PhosphoPICK-SNP: quantifying the effect of amino acid variants on protein phosphorylation

Precision proteogenomics reveals pan-cancer impact of germline variants

We investigate the impact of germline variants on cancer patients' proteomes, encompassing 1,064 individuals across 10 cancer types. We introduced an approach, "precision peptidomics," mapping 337,469 coding germline variants onto peptides from patients' mass spectrometry data, revealing their potential impact on post-translational modifications, protein stability, allele-specific expression, and protein structure by leveraging the relevant protein databases. We identified rare pathogenic and common germline variants in cancer genes potentially affecting proteomic features, including variants altering protein abundance and structure and variants in kinases (ERBB2 and MAP2K2) impacting phosphorylation. Precision peptidome analysis predicted destabilizing events in signal-regulatory protein alpha (SIRPA) and glial fibrillary acid protein (GFAP), relevant to immunomodulation and glioblastoma diagnostics, respectively. Genome-wide association studies identified quantitative trait loci for gene expression and protein levels, spanning millions of SNPs and thousands of proteins. Polygenic risk scores correlated with distal effects from risk variants. Our findings emphasize the contribution of germline genetics to cancer heterogeneity and high-throughput precision peptidomics.

Single-point mutations in disordered proteins: Linking sequence, ensemble, and function

Mutations in genomic DNA often result in single-point missense mutations in proteins. For folded proteins, the functional effect of these missense mutations can often be understood by their impact on structure. However, missense mutations in intrinsically disordered protein regions (IDRs) remain poorly understood. In IDRs, function can depend on the structural ensemble- the collection of accessible, interchanging conformations that is encoded in their amino acid sequence. We argue that, analogously to folded proteins, single-point mutations in IDRs can alter their structural ensemble, and consequently alter their biological function. To make this argument, we first provide experimental evidence from the literature showcasing how single-point missense mutations in IDRs affect their ensemble dimensions. Then, we use genomic data from patients to show that disease-linked missense mutations occurring in IDRs can, in many cases, significantly alter IDR structural ensembles. We hope this analysis prompts further study of disease-linked, single-point mutations in IDRs.

Old Passengers as New Drivers: Chromosomal Passenger Proteins Engage in Translesion Synthesis

Survivin is known for its dual biological role in apoptosis inhibition and mitotic progression. In addition to its being part of the chromosomal passenger complex (CPC), recent findings suggest additional roles for Survivin in the DNA damage response, further contributing to therapy resistance. In this study, we investigated the role of Survivin and the CPC proteins in the cellular response to irradiation with a focus on DNA replication processes. As is known, ionizing radiation leads to an increased expression of Survivin and its accumulation in nuclear foci, which we now know to be specifically localized to centromeric heterochromatin. The depletion of Survivin and Aurora B increases the DNA damage marker γH2AX, indicative of an impaired repair capacity. The presence of Survivin and the CPC in nuclear foci that we already identified during the S phase co-localize with the proliferating cell nuclear antigen (PCNA), further implying a potential role during replication. Indeed, Survivin knockdown reduced replication fork speed as assessed via DNA fiber assays. Mechanistically, we identified a PIP-box motif in INCENP mediating the interaction with PCNA to assist in managing damage-induced replication stress. Survivin depletion forces cells to undergo unphysiological genome replication via mitotic DNA synthesis (MiDAS), resulting in chromosome breaks. Finally, we revealed that Aurora B kinase liberates Pol η by phosphorylating polymerase delta-interacting protein 2 (POLDIP2) to resume the replication of damaged sites via translesion synthesis. In this study, we assigned a direct function to the CPC in the transition from stalled replication forks to translesion synthesis, further emphasizing the ubiquitous overexpression of Survivin particularly in tumors. This study, for the first time, assigns a direct function to the chromosomal passenger complex, CPC, including Survivin, Aurora B kinase, Borealin, and INCENP, in the transition from stalled replication forks (involving PCNA binding) to translesion synthesis (liberating Pol η by phosphorylating POLDIP2), and thus in maintaining genomic integrity.

