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A Comparative Encyclopedia Of Dna Elements In The Mouse Genome
By Isabella Romeo 1, 2, †, Ingrid Guarnetti Prandi 3, †, Emanuela Giombini 4, Cesare Ernesto Maria Gruber 4, Daniele Pietrucci 3, 5, Stefano Borocci 3, 6, Nabil Abid 6, 7, Anna Fava 8, Andrea R. Beccari 8, Giovanni Chillemi 3 and Carmine Talarico 8, *
Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, Via S. Camillo de Lellis s.n.c., 01100 Viterbo, Italy
A large number of SARS-CoV-2 mutations in a short period of time has driven scientific research related to vaccines, new drugs, and antibodies to combat the new variants of the virus. Herein, we present a web portal containing the structural information, the tridimensional coordinates, and the molecular dynamics trajectories of the SARS-CoV-2 spike protein and its main variants. The Spike Mutants website can serve as a rapid online tool for investigating the impact of novel mutations on virus fitness. Taking into account the high variability of SARS-CoV-2, this application can help the scientific community when prioritizing molecules for experimental assays, thus, accelerating the identification of promising drug candidates for COVID-19 treatment. Below we describe the main features of the platform and illustrate the possible applications for speeding up the drug discovery process and hypothesize new effective strategies to overcome the recurrent mutations in SARS-CoV-2 genome.
Understanding The Aggregate Effects Of Disability Insurance
As of August 2022, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, accounted for more than 599 million infections and more than six million deaths worldwide (https://covid19.who.int, accessed on 1 October 2020). The incessant rise in the number of cases despite the development of vaccines and the resulting immunization process reflects the impact of new variants of SARS-CoV-2 globally. Indeed, the evolution of SARS-CoV-2 was caused by the acquisition of several mutations since the pandemic started, as reported in the GISAID database (https://www.gisaid.org, accessed on 1 October 2020), which collected more than one million SARS-CoV-2 sequences. It is important to point out that the selection of random mutations stands out as one of the main mechanisms of acquiring resistance and represents a relevant phenomenon in viruses that mutate at high frequencies. RNA viruses, for instance, have a mutation rate estimated at 10
Per nucleotide per replication [1, 2]. In particular, the mutations were classified as variants of concern (VOCs), which included Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2); variants of interest (VOI), comprising Eta (B.1.525), Iota (B.1.126), Kappa (B.1.617.1), Lambda (C.37), Omicron (BA.1), Zeta (P.2 (484 K.V2), and Ihu (B.1.640.1); and variants of alert (VOA) (https://www.who.int/en/activities/tracking-SARS-CoV-2-variants, accessed on 1 October 2020) [3, 4]. However, this classification is dynamic, based on the spread of different variants over time and their clinical significance. In fact, these variants were able to emerge at the same time in multiple locations that are independent of each other, and then they were no longer circulating after a variable period. Chronologically, we have witnessed the emergence of B.1.1.7 in the United Kingdom (UK) [5], then B.1.351 in South Africa [6], followed by P.1 in Brazil [7], and B.1.617 in India [8]. These new variants show multiple mutations on their spike (S) glycoprotein and spread rapidly across the globe, resulting in more virulence. In December 2020, the B.1.1.7 variant was identified in the southeastern United Kingdom (UK), hence the name “UK variant”, and it is marked by 23 genetic mutations, compared to the original genome sequence that was first detected in Wuhan, China.
As previously reported, other VOCs have also been isolated in South Africa, India, and Brazil and have been investigated for their enhanced contagiousness and resistance to neutralization. B.1.351 or the Beta variant that emerged in South Africa, is characterized by 18 mutations and 3 amino acid deletions in the S glycoprotein [9]. P.1 variant, also known as Gamma, was detected in December 2020 and has spread in an accelerated way across Manaus, Brazil. Another variant, identified as B.1.617, has been isolated from Maharashtra, India and is subdivided into three sub-lineages, such as B.1.617.1, B.1.617.2, and B.1.617.3 [10, 11]. In November 2021, the emergence of the new variant detected in Botswana and South Africa, termed Omicron, alarmed the community due to the high number of changes in the spike protein, the increased transmission efficiency, and its ability to escape from neutralizing antibodies. However, clinical studies have reported that the rapidly spreading Omicron variant was less dangerous than its predecessor, the Delta variant [12]. The main differences found in this variant are to be attributed to the N-terminal domain (NTD): deletion of V143, Y144, and Y145 residues and a 3-amino-acid insertion of “EPE” at position 214. However, accumulating evidence suggests high transmission fitness due to its higher affinity to ACE2 receptors and efficient immune evasion, compared to the other VOCs [13].
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To date, thanks to sequencing, the identification of emerging SARS-CoV-2 variants and sets of mutations potentially linked to changes in viral properties has been achieved. Due to these possible changes, the understanding of the functional consequences of VOCs and VOIs needs to rapidly expand. To do this, much of this knowledge should concern the impact of these variants in terms of conformational changes in the S glycoprotein, which could enhance receptor recognition and affect the production of neutralizing antibodies.
