Access to the most up-to-date information on medical countermeasures is important for the research and development of effective treatments for viruses and marine toxins. However, there is a lack of comprehensive databases that curate data on viruses and marine toxins, making decisions on medical countermeasures slow and difficult. In this work, we employ two large language models (LLMs) of ChatGPT and Grok to design two comprehensive databases of therapeutic countermeasures for five viruses of Lassa, Marburg, Ebola, Nipah, and Venezuelan equine encephalitis, as well as marine toxins. With high-level human-provided inputs, the two LLMs identify public databases containing data on the five viruses and marine toxins, collect relevant information from these databases and the literature, iteratively cross-validate the collected information, and design interactive webpages for easy access to the curated, comprehensive databases. Notably, the ChatGPT LLM is employed to design agentic AI workflows (consisting of two AI agents for research and decision-making) to rank countermeasures for viruses and marine toxins in the databases. Together, our work explores the potential of LLMs as a scala
Metatranscriptomic sequencing has expanded our knowledge of the RNA virosphere far more rapidly than novel viruses can be taxonomically classified. Taxonomic assignment above the family level is particularly difficult because the RNA-dependent RNA polymerase (RdRp) is often the only gene retained across RNA viruses yet exhibits little sequence similarity among highly divergent viruses. Here we show that RdRp protein structure retains taxonomic signal at evolutionary depths where RdRp primary sequence similarity has largely collapsed, and that the organization of this signal is consistent with the current ICTV hierarchy. Based on this, we developed ViraClass, a hierarchical framework for RNA virus taxonomic placement that uses RdRp structure for rank-by-rank assignment from phylum to genus, stopping at the deepest rank supported by confidence thresholds, and calibrated structural clustering for viruses that remain outside existing reference space. Across random-split, prospective and taxonomic hold-out benchmarks, ViraClass outperforms sequence-based and genome-content baselines. The largest gains emerge at deep evolutionary distances, in benchmarks that withhold entire families, or
The paper continues the study of the phenomenon of local immunodeficiency (LI) in viral cross-immunoreactivity networks, with a focus on the roles and interactions between altruistic and persistent viral variants. As always, only the state of stable (i.e. observable) LI is analysed. First, we show that a single altruistic viral variant has an upper limit for the number of persistent viral variants that it can support. Our findings reveal that in viral cross-immunoreactivity networks, altruistic viruses act essentially autonomously from each other. Namely, connections between altruistic viruses do not change neither their qualitative roles, nor the quantitative values of the strengths of their connections in the CRNs. In other words, each altruistic virus does exactly the same actions and with the same strengths with or without presence of other altruistic viruses. However, having more altruistic viruses allows to keep sizes of populations of persistent viruses at the higher levels. Likewise, the strength of the immune response against any altruistic virus remains at the same constant level regardless of how many persistent viruses this altruistic virus supports, i.e. shields from t
Antigenic escape constitutes the main mechanism allowing rapidly evolving viruses to achieve endemicity. Beyond granting immune escape, empirical evidence also suggests that mutations of viruses might increase their inter-host infectiousness. While both mechanisms are well-studied individually, their combined effects on viral endemicity remain to be explored. Here we propose a minimal eco-evolutionary framework to simulate epidemic outbreaks generated by pathogens evolving both their infectiousness and immune escape. Our results reveal that the main driver of viral evolution shifts over time: from intrinsic selection for infectiousness at early stages of the outbreak to antigenic diversification in the transition to the endemic phase. We find that the evolution in both traits during the first epidemic wave plays a critical role in determining long-term viral persistence. Evolution in infectiousness enhances the endemicity of viruses, especially in viruses with lower baseline infectiousness due to the longer duration of their first epidemic wave. Likewise, control policies flattening epidemic curves might increase viral endemicity as a result of the greater antigenic diversity gener
This paper analyzes the role of neutral viruses in the phenomenon of local immunodeficiency. We show that, even in the absence of altruistic viruses, neutral viruses can support the existence of persistent viruses, and thus local immunodeficiency. However, in all such cases neutral viruses can maintain only bounded (relatively small) concentration of persistent viruses. Moreover, in all such cases the state of local immunodeficiency could only be marginally stable, while it is known that altruistic viruses can maintain stable local immunodeficiency. We also present an absolutely minimal cross-immunoreactivity network where a stable and robust state of local immunodeficiency can be maintained. It is now a challenge to synthetic biology to build such small networks with stable local immunodeficiency. Another important challenge for biology is to understand which types of viruses can play a role of persistent, altrustic and neutral ones, and whether a role which a given virus plays depends on the structure (topology) of a given cross-immunoreactivity network.
