bound to each of 154 different molecules chosen from 5 groups of compounds to determine which molecule(s) might be capable of inhibiting mutant neuraminidase 9, leading to the discovery of the lead compound of potent mutant NA9 inhibitors. This compound, together with other mutations occurring to NA9 identified in the study, would be used as data for further research regarding neuraminidase inhibitors and synthesizing new viable medications used in the fight against the virus.

In silico modification of oseltamivir as neuraminidase inhibitor of influenza A virus subtype H1N1

This research focused on the modification of the functional groups of oseltamivir as neuraminidase inhibitor against influenza A virus subtype H1N1. Interactions of three of the best ligands were evaluated in the hydrated state using molecular dynamics simulation at two different temperatures. The docking result showed that AD3BF2D ligand (N-[(1S,6R)-5-amino-5-{[(2R,3S,4S)-3,4-dihydroxy-4-(hydroxymethyl) tetrahydrofuran-2-yl]oxy}-4-formylcyclohex-3-en-1-yl]acetamide-3-(1-ethylpropoxy)-1-cyclohexene-1-carboxylate) had better binding energy values than standard oseltamivir. AD3BF2D had several interactions, including hydrogen bonds, with the residues in the catalytic site of neuraminidase as identified by molecular dynamics simulation. The results showed that AD3BF2D ligand can be used as a good candidate for neuraminidase inhibitor to cope with influenza A virus subtype H1N1.

A novel influenza virus neuraminidase inhibitor AV5027

A medium-sized focused library of novel Oseltamivir structural analogues with promising antiviral activity was successfully synthesized using a combinatorial approach. The synthesized compounds were then thoroughly evaluated in neuraminidase- and cell-based assays. As a result, (3R,4R,5S)-4-(2,2-difluoroacetylamino)-5-amino-3-(1-ethyl-propoxy)-cyclohex-1-enecarboxylic acid (AV5027) was identified as novel Hit-compound with picomolar potency. QSAR analysis was carried out based on the obtained biological data. Computational modeling was performed using a 3D-molecular docking approach and classical regression analysis. The developed integral model demonstrated a sufficient prediction accuracy and tolerance to evaluate compounds based on their potential activity against neuraminidase (NA) at least within the scaffold. Several compounds from the series can be reasonably regarded as promising anti-influenza drug-candidates.

An overview of the highly pathogenic H5N1 influenza virus

Since the first human case of H5N1 avian influenza virus infection was reported in 1997, this highly pathogenic virus has infected hundreds of people around the world and resulted in many deaths. The ability of H5N1 to cross species boundaries, and the presence of polymorphisms that enhance virulence, present challenges to developing clear strategies to prevent the pandemic spread of this highly pathogenic avian influenza (HPAI) virus. This review summarizes the current understanding of, and recent research on, the avian influenza H5N1 virus, including transmission, virulence, pathogenesis, clinical characteristics, treatment and prevention.

Structural position correlation analysis (SPCA) for protein family

Background

The proteins in a family, which perform the similar biological functions, may have very different amino acid composition, but they must share the similar 3D structures, and keep a stable central region. In the conservative structure region similar biological functions are performed by two or three catalytic residues with the collaboration of several functional residues at key positions. Communication signals are conducted in a position network, adjusting the biological functions in the protein family.

Methodology

A computational approach, namely structural position correlation analysis (SPCA), is developed to analyze the correlation relationship between structural segments (or positions). The basic hypothesis of SPCA is that in a protein family the structural conservation is more important than the sequence conservation, and the local structural changes may contain information of biology functional evolution. A standard protein P⁽⁰⁾ is defined in a protein family, which consists of the most-frequent amino acids and takes the average structure of the protein family. The foundational variables of SPCA is the structural position displacements between the standard protein P⁽⁰⁾ and individual proteins P_i of the family. The structural positions are organized as segments, which are the stable units in structural displacements of the protein family. The biological function differences of protein members are determined by the position structural displacements of individual protein P_i to the standard protein P⁽⁰⁾. Correlation analysis is used to analyze the communication network among segments.

Conclusions

The structural position correlation analysis (SPCA) is able to find the correlation relationship among the structural segments (or positions) in a protein family, which cannot be detected by the amino acid sequence and frequency-based methods. The functional communication network among the structural segments (or positions) in protein family, revealed by SPCA approach, well illustrate the distantly allosteric interactions, and contains valuable information for protein engineering study.

Molecular dynamics directed CoMFA studies on carbocyclic neuraminidase inhibitors

Zanamivir is the known potent anti-influenza agent targeting the key enzyme neuraminidase that cleaves sialic acid from cell receptors allowing release of newly formed virions. Molecular dynamics simulation was carried out to determine the dynamic behavior of Zanamivir upon its binding to flexible loops of neuraminidase and to analyse its interactions in the bioactive state. Neuraminidase exhibits wide range of affinity with structurally similar compounds. CoMFA study was used to determine quantitative structure-activity relationship for 36 carbocyclic Neuraminidase inhibitors (NIs). The CoMFA model was also successfully built using cross-validated r²cv = 0.580 and r²pred = 0.638.

