Single Amino Acid Variants Research Articles

Mutant Proteomics of Lung Adenocarcinomas Harboring Different EGFR Mutations.

Epidermal growth factor receptor EGFR major driver mutations may affect downstream molecular networks and pathways, which would influence treatment outcomes of non-small cell lung cancer (NSCLC). This study aimed to unveil profiles of mutant proteins expressed in lung adenocarcinomas of 36 patients harboring representative driver EGFR mutations (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Surprisingly, the orthogonal partial least squares discriminant analysis performed for identified mutant proteins demonstrated the profound differences in distance among the different EGFR mutation groups, suggesting that cancer cells harboring L858R or Ex19del emerge from cellular origins different from L858R/Ex19del-negative cells. Weighted gene coexpression network analysis, together with over-representative analysis, identified 18 coexpressed modules and their eigen proteins. Pathways enriched differentially for both the L858R and Ex19del mutations included carboxylic acid metabolic process, cell cycle, developmental biology, cellular responses to stress, mitotic prophase, cell proliferation, growth, epithelial to mesenchymal transition (EMT), and immune system. The IPA causal network analysis identified the highly activated networks of PARPBP, HOXA1, and APH1 under the L858R mutation, whereas those of ASGR1, APEX1, BUB1, and MAPK10 were highly activated under the Ex19del mutation. Interestingly, the downregulated causal network of osimertinib intervention showed the highest significance in overlap p-value among most causal networks predicted under the L858R mutation. We also identified the causal network of MAPK interacting serine/threonine kinase 1/2 (MNK1/2) highly activated differentially under the L858R mutation. Tumor-suppressor AMOT, a component of the Hippo pathways, was highly inhibited commonly under both L858R and Ex19del mutations. Our results could identify disease-related protein molecular networks from the landscape of single amino acid variants. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

The Catalytic Cycle of the Antioxidant and Cancer-Associated Human NQO1 Enzyme: Hydride Transfer, Conformational Dynamics and Functional Cooperativity.

Human NQO1 [NAD(H):quinone oxidoreductase 1] is a multi-functional and stress-inducible dimeric protein involved in the antioxidant defense, the activation of cancer prodrugs and the stabilization of oncosuppressors. Despite its roles in human diseases, such as cancer and neurological disorders, a detailed characterization of its enzymatic cycle is still lacking. In this work, we provide a comprehensive analysis of the NQO1 catalytic cycle using rapid mixing techniques, including multiwavelength and spectral deconvolution studies, kinetic modeling and temperature-dependent kinetic isotope effects (KIEs). Our results systematically support the existence of two pathways for hydride transfer throughout the NQO1 catalytic cycle, likely reflecting that the two active sites in the dimer catalyze two-electron reduction with different rates, consistent with the cooperative binding of inhibitors such as dicoumarol. This negative cooperativity in NQO1 redox activity represents a sort of half-of-sites activity. Analysis of KIEs and their temperature dependence also show significantly different contributions from quantum tunneling, structural dynamics and reorganizations to catalysis at the two active sites. Our work will improve our understanding of the effects of cancer-associated single amino acid variants and post-translational modifications in this protein of high relevance in cancer progression and treatment.

Proteomic variations of esophageal squamous cell carcinoma revealed by combining RNA-seq proteogenomics and G-PTM search strategy

