scholarly journals Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike RBD and human ACE2

2021 ◽  
Vol 118 (42) ◽  
pp. e2106480118
Author(s):  
Chen Chen ◽  
Veda Sheersh Boorla ◽  
Deepro Banerjee ◽  
Ratul Chowdhury ◽  
Victoria S. Cavener ◽  
...  
Keyword(s):  
Amino Acid ◽  
Binding Affinity ◽  
Computational Prediction ◽  
Single Amino Acid ◽  
Binding Affinities ◽  
Generalized Born ◽  
Angiotensin Converting Enzyme 2 ◽  
Novel Variants ◽  
Multiple Amino Acid ◽  
Correction Terms

The association of the receptor binding domain (RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein with human angiotensin-converting enzyme 2 (hACE2) represents the first required step for cellular entry. SARS-CoV-2 has continued to evolve with the emergence of several novel variants, and amino acid changes in the RBD have been implicated with increased fitness and potential for immune evasion. Reliably predicting the effect of amino acid changes on the ability of the RBD to interact more strongly with the hACE2 can help assess the implications for public health and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD−hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born solvation, hydrogen bonding, π−π packing, and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity changes using the decomposed energy terms as descriptors. The computational base achieves a validation accuracy of 82.8% for classifying single–amino acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient of 0.73 between predicted and experimentally calculated changes in binding affinities. Both metrics are calculated using a fivefold cross-validation test. Our method thus sets up a framework for screening binding affinity changes caused by unknown single– and multiple–amino acid changes offering a valuable tool to predict host adaptation of SARS-CoV-2 variants toward tighter hACE2 binding.

scholarly journals Computational prediction of the effect of amino acid changes on the binding affinity between SARS-CoV-2 spike protein and the human ACE2 receptor

2021 ◽  
Author(s):  
Chen Chen ◽  
Veda Sheeresh Boorla ◽  
Deepro Banerjee ◽  
Ratul Chowdhury ◽  
Victoria S Cavener ◽  
...  
Keyword(s):  
Amino Acid ◽  
Binding Affinity ◽  
Viral Entry ◽  
Computational Prediction ◽  
Single Amino Acid ◽  
Binding Affinities ◽  
Generalized Born ◽  
The Public ◽  
Affinity Change ◽  
Multiple Amino Acid

The association of the receptor binding domain (RBD) of SARS-CoV-2 viral spike with human angiotensin converting enzyme (hACE2) represents the first required step for viral entry. Amino acid changes in the RBD have been implicated with increased infectivity and potential for immune evasion. Reliably predicting the effect of amino acid changes in the ability of the RBD to interact more strongly with the hACE2 receptor can help assess the public health implications and the potential for spillover and adaptation into other animals. Here, we introduce a two-step framework that first relies on 48 independent 4-ns molecular dynamics (MD) trajectories of RBD-hACE2 variants to collect binding energy terms decomposed into Coulombic, covalent, van der Waals, lipophilic, generalized Born electrostatic solvation, hydrogen-bonding, π-π packing and self-contact correction terms. The second step implements a neural network to classify and quantitatively predict binding affinity using the decomposed energy terms as descriptors. The computational base achieves an accuracy of 82.2% in terms of correctly classifying single amino-acid substitution variants of the RBD as worsening or improving binding affinity for hACE2 and a correlation coefficient r of 0.69 between predicted and experimentally calculated binding affinities. Both metrics are calculated using a 5-fold cross validation test. Our method thus sets up a framework for effectively screening binding affinity change with unknown single and multiple amino-acid changes. This can be a very valuable tool to predict host adaptation and zoonotic spillover of current and future SARS-CoV-2 variants.

scholarly journals Genetic Variation and Evolution of the 2019 Novel Coronavirus

2021 ◽  
pp. 1-13
Author(s):  
Salvatore Dimonte ◽  
Muhammed Babakir-Mina ◽  
Taib Hama-Soor ◽  
Salar Ali
Keyword(s):  
Amino Acid ◽  
Receptor Binding ◽  
Rapid Evolution ◽  
Single Amino Acid ◽  
Angiotensin Converting Enzyme 2 ◽  
New Type ◽  
Amino Acid Mutations ◽  
Host Infection ◽  
Human Coronaviruses ◽  
Novel Coronavirus

