Prediction of mutations engineered by randomness in H5N1 neuraminidases from influenza A virus

Summary. In this proof-of-concept study, we attempt to determine whether the cause-mutation relationship defined by randomness is protein dependent by predicting mutations in H5N1 neuraminidases from influenza A virus, because we have recently conducted several concept-initiated studies on the prediction of mutations in hemagglutinins from influenza A virus. In our concept-initiated studies, we defined the randomness as a cause for mutation, upon which we built a cause-mutation relationship, which is then switched into the classification problem because the occurrence and non-occurrence of mutations can be classified as unity and zero. Thereafter, we used the logistic regression and neural network to solve this classification problem to predict the mutation positions in hemagglutinins, and then used the amino acid mutating probability to predict the would-be-mutated amino acids. As the previous results were promising, we extend this approach to other proteins, such as H5N1 neuraminidase in this study, and further address various issues raised during the development of this approach. The result of this study confirms that we can use this cause-mutation relationship to predict the mutations in H5N1 neuraminidases. Authors’ address: Guang Wu, Computational Mutation Project, DreamSciTech Consulting 301, Building 12, Nanyou A-zone, Jiannan Road, Shenzhen, Guangdong Province CN-518054, China     

Timing of mutation in influenza A virus hemagglutinins by means of amino-acid distribution rank and fast Fourier transform

In this study, we calculated the amino-acid distribution rank of 1201 hemagglutinins from influenza A viruses dated from 1918 to 2004 in order to compare them with respect to subtypes, species and years. After noticing fluctuations in distribution rank along the time course, we used the fast Fourier transform to determine the mutation periodicity of the hemagglutinins. Then we estimated our position at the current cycle of hemagglutinin evolutionary process to determine how many years remain before the next possible outbreak of influenza and bird flu. Finally, we used the trend channel to outlook the future of hemagglutinins for the next half a century. As our study covers almost all the full-length amino-acid sequences of hemagglutinins from various influenza A viruses, the conclusions will be valid for years until the number of hemagglutinins in Protein Databank is significantly increased.     

Prediction of mutations in H5N1 hemagglutinins from influenza A virus

In this study, we determine the mutation relation among 333 H5N1 hemagglutinins of influenza A viruses according to their amino acid and RNA codon sequences. Then, we calculate seven probabilistic numbers, which have been developed by us since 1999, for each amino acid in these hemagglutinins. With the seven numeric numbers as independents and the probability of occurrence of mutation at each hemagglutinin position as dependent, we use the logistic regression to model 967 missense point mutations from 333 hemagglutinins to get the population estimates. Thereafter, we predict the future mutation positions in H5N1 hemagglutinin. Finally, we use the translation probabilities between RNA codons and mutated amino acids to predict the would-be-mutated amino acids in H5N1 hemagglutinin.     

Prediction of Mutations Initiated by Internal Power in H3N2 Hemagglutinins of Influenza A Virus from North America

Probably the best way to predict mutations is to find the cause for mutations, by which the cause–mutation relationship can be built. However, many causes which have resulted in mutations in the past might not leave any trace due to the changes in environments. As well, the current proteins may not be sensitive to the causes, which led to mutations in the past, because of evolution. Thus we might have recorded many mutations, but few of their corresponding causes, and it would be difficult to establish the one-to-one cause–mutation relationship. However, the internal power engineering mutations within a protein would exist, of which randomness should play an important role. Since 1999, we have developed three methods to quantify the randomness within a protein by which we can build a cause–mutation relationship because we can classify the occurrence and non-occurrence of mutation as unity and zero, and transfer this relationship into the classification problem, which can be solved using logistic regression. Recently, we used the logistic regression to predict the mutation positions in H5N1 hemagglutinins from influenza A virus, and applied the amino-acid mutating probability to predict the would-be-mutated amino acids at predicted positions as the concept-initiated study. However, we still need to conduct many proof-of-concept studies to test whether this cause–mutation relationship is independent of protein subtypes, whether the logistic regression is powerful enough, etc. In this study, we attempted to use the logistic regression to predict the mutation positions in H3N2 hemagglutinins of influenza A virus from North America to answer the questions in the proof-of-concept stage.     