BackgroundCancer that arises from epithelial cells of the esophagus is called esophagus squamous cell carcinoma (ESCC) and is mostly observed in developing nations. Evaluation of cancer genomes and its regulation into proteins plays a predominant role in understanding the cancer progressions. Mass-spectrometry-based proteomics is a consequential tool to estimate proteomic variation and posttranslational modifications (PTMs) from standard protein databases. Post-translational modifications play a crucial role in protein folding and PTMs can be accounted for as a biological signal to interpret the structural changes and transition order of proteins. Functional validation of cancer-related mutations can explain the effects of mutations on genes and the identification of Oncogenes and tumor suppressor genes. Therefore, we present a study on protein variations to interpret the structural changes and transition order of proteins in ESCC carcinogenesis. MethodologyWe are using a bottom-up proteomics approach with Galaxy-P framework and RNA sequence data analysis to generate the sample-specific databases containing details of RNA splicing and variant peptides. Once the database generated with information on variable modification, only the curated PTMs at specific positions are considered to perform spectral matching. Proteogenomics mapping was performed to identify protein variations in ESCC. ResultsRNA-sequence proteogenomics with G-PTM (Global Post-Translational Modification) searching strategy has revealed proteomic events including several peptides that contain single amino acid variations, novel splice junction peptides and posttranslationally modified peptides. Proteogenomic mapping exhibited the splice junction peptides mapped predominantly for Malic enzyme exon type (ME-3) and MCM7 protein-coding genes that promote cancer progression, found to be exhibited in ESCC samples. Approximately 25 ± types of PTM modifications were recorded, and Protein Phosphorylation was largely noted. ConclusionESCC cancer prognosis at the molecular level enables a better understanding of cancer carcinogenesis and protein modifications can be used as potential biomarkers.

Leveraging Peptide Sequence Modification to Promote Assembly of Chiral Helical Gold Nanoparticle Superstructures.

Peptide conjugate molecules comprising a gold-binding peptide (e.g., AYSSGAPPMPPF) attached to an aliphatic tail have proven to be powerful agents for directing the synthesis and assembly of gold nanoparticle superstructures, in particular chiral helices having interesting plasmonic chiroptical properties. The composition and structure of these molecular agents can be tailored to carefully tune the structure and properties of gold nanoparticle single and double helices. To date, modifications to the β-sheet region (AYSSGA) of the peptide sequence have not been exploited to control the metrics and assembly of such superstructures. We report here that systematic peptide sequence variation in a series of gold-binding peptide conjugate molecules can be leveraged not only to affect the assembly of peptide conjugates but also to control the synthesis, assembly, and optical properties of gold nanoparticle superstructures. Depending upon the hydrophobicity of a single-amino acid variant, the conjugates yield either dispersed gold nanoparticles or helical superstructures. These results provide evidence that subtle changes to peptide sequence, via single-amino acid variation in the β-sheet region, can be leveraged to program structural control in chiral gold nanoparticle superstructures.

Comprehensive exploration of the translocation, stability and substrate recognition requirements in VIM-2 lactamase.

Metallo-β-lactamases (MBLs) degrade a broad spectrum of β-lactam antibiotics, and are a major disseminating source for multidrug resistant bacteria. Despite many biochemical studies in diverse MBLs, molecular understanding of the roles of residues in the enzyme's stability and function, and especially substrate specificity, is lacking. Here, we employ deep mutational scanning (DMS) to generate comprehensive single amino acid variant data on a major clinical MBL, VIM-2, by measuring the effect of thousands of VIM-2 mutants on the degradation of three representative classes of β-lactams (ampicillin, cefotaxime, and meropenem) and at two different temperatures (25°C and 37°C). We revealed residues responsible for expression and translocation, and mutations that increase resistance and/or alter substrate specificity. The distribution of specificity-altering mutations unveiled distinct molecular recognition of the three substrates. Moreover, these function-altering mutations are frequently observed among naturally occurring variants, suggesting that the enzymes have continuously evolved to become more potent resistance genes.

Optimized High-Yield Purification of Obesity-Associated Melanocortin 4 Receptor.

