Biology subjects

Papaleo, E.

Publications and source records attributed to Papaleo, E..

At least 19 recordsLinked to original sources

bioRxiv2026

EnZight: A Structure-Guided Algorithm to Identify and Prioritize Substitution Hotspots for Enzyme Engineering

Homologous protein structures contain valuable information about tolerated sequence variation. However, translating this information into practical enzyme design strategies remains challenging. Here we present EnZight, a user-friendly web server that integrates homologous structural alignment with intuitive visualization to identify substitution hotspots in protein cores. EnZight exploits structurally aligned homologs to identify positions where the surrounding structural environment is conserved while the residue at the position varies across homologs. This enables prediction of substitutions that preserve fold integrity while modulating function and thermostability. The approach further provides interactive structural outputs that allow users to inspect and prioritize substitutions manually. To validate the use of EnZight, we used a polyurethane-degrading amidase as a proof of concept. We constructed 34 variants, and 97% were successfully expressed, indicating high foldability of the predicted substitutions. Several substitutions improved both catalytic turnover and thermostability, and, importantly, beneficial substitutions combined additively, enabling stepwise accumulation of improvements. The best triple mutant variant exhibited a six-fold increase in catalytic turnover and 2{degrees}C increase in apparent melting temperature. Enhanced activity toward the pharmaceutical micropollutant flutamide further demonstrates EnZight's broad applicability in identifying substitutions that enable enzyme optimization across diverse substrates.

bioengineering↗

bioRxiv2026

The molecular triggers of human labour: a longitudinal plasma proteomics study

Spontaneous labour onset is a precisely timed physiological transition that determines outcomes for millions of pregnancies annually, yet its upstream molecular triggers remain unknown. Here we report a longitudinal plasma proteomics study using the Olink Explore HT platform to measure over 5,400 protein targets in repeated blood samples (median 13 samples per woman) taken from 40 women in the last month up to spontaneous term labour and delivery. Combining longitudinal trajectory modelling with interpretable machine learning for labour timing prediction, we identified five proteins - AFP, ACTA2, ANGPT2, IL1RL1, and LMOD1 - that change in a reproducible sequential order preceding labour onset across all 40 women. IL1RL1, encoding both soluble and membrane-bound ST2, the receptors for IL-33, rose earliest and most consistently, preceding declining AFP and ANGPT2 and a late rise in the smooth muscle contractile proteins ACTA2 and LMOD1. The temporal ordering of these proteins is consistent with a two-phase biological cascade in which feto-placental maturation and vascular remodelling precede activation of the IL-33/ST2 alarmin axis, until the soluble/membrane bound ST2 (sST2/ST2L) balance shifts towards membrane-bound signaling, driving myometrial contractile priming. Mendelian randomisation provided independent human genetic support for the involvement of the IL-33/ST2 axis in labour timing. These findings provide longitudinal plasma proteomic evidence implicating activation of this axis in the timing of spontaneous human labour and identify LMOD1 as a novel circulating marker of myometrial activation.

systems biology↗

bioRxiv2026

Evaluating FoldX5.1 for MAVISp Stability Data Collection

MAVISp (Multi-layered Assessment of VarIants by Structure for proteins) is a structure-based framework for facilitating mechanistic interpretation of missense variants, with protein stability as one of its core analytical layers. When software tools are updated, a key consideration for database curation is whether the new version can be adopted without compromising compatibility with existing entries. This study evaluated the effect of replacing FoldX5 with FoldX5.1 on the results of the MAVISp stability workflow. We compared predicted changes in folding free energy for 539,809 shared variants across 119 proteins. We found high overall agreement with a mean Pearson correlation of 0.933 and a mean Cohen coefficient of 0.814. Most proteins showed strong concordance. The number of disagreements was higher at sites with low AlphaFold2 confidence. These outliers did not display systematic inter-version bias, as mean shifts in folding free energies between versions were minimal. Collectively, these findings support adopting FoldX5.1 for future MAVISp data collection. We will include a transition period, during which existing entries retain FoldX5 annotations until their scheduled annual update, while new or updated entries are processed with FoldX5.1. To facilitate this transition, the FoldX software version has been added as a new metadata annotation in the MAVISp database.

