Mutations In Protein Sequences Research Articles

DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates.

The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict ${k}_{cat}$ and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on ${k}_{cat}$ values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.

Probing protein stability: towards a computational atomistic, reliable, affordable, and improvable model.

We present an improved application of a recently proposed computational method designed to evaluate the change of free energy as a function of the average value of a suitably chosen collective variable in proteins. The method is based on a full atomistic description of the protein and its environment. The goal is to understand how the protein melting temperature changes upon single-point mutations, because the sign of the temperature variation will allow us to discriminate stabilizing vs. destabilizing mutations in protein sequences. In this refined application the method is based on altruistic well-tempered metadynamics, a variant of multiple-walkers metadynamics. The resulting metastatistics is then modulated by the maximal constrained entropy principle. The latter turns out to be especially helpful in free-energy calculations as it is able to alleviate the severe limitations of metadynamics in properly sampling folded and unfolded configurations. In this work we apply the computational strategy outlined above in the case of the bovine pancreatic trypsin inhibitor, a well-studied small protein, which is a reference for computer simulations since decades. We compute the variation of the melting temperature characterizing the folding-unfolding process between the wild-type protein and two of its single-point mutations that are seen to have opposite effect on the free energy changes. The same approach is used for free energy difference calculations between a truncated form of frataxin and a set of five of its variants. Simulation data are compared to in vitro experiments. In all cases the sign of the change of melting temperature is reproduced, under the further approximation of using an empirical effective mean-field to average out protein-solvent interactions.

Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2

SummaryDeep mutational scanning (DMS) experiments have been performed on SARS-CoV-2’s spike receptor-binding domain (RBD) and human angiotensin-converting enzyme 2 (ACE2) zinc-binding peptidase domain—both central players in viral infection and evolution and antibody evasion—quantifying how mutations impact biochemical phenotypes. We modeled biochemical phenotypes from massively parallel assays, using neural networks trained on protein sequence mutations in the virus and human host. Neural networks were significantly predictive of binding affinity, protein expression, and antibody escape, learning complex interactions and higher-order features that are difficult to capture with conventional methods from structural biology. Integrating the physicochemical properties of amino acids, such as hydrophobicity and long-range non-bonded energy per atom, significantly improved prediction (empirical p < 0.01). We observed concordance of the neural network predictions with molecular dynamics (multiple 500 ns or 1 μs all-atom) simulations of the spike protein-ACE2 interface, with critical implications for the use of deep learning to dissect molecular mechanisms.

Bacterial translation machinery for deliberate mistranslation of the genetic code

Inaccurate expression of the genetic code, also known as mistranslation, is an emerging paradigm in microbial studies. Growing evidence suggests that many microbial pathogens can deliberately mistranslate their genetic code to help invade a host or evade host immune responses. However, discovering different capacities for deliberate mistranslation remains a challenge because each group of pathogens typically employs a unique mistranslation mechanism. In this study, we address this problem by studying duplicated genes of aminoacyl-transfer RNA (tRNA) synthetases. Using bacterial prolyl-tRNA synthetase (ProRS) genes as an example, we identify an anomalous ProRS isoform, ProRSx, and a corresponding tRNA, tRNAProA, that are predominately found in plant pathogens from Streptomyces species. We then show that tRNAProA has an unusual hybrid structure that allows this tRNA to mistranslate alanine codons as proline. Finally, we provide biochemical, genetic, and mass spectrometric evidence that cells which express ProRSx and tRNAProA can translate GCU alanine codons as both alanine and proline. This dual use of alanine codons creates a hidden proteome diversity due to stochastic Ala→Pro mutations in protein sequences. Thus, we show that important plant pathogens are equipped with a tool to alter the identity of their sense codons. This finding reveals the initial example of a natural tRNA synthetase/tRNA pair for dedicated mistranslation of sense codons.