Timing of mutation in hemagglutinins from influenza A virus by means of unpredictable portion of amino-acid pair and fast Fourier transform

In this study, we calculate the unpredictable portion of amino-acid pairs, which has been developed by us over the last several years, of 1201 hemagglutinins from influenza A viruses dated from 1918 to 2004 in order to compare them with respect to subtypes, species, and years. After noticing the fluctuations of unpredictable portion along the time course, we use the fast Fourier transform to find the mutation periodicity of hemagglutinins. Then we estimate our position at the current cycle of hemagglutinin evolutionary process to determine how many years remain before the next outbreak of influenza and bird flu. Finally, we use the trend line and channel to outlook the hemagglutinins for the next half a century. As our study covers almost all the full-length amino-acid sequences of hemagglutinins from various influenza A viruses, the conclusion will be valid for years until the number of hemagglutinins in protein databank will be significantly increased.     

Molecular Dynamics Simulations Suggest that Electrostatic Funnel Directs Binding of Tamiflu to Influenza N1 Neuraminidases

Oseltamivir (Tamiflu) is currently the frontline antiviral drug employed to fight the flu virus in infected individuals by inhibiting neuraminidase, a flu protein responsible for the release of newly synthesized virions. However, oseltamivir resistance has become a critical problem due to rapid mutation of the flu virus. Unfortunately, how mutations actually confer drug resistance is not well understood. In this study, we employ molecular dynamics (MD) and steered molecular dynamics (SMD) simulations, as well as graphics processing unit (GPU)-accelerated electrostatic mapping, to uncover the mechanism behind point mutation induced oseltamivir-resistance in both H5N1 “avian” and H1N1pdm “swine” flu N1-subtype neuraminidases. The simulations reveal an electrostatic binding funnel that plays a key role in directing oseltamivir into and out of its binding site on N1 neuraminidase. The binding pathway for oseltamivir suggests how mutations disrupt drug binding and how new drugs may circumvent the resistance mechanisms.     

Fate of 130 hemagglutinins from different influenza A viruses

In this study, we use our probabilistic models to analyze 130 hemagglutinins from different influenza A virus in order to gain the insight into their fate. The results provide three lines of evidence regarding the H5, H6, and H9 hemagglutinins: (i) the H5 hemagglutinins are more sensitive to mutations, this is the current state of the H5, H6, and H9 hemagglutinins; (ii) the H5 hemagglutinins had experienced more mutations in the past, this is the history of the H5, H6, and H9 hemagglutinins; and (iii) the H6 hemagglutinins has a bigger potential towards future mutations, this is the future of the H5, H6, and H9 hemagglutinins. Furthermore, this study gives two clues on the mutation tendency that is a degeneration process and the species susceptibility that is the chickens and ducks.     

Analysis of distributions of amino acids and amino acid pairs in human tumour necrosis factor precursor and its eight mutations according to a random mechanism

In this study, we use the random principle to analyse the distributions of amino acids and amino acid pairs in human tumour necrosis factor precursor (TNF-!) and its eight mutations, to compare the measured distribution probability with the theoretical distribution probability and to rank the measured distribution probability against the theoretical distribution probability. In this way, we can suggest that distributions with a high random rank should not be deliberately evolved and conserved and those with a low random rank should be deliberately evolved and conserved in human TNF-!. An increased distribution probability in a mutation means probabilistically that the mutation is more likely to occur spontaneously, whereas a decreased distribution probability in a mutation means probabilistically that the mutation is less likely to occur spontaneously and perhaps is more related to a certain cause. The results, for example, show that the distributions of 30% of the amino acids are identical with their probabilistic simplest distributions, and the distributions of some of the remaining amino acids are very close to their probabilistic simplest distributions. With respect to probabilities of distributions of amino acids in mutations, the results show that mutations lead to an increase in eight probabilities, which are thus more likely to occur. Eight probabilities decrease and are thus less likely to occur. With respect to the random ranks against the theoretical probabilities of distributions of amino acids, the results show that mutations lead to an increase in seven and a decrease in seven probabilities, with two probabilities unchanged.     

