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Register a new userQuantitative Interpretations of Energetic Features and Key Residues at SARS Coronavirus Spike Receptor-Binding Domain and ACE2 Receptor Interface
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The wide spread of coronavirus disease 2019 (COVID-19) has declared a global health emergency. As one of the most important targets for antibody and drug developments, Spike RBD-ACE2 interface has received extensive attention. Here, using molecular dynamics simulations, we explicitly evaluated the binding energetic features of the RBD-ACE2 complex of both SARS-CoV and SARS-CoV-2 to find the key residues. Although the overall ACE2-binding mode of the SARS-CoV-2 RBD is nearly identical to that of the SARS-CoV RBD, the difference in binding affinity is as large as -16.35 kcal/mol. Energy decomposition analyses identified three binding patches in the SARS-CoV-2 RBD and eleven key residues (Phe486, Tyr505, Asn501, Tyr489, Gln493, Leu455 and etc) which are believed to be the main targets for drug development. The dominating forces are from van der Waals attractions and dehydration of these residues. It is also worth mention that we found seven mutational sites (Lys417, Leu455, Ala475, Gly476, Glu484, Gln498 and Val503) on SARS-CoV-2 which unexpectedly weakened the RBD-ACE2 binding. Very interestingly, the most repulsive residue at the RBD-ACE2 interface (E484), is found to be mutated in the latest UK variant, B1.1.7, cause complete virus neutralization escapes from highly neutralizing COVID-19 convalescent plasma. Our present results indicate that at least from the energetic point of view such E484 mutation may have beneficial effects on ACE2 binding. The present study provides a systematical understanding, from the energetic point of view, of the binding features of SARS-CoV-2 RBD with ACE2 acceptor. We hope that the present findings of three binding patches, key attracting residues and unexpected mutational sites can provide insights to the design of SARS-CoV-2 drugs and identification of cross-active antibodies.
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Aldosterone, the main physiological mineralocorticoid in humans and other terrestrial vertebrates, first appears in lungfish, which are lobe-finned fish that are forerunners of terrestrial vertebrates. Aldosterone activation of the MR regulates internal homeostasis of water, sodium and potassium, which was critical in the conquest of land by vertebrates. We studied transcriptional activation of the slender African lungfish MR by aldosterone, other corticosteroids and progesterone and find that aldosterone, 11-deoxycorticosterone, 11-deoxycortisol and progesterone have half-maximal responses (EC50s) below 1 nM and are potential physiological mineralocorticoids. In contrast, EC50s for corticosterone and cortisol were 23 nM and 66 nM, respectively. Unexpectedly, truncated lungfish MR, consisting of the DNA-binding, hinge and steroid-binding domains, had a stronger response to corticosteroids and progesterone than full-length lungfish MR, indicating that the N-terminal domain represses steroid activation of lungfish MR, unlike human MR in which the N-terminal domain contains an activation function. BLAST searches of GenBank did not retrieve a GR ortholog, leading us to test dexamethasone and triamcinolone for activation of lungfish MR. At 10 nM, both synthetic glucocorticoids are about 4-fold stronger than 10 nM aldosterone in activating full-length lungfish MR, leading us to propose that lungfish MR also functions as a GR.
In most of the recent immunological literature the differences across antigen receptor populations are examined via non-parametric statistical measures of species overlap and diversity borrowed from ecological studies. While this approach is robust in a wide range of situations, it seems to provide little insight into the underlying clonal size distribution and the overall mechanism differentiating the receptor populations. As a possible alternative, the current paper presents a parametric method which adjusts for the data under-sampling as well as provides a unifying approach to simultaneous comparison of multiple receptor groups by means of the modern statistical tools of unsupervised learning. The parametric model is based on a flexible multivariate Poisson-lognormal distribution and is seen to be a natural generalization of the univariate Poisson-lognormal models used in ecological studies of biodiversity patterns. The procedure for evaluating models fit is described along with the public domain software developed to perform the necessary diagnostics. The model-driven analysis is seen to compare favorably vis a vis traditional methods when applied to the data from T-cell receptors in transgenic mice populations.
We study kinetic model of Nuclear Receptor Binding to Promoter Regions. This model is written as a system of ordinary differential equations. Model reduction techniques have been used to simplify chemical kinetics.In this case study, the technique of Pseudo-first order approximation is applied to simplify the reaction rates. CellDesigner has been used to draw the structures of chemical reactions of Nuclear Receptor Binding to Promoter Regions. After model reduction, the general analytical solution for reduced model is given and the number of species and reactions are reduced from 9 species and 6 reactions to 6 species and 5 reactions.
B cells develop high affinity receptors during the course of affinity maturation, a cyclic process of mutation and selection. At the end of affinity maturation, a number of cells sharing the same ancestor (i.e. in the same clonal family) are released from the germinal center, their amino acid frequency profile reflects the allowed and disallowed substitutions at each position. These clonal-family-specific frequency profiles, called substitution profiles, are useful for studying the course of affinity maturation as well as for antibody engineering purposes. However, most often only a single sequence is recovered from each clonal family in a sequencing experiment, making it impossible to construct a clonal-family-specific substitution profile. Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, we present the method Substitution Profiles Using Related Families (SPURF), a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence. Using this framework, we show that substitution profiles from similar clonal families can be leveraged together with simulated substitution profiles and germline gene sequence information to improve prediction. We fit this model on a large public dataset and validate the robustness of our approach on an external dataset. Furthermore, we provide a command-line tool in an open-source software package (https://github.com/krdav/SPURF) implementing these ideas and providing easy prediction using our pre-fit models.
SARS-CoV-2 is what has caused the COVID-19 pandemic. Early viral infection is mediated by the SARS-CoV-2 homo-trimeric Spike (S) protein with its receptor binding domains (RBDs) in the receptor-accessible state. We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs). Our study reveals that the NTD acts as a wedge and plays a crucial regulatory role in the conformational changes of the S protein. The complete RBD structural transition is allowed only when the neighboring NTD that typically prohibits the RBDs movements as a wedge detaches and swings away. Based on this NTD wedge model, we propose that the NTD-RBD interface should be a potential drug target.
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