Sequence-based genome-wide association study of individual milk mid-infrared wavenumbers in mixed-breed dairy cattle

BackgroundFourier-transform mid-infrared (FT-MIR) spectroscopy provides a high-throughput and inexpensive method for predicting milk composition and other novel traits from milk samples. While there have been many genome-wide association studies (GWAS) conducted on FT-MIR predicted traits, there have been few GWAS for individual FT-MIR wavenumbers. Using imputed whole-genome sequence for 38,085 mixed-breed New Zealand dairy cattle, we conducted GWAS on 895 individual FT-MIR wavenumber phenotypes, and assessed the value of these direct phenotypes for identifying candidate causal genes and variants, and improving our understanding of the physico-chemical properties of milk.ResultsSeparate GWAS conducted for each of 895 individual FT-MIR wavenumber phenotypes, identified 450 1-Mbp genomic regions with significant FT-MIR wavenumber QTL, compared to 246 1-Mbp genomic regions with QTL identified for FT-MIR predicted milk composition traits. Use of mammary RNA-seq data and gene annotation information identified 38 co-localized and co-segregating expression QTL (eQTL), and 31 protein-sequence mutations for FT-MIR wavenumber phenotypes, the latter including a null mutation in the ABO gene that has a potential role in changing milk oligosaccharide profiles. For the candidate causative genes implicated in these analyses, we examined the strength of association between relevant loci and each wavenumber across the mid-infrared spectrum. This revealed shared association patterns for groups of genomically-distant loci, highlighting clusters of loci linked through their biological roles in lactation and their presumed impacts on the chemical composition of milk.ConclusionsThis study demonstrates the utility of FT-MIR wavenumber phenotypes for improving our understanding of milk composition, presenting a larger number of QTL and putative causative genes and variants than found from FT-MIR predicted composition traits. Examining patterns of significance across the mid-infrared spectrum for loci of interest further highlighted commonalities of association, which likely reflects the physico-chemical properties of milk constituents.

Comprehensive comparative genomic and microsatellite analysis of SARS, MERS, BAT‐SARS, and COVID‐19 coronaviruses

The coronavirus disease 2019 (COVID‐19) pandemic has spread around the globe very rapidly. Previously, the evolution pattern and similarity among the COVID‐19 causative organism severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and causative organisms of other similar infections have been determined using a single type of genetic marker in different studies. Herein, the SARS‐CoV‐2 and related β coronaviruses Middle East respiratory syndrome coronavirus (MERS‐CoV), SARS‐CoV, bat coronavirus (BAT‐CoV) were comprehensively analyzed using a custom‐built pipeline that employed phylogenetic approaches based on multiple types of genetic markers including the whole genome sequences, mutations in nucleotide sequences, mutations in protein sequences, and microsatellites. The whole‐genome sequence‐based phylogeny revealed that the strains of SARS‐CoV‐2 are more similar to the BAT‐CoV strains. The mutational analysis showed that on average MERS‐CoV and BAT‐CoV genomes differed at 134.21 and 136.72 sites, respectively, whereas the SARS‐CoV genome differed at 26.64 sites from the reference genome of SARS‐CoV‐2. Furthermore, the microsatellite analysis highlighted a relatively higher number of average microsatellites for MERS‐CoV and SARS‐CoV‐2 (106.8 and 107, respectively), and a lower number for SARS‐CoV and BAT‐CoV (95.8 and 98.5, respectively). Collectively, the analysis of multiple genetic markers of selected β viral genomes revealed that the newly born SARS‐COV‐2 is closely related to BAT‐CoV, whereas, MERS‐CoV is more distinct from the SARS‐CoV‐2 than BAT‐CoV and SARS‐CoV.

Profiling of Initial Available SARS-CoV-2 Sequences from Iranian Related COVID-19 Patients.

The etiologic agent SARS-CoV-2 has caused the outbreak of COVID-19 which is spread widely around the world. It is vital to uncover and investigate the full genome sequence of SARS-CoV-2 throughout the world to track changes in this virus. To this purpose, SARS-CoV-2 full genome sequence profiling of 20 patients in Iran and different countries that already had a travel history to Iran or contacts with Iranian cases were provided from the GISAID database. The bioinformatics analysis showed 44 different nucleotide mutations that caused 26 nonsynonymous mutations in protein sequences with regard to the reference full genome of the SARS-CoV-2 sequence (NC_045512.2). R207C, V378I, M2796I, L3606F, and A6407V in ORF1ab were common mutations in these sequences. Also, some of the detected mutations only were found in Iranian data in comparison with all the available sequences of SARS-CoV-2. The position of S protein mutations showed they were far from the binding site of this protein with angiotensin-converting enzyme-2 (ACE2) as the host cell receptor. These results can be helpful to design specific diagnostic tests, trace the SARS-CoV-2 sequence changes in Iran, and explore therapeutic drugs and vaccines.

From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes

BackgroundThe protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function.ResultsRecent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation.ConclusionsThe proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction.

Compensatory Mutations in Protein Sequences from Big-Data

Compensatory Mutations in Protein Sequences from Big-Data

A Computational Approach From Gene to Structure Analysis of the Human ABCA4 Transporter Involved in Genetic Retinal Diseases.