Imported parakeets harbor H9N2 influenza A viruses that are genetically closely related to those transmitted to humans in Hong Kong

In 1997 and 1998, H9N2 influenza A viruses were isolated from the respiratory organs of Indian ring-necked parakeets (Psittacula Krameri manillensis) that had been imported from Pakistan to Japan. The two isolates were closely related to each other (>99% as determined by nucleotide analysis of eight RNA segments), indicating that H9N2 viruses of the same lineage were maintained in these birds for at least 1 year. The hemagglutinins and neuraminidases of both isolates showed >97% nucleotide identity with those of H9N2 viruses isolated from humans in Hong Kong in 1999, while the six genes encoding internal proteins were >99% identical to the corresponding genes of H5N1 viruses recovered during the 1997 outbreak in Hong Kong. These results suggest that the H9N2 parakeet viruses originating in Pakistan share an immediate ancestor with the H9N2 human viruses. Thus, influenza A viruses with the potential to be transmitted directly to humans may be circulating in captive birds worldwide.     

Viral surface geometry shapes influenza and coronavirus spike evolution through antibody pressure

The evolution of circulating viruses is shaped by their need to evade antibody response, which mainly targets the viral spike. Because of the high density of spikes on the viral surface, not all antigenic sites are targeted equally by antibodies. We offer here a geometry-based approach to predict and rank the probability of surface residues of SARS spike (S protein) and influenza H1N1 spike (hemagglutinin) to acquire antibody-escaping mutations utilizing in-silico models of viral structure. We used coarse-grained MD simulations to estimate the on-rate (targeting) of an antibody model to surface residues of the spike protein. Analyzing publicly available sequences, we found that spike surface sequence diversity of the pre-pandemic seasonal influenza H1N1 and the sarbecovirus subgenus highly correlates with our model prediction of antibody targeting. In particular, we identified an antibody-targeting gradient, which matches a mutability gradient along the main axis of the spike. This identifies the role of viral surface geometry in shaping the evolution of circulating viruses. For the 2009 H1N1 and SARS-CoV-2 pandemics, a mutability gradient along the main axis of the spike was not observed. Our model further allowed us to identify key residues of the SARS-CoV-2 spike at which antibody escape mutations have now occurred. Therefore, it can inform of the likely functional role of observed mutations and predict at which residues antibody-escaping mutation might arise.     

Improvement of model for prediction of hemagglutinin mutations in H5N1 influenza viruses with distinguishing of arginine, leucine and serine

In a continuation of our attempt to predict mutations in proteins from influenza A virus, this study attempted to answer the question of whether distinguishing between arginine, leucine and serine can improve the predictability as these residues are governed by different probabilistic mechanism translating from RNA codons to amino acids. In this study, we made the prediction based on the mutation relation among 299 H5N1 hemagglutinins of influenza A virus. Then, we compared the results based on the distinguishing of arginine, leucine and serine with the results without distinguishing of arginine, leucine and serine. The results show that the prediction together with distinguishing between arginine, leucine and serine is better than prediction without distinguishing between these residues.     

Three sampling strategies to predict mutations in H5N1 hemagglutitins from influenza A virus

After several studies on prediction of mutation, we examine the effect of three sampling strategies, the sampling based on years, the sampling based on number of mutations, and the sampling based on the unpredictable portion of amino-acid pairs, on the prediction performance in H5N1 hemagglutinins. The results show that the sampling strategy does play an important role in prediction, which should be taken into account when predicting the next generation of mutations in proteins from influenza A virus.     

In silico analysis of genes nucleoprotein, neuraminidase and hemagglutinin: a comparative study on different strains of influenza A (Bird flu) virus sub-type H5N1

The avian influenza (bird flu) is an infectious disease of birds, ranging from a mild to a severe form of illness. Influenza viruses pose significant challenges to both human and animal health. The proteins, nucleoprotein (NP), neuraminidase (NA) and hemagglutinin (HA) of influenza A virus (Bird flu virus) sub-type A/Hatay/2004/(H5N1) from chicken were selected for this study. Our in silico analysis predicted that HA of influenza A virus is highly sensitive to mutations and hence it is significant for its pathogenic nature. None of the mutations was detected as an important change except in NA where K332R was at a PKC phosphorylation site. Analysis of the sequence comparison showed that the maximum number of mutations were observed in HA. These mutations are significant as they are involved in change in polarity or hydrophobicity as well as in propensity of each amino acid residue to stabilize the secondary structure. The program MAPMUTATION can be used to monitor the mutations, and predict the trend of mutations.     