bioinformatics↗

bioRxiv2026

Decoding ATG9A Variation: A Comprehensive Structural Investigation of All Missense Variants

Macroautophagy (hereafter autophagy) is a cellular recycling pathway that requires different ATG (autophagy-related) proteins to generate double-membraned autophagosomes. ATG9A, a multi-spanning membrane protein, plays a crucial role in this process as the only transmembrane component of the core autophagy machinery. ATG9A functions as a lipid scramblase, redistributing lipids between membrane leaflets for the expanding autophagosome membrane. Structural studies have revealed that ATG9A forms a homotrimer with an interlocked domain-swapped architecture and a network of internal hydrophilic cavities. This configuration underlies its role in lipid transfer and membrane remodeling together with the lipid transporter ATG2A. ATG9A dysfunction has also been linked to human disease, as specific ATG9A mutations cause neurodevelopmental or neurodegenerative phenotypes. Additionally, ATG9A is altered in cancer, promoting pro-tumorigenic traits. However, most missense variants in ATG9A remain uncharacterized, posing a significant challenge for interpreting genomic data. In this study, we employed in silico saturation mutagenesis approach using the MAVISp (Multi-layered Assessment of VarIants by Structure) framework to predict the impact of every missense mutation in ATG9A. By analyzing multiple structural assemblies of ATG9A (monomer, trimer, and the ATG9A-ATG2A complex), we evaluated diverse mechanistic indicators of variant impact, including protein stability, long-range conformational changes, effects on multimerization interfaces, and alterations in post-translational modifications. We integrated the structure-based predictions with Variant Effect Predictors from recent deep-learning or evolutionary-based models and cross-referenced known variants catalogued in ClinVar, COSMIC, and cBioPortal. Finally, we predicted mechanistic indicators for all possible variants with structural coverage not yet reported in the disease-related databases supported by MAVISp. Our analyses identified a group of potentially damaging variants in ATG9A and the possible molecular mechanisms underlying their effects. Together, this work provides a roadmap for interpreting missense variants in autophagy regulators and highlights specific ATG9A mutations that deserve further investigation in the context of human disease.

bioinformatics↗

bioRxiv2025

Interpreting the Effects of DNA Polymerase Variants at the Structural Level

Genetic variants in the DNA polymerase enzymes POLE and POLD1 can affect protein function by altering stability, catalysis, DNA binding, and interactions with other biomolecules. Understanding the structural basis of these variants is important for a comprehensive interpretation of variant impact. In this study, we used MAVISp, a modular structure-based framework, and molecular dynamics simulations to analyze over 60,000 missense variants of POLE and POLD1. By integrating results from changes in folding and binding free energies, local alterations in the proximity of the active or phosphorylation sites, we provided a detailed structural interpretation of variants reported across various databases, including ClinVar, COSMIC, and cBioPortal. Moreover, we predicted the functional consequences of variants not found yet in disease-related databases, thereby creating a comprehensive catalogue for future studies. Of note, our approach enabled us to classify 364 Variants of Uncertain Significance (VUS) as PP3 evidence and 323 as BP4 evidence, in accordance with the American College of Medical Genetics and Genomics (ACMG) guidelines. Additionally, we identified a group of variants that could alter the native orientation of the residues within the catalytic site of the exonuclease domain, such as POLE variants P297S and P436R. Finally, we identified a group of variants predicted to affect DNA-binding affinity and rationalized their effects in terms of different energetic contributions and structural features. Collectively, our results not only advance our understanding of protein variant effects in POLE and POLD1 at the structural level but also support future studies aimed at variant classification, variant prioritization for experimental studies, and functional interpretation across diverse biological contexts.

bioinformatics↗

bioRxiv2025

In-Silico Stability Predictors: Investigation of Performance Towards balanced Experimental Data