Obesity has emerged as a global public health challenge associated with increased risk of hyperlipidemia and hypertension. It contributes to high sympathetic activity and increased catecholamine levels. The hypothalamic melanocortin system is known to regulate the energy homeostasis. The role of melanocortin 4 receptor (MC4R) has been demonstrated pharmacologically and in animal studies, which showed that severe obesity in MC4R knockout mice was caused by increased food intake and decreased energy consumption. Over 70 multiple different mis- -sense and nonsense mutations in hMC4R have been found at a high frequency of 2-8% in severe early onset or hereditary obesity. The single amino acid variation (D90N) located in the second transmembrane domain (TM2) of MC4R results in accelerated growth and childhood onset obesity. Interestingly, the functional characterization of D90N hMC4R mutant TM2 (m-hMC4R-TM2) revealed normal cell surface expression and binding with agonist similar to the hMC4R wild-type TM2 (wt-hMC4R-TM2) but loss of signal transduction mediated via Gs/adenylyl cyclase activation. It is essential to delineate the three-dimensional structure of MC4Rs in order to elucidate their functional aspects. In this study, we demonstrate the optimized expression and isolation of wt/m-hMC4R-TM2 proteins under different chemical cleavage reaction times and purification procedures via SDS precipitation. The solid-state NMR spectroscopy was carried out to study the structure of wt/m-hMC4R- TM2 protein in the anisotropic phospholipid bicelles. The KSI-wt/m-hMC4R-TM2 fusion proteins developed in cell culture with LB medium. In order to isolate the expressed fusion protein from the cell, ultrasonication, Ni-NTA affinity chromatography, dialysis, and lyophilization techniques were used. Then, to obtain a protein with higher purity and higher yield, the CNBr chemical cleavage time was subdivided into 30 minutes, 1 h, 2 h, 3 h, and 4 h. Purification process was performed using FPLC, and 100 mM KCl and dialysis were used to remove the SDS. CD spectrometer, MALDI-TOF, solution-state NMR, and solid-state NMR were used to confirmed purity and structure of the wt/m-hMC4R-TM2. The precipitation method was used to remove the SDS bound to proteins as KCl-SDS. We optimized the 2 h cleavage reaction times for both wt-hMC4R-TM2 and m-hMC4R-TM2 depending on the purity based on mass spectra and 1H-15N HSQC spectra and the yield after final purification. The 1D 1H-15N CP (Cross polarization) solid-state NMR spectra suggest that the wt/m-hMC4R- TM2 undergo rotational diffusion around a perpendicular axis along the bilayer normal. We expressed wt/m-hMC4R-TM2 in E.coli and optimized the isolation and purification process, especially CNBr chemical cleavage time. The efficiency of KCl-SDS precipitation was confirmed via MALDI-TOF MS and the pure proteins obtained using this method were characterized by CD spectroscopy and solution-state NMR. The results of 1H-15N HSQC spectra in solution- state NMR also show the probability for structural studies. The 1D 1H-15N CP solid-state NMR spectra indicate that most of the residues in both the wt/m-hMC4R-TM2 peptides are integrated into the membrane.

An Integrated Deep-Mutational-Scanning Approach Provides Clinical Insights on PTEN Genotype-Phenotype Relationships

Germline variation in PTEN results in variable clinical presentations, including benign and malignant neoplasia and neurodevelopmental disorders. Despite decades of research, it remains unclear how the PTEN genotype is related to clinical outcomes. In this study, we combined two recent deep mutational scanning (DMS) datasets probing the effects of single amino acid variation on enzyme activity and steady-state cellular abundance with a large, well-curated clinical cohort of PTEN-variant carriers. We sought to connect variant-specific molecular phenotypes to the clinical outcomes of individuals with PTEN variants. We found that DMS data partially explain quantitative clinical traits, including head circumference and Cleveland Clinic (CC) score, which is a semiquantitative surrogate of disease burden. We built logistic regression models that use DMS and CADD scores to separate clinical PTEN variation from gnomAD control-only variation with high accuracy. By using a survival-like analysis, we identified molecular phenotype groups with differential risk of early cancer onset as well as lifetime risk of cancer. Finally, we identified classes of DMS-defined variants with significantly different risk levels for classical hamartoma-related features (odds ratio [OR] range of 4.1-102.9). In stark contrast, the risk for developing autism or developmental delay does not significantly change across variant classes (OR range of 5.4-12.4). Together, these findings highlight the potential impact of combining DMS datasets with rich clinical data and provide new insights that might guide personalized clinical decisions for PTEN-variant carriers.

Mutation severity spectrum of rare alleles in the human genome is predictive of disease type.

The human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs, and the version incorporating population and gene-level information (DeepSAV+PG) has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs in the human population into a quantity termed “mutation severity measure” for each human protein-coding gene. It reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.

