Biology subjects

Chakravarty, A.

Publications and source records attributed to Chakravarty, A..

3 recordsLinked to original sources

bioRxiv2022

Machine Learning for the Identification of Viral Attachment Machinery from Respiratory Virus Sequences

At the outset of an emergent viral respiratory pandemic, sequence data is among the first molecular information available. As viral attachment machinery is a key target for therapeutic and prophylactic interventions, rapid identification of viral "spike" proteins from sequence can significantly accelerate the development of medical countermeasures. For five families of respiratory viruses, covering the vast majority of airborne and droplet-transmitted diseases, host cell entry is mediated by the binding of viral surface glycoproteins that interact with a host cell receptor. In this report it is shown that sequence data for an unknown virus belonging to one of the five families above provides sufficient information to identify the protein(s) responsible for viral attachment and to permit an assignment of viral family. Random forest models that take as input a set of respiratory viral sequences can classify the protein as "spike" vs. non-spike based on predicted secondary structure elements alone (with 97.8 % correctly classified) or in combination with N-glycosylation related features (with 98.1 % correctly classified). In addition, a Random Forest model developed using the same dataset and only secondary structural elements was able to predict the respiratory virus family of each protein sequence correctly 89.0 % of the time. Models were validated through 10-fold cross-validation as well as bootstrapping. Surprisingly, we showed that secondary structural element and N-glycosylation features were sufficient for model generation. The ability to rapidly identify viral attachment machinery directly from sequence data holds the potential to accelerate the design of medical countermeasures for future pandemics.

bioinformatics↗

bioRxiv2021

Using mixed-effects modeling to estimate decay kinetics of response to SARS-CoV-2 infection

The duration of natural immunity in response to SARS-CoV-2 is a matter of some debate in the literature at present. For example, in a recent publication characterizing SARS-CoV-2 immunity over time, the authors fit pooled longitudinal data, using fitted slopes to infer the duration of SARS-CoV-2 immunity. In fact, such approaches can lead to misleading conclusions as a result of statistical model-fitting artifacts. To exemplify this phenomenon, we reanalyzed one of the markers (pseudovirus neutralizing titer) in the publication, using mixed-effects modeling, a methodology better suited to longitudinal datasets like these. Our findings showed that the half-life was both longer and more variable than reported by the authors. The example selected by us here illustrates the utility of mixed-effects modeling in provide more accurate estimates of the duration and heterogeneity of half-lives of molecular and cellular biomarkers of SARS-CoV-2 immunity.

immunology↗

bioRxiv2020

Scale-free structure of cancer networks and their vulnerability to hub-directed combination therapy

Background The effectiveness of many targeted therapies is limited by toxicity and the rise of drug resistance. A growing appreciation of the inherent redundancies of cancer signaling has led to a rise in the number of combination therapies under development, but a better understanding of the overall cancer network topology would provide a conceptual framework for choosing effective combination partners. In this work, we explore the scale-free nature of cancer protein-protein interaction networks in 14 indications. Scale-free networks, characterized by a power-law degree distribution, are known to be resilient to random attack on their nodes, yet vulnerable to directed attacks on their hubs (their most highly connected nodes).Results Consistent with the properties of scale-free networks, we find that lethal genes are associated with ∼5-fold higher protein connectivity partners than non-lethal genes. This provides a biological rationale for a hub-centered combination attack. Our simulations show that combinations targeting hubs can efficiently disrupt 50% of network integrity by inhibiting less than 1% of the connected proteins, whereas a random attack can require inhibition of more than 30% of the connected proteins.Conclusions We find that the scale-free nature of cancer networks makes them vulnerable to focused attack on their highly connected protein hubs. Thus, we propose a new strategy for designing combination therapies by targeting hubs in cancer networks that are not associated with relevant toxicity networks.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