Study of Tamiflu sensitivity to variants of A/H5N1 virus using different force fields

An accurate estimation of binding free energy of a ligand to receptor ΔG(bind) is one of the most important problems in drug design. The success of solution of this problem is expected to depend on force fields used for modeling a ligand-receptor complex. In this paper, we consider the impact of four main force fields, AMBER99SB, CHARMM27, GROMOS96 43a1, and OPLS-AA/L, on the binding affinity of Oseltamivir carboxylate to the wild-type and Y252H, N294S, and H274Y mutants of glycoprotein neuraminidase from the pandemic A/H5N1 virus. Having used the molecular mechanic-Poisson-Boltzmann surface area method, we have shown that ΔG(bind), obtained by AMBER99SB, OPLS-AA/L, and CHARMM27, shows the high correlation with the available experimental data. They correctly capture the binding ranking Y252H → WT → N294S → H274Y observed in experiments (Collins, P. J. et al. Nature 2008, 453, 1258). In terms of absolute values of binding scores, results obtained by AMBER99SB are in the nearest range with experiments, while OPLS-AA/L, which is applied to study binding of Oseltamivir to the influenza virus for the first time, gives rather big negative values for ΔG(bind). GROMOS96 43a1 provides a lower correlation as it supports Oseltamivir to be more resistant to N294S than H274Y. Our study suggests that force fields have pronounced influence on theoretical estimations of binding free energy of a ligand to receptor. The effect of all-atom models on dynamics of the binding pocket as well as on the hydrogen-bond network between Oseltamivir and receptors is studied in detail. The hydrogen network, obtained by GROMOS, is weakest among four studied force fields.

Progress in structure-based drug design against influenza A virus

INTRODUCTION

The 2009-H1N1 influenza pandemic has prompted new global efforts to develop new drugs and drug design techniques to combat influenza viruses. While there have been a number of attempts to provide drugs to treat influenza, drug resistance has been a major problem with only four drugs currently approved by the FDA for its treatment.

AREAS COVERED

In this review, the drug-resistant problem of influenza A viruses is discussed and summarized. The article also introduces the experimental and computational structures of drug targeting proteins, neuraminidases, and of the M2 proton channel. Furthermore, the article illustrates the latest drug candidates and techniques of computer-aided drug design with examples of their application, including virtual in silico screening and scoring, AutoDock and evolutionary technique AutoGrow.

EXPERT OPINION

Structure-based drug design is the inventive process for finding new drugs based on the structural knowledge of the biological target. Computer-aided drug design strategies and techniques will make drug discovery more effective and economical. It is anticipated that the recent advances in structure-based drug design techniques will greatly help scientists to develop more powerful and specific drugs to fight the next generation of influenza viruses.

Design, synthesis, and biological activity of thiazole derivatives as novel influenza neuraminidase inhibitors

A series of novel influenza neuraminidase (NA) inhibitors based on thiazole core were synthesized and evaluated for their ability to inhibit NA of influenza A virus (H(3)N(2)). All compounds were synthesized in good yields starting from commercially available 2-amino-4-thiazole-acetic ester using a suitable synthetic strategy. These compounds showed moderate inhibitory activity against influenza A NA. The most potent compound of this series is compound 4d (IC(50) = 3.43 μM), which is about 20-fold less potent than oseltamivir, and could be used to design novel influenza NA inhibitors that exhibit increased activity based on thiazole ring.

Evaluation of a Set of C9 N-acyl Neu5Ac2en Mimetics as Viral Sialidase Selective Inhibitors

Identification of selective influenza viral sialidase inhibitors is highly desirable in order to minimize or avoid the adverse effects due to the possible inhibition of endogenous human sialidases. We recently reported the evaluation of C9 N-acyl Neu5Ac2en mimetics as probes for human sialidases. Herein, we describe the in vitro activity of the same set of C9 N-acyl Neu5Ac2en mimetics against sialidases expressed by influenza virus A/PR/8/34 (H1N1), A/Memphis/1/72 (H3N2), and A/Duck/313/78 (H5N3) strains. Compound 8 is identified as a promising starting point for the development of viral sialidase selective inhibitors. Multiple sequence alignment and molecular docking techniques are also performed to explore the plausible interaction of compound 8 with viral sialidases.