The aim of this article is to report the investigation of the structural features of ABCA4, a protein associated with a genetic retinal disease. A new database collecting knowledge of ABCA4 structure may facilitate predictions about the possible functional consequences of gene mutations observed in clinical practice. In order to correlate structural and functional effects of the observed mutations, the structure of mouse P-glycoprotein was used as a template for homology modeling. The obtained structural information and genetic data are the basis of our relational database (ABCA4Database). Sequence variability among all ABCA4-deposited entries was calculated and reported as Shannon entropy score at the residue level. The three-dimensional model of ABCA4 structure was used to locate the spatial distribution of the observed variable regions. Our predictions from structural in silico tools were able to accurately link the functional effects of mutations to phenotype. The development of the ABCA4Database gathers all the available genetic and structural information, yielding a global view of the molecular basis of some retinal diseases. ABCA4 modeled structure provides a molecular basis on which to analyze protein sequence mutations related to genetic retinal disease in order to predict the risk of retinal disease across all possible ABCA4 mutations. Additionally, our ABCA4 predicted structure is a good starting point for the creation of a new data analysis model, appropriate for precision medicine, in order to develop a deeper knowledge network of the disease and to improve the management of patients.

Species Distribution and In Vitro Azole Susceptibility of Aspergillus Section Nigri Isolates from Clinical and Environmental Settings

Aspergillus section Nigri includes species of interest for animal and human health, although studies on species distribution are limited to human cases. Data on the antifungal susceptibilities and the molecular mechanism of triazole resistance in strains belonging to this section are scant. Forty-two black Aspergillus strains from human patients (16 isolates), animals (14 isolates), and the environment (12 isolates) were molecularly characterized and their in vitro triazole susceptibilities investigated. Aspergillus tubingensis was isolated from humans, animals, and environmental settings, whereas Aspergillus awamori and Aspergillus niger were isolated exclusively from humans. Phylogenetic analyses of β-tubulin and calmodulin gene sequences were concordant in differentiating A. tubingensis from A. awamori and A. niger Voriconazole and posaconazole (PSZ) were the most active triazoles. One A. tubingensis strain was resistant to itraconazole and PSZ and one A. niger strain to PSZ. Sequence analysis of the cyp51A gene revealed different sequence types within a species, and A. tubingensis strains were also phylogenetically distinct from A. awamori/A. niger strains according to the strain origin and susceptibility profile. Genetic analysis of the cyp51A sequences suggests that two nonsynonymous mutations resulting in amino acid substitutions in the CYP51A protein (changes of L to R at position 21 [L21R] and of Q to R at position 228 [Q228R]) might be involved in azole resistance. Though azole resistance in black Aspergillus isolates from animals and rural environments does not represent a threat to public health in Southern Italy, the use of triazoles in the clinical setting needs to better monitored. The cyp51A sequence is useful for the molecular identification of black Aspergillus, and point mutations in protein sequences could be responsible for azole resistance phenomena.

Integrative visual analysis of protein sequence mutations

BackgroundAn important aspect of studying the relationship between protein sequence, structure and function is the molecular characterization of the effect of protein mutations. To understand the functional impact of amino acid changes, the multiple biological properties of protein residues have to be considered together.ResultsHere, we present a novel visual approach for analyzing residue mutations. It combines different biological visualizations and integrates them with molecular data derived from external resources. To show various aspects of the biological information on different scales, our approach includes one-dimensional sequence views, three-dimensional protein structure views and two-dimensional views of residue interaction networks as well as aggregated views. The views are linked tightly and synchronized to reduce the cognitive load of the user when switching between them. In particular, the protein mutations are mapped onto the views together with further functional and structural information. We also assess the impact of individual amino acid changes by the detailed analysis and visualization of the involved residue interactions. We demonstrate the effectiveness of our approach and the developed software on the data provided for the BioVis 2013 data contest.ConclusionsOur visual approach and software greatly facilitate the integrative and interactive analysis of protein mutations based on complementary visualizations. The different data views offered to the user are enriched with information about molecular properties of amino acid residues and further biological knowledge.

PuF, an antimetastatic and developmental signaling protein, interacts with the Alzheimer's amyloid-β precursor protein via a tissue-specific proximal regulatory element (PRE).

