Mutation Effects on Structure and Dynamics: Adaptive Evolution of the SARS-CoV-2 Main Protease.

The main protease of SARS-CoV-2 (Mpro) plays a critical role in viral replication; although it is relatively conserved, Mpro has nevertheless evolved over the course of the COVID-19 pandemic. Here, we examine phenotypic changes in clinically observed variants of Mpro, relative to the originally reported wild-type enzyme. Using atomistic molecular dynamics simulations, we examine effects of mutation on protein structure and dynamics. In addition to basic structural properties such as variation in surface area and torsion angles, we use protein structure networks and active site networks to evaluate functionally relevant characters related to global cohesion and active site constraint. Substitution analysis shows a continuing trend toward more hydrophobic residues that are dependent on the location of the residue in primary, secondary, tertiary, and quaternary structures. Phylogenetic analysis provides additional evidence for the impact of selective pressure on mutation of Mpro. Overall, these analyses suggest evolutionary adaptation of Mpro toward more hydrophobicity and a less-constrained active site in response to the selective pressures of a novel host environment.

Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.

Sequence Characterization and Molecular Modeling of Clinically Relevant Variants of the SARS-CoV-2 Main Protease.

The SARS-CoV-2 main protease (Mpro) is essential to viral replication and cleaves highly specific substrate sequences, making it an obvious target for inhibitor design. However, as for any virus, SARS-CoV-2 is subject to constant neutral drift and selection pressure, with new Mpro mutations arising over time. Identification and structural characterization of Mpro variants is thus critical for robust inhibitor design. Here we report sequence analysis, structure predictions, and molecular modeling for seventy-nine Mpro variants, constituting all clinically observed mutations in this protein as of April 29, 2020. Residue substitution is widely distributed, with some tendency toward larger and more hydrophobic residues. Modeling and protein structure network analysis suggest differences in cohesion and active site flexibility, revealing patterns in viral evolution that have relevance for drug discovery.

A cyclic peptide inhibitor of the SARS-CoV-2 main protease.

This paper presents the design and study of a first-in-class cyclic peptide inhibitor against the SARS-CoV-2 main protease (Mpro). The cyclic peptide inhibitor is designed to mimic the conformation of a substrate at a C-terminal autolytic cleavage site of Mpro. The cyclic peptide contains a [4-(2-aminoethyl)phenyl]-acetic acid (AEPA) linker that is designed to enforce a conformation that mimics a peptide substrate of Mpro. In vitro evaluation of the cyclic peptide inhibitor reveals that the inhibitor exhibits modest activity against Mpro and does not appear to be cleaved by the enzyme. Conformational searching predicts that the cyclic peptide inhibitor is fairly rigid, adopting a favorable conformation for binding to the active site of Mpro. Computational docking to the SARS-CoV-2 Mpro suggests that the cyclic peptide inhibitor can bind the active site of Mpro in the predicted manner. Molecular dynamics simulations provide further insights into how the cyclic peptide inhibitor may bind the active site of Mpro. Although the activity of the cyclic peptide inhibitor is modest, its design and study lays the groundwork for the development of additional cyclic peptide inhibitors against Mpro with improved activities.

Cutting Through the Noise: Predictors of Successful Online Message Retransmission in the First 8 Months of the COVID-19 Pandemic.

In this paper, we investigate how message construction, style, content, and the textual content of embedded images impacted message retransmission over the course of the first 8 months of the coronavirus disease 2019 (COVID-19) pandemic in the United States. We analyzed a census of public communications (n = 372,466) from 704 public health agencies, state and local emergency management agencies, and elected officials posted on Twitter between January 1 and August 31, 2020, measuring message retransmission via the number of retweets (ie, a message passed on by others), an important indicator of engagement and reach. To assess content, we extended a lexicon developed from the early months of the pandemic to identify key concepts within messages, employing it to analyze both the textual content of messages themselves as well as text included within embedded images (n = 233,877), which was extracted via optical character recognition. Finally, we modelled the message retransmission process using a negative binomial regression, which allowed us to quantify the extent to which particular message features amplify or suppress retransmission, net of controls related to timing and properties of the sending account. In addition to identifying other predictors of retransmission, we show that the impact of images is strongly driven by content, with textual information in messages and embedded images operating in similar ways. We offer potential recommendations for crafting and deploying social media messages that can "cut through the noise" of an infodemic.

The First 60 Days: American Public Health Agencies' Social Media Strategies in the Emerging COVID-19 Pandemic.

In this paper, we capture, identify, and describe the patterns of longitudinal risk communication from public health communicating agencies on Twitter during the first 60 days of the response to the novel coronavirus disease 2019 (COVID-19) pandemic. We collected 138,546 tweets from 696 targeted accounts from February 1 to March 31, 2020, employing term frequency-inverse document frequency to identify keyword hashtags that were distinctive on each day. Our team conducted inductive content analysis to identify emergent themes that characterize shifts in public health risk communication efforts. As a result, we found 7 distinct periods of communication in the first 60 days of the pandemic, each characterized by a differing emphasis on communicating information, individual and collection action, sustaining motivation, and setting social norms. We found that longitudinal risk communication in response to the COVID-19 pandemic shifted as secondary threats arose, while continuing to promote pro-social activities to reduce impact on vulnerable populations. Identifying patterns of risk communication longitudinally allows public health communicators to observe changes in topics and priorities. Observations from the first 60 days of the COVID-19 pandemic prefigures ongoing messaging needs for this event and for future disease outbreaks.

COVID-19: Retransmission of official communications in an emerging pandemic.

As the most visible face of health expertise to the general public, health agencies have played a central role in alerting the public to the emerging COVID-19 threat, providing guidance for protective action, motivating compliance with health directives, and combating misinformation. Social media platforms such as Twitter have been a critical tool in this process, providing a communication channel that allows both rapid dissemination of messages to the public at large and individual-level engagement. Message dissemination and amplification is a necessary precursor to reaching audiences, both online and off, as well as inspiring action. Therefore, it is valuable for organizational risk communication to identify strategies and practices that may lead to increased message passing among online users. In this research, we examine message features shown in prior disasters to increase or decrease message retransmission under imminent threat conditions to develop models of official risk communicators' messages shared online from February 1, 2020-April 30, 2020. We develop a lexicon of keywords associated with risk communication about the pandemic response, then use automated coding to identify message content and message structural features. We conduct chi-square analyses and negative binomial regression modeling to identify the strategies used by official risk communicators that respectively increase and decrease message retransmission. Findings show systematic changes in message strategies over time and identify key features that affect message passing, both positively and negatively. These results have the potential to aid in message design strategies as the pandemic continues, or in similar future events.

Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity.

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.