MinProtMaxVP: Generating a minimized number of protein variant sequences containing all possible variant peptides for proteogenomic analysis

Identifying single-amino-acid variants (SAVs) from mass spectrometry-based experiments is critical for validating single-nucleotide variants (SNVs) at the protein level to facilitate biomedical research. Currently, two approaches are usually applied to convert SNV annotations into SAV-harboring protein sequences. One approach generates one sequence containing exactly one SAV, and the other all SAVs. However, they may neglect the possibility of SAV combinations, e.g., haplotypes, existing in bio-samples. Therefore, it is necessary to consider all SAV combinations of a protein when generating SAV-harboring protein sequences. In this paper, we propose MinProtMaxVP, a novel approach which selects a minimized number of SAV-harboring protein sequences generated from the exhaustive approach, while still accommodating all possible variant peptides, by solving a classic set covering problem. Our study on known haplotype variations of TAS2R38 justifies the necessity for MinProtMaxVP to consider all combinations of SAVs. The performance of MinProtMaxVP is demonstrated by an in silico study on OR2T27 with five SAVs and real experimental data of the HEK293 cell line. Furthermore, assuming simulated somatic and germline variants of OR2T27 in tumor and normal tissues demonstrates that when adopting the appropriate somatic and germline SAV integration strategy, MinProtMaxVP is adaptable to labeling and label-free mass spectrometry-based experiments. SignificanceWe present MinProtMaxVP, a novel approach to generate SAV-harboring protein sequences for constructing a customized protein sequence database, which is used in database searching for variant peptide identification. This approach outperforms the existing approaches in generating all possible variant peptides to be included in protein sequences and possibly leading to identification of more variant peptides in proteogenomic analysis.

Screening of OXA-244 producers, a difficult-to-detect and emerging OXA-48 variant?

OXA-244, a single amino acid variant of OXA-48, demonstrates weaker hydrolytic activity towards carbapenems and temocillin compared with OXA-48. Of note, these antimicrobials are present in high concentrations in several carbapenemase-producing Enterobacterales (CPE) screening media. As a result, some screening media fail to grow OXA-244-producing isolates, while the prevalence of OXA-244 producers is constantly increasing in France. Here, we evaluate the performance of three commercially available CPE screening media [ChromID® CARBA SMART (bioMérieux), Brilliance™ CRE (Thermo Fisher) and mSuperCARBA™ (MAST Diagnostic)] for their ability to detect OXA-244 producers (n = 101). As OXA-244 producers may also express an ESBL, two additional ESBL screening media were tested (Brilliance™ ESBL and ChromID® BLSE). MICs of temocillin and imipenem were determined by broth microdilution. The clonality of OXA-244-producing Escherichia coli isolates (n = 97) was assessed by MLST. Overall, the sensitivity of the ChromID® CARBA SMART, Brilliance™ CRE and mSuperCARBA™ media were 14% (95% CI = 8.1%-22.5%), 54% (95% CI = 43.3%-63.4%) and 99% (95% CI = 93.8%-100%), respectively, for the detection of OXA-244 producers. Among the 101 OXA-244-producing isolates, 96% were E. coli and 77%-78% grew on ESBL screening media. MLST analysis identified five main STs among OXA-244-producing E. coli isolates: ST38 (n = 37), ST361 (n = 17), ST69 (n = 12), ST167 (n = 11) and ST10 (n = 8). Our results demonstrated that the mSuperCARBA™ medium is very efficient in the detection of OXA-244 producers, unlike the ChromID® CARBA SMART medium. The high prevalence of ESBLs among OXA-244 producers allowed detection of 77%-78% of them using ESBL-specific screening media.

Complementary computational and experimental evaluation of missense variants in the ROMK potassium channel.

