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Gutnik, D.; Evseev, P.; Miroshnikov, K.; Shneider, M. AlphaFold Predictions in Viral Research. Encyclopedia. Available online: https://encyclopedia.pub/entry/44035 (accessed on 06 May 2024).
Gutnik D, Evseev P, Miroshnikov K, Shneider M. AlphaFold Predictions in Viral Research. Encyclopedia. Available at: https://encyclopedia.pub/entry/44035. Accessed May 06, 2024.
Gutnik, Daria, Peter Evseev, Konstantin Miroshnikov, Mikhail Shneider. "AlphaFold Predictions in Viral Research" Encyclopedia, https://encyclopedia.pub/entry/44035 (accessed May 06, 2024).
Gutnik, D., Evseev, P., Miroshnikov, K., & Shneider, M. (2023, May 09). AlphaFold Predictions in Viral Research. In Encyclopedia. https://encyclopedia.pub/entry/44035
Gutnik, Daria, et al. "AlphaFold Predictions in Viral Research." Encyclopedia. Web. 09 May, 2023.
AlphaFold Predictions in Viral Research
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This entry is adapted from the peer-reviewed paper 10.3390/cimb45040240

AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design.

AlphaFold viruses bacteriophages

1. Introduction

Prediction of the three-dimensional structure of proteins is a difficult task. For a long time, the main prediction methods included comparative modelling (homology modelling), threading and ab initio and machine-learning approaches [1][2]. The development of end-to-end machine-learning approaches in recent years has resulted in the emergence of new techniques that can often outperform other methods [3][4]. Moreover, recent progress associated with deep-learning methods enables speculation about a revolution in protein-structure prediction [5]. One of the most popular deep-learning techniques is Alphabet–Google DeepMind’s neural network-based end-to-end solution AlphaFold2 (AlphaFold, AF2), which was presented in the CASP14 competition [6], the second iteration of the AlphaFold system entered in CASP13 [7]. AlphaFold employs a deep-learning approach and a conventional neural network. This technique is able to predict the distance and torsion distribution of proteins, using training schemes of experimentally determined PDB structures, protein primary sequences and the multiple sequence alignment (MSA) of proteins. In CASP14, AlphaFold2 structures had a median backbone accuracy of 0.96 Å RMSD95 (Cα root-mean-square deviation at 95% residue coverage) and an all-atom accuracy of 1.5 Å RMSD95. The corresponding values for the prediction of the best alternative method were 2.8 Å and 3.5 Å [6]. The high level of accuracy of AlphaFold2 predictions boosted the popularity of this technique. One might even talk about “AlphaFold mania”, given the astonishing increase in the number of journal articles and preprints citing AlphaFold2 AI software [8]. 