We propose an approach based on a combination of physical, chemical, and mathematical methods to identify and characterize virulent influenza A viruses (IAVs) through the analysis of the hemagglutinin protein. These methods include the isoelectric point, extreme value theory, and tree-like classification. The characterization process involves molecular and biological aspects. This procedure was applied to an IAV sample that included strains related to known influenza pandemics. The results provided clear position and amino acid pairs that identify these virulent viruses. These results show that our approach is promising to contribute new methodologies to identify and characterize virulent IAVs.
Antigenic variation is the main immune escape mechanism for RNA viruses like influenza or SARS-CoV-2. While high mutation rates promote antigenic escape, they also induce large mutational loads and reduced fitness. It remains unclear how this cost-benefit trade-off selects the mutation rate of viruses. Using a traveling wave model for the co-evolution of viruses and host immune systems in a finite population, we investigate how immunity affects the evolution of the mutation rate and other non-antigenic traits, such as virulence. We first show that the nature of the wave depends on how cross-reactive immune systems are, reconciling previous approaches. The immune-virus system behaves like a Fisher wave at low cross-reactivities, and like a fitness wave at high cross-reactivities. These regimes predict different outcomes for the evolution of non-antigenic traits. At low cross-reactivities, the evolutionarily stable strategy is to maximize the speed of the wave, implying a higher mutation rate and increased virulence. At large cross-reactivities, where our estimates place H3N2 influenza, the stable strategy is to increase the basic reproductive number, keeping the mutation rate to a m
In 2022, a group of basic and clinical virologists, bioinformaticians, and evolutionary and structural biologists met in Oxford, UK, to develop a consensus on methodologies used to classify viruses. They concluded that virus taxonomy, which is hierarchical and based on evolution, is only one of many possible ways to classify viruses. This taxonomy, while satisfying the four principles they set out, faces difficulties in coordinating with other classification systems useful to clinicians, infectious disease specialists, agronomists, etc. One example discussed is the grouping of different viral strains that cause different diseases into the species Enterovirus C. Here we show that the use of a previously proposed variant of a natural virus classification system based on the use of Neural Replicator Analysis can resolve this contradiction by establishing the fine structure of the Enterovirus C species, in which strains that cause different diseases are placed in several different cells of the binomial table of viruses. A key element in enabling this is the sophisticated preprocessing of the original viral genomes using neural replicators.
The rapid worldwide spread of severe viral infections, often involving novel modifications of viruses, poses major challenges to our health care systems. This means that tools that can efficiently and specifically diagnose viruses are much needed. To be relevant for a broad application in local health care centers, such tools should be relatively cheap and easy to use. Here we discuss the biophysical potential for the macroscopic detection of viruses based on the induction of a mechanical stress in a bundle of pre-stretched DNA molecules upon binding of viruses to the DNA. We show that the affinity of the DNA to the charged virus surface induces a local melting of the double-helix into two single-stranded DNA. This process effects a mechanical stress along the DNA chains leading to an overall contraction of the DNA. Our results suggest that when such DNA bundles are incorporated in a supporting matrix such as a responsive hydrogel, the presence of viruses may indeed lead to a significant, macroscopic mechanical deformation of the matrix. We discuss the biophysical basis for this effect and characterize the physical properties of the associated DNA melting transition. In particular,
Identifying new viral threats, and developing long term defences against current and future computer viruses, requires an understanding of their behaviour, structure and capabilities. This paper aims to advance this understanding by further developing the abstract theory of computer viruses. A method of providing abstract definitions for classes of viruses is presented in this paper, which addresses inadequacies of previous techniques. Formal definitions for some classes of viruses are then provided, which correspond to existing informal definitions. To relate the abstract theory to the real world, the connection between the abstract definitions and concrete virus implementations is examined. The use of the proposed method in studying the fundamental properties of computer viruses is discussed.