Single nucleotide polymorphism-based biomarker in primary hypertension

Primary hypertension is a multiplex and multifactorial disease influenced by various strong components including genetics. Extensive research such as Genome-wide association studies and candidate gene studies have revealed various single nucleotide polymorphisms (SNPs) related to hypertension, providing insights into the genetic basis of the condition. This review summarizes the current status of SNP research in primary hypertension, including examples of hypertension-related SNPs, their location, function, and frequency in different populations. The potential clinical implications of SNP research for primary hypertension management are also discussed, including disease risk prediction, personalized medicine, mechanistic understanding, and lifestyle modifications. Furthermore, this review highlights emerging technologies and methodologies that have the potential to revolutionize the vast understanding of the basis of genetics in primary hypertension. Gene editing holds the potential to target and correct any kind of genetic mutations that contribute to the development of hypertension or modify genes involved in blood pressure regulation to prevent or treat the condition. Advances in computational biology and machine learning enable researchers to analyze large datasets and identify complex genetic interactions contributing to hypertension risk. In conclusion, SNP research in primary hypertension is rapidly evolving with emerging technologies and methodologies that have the potential to transform the knowledge about genetic basis related to the condition. These advances hold promise for personalized prevention and treatment strategies tailored to an individual's genetic profile ultimately improving patient outcomes and reducing healthcare costs.

VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis

Disrupted protein phosphorylation due to genetic variation is a widespread phenomenon that triggers oncogenic transformation of healthy cells. However, few relevant phosphorylation disruption events have been verified due to limited biological experimental methods. Because of the lack of reliable benchmark datasets, current bioinformatics methods primarily use sequence-based traits to study variant impact on phosphorylation (VIP). Here, we increased the number of experimentally supported VIP events from less than 30 to 740 by manually curating and reanalyzing multi-omics data from 916 patients provided by the Clinical Proteomic Tumor Analysis Consortium. To predict VIP events in cancer cells, we developed VIPpred, a machine learning method characterized by multidimensional features that exhibits robust performance across different cancer types. Our method provided a pan-cancer landscape of VIP events, which are enriched in cancer-related pathways and cancer driver genes. We found that variant-induced increases in phosphorylation events tend to inhibit the protein degradation of oncogenes and promote tumor suppressor protein degradation. Our work provides new insights into phosphorylation-related cancer biology as well as novel avenues for precision therapy.

RMDisease V2.0: an updated database of genetic variants that affect RNA modifications with disease and trait implication

Recent advances in epitranscriptomics have unveiled functional associations between RNA modifications (RMs) and multiple human diseases, but distinguishing the functional or disease-related single nucleotide variants (SNVs) from the majority of 'silent' variants remains a major challenge. We previously developed the RMDisease database for unveiling the association between genetic variants and RMs concerning human disease pathogenesis. In this work, we present RMDisease v2.0, an updated database with expanded coverage. Using deep learning models and from 873 819 experimentally validated RM sites, we identified a total of 1 366 252 RM-associated variants that may affect (add or remove an RM site) 16 different types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G, A-to-I, ac4C, Am, Cm, Um, Gm, hm5C, D and f5C) in 20 organisms (human, mouse, rat, zebrafish, maize, fruit fly, yeast, fission yeast, Arabidopsis, rice, chicken, goat, sheep, pig, cow, rhesus monkey, tomato, chimpanzee, green monkey and SARS-CoV-2). Among them, 14 749 disease- and 2441 trait-associated genetic variants may function via the perturbation of epitranscriptomic markers. RMDisease v2.0 should serve as a useful resource for studying the genetic drivers of phenotypes that lie within the epitranscriptome layer circuitry, and is freely accessible at: www.rnamd.org/rmdisease2.