The renal outer medullary potassium (ROMK) channel is essential for potassium transport in the kidney, and its dysfunction is associated with a salt-wasting disorder known as Bartter syndrome. Despite its physiological significance, we lack a mechanistic understanding of the molecular defects in ROMK underlying most Bartter syndrome-associated mutations. To this end, we employed a ROMK-dependent yeast growth assay and tested single amino acid variants selected by a series of computational tools representative of different approaches to predict each variants’ pathogenicity. In one approach, we used in silico saturation mutagenesis, i.e. the scanning of all possible single amino acid substitutions at all sequence positions to estimate their impact on function, and then employed a new machine learning classifier known as Rhapsody. We also used two additional tools, EVmutation and Polyphen-2, which permitted us to make consensus predictions on the pathogenicity of single amino acid variants in ROMK. Experimental tests performed for selected mutants in different classes validated the vast majority of our predictions and provided insights into variants implicated in ROMK dysfunction. On a broader scope, our analysis suggests that consolidation of data from complementary computational approaches provides an improved and facile method to predict the severity of an amino acid substitution and may help accelerate the identification of disease-causing mutations in any protein.

Variant effect predictions capture some aspects of deep mutational scanning experiments

BackgroundDeep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants – missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs.ResultsOn a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same. Traditional methods such as SNAP2 correlated slightly more with measurements and better classified binary states (effect or neutral). Envision appeared to better estimate the precise degree of effect. Most surprising was that the simple naïve conservation approach using PSI-BLAST in many cases outperformed other methods. All methods captured beneficial effects (gain-of-function) significantly worse than deleterious (loss-of-function). For the few proteins with multiple independent experimental measurements, experiments differed substantially, but agreed more with each other than with predictions.ConclusionsDMS provides a new powerful experimental means of understanding the dynamics of the protein sequence space. As always, promising new beginnings have to overcome challenges. While our results demonstrated that DMS will be crucial to improve variant effect prediction methods, data diversity hindered simplification and generalization.

Family-specific analysis of variant pathogenicity prediction tools.

Using the presently available datasets of annotated missense variants, we ran a protein family-specific benchmarking of tools for predicting the pathogenicity of single amino acid variants. We find that despite the high overall accuracy of all tested methods, each tool has its Achilles heel, i.e. protein families in which its predictions prove unreliable (expected accuracy does not exceed 51% in any method). As a proof of principle, we show that choosing the optimal tool and pathogenicity threshold at a protein family-individual level allows obtaining reliable predictions in all Pfam domains (accuracy no less than 68%). A functional analysis of the sets of protein domains annotated exclusively by neutral or pathogenic mutations indicates that specific protein functions can be associated with a high or low sensitivity to mutations, respectively. The highly sensitive sets of protein domains are involved in the regulation of transcription and DNA sequence-specific transcription factor binding, while the domains that do not result in disease when mutated are responsible for mediating immune and stress responses. These results suggest that future predictors of pathogenicity and especially variant prioritization tools may benefit from considering functional annotation.

Comprehensive Detection of Single Amino Acid Variants and Evaluation of Their Deleterious Potential in a PANC-1 Cell Line.

Identifying single amino acid variants (SAAVs) in cancer is critical for precision oncology. Several advanced algorithms are now available to identify SAAVs, but attempts to combine different algorithms and optimize them on large data sets to achieve a more comprehensive coverage of SAAVs have not been implemented. Herein, we report an expanded detection of SAAVs in the PANC-1 cell line using three different strategies, which results in the identification of 540 SAAVs in the mass spectrometry data. Among the set of 540 SAAVs, 79 are evaluated as deleterious SAAVs based on analysis using the novel AssVar software in which one of the driver mutations found in each protein of KRAS, TP53, and SLC37A4 is further validated using independent selected reaction monitoring (SRM) analysis. Our study represents the most comprehensive discovery of SAAVs to date and the first large-scale detection of deleterious SAAVs in the PANC-1 cell line. This work may serve as the basis for future research in pancreatic cancer and personal immunotherapy and treatment.

Binding and Regulation of Transcription by Yeast Ste12 Variants To Drive Mating and Invasion Phenotypes.