<b><i>Introduction:</i></b> SARS-CoV-2 is a new type of coronavirus causing a pandemic severe acute respiratory syndrome (SARS-2). Coronaviruses are very diverting genetically and mutate so often periodically. The natural selection of viral mutations may cause host infection selectivity and infectivity. <b><i>Methods:</i></b> This study was aimed to indicate the diversity between human and animal coronaviruses through finding the rate of mutation in each of the spike, nucleocapsid, envelope, and membrane proteins. <b><i>Results:</i></b> The mutation rate is abundant in all 4 structural proteins. The most number of statistically significant amino acid mutations were found in spike receptor-binding domain (RBD) which may be because it is responsible for a corresponding receptor binding in a broad range of hosts and host selectivity to infect. Among 17 previously known amino acids which are important for binding of spike to angiotensin-converting enzyme 2 (ACE2) receptor, all of them are conservative among human coronaviruses, but only 3 of them significantly are mutated in animal coronaviruses. A single amino acid aspartate-454, that causes dissociation of the RBD of the spike and ACE2, and F486 which gives the strength of binding with ACE2 remain intact in all coronaviruses. <b><i>Discussion/Conclusion:</i></b> Observations of this study provided evidence of the genetic diversity and rapid evolution of SARS-CoV-2 as well as other human and animal coronaviruses.

scholarly journals The Differential Binding Affinities of the Luteinizing Hormone (LH)/Choriogonadotropin Receptor for LH and Choriogonadotropin Are Dictated by Different Extracellular Domain Residues

2005 ◽  
Vol 19 (5) ◽  
pp. 1263-1276 ◽  
Author(s):  
Colette Galet ◽  
Mario Ascoli
Keyword(s):  
Amino Acid ◽  
Ligand Binding ◽  
Binding Affinity ◽  
Extracellular Domain ◽  
Binding Affinities ◽  
Amino Acid Residues ◽  
Identify Amino Acid ◽  
Β Sheets ◽  
Leucine Rich Repeats ◽  
High Degree

Abstract The high degree of amino acid sequence homology and the divergent ligand binding affinities of the rat (r) and human (h) LH receptors (LHRs) allowed us to identify amino acid residues of their extracellular domain that are responsible for the different binding affinities of bovine (b) and hLH, and human choriogonadotropin (hCG) to the hLHR and rLHR. Because of the proposed importance of the β-sheets of the leucine-rich repeats (LRRs) of the extracellular domain of the LHR on hormone binding, we examined 10 divergent residues present in these regions by analyzing two complementary sets of mutants in which hLHR residues were substituted with the corresponding rLHR residues and vice versa. These experiments resulted in the identification of a single residue (a Ile or Ser in the C-terminal end of LRR2 of the hLHR or rLHR, respectively) that is important for hLH binding affinity. Surprisingly, however, this residue does not affect hCG or for bLH binding affinity. In fact, the results obtained with bLH and hCG show that several of the divergent residues in the β-sheets of LRR1–9 affect bLH binding affinity, but none of them affect hCG binding affinity. Importantly, our results also emphasize the involvement of residues outside of the β-sheets of the LRRs of the LHR in ligand binding affinity. This finding has to be considered in future models of the interaction of LH/CG with the LHR.

scholarly journals rasH mutants deficient in GTP binding.

1986 ◽  
Vol 6 (9) ◽  
pp. 3291-3294 ◽  
Author(s):  
C J Der ◽  
B T Pan ◽  
G M Cooper
Keyword(s):  
Amino Acid ◽  
Binding Affinity ◽  
Binding Proteins ◽  
Single Amino Acid ◽  
Amino Acid Substitutions ◽  
3T3 Cells ◽  
Protein Residues ◽  
Gtp Binding ◽  
Nih 3T3 ◽  
P21 Proteins

Single amino acid substitutions were introduced into a region of the rasH protein (residues 116, 117, and 119) homologous to a variety of diverse GTP-binding proteins. Each of the mutant p21 proteins displayed a significant reduction (10- to 5,000-fold) in GTP binding affinity. Activated rasH proteins deficient in GTP binding were unaltered in their ability to morphologically transform NIH 3T3 cells.

scholarly journals Computational prediction of the tolerance to amino-acid deletion in green-fluorescent protein

2016 ◽  
Author(s):  
Eleisha L. Jackson ◽  
Stephanie J. Spielman ◽  
Claus O. Wilke
Keyword(s):  
Green Fluorescent Protein ◽  
Protein Structure ◽  
Amino Acid ◽  
Protein Design ◽  
Fluorescent Protein ◽  
Computational Prediction ◽  
Single Amino Acid ◽  
Dimensional Structure ◽  
Amino Acid Deletion ◽  
Green Fluorescent

