The Impact of Protein Dynamics on Residue-Residue Coevolution and Contact Prediction

The need to maintain protein structure constrains evolution at the sequence level, and patterns of coevolution in homologous protein sequences can be used to predict their 3D structures with high accuracy. Our understanding of the relationship between protein structure and evolution has traditionally been benchmarked by computational models ability to predict contacts from a single representative, experimentally determined structure per protein family. However, proteins in vivo are highly dynamic and can adopt multiple functionally relevant conformations. Here we demonstrate that interactions that stabilize alternate conformations, as well those that mediate conformational changes, impose an underappreciated but significant set of evolutionary constraints. We analyze the extent of these constraints over 56 paralogous G protein coupled receptors (GPCRs), {beta}-arrestin and the human SARS-CoV2 receptor ACE2. Specifically, we observe that contacts uniquely found in molecular dynamics (MD) simulation data and alternate-conformation crystal structures are successfully predicted by unsupervised language models. In GPCRs, adding these contacts as positives increases the percentage of top contacts classified as true positives, as predicted by a state-of-the-art language model, from 69% to 87%. Our results show that protein dynamics impose constraints on molecular evolution and demonstrate the ability of unsupervised language models to measure these constraints.

Longitudinal single-cell epitope and RNA-sequencing reveals the immunological impact of type 1 interferon autoantibodies in critical COVID-19

Type I interferon (IFN-I) neutralizing autoantibodies have been found in some critical COVID-19 patients; however, their prevalence and longitudinal dynamics across the disease severity scale, and functional effects on circulating leukocytes remain unknown. Here, in 284 COVID-19 patients, we found IFN-I autoantibodies in 19% of critical, 6% of severe and none of the moderate cases. Longitudinal profiling of over 600,000 peripheral blood mononuclear cells using multiplexed single-cell epitope and transcriptome sequencing from 54 COVID-19 patients, 15 non-COVID-19 patients and 11 non-hospitalized healthy controls, revealed a lack of IFN-I stimulated gene (ISG-I) response in myeloid cells from critical cases, including those producing anti-IFN-I autoantibodies. Moreover, surface protein analysis showed an inverse correlation of the inhibitory receptor LAIR-1 with ISG-I expression response early in the disease course. This aberrant ISG-I response in critical patients with and without IFN-I autoantibodies, supports a unifying model for disease pathogenesis involving ISG-I suppression via convergent mechanisms.

Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning

Mitigating transmission of SARS-CoV-2 has been complicated by the inaccessibility and, in some cases, inadequacy of testing options to detect present or past infection. Immunochromatographic lateral flow assays (LFAs) are a cheap and scalable modality for tracking viral transmission by testing for serological immunity, though systematic evaluations have revealed the low performance of some SARS-CoV-2 LFAs. Here, we re-analyzed existing data to present a proof-of-principle machine learning framework that may be used to inform the pairing of LFAs to achieve superior classification performance while enabling tunable False Positive Rates optimized for the estimated seroprevalence of the population being tested.

The loss of expression of transforming growth factor-beta receptors correlates with the histopathologic tumor grade in bladder transitional cell carcinoma patients

Transforming growth factor-beta (TGF-beta), a pleiotropic growth factor, is a potent inhibitor of cellular proliferation in cells of epithelial origin. Recently, it has been suggested that a loss of sensitivity to TGF-beta through a loss of expression of TGF-beta receptors T beta R-I and T beta R-II--is associated with tumor initiation and progression. Therefore, to investigate the relationship between TGF-beta receptors expression and carcinogenesis of bladder TCC, this study examined the expression of T beta R-I and T beta R-II in 46 bladder TCC patients using immunohistochemistry. Since histopathological grade is a widely accepted marker of prognosis, the results were compared in relation to the three grades of bladder TCC. The results demonstrated that the loss of TGF-beta receptors expression is associated with increasing histopathological grades of bladder TCC. Specifically, both T beta R-I and T beta R-II were readily detected in all 10 normal bladder mucosa specimens. Likewise, all 6 specimens of grade I TCC samples expressed high levels of both TGF-beta receptors. However, among grade II TCC samples, T beta R-I and T beta R-II were detected in 78% and 89%, respectively: among grade III TCC samples, T beta R-I and T beta R-II were detected in 45% and 41%, respectively. These results suggested that loss of sensitivity to TGF-beta may play a role in the progression of TCC from low to high grade disease.