Change in protein stability, quantified as the change in Gibbs free energy of folding ({Delta}{Delta}G) in kcal/mol, plays a crucial role in functional alterations of proteins, with misfolding and destabilization commonly associated with pathogenicity. The past two decades have brought the development of bioinformatics tools leveraging evolutionary knowledge, empirical force fields, and machine learning to predict stability alterations. However, existing tools are often optimized towards or trained on limited experimental data, leading to unbalanced datasets and potential overfitting. The research objective is to benchmark selected stability predictors using an unbiased and balanced dataset, with AlphaFold structures as input. We demonstrate a performance decline when balancing data across amino acids, stabilizing and destabilizing mutations, and protein representatives, highlighting that redundancy alone is insufficient for benchmarking correction. Additionally, we illustrate that a protein structure ensemble from molecular dynamics acts as a superior input compared to a single static structure. At the same time, coarse-grained methodologies tend to decrease the output quality.

bioinformatics↗

bioRxiv2025

Role of N-glycosylation as a determinant of ATG9A conformations and activity

In this study we investigate the effects of glycosylation at position N99 on the structural dynamics and lipid scrambling activity of ATG9A, a key autophagy protein, using microsecond all-atom molecular dynamics (MD) simulations. ATG9A is an integral membrane protein involved in autophagosome biogenesis, and glycosylation at N99 is known to play a critical, yet poorly understood role in its function. The MD simulations revealed that the hydrophilic central cavity of ATG9A supports lipid reorientation and partial transbilayer movements, consistent with its lipid scrambling activity observed experimentally. N-glycosylation at N99 was found to enhance cooperative interactions between protomers, facilitating lipid insertion and traversal within the central cavity. These findings align with the proposed mechanism of ATG9A role in lipid redistribution across the phagophore membrane during autophagy. However, mutagenesis experiments that abolish N-glycosylation in ATG9A (ATG9AN99A and ATG9AN99D mutants) did not show a significant change in autophagy flux, suggesting that further experimental approaches, such as lipid scramblase assays, are needed to pinpoint the function of glycosylation. In this study we also observed an asymmetric protomer conformations in ATG9A, contrasting with symmetric structures obtained from cryo-EM, suggesting that the structural heterogeneity of the protein could be further explored in cryo-EM datasets. Overall, the study highlights the importance of incorporating glycosylation in computational studies of membrane proteins and offers valuable insights into the molecular mechanisms of lipid transport in autophagy, with potential implications for other lipid scramblases and flippases.

biophysics↗

bioRxiv2024

Point mutations of the mitochondrial chaperone TRAP1 affect its functions and pro-neoplastic activity.

The mitochondrial chaperone TRAP1 is a key regulator of cellular homeostasis and its activity has important implications in neurodegeneration, ischemia and cancer. Recent evidence has indicated that TRAP1 mutations are involved in several disorders, even though the structural basis for the impact of point mutations on TRAP1 functions has never been studied. By exploiting a modular structure-based framework and molecular dynamics simulations, we investigated the effect of five TRAP1 mutations on its structure and stability. Each mutation differentially impacts long-range interactions, intra and inter-protomer dynamics and ATPase activity. Changes in these parameters influence TRAP1 functions, as revealed by their effects on the activity of the TRAP1 interactor succinate dehydrogenase (SDH). In keeping with this, TRAP1 point mutations affect the growth and migration of aggressive sarcoma cells, and alter sensitivity to a selective TRAP1 inhibitor. Our work provides new insights on the structure-activity relationship of TRAP1, identifying crucial amino acid residues that regulate TRAP1 proteostatic functions and pro-neoplastic activity.

cancer biology↗

bioRxiv2024

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data. Moonlight2R is available on GitHub (https://github.com/ELELAB/Moonlight2R) and BioCondcutor (https://bioconduc-tor.org/packages/release/bioc/html/Moonlight2R.html). The associated case studies presented here are available on GitHub (https://github.com/ELELAB/Moon-light2_GMA_case_studies) and OSF (https://osf.io/j4n8q/). Author summaryCancer is a complex disease and a main cause of mortality worldwide. This heterogeneous disease arises due to accumulation of changes which occur in driver genes that drive cancer progression when they are altered. These driver genes are commonly divided into oncogenes, which promote cancer, and tumor suppressors, which prevent it. A major goal of cancer research is identifying these driver genes, crucial for increasing our current understanding of cancer biology and for developing novel treatment approaches. A large number of cancer driver genes have already been identified. However, the underlying mechanisms for the alterations in these genes is challenging to predict given their context-dependent behavior and the complexity of cancer. Such explanations are the focus of this study with the aim of providing evidence of why certain genes do not function normally in cancer. Within this context, we present new functionalities to our previously developed cancer driver predictive framework, Moonlight. These new functionalities integrate multiple data types to predict oncogenes and tumor suppressors in a systems-biology-oriented manner that is freely available as a R package for the community.

