Dataset Information

Examining protein surface structure in highly conserved sequence variants with mass spectrometry.

ABSTRACT: A simple mass spectrometry-based method capable of examining protein structure called SNAPP (selective noncovalent adduct protein probing) is used to evaluate the structural consequences of point mutations in naturally occurring sequence variants from different species. SNAPP monitors changes in the attachment of noncovalent adducts to proteins as a function of structural state. Mutations that lead to perturbations to the electrostatic surface structure of a protein affect noncovalent attachment and are easily observed with SNAPP. Mutations that do not alter the tertiary structure or electrostatic surface structure yield similar results by SNAPP. For example, bovine, porcine, and human insulin all have very similar backbone structures and no basic or acidic residue mutations, and the SNAPP distributions for all three proteins are very similar. In contrast, four variants of cytochrome c (cytc) have varying degrees of sequence homology, which are reflected in the observed SNAPP distributions. Bovine and pigeon cytc have several basic or acidic residue substitutions relative to horse cytc, but the SNAPP distributions for all three proteins are similar. This suggests that these mutations do not significantly influence the protein surface structure. On the other hand, yeast cytc has the least sequence homology and exhibits a unique, though related, SNAPP distribution. Even greater differences are observed for lysozyme. Hen and human lysozyme have identical tertiary structures but significant variations in the locations of numerous basic and acidic residues. The SNAPP distributions are quite distinct for the two forms of lysozyme, suggesting significant differences in the surface structures. In summary, SNAPP experiments are relatively easy to perform, require minimal sample consumption, and provide a facile route for comparison of protein surface structure between highly homologous proteins.

SUBMITTER: Tao Y

PROVIDER: S-EPMC3307800 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Examining protein surface structure in highly conserved sequence variants with mass spectrometry.

Tao Yuanqi Y Julian Ryan R RR

Biochemistry 20120210 8

A simple mass spectrometry-based method capable of examining protein structure called SNAPP (selective noncovalent adduct protein probing) is used to evaluate the structural consequences of point mutations in naturally occurring sequence variants from different species. SNAPP monitors changes in the attachment of noncovalent adducts to proteins as a function of structural state. Mutations that lead to perturbations to the electrostatic surface structure of a protein affect noncovalent attachment ...[more]

PMID: 22320248

Similar Datasets

Project description:Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein-protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While de novo structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein-protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.

Project description:Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are characterized by differences in transmissibility and response to therapeutics. Therefore, discriminating among them is vital for surveillance, infection prevention, and patient care. While whole viral genome sequencing (WGS) is the "gold standard" for variant identification, molecular variant panels have become increasingly available. Most, however, are based on limited targets and have not undergone comprehensive evaluation. We assessed the diagnostic performance of the highly multiplexed Agena MassARRAY ® SARS-CoV-2 Variant Panel v3 to identify variants in a diverse set of 391 SARS-CoV-2 clinical RNA specimens collected across our health systems in New York City, USA as well as in Bogotá, Colombia (September 2, 2020 - March 2, 2022). We demonstrate almost perfect levels of interrater agreement between this assay and WGS for 9 of 11 variant calls (κ ≥ 0.856) and 25 of 30 targets (κ ≥ 0.820) tested on the panel. The assay had a high diagnostic sensitivity (≥93.67%) for contemporary variants (e.g., Iota, Alpha, Delta, Omicron [BA.1 sublineage]) and a high diagnostic specificity for all 11 variants (≥96.15%) and all 30 targets (≥94.34%) tested. Moreover, we highlight distinct target patterns that can be utilized to identify variants not yet defined on the panel including the Omicron BA.2 and other sublineages. These findings exemplify the power of highly multiplexed diagnostic panels to accurately call variants and the potential for target result signatures to elucidate new ones.ImportanceThe continued circulation of SARS-CoV-2 amidst limited surveillance efforts and inconsistent vaccination of populations has resulted in emergence of variants that uniquely impact public health systems. Thus, in conjunction with functional and clinical studies, continuous detection and identification are quintessential to inform diagnostic and public health measures. Furthermore, until WGS becomes more accessible in the clinical microbiology laboratory, the ideal assay for identifying variants must be robust, provide high resolution, and be adaptable to the evolving nature of viruses like SARS-CoV-2. Here, we highlight the diagnostic capabilities of a highly multiplexed commercial assay to identify diverse SARS-CoV-2 lineages that circulated at over September 2, 2020 - March 2, 2022 among patients seeking care at our health systems. This assay demonstrates variant-specific signatures of nucleotide/amino acid polymorphisms and underscores its utility for detection of contemporary and emerging SARS-CoV-2 variants of concern.

Dataset Information

Examining protein surface structure in highly conserved sequence variants with mass spectrometry.

Publications

Examining protein surface structure in highly conserved sequence variants with mass spectrometry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets