scholarly journals Mutations within the P2 Domain of Norovirus Capsid Affect Binding to Human Histo-Blood Group Antigens: Evidence for a Binding Pocket

2003 ◽  
Vol 77 (23) ◽  
pp. 12562-12571 ◽  
Author(s):  
Ming Tan ◽  
Pengwei Huang ◽  
Jaroslaw Meller ◽  
Weiming Zhong ◽  
Tibor Farkas ◽  
...  
Keyword(s):  
Amino Acid ◽  
Blood Group ◽  
Binding Pocket ◽  
Single Amino Acid ◽  
Blood Group Antigens ◽  
Sequence Alignments ◽  
Amino Acid Mutation ◽  
Multiple Sequence ◽  
Amino Acid Mutations ◽  
Function Relationship

ABSTRACT Noroviruses (NORs) are an important cause of acute gastroenteritis. Recent studies of NOR receptors showed that different NORs bind to different histo-blood group antigens (HBGAs), and at least four distinct binding patterns were observed. To determine the structure-function relationship for NORs and their receptors, two strains representing two of the four binding patterns were studied. Strain VA387 binds to HBGAs of A, B, and O secretors, whereas strain MOH binds to HBGAs of A and B secretors only. Using multiple sequence alignments, homology modeling, and structural analysis of NOR capsids, we identified a plausible “pocket” in the P2 domain that may be responsible for binding to HBGA receptors. This pocket consists of a conserved RGD/K motif surrounded by three strain-specific hot spots (N302, T337, and Q375 for VA387 and N302, N338, and E378 for MOH). Subsequent mutagenesis experiments demonstrated that all four sites played important roles in binding. A single amino acid mutation at T337 (to A) in VA387 or a double amino acid mutation at RN338 (to TT) in MOH abolished binding completely. Change of the entire RGD motif to SAS abolished binding in case of VA387, whereas single amino acid mutations in that motif did not have an apparent effect on binding to A and B antigens but decreased binding to H antigen. Multiple mutations at the RGK motif of MOH (SIRGK to TFRGD) completely knocked out the binding. Mutation of N302 or Q375 in VA387 affected binding to type O HBGA only, while switch mutants with three amino acid changes at either site from MOH to VA387 resulted in a weak binding to type O HBGAs. A further switch mutant with three amino acid changes at E378 from MOH to VA387 diminished the binding to type A HBGA only. Taken together, our data indicate that the binding pocket likely exists on NOR capsids. Direct evidence of this hypothesis requires crystallography studies.

Predicting Structural and Serologic Modifications in Blood Group Antigens by Homology Modeling.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 451-451
Author(s):  
Connie M. Westhoff ◽  
Dwane E. Wylie
Keyword(s):  
Crystal Structure ◽  
Amino Acid ◽  
Homology Modeling ◽  
Blood Group ◽  
Amino Acid Change ◽  
Single Amino Acid ◽  
Side Chain ◽  
Blood Group Antigens ◽  
Aromatic Side Chain ◽  
Transport Channel

Abstract Homology modeling of blood group proteins offers the possibility of predicting the effect of amino acid changes on serologic phenotype and immunogenicity. The location of an amino acid change within known structural motifs, its phylogenetic conservation, and its proximity to known epitopes give insight into its potential effect on protein structure and, consequently, its clinical significance. We applied this approach to investigate the loss of membrane expression of the Dombrock blood group antigens in a patient with a single amino acid change and to investigate RhD alterations in weak D phenotypes. The Dombrock homology model was derived with rat ART2.2 crystal structure as template. For the RhD model, the crystal structure of the Rh-like-ammonia transporter from Nitrosomonas europaea was used. Protein alignment was derived with Clustal X, adjusted visually, and submitted to the Swiss Modeling server. Models were viewed with Deep View Swiss Pdb Viewer. The Dombrock null containes a Phe62Ser substitution. This Phe (F) residue is located in an FDDQY motif near the COOH terminus. This region of the protein also contains a HYYLT motif. These two motifs are highly conserved in the ART protein family and contribute several aromatic amino acids to this region of the molecule. Aromatic side chain interactions between these residues could contribute to the stability of the Do protein. In support, the distance in the ART2.2 crystal structure between Phe in FDDQY and His in HYYLT is 3.7 Å, which is the appropriate distance for aromatic side chain interactions. This is also the measured distance between these two residues in the Do model. Thus, protein modeling indicates that the Phe62Ser mutation disrupts important stacking interactions between Phe62 and His160. When amino acid changes causing weak D phenotypes were examined, some of those affecting expression of RhD were located near the vestigial transport channel. These include the Trp220Arg mutation (weak D Type 16). This Trp residue is part of the transport channel in Nitrosomonas and is conserved in Rh proteins of almost all species. Its role in maintaining Rh structure is indicated by the dramatic effect its modification has on protein and epitope expression. Additionally, Arg114Trp change (weak D Type 17), which is also near the channel, reduces D expression to only 66 antigen sites/cell. GlyXXXGly motifs stabilize interactions of adjacent alpha helices in membrane proteins. Evidence for a role in stabilization of RhD is revealed by the Gly282Asp mutation (weak D Type 15) which is part of such a motif. In addition, a D-epitope in loop 3 is near the 282Asp residue. Alteration of helical packing accompanied by epitope conformation could explain production of anti-D in patients with weak D Type 15. Homology modeling is an important tool for understanding the structure and serologic bases of blood group proteins and will continue to give important insight as more protein crystal structures become available.