AbstractProteins evolve through two primary mechanisms: substitution, where mutations alter a protein’s amino-acid sequence, and insertions and deletions (indels), where amino acids are either added to or removed from the sequence. Protein structure has been shown to influence the rate at which substitutions accumulate across sites in proteins, but whether structure similarly constrains the occurrence of indels has not been rigorously studied. Here, we investigate the extent to which structural properties known to covary with protein evolutionary rates might also predict protein tolerance to indels. Specifically, we analyze a publicly available dataset of single–amino-acid deletion mutations in enhanced green fluorescent protein (eGFP) to assess how well the functional effect of deletions can be predicted from protein structure. We find that weighted contact number (WCN), which measures how densely packed a residue is within the protein’s three-dimensional structure, provides the best single predictor for whether eGFP will tolerate a given deletion. We additionally find that using protein design to explicitly model deletions results in improved predictions of functional status when combined with other structural predictors. Our work suggests that structure plays fundamental role in constraining deletions at sites in proteins, and further that similar biophysical constraints influence both substitutions and deletions. This study therefore provides a solid foundation for future work to examine how protein structure influences tolerance of more complex indel events, such as insertions or large deletions.

scholarly journals Target-Site Mutations Conferring Herbicide Resistance

Plants ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 382 ◽  
Author(s):  
Brent P. Murphy ◽  
Patrick J. Tranel
Keyword(s):  
Amino Acid ◽  
Herbicide Resistance ◽  
Target Site ◽  
Single Amino Acid ◽  
Cross Resistance ◽  
Target Sites ◽  
Resistance Patterns ◽  
Experimental Approaches ◽  
Sites Of Action ◽  
Multiple Amino Acid

Mutations conferring evolved herbicide resistance in weeds are known in nine different herbicide sites of action. This review summarizes recently reported resistance-conferring mutations for each of these nine target sites. One emerging trend is an increase in reports of multiple mutations, including multiple amino acid changes at the glyphosate target site, as well as mutations involving two nucleotide changes at a single amino acid codon. Standard reference sequences are suggested for target sites for which standards do not already exist. We also discuss experimental approaches for investigating cross-resistance patterns and for investigating fitness costs of specific target-site mutations.

scholarly journals Computational redesign of Fab CC12.3 with substantially better predicted binding affinity to SARS-CoV-2 than human ACE2 receptor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wantanee Treewattanawong ◽  
Thassanai Sitthiyotha ◽  
Surasak Chunsrivirot
Keyword(s):  
Binding Affinity ◽  
Protein Design ◽  
Neutralizing Antibodies ◽  
Neutralizing Antibody ◽  
Binding Affinities ◽  
Receptor Binding Domain ◽  
Fab Fragment ◽  
Angiotensin Converting Enzyme 2 ◽  
Large Numbers ◽  
Better Than

AbstractSARS-CoV-2 is responsible for COVID-19 pandemic, causing large numbers of cases and deaths. It initiates entry into human cells by binding to the peptidase domain of angiotensin-converting enzyme 2 (ACE2) receptor via its receptor binding domain of S1 subunit of spike protein (SARS-CoV-2-RBD). Employing neutralizing antibodies to prevent binding between SARS-CoV-2-RBD and ACE2 is an effective COVID-19 therapeutic solution. Previous studies found that CC12.3 is a highly potent neutralizing antibody that was isolated from a SARS-CoV-2 infected patient, and its Fab fragment (Fab CC12.3) bound to SARS-CoV-2-RBD with comparable binding affinity to ACE2. To enhance its binding affinity, we employed computational protein design to redesign all CDRs of Fab CC12.3 and molecular dynamics (MD) to validate their predicted binding affinities by the MM-GBSA method. MD results show that the predicted binding affinities of the three best designed Fabs CC12.3 (CC12.3-D02, CC12.3-D05, and CC12.3-D08) are better than those of Fab CC12.3 and ACE2. Additionally, our results suggest that enhanced binding affinities of CC12.3-D02, CC12.3-D05, and CC12.3-D08 are caused by increased SARS-CoV-2-RBD binding interactions of CDRs L1 and L3. This study redesigned neutralizing antibodies with better predicted binding affinities to SARS-CoV-2-RBD than Fab CC12.3 and ACE2. They are promising candidates as neutralizing antibodies against SARS-CoV-2.

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