Amino acid substitutions are commonly found in human transcription factors, yet the functional consequences of much of this variation remain unknown, even in well-characterized DNA-binding domains. Here, we examine how six single-amino acid variants in the DNA-binding domain of Ste12-a yeast transcription factor regulating mating and invasion-alter Ste12 genome binding, motif recognition, and gene expression to yield markedly different phenotypes. Using a combination of the "calling-card" method, RNA sequencing, and HT-SELEX (high throughput systematic evolution of ligands by exponential enrichment), we find that variants with dissimilar binding and expression profiles can converge onto similar cellular behaviors. Mating-defective variants led to decreased expression of distinct subsets of genes necessary for mating. Hyper-invasive variants also decreased expression of subsets of genes involved in mating, but increased the expression of other subsets of genes associated with the cellular response to osmotic stress. While single-amino acid changes in the coding region of this transcription factor result in complex regulatory reconfiguration, the major phenotypic consequences for the cell appear to depend on changes in the expression of a small number of genes with related functions.

Porphobilinogen synthase: An equilibrium of different assemblies in human health.

Porphobilinogen synthase (PBGS) is an essential enzyme that catalyzes an early step in heme biosynthesis. An unexpected human PBGS quaternary structure dynamic drove the definition of morpheeins, which are protein multimers that dissociate, change shape, and re-assemble differently with functional consequences. Each PBGS monomer has two domains that can reposition through a hinge motion. Human PBGS exists in an equilibrium among high activity octamer, low activity hexamer, and low mole-fraction dimer in which the hinge motion occurs. The dimer conformation dictates the multimer architecture. An octamer-specific inter-subunit interaction responds to pH, resulting in a pH-dependence to the octamer-hexamer equilibrium. An inborn error of metabolism, ALAD porphyria, is caused by single amino acid substitutions that stabilize the hexamer relative to octamer. Drugs that stabilize the PBGS hexamer result in a drug side effect that can exacerbate porphyria. PBGS is essential for all organisms that require respiration, photosynthesis, or methanogenesis. Consequently, phylogenetic variation in PBGS multimerization equilibria provides insight into how Nature has harnessed oligomeric variation in the control of protein function. The dynamic multimerization of PBGS revealed the morpheein mechanism for allostery, a structural basis for inborn errors of metabolism, a quaternary structure focus for drug discovery and/or drug side effects, and a pathway toward new antibiotics or herbicides. The fortuitous discovery of PBGS quaternary structure dynamics arose from characterization of a low-activity single amino acid variant that dramatically stabilized the hexamer, whose existence had previously gone unnoticed.

Characterization of Single Amino Acid Variations in an EDTA-Tolerating Non-specific Nuclease from the Ice-Nucleating Bacterium Pseudomonas syringae.

Non-specific nuclease (NSN) can be applied in industrial downstream processing to remove nucleic acids from crude protein extracts or in cell-sorting systems to degrade nucleic acids derived from lysed cells. PsNuc from the ice-nucleating bacterium Pseudomonas syringae has the ability to decompose double- and single-stranded DNA in linear or circular form and RNA. It is not affected by the presence of metal-ion chelators such as EDTA and tolerates several protease inhibitors and reducing agents. A multiple sequence alignment of PsNuc with closely related enzymes (97-99% identity on the protein level) within the family Pseudomonaceae revealed the presence of only six amino acid residues that are variable in putative NSN from different members of the genus Pseudomonas. Single amino acid variants were produced in recombinant form in Escherichia coli, purified, and characterized. They showed similar activity compared to PsNuc, but a single variant even displayed an improved performance with an activity of > 20,000U/mg at 35°C, while amino acid residues S148 and V161 were found to be essential for enzymatic functionality. These results suggest that homologous nucleases from Pseudomonaceae display high activity levels in a metal-ion-independent manner and are therefore of interest for applications in biotechnology.

Open-pFind Enhances the Identification of Missing Proteins from Human Testis Tissue.