2. Application of AF2 for Research on Eukaryotic Viruses

2.1. Application of AlphaFold for SARS-CoV-2 Research

The outbreak of severe acute respiratory syndrome caused by coronavirus 2 (SARS-CoV-2, realm Ribozyviria, class Pisoniviricetes, order Nidovirales, family Coronaviridae, genus Betacoronavirus) and the spread of associated infection boosted research on coronaviruses. The structure of SARS-CoV-2 spike (S) glycoprotein, the main target of antibodies, has been determined by cryo-electron microscopy and was used in the development of vaccines and inhibitors [9][10]. S glycoprotein promotes entry into the cell. Another target of drug design is main protease cutting the initial translated propeptide into functional viral proteins. The crystal structure of the SARS-CoV-2 main protease was also obtained experimentally [11].
To assist the solution of tasks related to general research and drug design, different structure prediction techniques, including AlphaFold, were used for prediction of SARS-CoV-2 proteins [12][13][14][15][16][17]. The main task was probably the investigation of the mechanism of interaction of the SARS-CoV-2 receptor-binding protein (RBP), which is the SARS-CoV-2 spike, and the angiotensin-converting enzyme 2 (ACE2) receptor. AF2 predictions enabled clarification of the structural features of monomeric and multimeric formulations of the vaccine and suggested that monomeric formulation presents more antigenic epitopes [14]. The emergence of new immune-escaping variants of SARS-CoV-2, such as Omicron BA1, made it important to study potential mutation sites that do not yet exist in nature but could increase the binding affinity of RBD and the receptor [16]. AF2 predictions were successfully used to find an explanation for the observed reduction in the neutralisation of SARS-CoV-2 variants of concern compared with other variants [15]. AF2 predictions can be combined with molecular dynamics simulations to improve modelling accuracy [18] and to predict the physical properties of proteins. Such models can be used for studies of both qualitative and quantitative aspects of the formation of the quaternary structure of proteins [17]. AlphaFold models are useful for revealing possible ligand binding sites. Together with virtual screening and in silico validation, these approaches provide the basis for the biological testing of new drugs and for the repurposing of natural products [12].
The accuracy of predicted structures can be assessed using computational techniques [19] and via experimental methods, e.g., optical spectroscopy or measurement of solution residual dipolar couplings data (RDCs) [20][21]. A meticulous evaluation of the concordance of AF2 models of the SARS-CoV-2 homodimeric 3C-like protease (Mpro) with residual dipolar couplings (RDCs) measured in solution for 15N–1HN and 13C′–1HN atom pairs indicated the close agreement of AlphaFold predictions with experimental data [20].
Interestingly, the high level of accuracy of AF2 predictions makes it possible to use AlphaFold predictions to determine a macromolecular structure from crystallographic diffraction experiments. It has been shown that a template-free AF2 model, generated by the AlphaFold2 group, was of sufficient quality to phase the native SARS-CoV-2 ORF8 dataset by molecular replacement, overcoming the limitations of the crystallographic phasing problem [13]. However, a comparison of RMSD (root mean square deviation of atomic positions) values of SARS-CoV-2 spike RBD, the laboratory-derived structure with both trRosetta-generated models [22] and models generated by AlphaFold v2.1.0, indicated the high level of accuracy of both methods, but the better results were obtained with trRosetta.