BackgroundAlzheimer’s disease (AD) is intimately tied to amyloid-β (Aβ) peptide. Extraneuronal brain plaques consisting primarily of Aβ aggregates are a hallmark of AD. Intraneuronal Aβ subunits are strongly implicated in disease progression. Protein sequence mutations of the Aβ precursor protein (APP) account for a small proportion of AD cases, suggesting that regulation of the associated gene (APP) may play a more important role in AD etiology. The APP promoter possesses a novel 30 nucleotide sequence, or “proximal regulatory element” (PRE), at −76/−47, from the +1 transcription start site that confers cell type specificity. This PRE contains sequences that make it vulnerable to epigenetic modification and may present a viable target for drug studies. We examined PRE-nuclear protein interaction by gel electrophoretic mobility shift assay (EMSA) and PRE mutant EMSA. This was followed by functional studies of PRE mutant/reporter gene fusion clones.ResultsEMSA probed with the PRE showed DNA-protein interaction in multiple nuclear extracts and in human brain tissue nuclear extract in a tissue-type specific manner. We identified transcription factors that are likely to bind the PRE, using competition gel shift and gel supershift: Activator protein 2 (AP2), nm23 nucleoside diphosphate kinase/metastatic inhibitory protein (PuF), and specificity protein 1 (SP1). These sites crossed a known single nucleotide polymorphism (SNP). EMSA with PRE mutants and promoter/reporter clone transfection analysis further implicated PuF in cells and extracts. Functional assays of mutant/reporter clone transfections were evaluated by ELISA of reporter protein levels. EMSA and ELISA results correlated by meta-analysis.ConclusionsWe propose that PuF may regulate the APP gene promoter and that AD risk may be increased by interference with PuF regulation at the PRE. PuF is targeted by calcium/calmodulin-dependent protein kinase II inhibitor 1, which also interacts with the integrins. These proteins are connected to vital cellular and neurological functions. In addition, the transcription factor PuF is a known inhibitor of metastasis and regulates cell growth during development. Given that APP is a known cell adhesion protein and ferroxidase, this suggests biochemical links among cell signaling, the cell cycle, iron metabolism in cancer, and AD in the context of overall aging.

Interactome-Wide Prediction of Protein-Protein Binding Sites Reveals Effects of Protein Sequence Variation in Arabidopsis thaliana

The specificity of protein-protein interactions is encoded in those parts of the sequence that compose the binding interface. Therefore, understanding how changes in protein sequence influence interaction specificity, and possibly the phenotype, requires knowing the location of binding sites in those sequences. However, large-scale detection of protein interfaces remains a challenge. Here, we present a sequence- and interactome-based approach to mine interaction motifs from the recently published Arabidopsis thaliana interactome. The resultant proteome-wide predictions are available via www.ab.wur.nl/sliderbio and set the stage for further investigations of protein-protein binding sites. To assess our method, we first show that, by using a priori information calculated from protein sequences, such as evolutionary conservation and residue surface accessibility, we improve the performance of interface prediction compared to using only interactome data. Next, we present evidence for the functional importance of the predicted sites, which are under stronger selective pressure than the rest of protein sequence. We also observe a tendency for compensatory mutations in the binding sites of interacting proteins. Subsequently, we interrogated the interactome data to formulate testable hypotheses for the molecular mechanisms underlying effects of protein sequence mutations. Examples include proteins relevant for various developmental processes. Finally, we observed, by analysing pairs of paralogs, a correlation between functional divergence and sequence divergence in interaction sites. This analysis suggests that large-scale prediction of binding sites can cast light on evolutionary processes that shape protein-protein interaction networks.

On the mechanisms of bananin activity against severe acute respiratory syndrome coronavirus

In a previous study, severe acute respiratory syndrome coronavirus (SARS‐CoV) was cultured in the presence of bananin, an effective adamantane‐related molecule with antiviral activity. In the present study, we show that all bananin‐resistant variants exhibit mutations in helicase and membrane protein, although no evidence of bananin interference on their mutual interaction has been found. A structural analysis on protein sequence mutations found in SARS‐CoV bananin‐resistant variants was performed. The S259/L mutation of SARS‐CoV helicase is always found in all the identified bananin‐resistant variants, suggesting a primary role of this mutation site for bananin activity. From a structural analysis of SARS‐CoV predicted helicase structure, S259 is found in a hydrophilic surface pocket, far from the enzyme active sites and outside the helicase dimer interface. The S/L substitution causes a pocket volume reduction that weakens the interaction between bananin and SARS‐CoV mutated helicase, suggesting a possible mechanism for bananin antiviral activity.