Although assessing changes in the interaction between SARS-CoV-2 epitopes and antibodies is difficult, the binding affinity between mutated S glycoprotein and both T-cell receptor (TCR) and ACE2 receptor could be predicted in silico to test the immune response and infectivity, respectively. In this regard, computational methods hold the potential to understand resistance mechanisms, shedding light on the elusive link between novel mutations present in the S glycoprotein and its targets. These methods exploit the available structural information on protein–ligand complexes and structural modeling of point mutations in the protein structure [14]. Reported examples of the use of structure-based methods include: (i) the application of molecular docking to predict resistance or susceptibility of antiviral targets to different inhibitors [15, 16]; (ii) the use of molecular dynamics simulations (MDs) to investigate the impact of mutations on enzyme stability and binding affinity on the receptor [17, 18] or to highlight long-range altered communications in wild-type (WT) and mutated S glycoproteins; (iii) the use of computational mutation scanning protocols to extract insights on free energy and binding affinity changes, resulting from the active site and non-active site mutations [19]. Even though these methods are constantly adding new pieces to the puzzle and opening opportunities in the understanding of drug resistance, they suffer from various drawbacks, such as being time consuming and offering limited predictive accuracy. As a result of such limitations, the primary challenge facing structure-based drug resistance prediction is to achieve an acceptable balance between prediction accuracy and computational efficiency to become both reliable and fast tools to be used in the clinical context [17]. In fact, some of the most recent reports describe the use of machine learning strategies merging both sequence and structural data in an attempt to achieve such a balance [20, 21]. In this perspective, several platforms have been developed to assemble sequencing data, provide information and tools to visualize the mutations present in the SARS-CoV-2 structures [22, 23], and distribute a plethora of experimental data and bioinformatic tools through the European COVID-19 Data Platform (https://www.covid19dataportal.org/, accessed on 1 October 2020) for different purposes. Another SARS-CoV-2 immuno-analytics platform has been developed to visualize multidimensional data to inform target selection in immunological research by combining genomic and whole-proteome analyses with in silico epitope predictions [24, 25]. Recently, in the context of drug discovery, user-friendly platforms were designed to identify promising drugs and targets for COVID-19, such as COVID19db [26] and COVID Moonshot [27]. Moreover, the EU-funded project, Exscalate4CoV (https://www.exscalate4cov.eu/, accessed on 1 October 2020), released additional web services, such as https://viralseq.exscalate4cov.eu/ and https://mediate.exscalate4cov.eu/ (accessed on 1 October 2020), to support the scientific community. The majority of created platforms collect data present in the literature and integrate databases that consider the SARS-CoV-2 targets, their mutations, and related clinical and immunological information. Unlike known platforms, we
To date, thanks to sequencing, the identification of emerging SARS-CoV-2 variants and sets of mutations potentially linked to changes in viral properties has been achieved. Due to these possible changes, the understanding of the functional consequences of VOCs and VOIs needs to rapidly expand. To do this, much of this knowledge should concern the impact of these variants in terms of conformational changes in the S glycoprotein, which could enhance receptor recognition and affect the production of neutralizing antibodies.
Although assessing changes in the interaction between SARS-CoV-2 epitopes and antibodies is difficult, the binding affinity between mutated S glycoprotein and both T-cell receptor (TCR) and ACE2 receptor could be predicted in silico to test the immune response and infectivity, respectively. In this regard, computational methods hold the potential to understand resistance mechanisms, shedding light on the elusive link between novel mutations present in the S glycoprotein and its targets. These methods exploit the available structural information on protein–ligand complexes and structural modeling of point mutations in the protein structure [14]. Reported examples of the use of structure-based methods include: (i) the application of molecular docking to predict resistance or susceptibility of antiviral targets to different inhibitors [15, 16]; (ii) the use of molecular dynamics simulations (MDs) to investigate the impact of mutations on enzyme stability and binding affinity on the receptor [17, 18] or to highlight long-range altered communications in wild-type (WT) and mutated S glycoproteins; (iii) the use of computational mutation scanning protocols to extract insights on free energy and binding affinity changes, resulting from the active site and non-active site mutations [19]. Even though these methods are constantly adding new pieces to the puzzle and opening opportunities in the understanding of drug resistance, they suffer from various drawbacks, such as being time consuming and offering limited predictive accuracy. As a result of such limitations, the primary challenge facing structure-based drug resistance prediction is to achieve an acceptable balance between prediction accuracy and computational efficiency to become both reliable and fast tools to be used in the clinical context [17]. In fact, some of the most recent reports describe the use of machine learning strategies merging both sequence and structural data in an attempt to achieve such a balance [20, 21]. In this perspective, several platforms have been developed to assemble sequencing data, provide information and tools to visualize the mutations present in the SARS-CoV-2 structures [22, 23], and distribute a plethora of experimental data and bioinformatic tools through the European COVID-19 Data Platform (https://www.covid19dataportal.org/, accessed on 1 October 2020) for different purposes. Another SARS-CoV-2 immuno-analytics platform has been developed to visualize multidimensional data to inform target selection in immunological research by combining genomic and whole-proteome analyses with in silico epitope predictions [24, 25]. Recently, in the context of drug discovery, user-friendly platforms were designed to identify promising drugs and targets for COVID-19, such as COVID19db [26] and COVID Moonshot [27]. Moreover, the EU-funded project, Exscalate4CoV (https://www.exscalate4cov.eu/, accessed on 1 October 2020), released additional web services, such as https://viralseq.exscalate4cov.eu/ and https://mediate.exscalate4cov.eu/ (accessed on 1 October 2020), to support the scientific community. The majority of created platforms collect data present in the literature and integrate databases that consider the SARS-CoV-2 targets, their mutations, and related clinical and immunological information. Unlike known platforms, we
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