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.     

北京野生水鸟迁徙规律及其监测策略初探

2005年全球暴发的禽流感疫情备受世界关注,候鸟带毒且野生水鸟是禽流感病毒的天然储库已被世界公认,我国野生动物疫源疫病监测工作已被提到重要议事日程.通过对2006～2008年监测数据的分析,发现北京市野生水鸟春季迁徙从2月下旬开始,4月初达到迁徙高峰;9月下旬开始秋季南迁,11月下旬达到迁徙高峰.并分别对北京地区雁鸭类、鹬鸻类、鹭类的迁徙规律进行了分析;根据北京的气候特点分析了野生水鸟的分布和迁徙特点;根据禽流感病毒与温度的关系,提出了北京的重点监测时期及物种.     

H7亚型禽流感病毒与禽类和人类的关系

2013年在中国首次发生了H7N9亚型流感病毒感染人事件,已经证实H7N9型禽流感是一种新型禽流感,是全球首次发现感染人类的新亚型流感病毒,以往这种病毒只在野生鸟类存在和传播。H7N9型禽流感病毒属于H7亚型中的一种,全球感染人的H7亚型病毒主要分为两大支系,即北美支系和欧亚支系,感染人的流感亚型也主要集中在H7N7,H7N3,H7N2等亚型上。为了清晰的了解H7亚型病毒的来龙去脉,本文重点讨论了A亚型流感病毒的宿主分布、H7亚型病毒感染禽类和人类的历史、H7亚型病毒的生物学特性以及未来研究展望。     

Breaking the HxNy outbreak

The latest emergence of influenza A (H1N1) virus outbreak demonstrated how swiftly a new strain of flu can evolve and spread around the globe. The A/H1N1 flu has been spreading at unprecedented speed, and further spread within the countries being affected and to other adjacent or far way countries is considered inevitable due to the rapid emigration of infected individuals across the world. In this bioinformation, we discuss the mechanism of evolution of a new HxNy strain and the essential criteria for potentially breaking the outbreak of these extremely harmful and rapidly evolving viral strains in the near future by taking the recent H1N1 pandemic as a classical paradigm.     

In silico analysis of drug-resistant mutant of neuraminidase (N294S) against oseltamivir

The recent H1N1 influenza pandemic has attracted worldwide attention due to the high infection rate. Oseltamivir is a new class of anti-viral agent approved for the treatment and prevention of influenza infections. The principal target for this drug is a virus surface glycoprotein, neuraminidase (NA), which facilitates the release of nascent virus and thus spreads infection. Until recently, only a low prevalence of neuraminidase inhibitor (NAI) resistance (<1 %) had been detected in circulating viruses. However, there have been reports of significant numbers of A (H1N1) influenza strains with a N294S neuraminidase mutation that was highly resistant to the NAI, oseltamivir. Hence, in the present study, we highlight the effect of point mutation-induced oseltamivir resistance in H1N1 subtype neuraminidases by molecular simulation approach. The docking analysis reveals that mutation (N294S) significantly affects the binding affinity of oseltamivir with mutant type NA. This is mainly due to the decrease in the flexibility of binding site residues and the difference in prevalence of hydrogen bonds in the wild and mutant structures. This study throws light on the possible effects of drug-resistant mutations on the large functionally important collective motions in biological systems.     

Structure function studies on different structural domains of nucleoprotein of H1N1 subtype

Recent 2009 flu pandemic is a global outbreak of a new strain of influenza A virus subtype H1N1. The H1N1 virus has crossed species barrier to human and apparently acquired the capability to transmit this disease from human to human. The NP is a multifunctional protein that not only encapsidates viral RNA (vRNA), but also forms homo-oligomer and thereby maintains RNP structure. It is also thought to be the key adaptor for virus and host cell interaction. Thus, it is one of the factor that play a key role in the pathogenesis of influenza A virus infection. Therefore, to understand the cause of pathogenicity of H1N1 virus, we have studied the structure-function relationship of different domains of NP. Our results showed that conservative mutation in NP of various strains were pathogenic in nature. However, non-conservative mutation slightly abrogated oligomerization and was therefore less pathogenic. Our results also suggest that beside tail and body domain, head domain may also participate in an oligomerization process.