In recent years, high-throughput technologies have contributed to the development of a more precise picture of the human proteome. However, 2129 proteins remain listed as missing proteins (MPs) in the newest neXtProt release (2019-02). The main reasons for MPs are a low abundance, a low molecular weight, unexpected modifications, membrane characteristics, and so on. Moreover, >50% of the MS/MS data have not been successfully identified in shotgun proteomics. Open-pFind, an efficient open search engine, recently released by the pFind group in China, might provide an opportunity to identify these buried MPs in complex samples. In this study, proteins and potential MPs were identified using Open-pFind and three other search engines to compare their performance and efficiency with three large-scale data sets digested by three enzymes (Glu-C, Lys-C, and trypsin) with specificity on different amino acid (AA) residues. Our results demonstrated that Open-pFind identified 44.7-93.1% more peptide-spectrum matches and 21.3-61.6% more peptide sequences than the second-best search engine. As a result, Open-pFind detected 53.1% more MP candidates than MaxQuant and 8.8% more candidate MPs than Proteome Discoverer. In total, 5 (PE2) of the 124 MP candidates identified by Open-pFind were verified with 2 or 3 unique peptides containing more than 9 AAs by using a spectrum theoretical prediction with pDeep and synthesized peptide matching with pBuild after spectrum quality analysis, isobaric post-translational modification, and single amino acid variant filtering. These five verified MPs can be saved as PE1 proteins. In addition, three other MP candidates were verified with two unique peptides (one peptide containing more than 9 AAs and the other containing only 8 AAs), which was slightly lower than the criteria listed by C-HPP and required additional verification information. More importantly, unexpected modifications were detected in these MPs. All MS data sets have been deposited into ProteomeXchange with the identifier PXD015759.

SAAVpedia: Identification, Functional Annotation, and Retrieval of Single Amino Acid Variants for Proteogenomic Interpretation.

Next-generation genome sequencing has enabled the discovery of numerous disease- or drug-response-associated nonsynonymous single nucleotide variants (nsSNVs) that alter the amino acid sequences of a protein. Although several studies have attempted to characterize pathogenic nsSNVs, few have been confirmed as single amino acid variants (SAAVs) at the protein level. Here we developed the SAAVpedia platform to identify, annotate, and retrieve pathogenic SAAV candidates from proteomic and genomic data. The platform consists of four modules: SAAVidentifier, SAAVannotator, SNV/SAAVretriever, and SAAVvisualizer. The SAAVidentifier provides a reference database containing 18 206 090 SAAVs and performs the identification and quality assessment of SAAVs. The SAAVannotator provides functional annotation with biological, clinical, and pharmacological information for the interpretation of condition-specific SAAVs. The SNV/SAAVretriever module enables bidirectional navigation between relevant SAAVs and nsSNVs with diverse genomic and proteomic data. SAAVvisualizer provides various statistical plots based on functional annotations of detected SAAVs. To demonstrate the utility of SAAVpedia, the proteogenomic pipeline with protein-protein interaction network analysis was applied to proteomic data from breast cancer and glioblastoma patients. We identified 1326 and 12 breast-cancer- and glioblastoma-related genes that contained one or more SAAVs, including BRCA2 and FAM49B, respectively. SAAVpedia is a suitable platform for confirming whether a genomic variant is maintained in an amino acid sequence. Furthermore, as a result of the SAAV discovery of these positive controls, the SAAVpedia could play a key role in the protein functional study for the Human Proteome Project (HPP).

Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade.

Members of the SAR11 order Pelagibacterales dominate the surface oceans. Their extensive diversity challenges emerging operational boundaries defined for microbial 'species' and complicates efforts of population genetics to study their evolution. Here, we employed single-amino acid variants (SAAVs) to investigate ecological and evolutionary forces that maintain the genomic heterogeneity within ubiquitous SAR11 populations we accessed through metagenomic read recruitment using a single isolate genome. Integrating amino acid and protein biochemistry with metagenomics revealed that systematic purifying selection against deleterious variants governs non-synonymous variation among very closely related populations of SAR11. SAAVs partitioned metagenomes into two main groups matching large-scale oceanic current temperatures, and six finer proteotypes that connect distant oceanic regions. These findings suggest that environmentally-mediated selection plays a critical role in the journey of cosmopolitan surface ocean microbial populations, and the idea 'everything is everywhere but the environment selects' has credence even at the finest resolutions.