SpikePro: a webserver to predict the fitness of SARS-CoV-2 variants

Abstract

Motivation: The SARS-CoV-2 virus has shown a remarkable ability to evolve and spread across the globe through successive waves of variants since the original Wuhan lineage. Despite all the efforts of the last 2 years, the early and accurate prediction of variant severity is still a challenging issue which needs to be addressed to help, for example, the decision of activating COVID-19 plans long before the peak of new waves. Upstream preparation would indeed make it possible to avoid the overflow of health systems and limit the most severe cases.

Results: We recently developed SpikePro, a structure-based computational model capable of quickly and accurately predicting the viral fitness of a variant from its spike protein sequence. It is based on the impact of mutations on the stability of the spike protein as well as on its binding affinity for the angiotensin-converting enzyme 2 (ACE2) and for a set of neutralizing antibodies. It yields a precise indication of the virus transmissibility, infectivity, immune escape and basic reproduction rate. We present here an updated version of the model that is now available on an easy-to-use webserver, and illustrate its power in a retrospective study of fitness evolution and reproduction rate of the main viral lineages. SpikePro is thus expected to be great help to assess the fitness of newly emerging SARS-CoV-2 variants in genomic surveillance and viral evolution programs.

Availability and implementation: SpikePro webserver http://babylone.ulb.ac.be/SpikePro/.

Supplementary information: Supplementary data are available at Bioinformatics online.