We study the population dynamics of lytic viruses which replicate slowly in dividing host cells within an organism or cell culture, and find a range of viral replication rates that allows viruses to persist, avoiding extinction of host cells or dilution of viruses at too rapid or too slow viral replication. For the within-host competition between multiple viral strains, a strain with a "stable" replication rate could outcompete another strain with a higher or lower replication rate, therefore natural selection of viruses stabilizes the viral persistence. However, when strains with higher and lower than the "stable" value replication rates are both present, competition between strains does not result in dominance of one strain, but in their coexistence.
Computer viruses exhibit many similarities with biological viruses. Thus, a closer examination of this association might lead to some new perspectives and, even, to new enhanced capabilities that will facilitate the overall effort to tackle and, why not, eradicate them. Game theory has long been considered as a useful tool for modeling viral behavior. In this paper, we establish certain, important we hope, correlations between a well-known virus, namely VirLock, with the bacteriophage $\phi6$. Moreover, following this line of thought, we also suggest efficient and, at the same time, practical strategies that may significantly alleviate the infection problems caused by VirLock and any other virus having similar traits.
The present paper presents the carrier-acoustic phonon scattering in the spherical and TMV viruses. We demonstrate theoretically that the absorption rate changes in spherical and TMV viruses according to the phonon energy while emission of phonon is limited by the hole energy. The obtained relaxation rate is then used to calculate the conductivity and mobility of viruses. The obtained conductivity for spherical and TMV viruses suggest that the TMV virus is more conducting and therefore may be a good candidate for the connector or wire to be used in the nanoelectronics. The value of resistance obtained for TMV virus is lower than the earlier reported resistance of DNA.
The fast growing market for smart phones coupled with their almost continuous online presence makes these devices the new targets of virus writers. It has been recently found that the topological spread of MMS (Multimedia Message Services) viruses is highly restricted by the underlying fragmentation of the call graph. In this paper, we study MMS viruses under another type of spreading behavior: scanning. We find that hybrid MMS viruses including some level of scanning are more dangerous to the mobile community than their standard topological counterparts. However, the effectiveness of both scanning and topological behaviors in MMS viruses can generally be limited by two controlling methods: (i) decreasing susceptible handsets' market share (OS it runs) and (ii) improving monitoring capacity to limit the frequency in which MMS messages can be sent by the mobile viruses.