bioinformatics↗

bioRxiv2024

Data-driven discovery of gene expression markers distinguishing pediatric acute lymphoblastic leukemia subtypes

Acute lymphoblastic leukemia (ALL), the most common cancer in children, is overall divided into two subtypes, B-cell precursor ALL (B-ALL) and T-cell ALL (T-ALL), which have different molecular characteristics. Despite massive progress in understanding the disease trajectories of ALL, ALL remains a major cause of death in children. Thus, further research exploring the biological foundations of ALL is essential. Here, we examined the diagnostic, prognostic, and therapeutic potential of gene expression data in pediatric patients with ALL. We discovered a subset of expression markers differentiating B- and T-ALL: CCN2, VPREB3, NDST3, EBF1, RN7SKP185, RN7SKP291, SNORA73B, RN7SKP255, SNORA74A, RN7SKP48, RN7SKP80, LINC00114, a novel gene (ENSG00000227706), and 7SK. The expression level of these markers all demonstrated significant effects on survival of the patients, comparing the two subtypes. We also discovered four expression subgroups in the expression data with eight genes driving separation between two of these predicted subgroups. A subset of the 14 markers could separate B- and T-ALL in an independent cohort of patients with ALL. This study can enhance our knowledge of the transcriptomic profile of different ALL subtypes.

cancer biology↗

bioRxiv2023

AVENGERS: Analysis of Variant Effects using Next Generation sequencing to Enhance BRCA2 Stratification

Accurate interpretation of genetic variation is a critical step towards realizing the potential of precision medicine. Sequencing-based genetic tests have uncovered a vast array of BRCA2 sequence variants. Due to limited clinical, familial and/or epidemiological data, thousands of variants are considered to be variants of uncertain significance (VUS). To determine the functional impact of VUSs, here we develop AVENGERS: Analysis of Variant Effects using NGs to Enhance BRCA2 Stratification, utilizing CRISPR-Cas9-based saturation genome editing (SGE) in a humanized-mouse embryonic stem cell line. We have categorized nearly all possible missense single nucleotide variants (SNVs) encompassing the C-terminal DNA binding domain of BRCA2. We have generated the function scores for 6270 SNVs, covering 95.5% of possible SNVs in exons 15-26 spanning residues 2479-3216, including 1069 unique missense VUS, with 81% functional and 14% found to be nonfunctional. Our classification aligns strongly with pathogenicity data from ClinVar, orthogonal functional assays and computational meta predictors. Our statistical classifier exhibits 92.2% sensitivity and 96% specificity in distinguishing clinically benign and pathogenic variants recorded in ClinVar. Furthermore, we offer proactive evidence for 617 SNVs being non-functional and 3396 SNVs being functional demonstrated by impact on cell growth and response to DNA damaging drugs like cisplatin and olaparib. This classification serves as a valuable resource for interpreting unidentified variants in the population and for physicians and genetic counselors assessing BRCA2 VUSs in patients.

genetics↗

bioRxiv2023

Acidic Sphingomyelinase Interactions with Lysosomal Membranes and Cation Amphiphilic Drugs: a Molecular Dynamics Investigation

