Modeling coronavirus spike protein dynamics: implications for immunogenicity and immune escape

Abstract

The ongoing COVID-19 pandemic is a global public health emergency requiring urgent development of efficacious vaccines. While concentrated research efforts have focused primarily on antibody-based vaccines that neutralize SARS-CoV-2, and several first-generation vaccines have either been approved or received emergency use authorization, it is forecasted that COVID-19 will become an endemic disease requiring updated second-generation vaccines. The SARS-CoV-2 surface spike (S) glycoprotein represents a prime target for vaccine development because antibodies that block viral attachment and entry, i.e., neutralizing antibodies, bind almost exclusively to the receptor-binding domain. Here, we develop computational models for a large subset of S proteins associated with SARS-CoV-2, implemented through coarse-grained elastic network models and normal mode analysis. We then analyze local protein domain dynamics of the S protein systems and their thermal stability to characterize structural and dynamical variability among them. These results are compared against existing experimental data and used to elucidate the impact and mechanisms of SARS-CoV-2 S protein mutations and their associated antibody binding behavior. We construct a SARS-CoV-2 antigenic map and offer predictions about the neutralization capabilities of antibody and S mutant combinations based on protein dynamic signatures. We then compare SARS-CoV-2 S protein dynamics to SARS-CoV and MERS-CoV S proteins to investigate differing antibody binding and cellular fusion mechanisms that may explain the high transmissibility of SARS-CoV-2. The outbreaks associated with SARS-CoV, MERS-CoV, and SARS-CoV-2 over the last two decades suggest that the threat presented by coronaviruses is ever-changing and long term. Our results provide insights into the dynamics-driven mechanisms of immunogenicity associated with coronavirus S proteins and present a new, to our knowledge, approach to characterize and screen potential mutant candidates for immunogen design, as well as to characterize emerging natural variants that may escape vaccine-induced antibody responses.

Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function

“…S3 B), that pass the extracted feature map to the synthesis subnet gradually to generate the protein contact map, [45] , [46] . SE-Concat is inspired from the SE-ResNet architecture, modified to use concatenation instead of summation, to reuse the extracted features and increase the information flow between layers [45] , [47] , [48] . The squeeze excitation (SE) block ( SI Fig.…”

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

“…The recent advances in computing resources and availability of large datasets, as well as the development of new AI algorithms, have opened the path to the use of AI in many different areas, including but not limited to image synthesis [ 121 ], speech recognition [ 122 , 123 ] and engineering [ 124 , 125 , 126 ]. AI has been also employed in healthcare industries for applications such as protein engineering [ 127 , 128 , 129 , 130 ], cancer detection [ 131 ], and drug discovery [ 132 , 133 ]. More specifically, AI algorithms have shown an outstanding capability to discover complex patterns and extract discriminative features from medical images, providing higher-quality analysis and better quantitative results efficiently and automatically.…”

An ensemble clustering method based on consistency cluster consensus approach and MapReduce model

“…Definition 4. Uncertain sample: The sample x is uncertain defined, when S x as the highest membership similarity is smaller than the predefined value of 𝛼 2 , as defined in Equation (10).…”

“…Ensemble clustering methods can produce better results compared to any of the essential clustering methods by combining several basic clustering methods and using the strengths of each method. 10,11 In fact, the most purpose of ensemble clustering is to produce optimal clusters using a combination of the results of several basic methods. 11 The consensus function in ensemble clustering cannot be configured on the basis of cluster averaging or simple voting.…”