2.2. Application of AlphaFold to Study Eukaryotic Viruses

AlphaFold is widely used in research on other eukaryotic viruses, including monkeypox virus (MPXV) [23][24][25][26], herpes simplex virus [27][28], hepatitis E virus (HEV) [29] and other viral pathogens of humans and economically significant animals and plants [30][31][32][33][34][35]. Monkeypox virus (MPXV) represents a new serious threat to human health. MPXV has spread to 110 countries (https://www.cdc.gov/poxvirus/mpox/response/2022/world-map.html, accessed on 1 March 2023). As of 1 March 2023, there were 86,231 confirmed cases worldwide, of which 84,858 cases occurred in locations that had not previously reported MPXV cases. Monkeypox virus is classified as a member of realm Varidnaviria, class Pokkesviricetes, order Chitovirales, family Poxviridae, genus Orthopoxvirus and is evolutionarily close to vaccinia virus (VACV), the smallpox virus. AlphaFold-derived structures of the recombinantly expressed MPXV antigen truncations to their VACV homologues have indicated that MPXV and VACV antigens are likely to achieve similar conformations [26]. The World Health Organisation (WHO) has recommended the current anti-smallpox drugs tecovirimat, brincidofovir and cidofovir for the treatment of monkeypox [36]. Brincidofovir and cidofovir inhibit DNA polymerase (DNAP), while tecovirimat is an inhibitor for poxvirus phospholipase D (protein F13) [37], but specific antiviral treatment requires new drugs.
MPXV DNA polymerase (DNAP) is a very important antiviral drug target. The laboratory-derived structure of MPXV DNAP was deposited in the RCSB PDB database (PDB code 8HG1) in mid-November 2022, and a paper describing this structure was published in January 2023 [38]. Before that, the AF2-derived structure was obtained and used in the search and design of new inhibitors of MPXV DNAP. The molecules found were predicted to bind to the MPXV DNAP with a binding energy comparable to that of brincidofovir and cidofovir. New MPXV DNAP inhibitors are important in the context of possible drug resistance, which can arise due to mutations in proteins of the DNA replication complex (RC). Studies of the effect of mutations in MPXV RC using AF2-generated models have suggested similar mechanisms of drug resistance to cidofovir in monkeypox and vaccinia viruses [24]. It appears that the use of highly accurate AlphaFold predictions can assist the forecasting of the emergence of drug-resistant variants of concern to improve preparedness for them.
The molecular mechanism of interaction of tecovirimat with the monkeypox phospholipase D (F13) was studied using AlphaFold models and molecular dynamics simulations [25]. The results suggested a detailed mechanism of inhibition of F13 by tecovirimat and supported the efficacy of tecovirimat against monkeypox virus, emphasising the importance of the availability of precise modelling for revealing molecular mechanisms of drug action.
The development of new drugs is barely possible without an understanding of the mechanisms of viral infection. This knowledge can often require robust structural analysis, which can make use of modern deep-learning structure prediction methods. AlphaFold can facilitate the elucidation of the functionality of viral proteins.
Herpesviruses constitute an important group of pathogens that infect animals, including humans. Herpesviruses infect most vertebrates, causing a lifelong latent infection [39]. Herpesviruses belong to the realm Duplodnaviria, class Herviviricetes, order Herpesvirales, and comprise the families Alloherpesviridae, Herpesviridae and Malacoherpesviridae [40]. Human herpesviruses belong to the family Herpesviridae. Herpes simplex virus 1 (HSV-1) (genus Alphaherpesviruse), residing in sensory neurons or sympathetic neurons, has been shown to severely modify infected cells and to remodel the composition and architecture of cellular membranes [27][41][42]. One of the HSV-1 proteins, phosphatase adaptor UL21, mediates dephosphorylation and accelerates the rate of ceramide to sphingomyelin conversion, altering cell membranes and influencing viral replication [27]. AlphaFold-Multimer modelling has revealed the details of the interaction of UL21 and viral protein UL16 and has enabled the suggestion of the functionality of domains of the latter protein using its structural features. Specific protein–protein interactions have been shown to be essential for lipid metabolism [27]. The use of AlphaFold has also shown that another HSV-1 protein, the tegument protein UL37, interacts with the cytoplasmic surface of the lipid membrane, suggesting that UL37 can be a peripheral membrane protein [28]. AlphaFold predictions have suggested the domain organisation of UL37, and assisted experimental studies and molecular dynamics simulation have clarified the structural features and molecular mechanisms of UL37 interactions.
Fundamentally similar tasks concerning research on other viral pathogens of animals, including humans, and plants can be made easier by the use of AlphaFold predictions. These tasks include mechanisms that are crucial for viral attachment, penetration, replication, release and other steps in the viral infection cycle. They can include the investigation of viral proteins and membranes [30][33][35], viral proteins and DNA [31] and studies of viral proteins, glycoproteins and their mutations [29][32][34]. It is noteworthy that AlphaFold predictions are often used as part of an integrated approach, making the planning of experiments easier and improving understanding of the results obtained.

