Online biophysical predictions for SARS-CoV-2 proteins
Título
Online biophysical predictions for SARS-CoV-2 proteins
Autor
Luciano Kagami, Joel Roca-Martínez, Jose Gavaldá-García, Pathmanaban Ramasamy, K. Anton Feenstra, Wim F. Vranken
Descripción
Abstract Background The SARS-CoV-2 virus, the causative agent of COVID-19, consists of an assembly of proteins that determine its infectious and immunological behavior, as well as its response to therapeutics. Major structural biology efforts on these proteins have already provided essential insights into the mode of action of the virus, as well as avenues for structure-based drug design. However, not all of the SARS-CoV-2 proteins, or regions thereof, have a well-defined three-dimensional structure, and as such might exhibit ambiguous, dynamic behaviour that is not evident from static structure representations, nor from molecular dynamics simulations using these structures. Main We present a website ( https://bio2byte.be/sars2/ ) that provides protein sequence-based predictions of the backbone and side-chain dynamics and conformational propensities of these proteins, as well as derived early folding, disorder, β-sheet aggregation, protein-protein interaction and epitope propensities. These predictions attempt to capture the inherent biophysical propensities encoded in the sequence, rather than context-dependent behaviour such as the final folded state. In addition, we provide the biophysical variation that is observed in homologous proteins, which gives an indication of the limits of their functionally relevant biophysical behaviour. Conclusion The https://bio2byte.be/sars2/ website provides a range of protein sequence-based predictions for 27 SARS-CoV-2 proteins, enabling researchers to form hypotheses about their possible functional modes of action.
Fecha
2021
Materia
covid-19, SARS-CoV-2, Proteins, biophysical features, Single sequence based predictions
Identificador
10.1186/s12860-021-00362-w
Fuente
Epidemiology and Health
Editor
Korean Society of Epidemiology
Cobertura
Cytology
Colección
Citación
Luciano Kagami, Joel Roca-Martínez, Jose Gavaldá-García, Pathmanaban Ramasamy, K. Anton Feenstra, Wim F. Vranken, “Online biophysical predictions for SARS-CoV-2 proteins,” SOCICT Open, consulta 20 de abril de 2026, https://www.socictopen.socict.org/items/show/9209.
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