PhosVarDeep: deep-learning based prediction of phospho-variants using sequence information

Human DNA sequencing has revealed numerous single nucleotide variants associated with complex diseases. Researchers have shown that these variants have potential effects on protein function, one of which is to disrupt protein phosphorylation. Based on conventional machine learning algorithms, several computational methods for predicting phospho-variants have been developed, but their performance still leaves considerable room for improvement. In recent years, deep learning has been successfully applied in biological sequence analysis with its efficient sequence pattern learning ability, which provides a powerful tool for improving phospho-variant prediction based on protein sequence information. In the study, we present PhosVarDeep, a novel unified deep-learning framework for phospho-variant prediction. PhosVarDeep takes reference and variant sequences as inputs and adopts a Siamese-like CNN architecture containing two identical subnetworks and a prediction module. In each subnetwork, general phosphorylation sequence features are extracted by a pre-trained sequence feature encoding network and then fed into a CNN module for capturing variant-aware phosphorylation sequence features. After that, a prediction module is introduced to integrate the outputs of the two subnetworks and generate the prediction results of phospho-variants. Comprehensive experimental results on phospho-variant data demonstrates that our method significantly improves the prediction performance of phospho-variants and compares favorably with existing conventional machine learning methods.

Functional Effects In Silico Prediction for Androgen Receptor Ligand-Binding Domain Novel I836S Mutation

Over 1000 mutations are described in the androgen receptor (AR) gene. Of those, about 600 were found in androgen insensitivity syndrome (AIS) patients, among which 400 mutations affect the ligand-binding domain (LBD) of the AR protein. Recently, we reported a novel missense mutation c.2507T>G I836S (ClinVarID: 974911) in a patient with complete AIS (CAIS) phenotype. In the present study, we applied a set of computational approaches for the structural analysis of the ligand-binding domains in a wild-type and mutant AR to evaluate the functional impact of the novel I836S mutation. We revealed that the novel I836S substitution leads to a shorter existence time of the ligand's gating tunnel and internal cavity, occurring only in the presence of S836 phosphorylation. Additionally, the analysis of phosphorylation of the 836 mutant residues explained the negative impact on AR homodimerization, since monomer surface changes indirectly impacted the binding site. Our analyses provide evidence that I836S causes disruptions of AR protein functionality and development of CAIS clinical features in patients.

Lamin post-translational modifications: emerging toggles of nuclear organization and function

Nuclear lamins are ancient type V intermediate filaments with diverse functions that include maintaining nuclear shape, mechanosignaling, tethering and stabilizing chromatin, regulating gene expression, and contributing to cell cycle progression. Despite these numerous roles, an outstanding question has been how lamins are regulated. Accumulating work indicates that a range of lamin post-translational modifications (PTMs) control their functions both in homeostatic cells and in disease states such as progeria, muscular dystrophy, and viral infection. Here, we review the current knowledge of the diverse types of PTMs that regulate lamins in a site-specific manner. We highlight methods that can be used to characterize lamin PTMs whose functions are currently unknown and provide a perspective on the future of the lamin PTM field.

RMDisease: a database of genetic variants that affect RNA modifications, with implications for epitranscriptome pathogenesis

Deciphering the biological impacts of millions of single nucleotide variants remains a major challenge. Recent studies suggest that RNA modifications play versatile roles in essential biological mechanisms, and are closely related to the progression of various diseases including multiple cancers. To comprehensively unveil the association between disease-associated variants and their epitranscriptome disturbance, we built RMDisease, a database of genetic variants that can affect RNA modifications. By integrating the prediction results of 18 different RNA modification prediction tools and also 303,426 experimentally-validated RNA modification sites, RMDisease identified a total of 202,307 human SNPs that may affect (add or remove) sites of eight types of RNA modifications (m6A, m5C, m1A, m5U, Ψ, m6Am, m7G and Nm). These include 4,289 disease-associated variants that may imply disease pathogenesis functioning at the epitranscriptome layer. These SNPs were further annotated with essential information such as post-transcriptional regulations (sites for miRNA binding, interaction with RNA-binding proteins and alternative splicing) revealing putative regulatory circuits. A convenient graphical user interface was constructed to support the query, exploration and download of the relevant information. RMDisease should make a useful resource for studying the epitranscriptome impact of genetic variants via multiple RNA modifications with emphasis on their potential disease relevance. RMDisease is freely accessible at: www.xjtlu.edu.cn/biologicalsciences/rmd.