A major part of the interactions involved in the assembly and stability of icosahedral, positive-sense single-stranded RNA (ssRNA+) viruses is electrostatic in nature, as can be inferred from the strong $pH$- and salt-dependence of their assembly phase diagrams. Electrostatic interactions do not act only between the capsid coat proteins (CPs), but just as often provide a significant contribution to the interactions of the CPs with the genomic RNA, mediated to a large extent by positively charged, flexible N-terminal tails of the CPs. In this work, we provide two clear and complementary definitions of an N-terminal tail of a protein, and use them to extract the tail sequences of a large number of CPs of ssRNA+ viruses. We examine the $pH$-dependent interplay of charge on both tails and CPs alike, and show that -- in contrast to the charge on the CPs -- the net positive charge on the N-tails persists even to very basic $pH$ values. In addition, we note a limit to the length of the wild-type genomes of those viruses which utilize positively charged tails, when compared to viruses without charged tails and similar capsid size. At the same time, we observe no clear connection between th
The canonical view of the interactions between viruses and their microbial hosts presumes that changes in host and virus fate require the initiation of infection of a host by a virus. That is, first virus particles diffuse randomly outside of host cells, then the virus genome enters the target host cell, and only then do intracellular dynamics and regulation of virus and host cell fate unfold. Intracellular dynamics may lead to the death of the host cell and release of viruses, to the elimination of the virus genome through cellular defense mechanisms, or the integration of the virus genome with the host as a chromosomal or extra-chromosomal element. Here we revisit this canonical view, inspired by recent experimental findings of Bautista and colleagues (mBio, 2015) in which the majority of target host cells can be induced into a dormant state when exposed to either active or de-activated viruses, even when viruses are present at low relative titer. We propose that both the qualitative phenomena and the quantitative time-scales of dormancy induction can be reconciled given the hypothesis that cellular physiology can be altered by contact on the surface of host cells rather than str
Giant viruses contain large genomes, encode many proteins atypical for viruses, replicate in large viral factories, and tend to infect protists. The giant virus replication factories can in turn be infected by so called virophages, which are smaller viruses that negatively impact giant virus replication. An example are Mimiviruses that infect the protist Acanthamoeba and that are themselves infected by the virophage Sputnik. This paper examines the evolutionary dynamics of this system, using mathematical models. While the models suggest that the virophage population will evolve to increasing degrees of giant virus inhibition, it further suggests that this renders the virophage population prone to extinction due to dynamic instabilities over wide parameter ranges. Implications and conditions required to avoid extinction are discussed. Another interesting result is that virophage presence can fundamentally alter the evolutionary course of the giant virus. While the giant virus is predicted to evolve towards increasing its basic reproductive ratio in the absence of the virophage, the opposite is true its presence. Therefore, virophages can not only benefit the host population directly
Background: Prokaryotic viruses, which infect bacteria and archaea, are the most abundant and diverse biological entities in the biosphere. To understand their regulatory roles in various ecosystems and to harness the potential of bacteriophages for use in therapy, more knowledge of viral-host relationships is required. High-throughput sequencing and its application to the microbiome have offered new opportunities for computational approaches for predicting which hosts particular viruses can infect. However, there are two main challenges for computational host prediction. First, the empirically known virus-host relationships are very limited. Second, although sequence similarity between viruses and their prokaryote hosts have been used as a major feature for host prediction, the alignment is either missing or ambiguous in many cases. Thus, there is still a need to improve the accuracy of host prediction. Results: In this work, we present a semi-supervised learning model, named HostG, to conduct host prediction for novel viruses. We construct a knowledge graph by utilizing both virus-virus protein similarity and virus-host DNA sequence similarity. Then graph convolutional network (G
The theory of computer viruses has been studied by several authors, though there is no systematic theoretical study up to now. The long time open question in this area is as follows: Is it possible to design a signature-free (including dynamic signatures which we will define late) virus? In this paper, we give an affirmative answer to this question from a theoretical viewpoint. We will introduce a new stronger concept: dynamic signatures of viruses, and present a method to design viruses which are static signature-free and whose dynamic signatures are hard to determine unless some cryptographic assumption fails. We should remark that our results are only for theoretical interest and may be resource intensive in practice.
Contaminated objects or surfaces, referred to as fomites, play a critical role in the spread of viruses, including SARS-CoV-2, the virus responsible for the COVID-19 pandemic. The long persistence of viruses (hours to days) on surfaces calls for an urgent need for surface disinfection strategies to intercept virus transmission and the spread of the disease. Elucidating the physicochemical processes and surface science underlying the adsorption and transfer of virus between surfaces, as well as their inactivation, are important in understanding how the disease is transmitted, and in developing effective interception strategies. This review aims to summarize the current knowledge and underlying physicochemical processes of virus transmission, in particular via fomites, and common disinfection approaches. Gaps in knowledge and needs for further research are also identified. The review focuses on SARS-CoV-2, but will supplement the discussions with related viruses.