Evidence obtained from ANOVA to reason cross-species infection and cross-subtype mutation in neuraminidases of influenza A viruses

The current pandemic of A/H1N1 influenza raises a serious question on cross-species infection and cross-subtype mutation because our previous focus on possible influenza pandemic laid on H5N1 subtype and the cross-species infection between avian and human. In this study, we analyse 3874 neuraminidases from influenza A viruses using anova to answer the question of if there is barrier between species and between subtypes. The results show that there is no cross-species barrier in some species, and the intra-species variation is larger than the inter-species variation in some species hosting the viruses, and the cross-subtype mutation is possible because there is no cross-subtype barrier in some subtypes and the intra-subtype variation is larger than the inter-subtype variation in some subtypes. These results highlight the state of barrier of influenza A virus, which can help us understand the current pandemic and manufacture more effective vaccines.

Trends in global warming and evolution of matrix protein 2 family from influenza A virus

The global warming is an important factor affecting the biological evolution, and the influenza is an important disease that threatens humans with possible epidemics or pandemics. In this study, we attempted to analyze the trends in global warming and evolution of matrix protein 2 family from influenza A virus, because this protein is a target of anti-flu drug, and its mutation would have significant effect on the resistance to anti-flu drugs. The evolution of matrix protein 2 of influenza A virus from 1959 to 2008 was defined using the unpredictable portion of amino-acid pair predictability. Then the trend in this evolution was compared with the trend in the global temperature, the temperature in north and south hemispheres, and the temperature in influenza A virus sampling site, and species carrying influenza A virus. The results showed the similar trends in global warming and in evolution of M2 proteins although we could not correlate them at this stage of study. The study suggested the potential impact of global warming on the evolution of proteins from influenza A virus.

Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design

In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.

On the structure-based design of novel inhibitors of H5N1 influenza A virus neuraminidase (NA)

The structure-based design of novel H5N1 neuraminidase inhibitors is currently a research topic of vital importance owing to both a recent pandemic threat by the worldwide spread of H5N1 avian influenza and the high resistance of H5N1 virus to the most widely used commercial drug, oseltamivir-OTV (Tamiflu). A specific criterion used in this work for determining fully acceptable conformations of potential inhibitors is a previous experimental proposal of exploiting potential benefits for drug design offered by the '150-cavity' adjacent to the NA active site. Using the crystal structure of H5N1 NA (PDB ID: 2hty) as the starting point, in a set of 54 inhibitors previously proposed by modifying the side chains of oseltamivir, 4 inhibitors were identified using two different computational strategies (ArgusLab4.0.1, FlexX-E3.0.1) both to lower the binding free energy (BFE) of oseltamivir and to have partially acceptable conformations. These 4 oseltamivr structure-based analogues were found to adopt the most promising conformations by identifying the guanidinium side chain of Arg156 as a prospective partner for making polar contacts, but none of the modified 4-amino groups of oseltamivir in the 4 favorable conformations was found to make polar contacts with the guanidinium side chain of Arg156. Hence, the structures of two additional inhibitors were designed and shown to further lower the binding free energy of OTV relative to the previous 54 inhibitors. These two novel structures clearly suggest that it may be possible for a new substituent to be developed by functional modifications at position of the 4-amino group of oseltamivir in order to make polar contacts with the guanidinium side chain of Arg156, and thereby enhance the binding of a more potent inhibitor. Several standpoints of vital importance for designing novel structures of potentially more effective H5N1 NA inhibitors are established.

Computational studies of the binding modes of A 2A adenosine receptor antagonists

A molecular docking study was performed on several structurally diverse A(2A) AR antagonists, including xanthines, and non-xanthine type antagonists to investigate their binding modes with A(2A) adenosine receptor (AR), one of the four subtypes of AR, which is currently of great interest as a target for therapeutic intervention, in particular for Parkinson's disease. The high-affinity binding site was found to be a hydrophobic pocket with the involvement of hydrogen bonding interactions as well as pi-pi stacking interactions with the ligands. The detailed binding modes for both xanthine and non-xanthine type A(2A) antagonists were compared and the essential features were extracted and converted to database searchable queries for virtual screening study of novel A(2A) AR antagonists. Findings from this study are helpful for elucidating the binding pattern of A(2A) AR antagonists and for the design of novel active ligands.

Analysis of ligand binding to proteins using molecular dynamics simulations

This work aims to explore theoretically the molecular mechanisms of ligand binding to proteins through the use of molecular dynamics simulations. The binding of sodium dodecyl sulfate (SDS) to cobra cardio toxin A3 (CTX A3) and thiourea (TOU) to lysozyme have been chosen as the two model systems. Data acquisitions were made by Gromacs software. To begin with, the collisions of ligand molecules with every residue of CTX A3 and lysozyme were evaluated. With this information in hand, the average numbers of collisions with each residue was defined and then assessed. Next, a measure of the affinity of a residue, P(i), referred to as conformational factor, toward a ligand molecule was established. Based on the results provided, all site-making residues for CTX A3 and lysozyme were identified. The results are in good agreement with the experimental data. Finally, based on this method, all site-making residues of bovine carbonic anhydrase (BCA) toward the SDS ligand were predicted.

