Validating Amino Acid Variants in Proteogenomics Using Sequence Coverage by Multiple Reads

TransEFVP: A Two-Stage Approach for the Prediction of Human Pathogenic Variants Based on Protein Sequence Embedding Fusion

Studying the effect of single amino acid variations (SAVs) on protein structure and function is integral to advancing our understanding of molecular processes, evolutionary biology, and disease mechanisms. Screening for deleterious variants is one of the crucial issues in precision medicine. Here, we propose a novel computational approach, TransEFVP, based on large-scale protein language model embeddings and a transformer-based neural network to predict disease-associated SAVs. The model adopts a two-stage architecture: the first stage is designed to fuse different feature embeddings through a transformer encoder. In the second stage, a support vector machine model is employed to quantify the pathogenicity of SAVs after dimensionality reduction. The prediction performance of TransEFVP on blind test data achieves a Matthews correlation coefficient of 0.751, an F1-score of 0.846, and an area under the receiver operating characteristic curve of 0.871, higher than the existing state-of-the-art methods. The benchmark results demonstrate that TransEFVP can be explored as an accurate and effective SAV pathogenicity prediction method. The data and codes for TransEFVP are available at https://github.com/yzh9607/TransEFVP/tree/master for academic use.

Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants

Proteogenomics (PG) integrates the proteome with the genome and transcriptome to refine gene models and annotation. Coupled with single-cell (SC) assays, PG effectively distinguishes heterogeneity among cell groups. Affiliating spatial information to PG reveals the high-resolution circuitry within SC atlases. Additionally, PG can investigate dynamic changes in protein-coding genes in plants across growth and development as well as stress and external stimulation, significantly contributing to the functional genome. Here we summarize existing PG research in plants and introduce the technical features of various methods. Combining PG with other omics, such as metabolomics and peptidomics, can offer even deeper insights into gene functions. We argue that the application of PG will represent an important font of foundational knowledge for plants.

Electrochemical biosensor for trypsin activity assay based on cleavage of immobilized tyrosine-containing peptide

In this work, we proposed a biosensor for trypsin proteolytic activity assay using immobilization of model peptides on screen-printed electrodes (SPE) modified with gold nanoparticles (AuNPs) prepared by electrosynthetic method. Sensing of proteolytic activity was based on electrochemical oxidation of tyrosine residues of peptides. We designed peptides containing N-terminal cysteine residue for immobilization on an SPE, modified with gold nanoparticles, trypsin-specific cleavage site and tyrosine residue as a redox label. The peptides were immobilized on SPE by formation of chemical bonds between mercapto groups of the N-terminal cysteine residues and AuNPs. After the incubation with trypsin, time-dependent cleavage of the immobilized peptides was observed by decline in tyrosine electrochemical oxidation signal. The kinetic parameters of trypsin, such as the catalytic constant (k_cat), the Michaelis constant (K_M) and the catalytic efficiency (k_cat/K_M), toward the CGGGRYR peptide were determined as 0.33 ± 0.01 min^-1, 198 ± 24 nM and 0.0016 min^-1 nM^-1, respectively. Using the developed biosensor, we demonstrated the possibility of analysis of trypsin specificity toward the peptides with amino acid residues disrupting proteolysis. Further, we designed the peptides with proline or glutamic acid residues after the cleavage site (CGGRPYR and CGGREYR), and trypsin had reduced activity toward both of them according to the existing knowledge of the enzyme specificity. The developed biosensor system allows one to perform a comparative analysis of the protease steady-state kinetic parameters and specificity toward model peptides with different amino acid sequences.

Finding haplotypic signatures in proteins

BACKGROUND

The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different co-occurring sets of alleles can encode different amino acid sequences: protein haplotypes. These protein haplotypes are present in biological samples and detectable by mass spectrometry, but they are not accounted for in proteomic searches. Consequently, the impact of haplotypic variation on the results of proteomic searches and the discoverability of peptides specific to haplotypes remain unknown.

FINDINGS

Here, we study how common genetic haplotypes influence the proteomic search space and investigate the possibility to match peptides containing multiple amino acid substitutions to a publicly available data set of mass spectra. We found that for 12.42% of the discoverable amino acid substitutions encoded by common haplotypes, 2 or more substitutions may co-occur in the same peptide after tryptic digestion of the protein haplotypes. We identified 352 spectra that matched to such multivariant peptides, and out of the 4,582 amino acid substitutions identified, 6.37% were covered by multivariant peptides. However, the evaluation of the reliability of these matches remains challenging, suggesting that refined error rate estimation procedures are needed for such complex proteomic searches.

CONCLUSIONS

As these procedures become available and the ability to analyze protein haplotypes increases, we anticipate that proteomics will provide new information on the consequences of common variation, across tissues and time.

Proteomic Analysis of Zebrafish Protein Recoding via mRNA Editing by ADAR Enzymes

RNA editing by adenosine deaminases of the ADAR family can lead to protein recoding, since inosine formed from adenosine in mRNA is complementary to cytosine; the resulting codon editing might introduce amino acid substitutions into translated proteins. Proteome recoding can have functional consequences which have been described in many animals including humans. Using protein recoding database derived from publicly available transcriptome data, we identified for the first time the recoding sites in the zebrafish shotgun proteomes. Out of more than a hundred predicted recoding events, ten substitutions were found in six used datasets. Seven of them were in the AMPA glutamate receptor subunits, whose recoding has been well described, and are conserved among vertebrates. Three sites were specific for zebrafish proteins and were found in the transmembrane receptors astrotactin 1 and neuregulin 3b (proteins involved in the neuronal adhesion and signaling) and in the rims2b gene product (presynaptic membrane protein participating in the neurotransmitter release), respectively. Further studies are needed to elucidate the role of recoding of the said three proteins in the zebrafish.