3. Application of AlphaFold for Research on Bacteriophages

Bacteriophages (a.k.a. phages) are viruses that infect and replicate in bacterial cells alone. Bacteriophages are ubiquitous—they can be found in water, soil and various living organisms [43]. The total number of bacteriophages can be estimated at 1031 viral particles, which is 10–100 times the number of cells [44]. The total mass of these particles is about a trillion tons [45]. Phages are also members of plant and animal microbiomes, including humans. For example, the human gastrointestinal tract contains more than 1012 phage virions [46]. The ability of bacteriophages to destroy the cells of pathogenic bacteria attracted the attention of scientists as early as the beginning of the 20th century. In recent decades, interest in bacteriophage therapy has begun to grow, primarily due to the spread of antibiotic resistance. Phage therapy has important advantages [47], including sustained bactericidal activity and “autodosing”, wherein the number of phages positively correlates with the number of host bacteria. Furthermore, phages have low intrinsic toxicity, and phage therapy is characterised by minimal disruption of normal flora and the lack of cross-resistance with antibiotics.
The practical use of phages for phage therapy requires an understanding of the structural bases of interactions of the host receptor and phage receptor-binding proteins (RBPs); the latter can include tail fibre and tail spike proteins (TFP and TSP). In addition, phage RBPs, as well as endolysins and ectolysins, the proteins that cause cell lysis, can be used as antibacterial agents by themselves [48][49]. The analysis of the structural features of phage RBPs and lysins can use modern deep-learning techniques, including AlphaFold. Together with experimental studies, AlphaFold predictions can be used to elucidate the domain organisation of TFP, TSP and cell-wall degrading enzymes, to reveal the sites of phage particle binding and enzymatic domains [48][50][51][52].
As well as in the case of eukaryotic viruses mentioned above, AlphaFold predictions can contribute to building the model of the viral particle [53][54] or the virion parts, including the attachment apparatus [50][55] and phage egress machinery [56]. All the steps of phage infection are accompanied by macromolecular interactions that include proteins, so AlphaFold’s highly accurate structural predictions can assist in the elucidation of the mechanisms of the formation of the phage nucleus [57], lysogeny maintenance [58] or anti-phage defence [59][60]. AlphaFold can also be useful in the trivial but relevant task of phage genome annotation, assisting the prediction of genes’ functionality. As of January 2023, 19,499 GenBank sequences, assigned to class Caudoviricetes, contained 1,731,815 coding regions, 67% of which were annotated as hypothetical proteins. In some cases, BLAST search and HMM-HMM motif comparisons fail to assign a function to proteins encoded in phage genomes, but analysis of fold of AF2-derived structures can assist to clarify this function [61].
It seems that no large-scale studies have been published on the accuracy of modelling using AF2 compared with the predictions of other algorithms. However, comparing the predicted average local distance difference test (lDDT) score of the 54 AF2-derived models of the major capsid protein and ATPase subunit of phage terminase indicated an impressive level of accuracy of the predictions [61]. Interestingly, structural predictions of more conserved terminase were more accurate than those of major capsid protein, (terminase lDDT mean: 0.988, median: 0.996; major capsid protein lDDT mean: 0.907, median: 0.929). The average lDDT of the ATPase domains extracted from the ATPase subunit of phage terminase models was even higher (mean: 0.998, median: 0.999). An evaluation of models of the same major capsid proteins, carried out using a different deep-learning algorithm, RoseTTAFold, showed a lower accuracy of prediction (lDDT mean: 0.634, median: 0.649) than with the AlphaFold models.

4. Application of AlphaFold for Evolutionary and Taxonomic Studies

Comparing structural similarity and specific structural features can clarify the evolutionary relationships between proteins. Furthermore, the emergence of new high-precision algorithms for predicting the structure of proteins, including AlphaFold, can enable the identification of evolutionary relationships between highly divergent discovered proteins, using the results of structural modelling. The evolution of proteins may be accompanied by the appearance of new domains, and comparative analysis of AF2-derived structures can help reveal patterns of protein evolution. Studies of bacteriophage tail sheath proteins, an important part of phages’ contractile injection system, have enabled the identification of the common core domain, including both N-terminal and C-terminal parts. The remaining variable parts consisting of one or more moderately conserved domains have, presumably, been added during phage evolution [62].
Structural similarity is widely used to evaluate evolutionary relationships between proteins whose amino acid sequence homology level is low or cannot be determined at all [63][64]. The structural similarity between two proteins can be assessed using root-mean-square deviation (RMSD) or other metrics such as template modelling score (TM-score) and DALI Z-score; the latter two metrics have a number of advantages over RMSD [11][65]. Clustering of experimentally determined structures of major capsid proteins using the DALI Z-score has already been used to illustrate the common origin of some viral groups and to cluster prokaryotic viruses [63][66]. Integrated use of both experimental structures and AF2-derived structures can be used for elucidation of evolutionary relationships and taxonomic classification of bacteriophages and eukaryotic viruses [67][68][69]. AlphaFold modelling and subsequent clustering have been used in taxonomic studies of archaeal viruses [66]. Clustering using AlphaFold showed interesting and often biologically meaningful results [61]. Clustering using structures predicted by AlphaFold showed interesting and often biologically meaningful results. It should also be noted that the native state of viral proteins can change according to the state of the viral particle (e.g., empty, full, expanded capsids) and according to the stage of viral particle assembly [70][71][72][73]. The correlation between structural similarity and sequence identity is not absolute due to conformational plasticity, solvent effects and ligand binding [74]. Most of these limitations apply to studies that involve experimentally determined structures, but, hypothetically, they could be exacerbated by structural prediction errors. Therefore, predicting the effectiveness of using AlphaFold for the analysis of structural similarity and evolutionary history, based only on the similarity of the predicted structures, seems to be a difficult task [61].

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Subjects: Virology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Daria Gutnik
,
Peter Evseev
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Konstantin Miroshnikov
,
Mikhail Shneider
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Revisions: 2 times (View History)
Update Date: 10 May 2023
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