Analysis of 3D structural differences in the IgG-binding domains based on the interresidue average-distance statistics

It is well-known that the IgG-binding domain from staphylococcal protein A folds into a 3 alpha helix bundle structure, while the IgG-binding domain of streptococcal protein G forms an (alpha + beta) structure. Recently, He et al. (Biochemistry 44:14055-14061, 2005) made mutants of these proteins from the wild types of protein A and protein G strains. These mutants are referred to as protein A219 and protein G311, and it was showed that these two mutants have different 3D structures, i.e., the 3 alpha helix bundle structure and the (alpha + beta) structure, respectively, despite the high sequence identity (59%). The purpose of our study was to clarify how such 3D structural differences are coded in the sequences with high homology. To address this problem, we introduce a predicted contact map constructed based on the interresidue average-distance statistics for prediction of folding properties of a protein. We refer to this map as an average distance map (ADM). Furthermore, the statistics of interresidue distances can be converted to an effective interresidue potential. We calculated the contact frequency of each residue of a protein in random conformations with this effective interresidue potential, and then we obtained values similar to phi values. We refer to this contact frequency of each residue as a p(mu) value. The comparison of the p(mu) values to the phi values for a protein suggests that p(mu) values reveal the information on the folding initiation site. Using these techniques, we try to extract the information on the difference in the 3D structures of protein A219 and protein G311 coded in their amino acid sequences in the present work. The results show that the ADM analyses and the p(mu) value analyses predict the information of folding initiation sites, which can be used to detect the 3D difference in both proteins.

Bridging protein local structures and protein functions

One of the major goals of molecular and evolutionary biology is to understand the functions of proteins by extracting functional information from protein sequences, structures and interactions. In this review, we summarize the repertoire of methods currently being applied and report recent progress in the field of in silico annotation of protein function based on the accumulation of vast amounts of sequence and structure data. In particular, we emphasize the newly developed structure-based methods, which are able to identify locally structural motifs and reveal their relationship with protein functions. These methods include computational tools to identify the structural motifs and reveal the strong relationship between these pre-computed local structures and protein functions. We also discuss remaining problems and possible directions for this exciting and challenging area.

Cleavage mechanism of the H5N1 hemagglutinin by trypsin and furin

The cleavage property of hemagglutinin (HA) by different proteases was the prime determinant for influenza A virus pathogenicity. In order to understand the cleavage mechanism, molecular modeling tools were utilized to study the coupled model systems of the proteases, i.e., trypsin and furin and peptides of the cleavage sites specific to H5N1 and H1 HAs, which constitute models of HA precursor in complex with cleavage proteases. The peptide segments ‘RERRRKKR ↓ G’ and ‘SIQSR ↓ G’ from the high pathogenic H5N1 H5 and the low pathogenic H1N1 H1 cleavage sites were docking to the trypsin and furin active pockets, respectively. It was observed through the docking studies that trypsin was able to recognize and cleave both the high pathogenic and low pathogenic hemagglutinin, while furin could only cleave the high pathogenic hemagglutinin. An analysis of binding energies indicated that furin got most of its selectivity due to the interactions with P₁, P₄, and P₆, while having less interaction with P₂ and little interactions with P₃, P₅, P₇, and P₈. Some mutations of H5N1 H5 cleavage sequence fitted less well into furin and would reduce high pathogenicity of the virus. These findings hint that we should focus at the subsites P₁, P₄, and P₆ for developing drugs against H5N1 viruses.

Prediction of mutations engineered by randomness in H5N1 hemagglutinins of influenza A virus

This is the continuation of our studies on the prediction of mutation engineered by randomness in proteins from influenza A virus. In our previous studies, we have demonstrated that randomness plays a role in engineering mutations because the measures of randomness in protein are different before and after mutations. Thus we built a cause-mutation relationship to count the mutation engineered by randomness, and conducted several concept-initiated studies to predict the mutations in proteins from influenza A virus, which demonstrated the possibility of prediction of mutations along this line of thought. On the other hand, these concept-initiated studies indicate the directions forwards the enhancement of predictability, of which we need to use the neural network instead of logistic regression that was used in those concept-initiated studies to enhance the predictability. In this proof-of-concept study, we attempt to apply the neural network to modeling the cause-mutation relationship to predict the possible mutation positions, and then we use the amino acid mutating probability to predict the would-be-mutated amino acids at predicted positions. The results confirm the possibility of use of internal cause-mutation relationship with neural network model to predict the mutation positions and use of amino acid mutating probability to predict the would-be-mutated amino acids.