scholarly journals Single Amino Acid Mutations in the Potato Immune Receptor R3a Expand Response to Phytophthora Effectors

2014 ◽  
Vol 27 (7) ◽  
pp. 624-637 ◽  
Author(s):  
María Eugenia Segretin ◽  
Marina Pais ◽  
Marina Franceschetti ◽  
Angela Chaparro-Garcia ◽  
Jorunn I. B. Bos ◽  
...  
Keyword(s):  
Amino Acid ◽  
Random Mutagenesis ◽  
Nucleotide Binding ◽  
Binding Pocket ◽  
Single Amino Acid ◽  
Effector Protein ◽  
Immune Receptor ◽  
Pathogen Effector ◽  
Oomycete Pathogen ◽  
Amino Acid Mutations

Both plants and animals rely on nucleotide-binding domain and leucine-rich repeat-containing (NB-LRR or NLR) proteins to respond to invading pathogens and activate immune responses. How plant NB-LRR proteins respond to pathogens is poorly understood. We undertook a gain-of-function random mutagenesis screen of the potato NB-LRR immune receptor R3a to study how this protein responds to the effector protein AVR3a from the oomycete pathogen Phytophthora infestans. R3a response can be extended to the stealthy AVR3aEM isoform of the effector while retaining recognition of AVR3aKI. Each one of eight single amino acid mutations is sufficient to expand the R3a response to AVR3aEM and other AVR3a variants. These mutations occur across the R3a protein, from the N terminus to different regions of the LRR domain. Further characterization of these R3a mutants revealed that at least one of them was sensitized, exhibiting a stronger response than the wild-type R3a protein to AVR3aKI. Remarkably, the N336Y mutation, near the R3a nucleotide-binding pocket, conferred response to the effector protein PcAVR3a4 from the vegetable pathogen P. capsici. This work contributes to understanding how NB-LRR receptor specificity can be modulated. Together with knowledge of pathogen effector diversity, this strategy can be exploited to develop synthetic immune receptors.

scholarly journals An integrated screening system for the selection of exemplary substrates for natural and engineered cytochrome P450s

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Naoki Kanoh ◽  
Ayano Kawamata-Asano ◽  
Kana Suzuki ◽  
Yusuke Takahashi ◽  
Takeshi Miyazawa ◽  
...  
Keyword(s):  
Amino Acid ◽  
Oxidation Products ◽  
Single Amino Acid ◽  
Cytochrome P450 Monooxygenases ◽  
Substrate Selectivity ◽  
Screening System ◽  
Amino Acid Mutation ◽  
P450 Monooxygenases ◽  
Amino Acid Mutations ◽  
Selection Of

AbstractInformation about substrate and product selectivity is critical for understanding the function of cytochrome P450 monooxygenases. In addition, comprehensive understanding of changes in substrate selectivity of P450 upon amino acid mutation would enable the design and creation of engineered P450s with desired selectivities. Therefore, systematic methods for obtaining such information are required. Herein, we developed an integrated P450 substrate screening system for the selection of “exemplary” substrates for a P450 of interest. The established screening system accurately selected the known exemplary substrates and also identified previously unknown exemplary substrates for microbial-derived P450s from a library containing sp3-rich synthetic small molecules. Synthetically potent transformations were also found by analyzing the reactions and oxidation products. The screening system was applied to analyze the substrate selectivity of the P450 BM3 mutants F87A and F87A/A330W, which acquired an ability to hydroxylate non-natural substrate steroids regio- and stereoselectively by two amino acid mutations. The distinct transition of exemplary substrates due to each single amino acid mutation was revealed, demonstrating the utility of the established system.

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 Prospective mapping of viral mutations that escape antibodies used to treat COVID-19

Science ◽  
2021 ◽  
Vol 371 (6531) ◽  
pp. 850-854 ◽  
Author(s):  
Tyler N. Starr ◽  
Allison J. Greaney ◽  
Amin Addetia ◽  
William W. Hannon ◽  
Manish C. Choudhary ◽  
...  
Keyword(s):  
Amino Acid ◽  
Infected Patient ◽  
Viral Escape ◽  
Single Amino Acid ◽  
Receptor Binding Domain ◽  
Amino Acid Mutation ◽  
The Individual ◽  
Viral Surveillance ◽  
Single Amino Acid Mutation

Antibodies are a potential therapy for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the risk of the virus evolving to escape them remains unclear. Here we map how all mutations to the receptor binding domain (RBD) of SARS-CoV-2 affect binding by the antibodies in the REGN-COV2 cocktail and the antibody LY-CoV016. These complete maps uncover a single amino acid mutation that fully escapes the REGN-COV2 cocktail, which consists of two antibodies, REGN10933 and REGN10987, targeting distinct structural epitopes. The maps also identify viral mutations that are selected in a persistently infected patient treated with REGN-COV2 and during in vitro viral escape selections. Finally, the maps reveal that mutations escaping the individual antibodies are already present in circulating SARS-CoV-2 strains. These complete escape maps enable interpretation of the consequences of mutations observed during viral surveillance.