Statistical method on nonrandom clustering with application to somatic mutations in cancer

BackgroundHuman cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.ResultsWe propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and β-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors.ConclusionsOur proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.

MAP MUTATION: a program for analyzing mutations in protein sequences

Avian influenza is an infectious disease of birds caused by type-A strains of the influenza virus. The unprecedented spread of the highly pathogenic avian influenza type A is a threat to veterinary and human health. Influenza viruses continuously undergo mutations and they lack proofreading mechanism. Hence, they evolve to new forms of the virus. We describe MAP MUTATION (developed using PERL script) to quickly compare two strains and display mutational information and report specific positions where mutation has occurred. The PERL script is available from the authors for non-commercial purposes.

Transcriptional homeostasis: a mechanism of protein quality control

Sickle cell anaemia confirms that minor mutations in protein sequence can have catastrophic effects. However, RNA transcription depends on polymerases that have low fidelity and introduce errors at a rate of 10 −5 substitutions/base synthesised. For many large proteins translation of these errors could result in significant loss of function, while for others (for example, cell surface signalling proteins) variability of protein expression could be advantageous. This paper outlines a mechanism that enables proteins to modulate error incorporation (variability) into RNAs sysnthesised by RNA polymerase – transcriptional homeostasis – thereby modulating, and error correcting, their own sysnthesis. Transcriptional homeostasis is a fundamental regulatory mechanism relevant to control of gene expression.

The human serum proteome: display of nearly 3700 chromatographically separated protein spots on two-dimensional electrophoresis gels and identification of 325 distinct proteins.

Plasma, the soluble component of the human blood, is believed to harbor thousands of distinct proteins, which originate from a variety of cells and tissues through either active secretion or leakage from blood cells or tissues. The dynamic range of plasma protein concentrations comprises at least nine orders of magnitude. Proteins involved in coagulation, immune defense, small molecule transport, and protease inhibition, many of them present in high abundance in this body fluid, have been functionally characterized and associated with disease processes. For example, protein sequence mutations in coagulation factors cause various serious disease states. Diagnosing and monitoring such diseases in blood plasma of affected individuals has typically been conducted by use of enzyme-linked immunosorbent assays, which using a specific antibody quantitatively measure only the affected protein in the tested plasma samples. The discovery of protein biomarkers in plasma for diseases with no known correlations to genetic mutations is challenging. It requires a highly parallel display and quantitation strategy for proteins. We fractionated blood serum proteins prior to display on two-dimensional electrophoresis (2-DE) gels using immunoaffinity chromatography to remove the most abundant serum proteins, followed by sequential anion-exchange and size-exclusion chromatography. Serum proteins from 74 fractions were displayed on 2-DE gels. This approach succeeded in resolving approximately 3700 distinct protein spots, many of them post-translationally modified variants of plasma proteins. About 1800 distinct serum protein spots were identified by mass spectrometry. They collapsed into 325 distinct proteins, after sequence homology and similarity searches were carried out to eliminate redundant protein annotations. Although a relatively insensitive dye, Coomassie Brilliant Blue G-250, was used to visualize protein spots, several proteins known to be present in serum in < 10 ng/mL concentrations were identified such as interleukin-6, cathepsins, and peptide hormones. Considering that our strategy allows highly parallel protein quantitation on 2-DE gels, it holds promise to accelerate the discovery of novel serum protein biomarkers.

Multiple separations facilitate identification of protein variants by mass spectrometry.

Identification of variant proteins from complex biological samples promises to contribute much to our understanding the etiology of pathological states. Characterization of variants, either due to genetic mutations in protein sequences or to post-translational modifications, is considerably more difficult than the simple protein identifications typical of most current proteomic investigations. Identification of a few peptides by database retrieval is not adequate when the goal is to have a complete understanding of the modifications of the protein. Although one advantage of mass spectrometry is its ability to obtain specific responses to several components, the complexity of biological samples is often overwhelming, resulting in spectra lacking useful information. For complex mixtures, isolation procedures before mass spectrometric analysis may need to include a variety of chromatographic and electrophoretic separation techniques. In this report, we illustrate how several preparative steps were essential for obtaining information about modified human lens beta-crystallins. The preparative techniques prior to mass spectrometry included size exclusion chromatography, reversed phase chromatography, two-dimensional gel electrophoresis, in situ digestion of the proteins and peptide trapping and washing before a final reversed phase high performance liquid chromatographic separation on-line to the mass spectrometer. This approach for isolation and analysis, when customized for other proteins, should find application in many studies where protein variants of complex mixtures are to be identified.