Phospho-Mimetic Mutation at Ser602 Inactivates Human TRPA1 Channel

The Transient Receptor Potential Ankyrin 1 (TRPA1) channel is an integrative molecular sensor for detecting environmental irritant compounds, endogenous proalgesic and inflammatory agents, pressure, and temperature. Different post-translational modifications participate in the discrimination of the essential functions of TRPA1 in its physiological environment, but the underlying structural bases are poorly understood. Here, we explored the role of the cytosolic N-terminal residue Ser602 located near a functionally important allosteric coupling domain as a potential target of phosphorylation. The phosphomimetic mutation S602D completely abrogated channel activation, whereas the phosphonull mutations S602G and S602N produced a fully functional channel. Using mutagenesis, electrophysiology, and molecular simulations, we investigated the possible structural impact of a modification (mutation or phosphorylation) of Ser602 and found that this residue represents an important regulatory site through which the intracellular signaling cascades may act to reversibly restrict or “dampen” the conformational space of the TRPA1 channel and promote its transitions to the closed state.

KSP: an integrated method for predicting catalyzing kinases of phosphorylation sites in proteins

BACKGROUND

Protein phosphorylation by kinases plays crucial roles in various biological processes including signal transduction and tumorigenesis, thus a better understanding of protein phosphorylation events in cells is fundamental for studying protein functions and designing drugs to treat diseases caused by the malfunction of phosphorylation. Although a large number of phosphorylation sites in proteins have been identified using high-throughput phosphoproteomic technologies, their specific catalyzing kinases remain largely unknown. Therefore, computational methods are urgently needed to predict the kinases that catalyze the phosphorylation of these sites.

RESULTS

We developed KSP, a new algorithm for predicting catalyzing kinases for experimentally identified phosphorylation sites in human proteins. KSP constructs a network based on known protein-protein interactions and kinase-substrate relationships. Based on the network, it computes an affinity score between a phosphorylation site and kinases, and returns the top-ranked kinases of the score as candidate catalyzing kinases. When tested on known kinase-substrate pairs, KSP outperforms existing methods including NetworKIN, iGPS, and PKIS.

CONCLUSIONS

We developed a novel accurate tool for predicting catalyzing kinases of known phosphorylation sites. It can work as a complementary network approach for sequence-based phosphorylation site predictors.

Using phosphoproteomics data to understand cellular signaling: a comprehensive guide to bioinformatics resources

Mass spectrometry-based phosphoproteomics is becoming an essential methodology for the study of global cellular signaling. Numerous bioinformatics resources are available to facilitate the translation of phosphopeptide identification and quantification results into novel biological and clinical insights, a critical step in phosphoproteomics data analysis. These resources include knowledge bases of kinases and phosphatases, phosphorylation sites, kinase inhibitors, and sequence variants affecting kinase function, and bioinformatics tools that can predict phosphorylation sites in addition to the kinase that phosphorylates them, infer kinase activity, and predict the effect of mutations on kinase signaling. However, these resources exist in silos and it is challenging to select among multiple resources with similar functions. Therefore, we put together a comprehensive collection of resources related to phosphoproteomics data interpretation, compared the use of tools with similar functions, and assessed the usability from the standpoint of typical biologists or clinicians. Overall, tools could be improved by standardization of enzyme names, flexibility of data input and output format, consistent maintenance, and detailed manuals.