Structural assessment of single amino acid mutations: application to TP53 function

2006 ◽  
Vol 27 (9) ◽  
pp. 926-937 ◽  
Author(s):  
Yum L. Yip ◽  
Vincent Zoete ◽  
Holger Scheib ◽  
Olivier Michielin
Keyword(s):  
Amino Acid ◽  
Single Amino Acid ◽  
Structural Assessment ◽  
Amino Acid Mutations

scholarly journals Embeddings from protein language models predict conservation and variant effects

2021 ◽  
Author(s):  
Céline Marquet ◽  
Michael Heinzinger ◽  
Tobias Olenyi ◽  
Christian Dallago ◽  
Michael Bernhofer ◽  
...  
Keyword(s):  
Protein Function ◽  
Language Models ◽  
Single Amino Acid ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Human Proteins ◽  
Entire Sequence ◽  
Embedding Methods ◽  
Better Than

Abstract The emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (LMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or marked amino acids from the context of entire sequence regions. Here, we explored how to benefit from learned protein LM representations (embeddings) to predict SAV effects. Although we have failed so far to predict SAV effects directly from embeddings, this input alone predicted residue conservation almost as accurately from single sequences as using multiple sequence alignments (MSAs) with a two-state per-residue accuracy (conserved/not) of Q2=80% (embeddings) vs. 81% (ConSeq). Considering all SAVs at all residue positions predicted as conserved to affect function reached 68.6% (Q2: effect/neutral; for PMD) without optimization, compared to an expert solution such as SNAP2 (Q2=69.8). Combining predicted conservation with BLOSUM62 to obtain variant-specific binary predictions, DMS experiments of four human proteins were predicted better than by SNAP2, and better than by applying the same simplistic approach to conservation taken from ConSeq. Thus, embedding methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. This allowed prediction of SAV effects for the entire human proteome (~20k proteins) within 17 minutes on a single GPU.

scholarly journals Embeddings from protein language models predict conservation and variant effects

2021 ◽  
Author(s):  
Céline Marquet ◽  
Michael Heinzinger ◽  
Tobias Olenyi ◽  
Christian Dallago ◽  
Kyra Erckert ◽  
...  
Keyword(s):  
Protein Function ◽  
Pearson Correlation ◽  
Performance Measure ◽  
Language Models ◽  
Single Amino Acid ◽  
Data Sets ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Human Proteins

AbstractThe emergence of SARS-CoV-2 variants stressed the demand for tools allowing to interpret the effect of single amino acid variants (SAVs) on protein function. While Deep Mutational Scanning (DMS) sets continue to expand our understanding of the mutational landscape of single proteins, the results continue to challenge analyses. Protein Language Models (pLMs) use the latest deep learning (DL) algorithms to leverage growing databases of protein sequences. These methods learn to predict missing or masked amino acids from the context of entire sequence regions. Here, we used pLM representations (embeddings) to predict sequence conservation and SAV effects without multiple sequence alignments (MSAs). Embeddings alone predicted residue conservation almost as accurately from single sequences as ConSeq using MSAs (two-state Matthews Correlation Coefficient—MCC—for ProtT5 embeddings of 0.596 ± 0.006 vs. 0.608 ± 0.006 for ConSeq). Inputting the conservation prediction along with BLOSUM62 substitution scores and pLM mask reconstruction probabilities into a simplistic logistic regression (LR) ensemble for Variant Effect Score Prediction without Alignments (VESPA) predicted SAV effect magnitude without any optimization on DMS data. Comparing predictions for a standard set of 39 DMS experiments to other methods (incl. ESM-1v, DeepSequence, and GEMME) revealed our approach as competitive with the state-of-the-art (SOTA) methods using MSA input. No method outperformed all others, neither consistently nor statistically significantly, independently of the performance measure applied (Spearman and Pearson correlation). Finally, we investigated binary effect predictions on DMS experiments for four human proteins. Overall, embedding-based methods have become competitive with methods relying on MSAs for SAV effect prediction at a fraction of the costs in computing/energy. Our method predicted SAV effects for the entire human proteome (~ 20 k proteins) within 40 min on one Nvidia Quadro RTX 8000. All methods and data sets are freely available for local and online execution through bioembeddings.com, https://github.com/Rostlab/VESPA, and PredictProtein.

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