Lysosomes are pivotal in cellular functions and disease, influencing cancer progression and therapy resistance with Acid Sphingomyelinase (ASM) governing their membrane integrity. Moreover, cation amphiphilic drugs (CADs) are known as ASM inhibitors and have anti-cancer activity, but the structural mechanisms of their interactions with the lysosomal membrane and ASM are poorly explored. Our study, leveraging all-atom explicit solvent molecular dynamics simulations, delves into the interaction of glycosylated ASM with the lysosomal membrane and the effects of one of the CAD representatives, i.e., ebastine on the membrane and ASM. Our results confirm the ASM association to the membrane through the saposin domain, previously only showed with coarse grained models. Furthermore, we elucidated the role of specific residues and ASM-induced membrane curvature in lipid recruitment and orientation. Ebastine also interferes with the association of ASM with the membrane at the level of a loop in the catalytic domain engaging in membrane interactions. Our computational approach, applicable to various CADs or membrane compositions, provides insights into ASM and CAD interaction with the membrane, offering a valuable tool for future studies.

bioinformatics↗

bioRxiv2023

ASM Variants in the Spotlight: A Structure-Based Atlas for Unraveling Pathogenic Mechanisms in Lysosomal Acid Sphingomyelinase

Lysosomal acid sphingomyelinase (ASM), a critical enzyme in lipid metabolism encoded by the SMPD1 gene, plays a crucial role in sphingomyelin hydrolysis in lysosomes. ASM deficiency leads to acid sphingomyelinase deficiency, a rare genetic disorder with diverse clinical manifestations, and the protein can be found mutated in other diseases. We employed a structure-based framework to comprehensively understand the functional implications of ASM variants, integrating pathogenicity predictions with molecular insights derived from molecular dynamics simulations in a lysosomal membrane environment. Our analysis, encompassing over 400 variants, establishes a structural atlas of missense variants of lysosomal ASM, associating mechanistic indicators with pathogenic potential. Our study highlights variants that influence structural stability or exert local and long-range effects at functional sites. To validate our predictions, we compared them to available experimental data on residual catalytic activity in 135 ASM variants. Notably, our findings also suggest applications of the resulting data for identifying cases suited for enzyme replacement therapy. This comprehensive approach enhances the understanding of ASM variants and provides valuable insights for potential therapeutic interventions.

bioinformatics↗

bioRxiv2023

Phosphorylation-induced rewiring of intermolecular interactions with LC3B: local and long-range effects on short linear motifs

Short Linear Motifs (SLiMs) play a pivotal role in mediating interactions between intrinsically disordered proteins and their binding partners. SLiMs exhibit sequence degeneracy and undergo regulation through post-translational modifications, including phosphorylation. The flanking regions surrounding the core motifs also exert a crucial role in shaping the modes of interaction. In this study, we aimed to integrate biomolecular simulations, in silico high-throughput mutational scans, and biophysical experiments to elucidate the structural details of phospho-regulation in a class of SLiMs crucial for autophagy, known as LC3 interacting regions (LIRs). As a case study, we investigated the interaction between optineurin and LC3B. Optineurin LIR perfectly exemplify a class of LIR where there is a complex interplay of different phosphorylations and a N-terminal helical flanking region to be disentangled. Our work unveils the unexplored role of the N-terminal flanking region upstream of the LIR core motif in contributing to the interaction interface. The results offer an atom-level perspective on the structural mechanisms and conformational alterations induced by phosphorylation in optineurin and LC3B recognition, along with of effects of mutations on the background of the phosphorylated form of the protein. Additionally, we assessed the impact of disease-related mutations on optineurin, accounting for different functional features. Notably, we established an approach based on Microfluidic Diffusional Sizing as a novel method to investigate the binding affinity of SLiMs to target proteins, enabling precise measurements of the dissociation constant for a selection of variants identified in the in silico mutational screening. Overall, our work provides a versatile toolkit to characterize other LIR-containing proteins and their modulation by phosphorylation or other phospho-regulated SLiMs, thereby advancing the understanding of important cellular processes.

biochemistry↗

bioRxiv2023

PDBminer to Find and Annotate Protein Structures for Computational Analysis

Structural bioinformatics and molecular modeling of proteins strongly depend on the protein structure selected for investigation. The choice of protein structure relies on direct application from the Protein Data Bank (PDB), homology- or de-novo modeling. Recent de-novo models, such as AlphaFold2, require little preprocessing and omit the need to navigate the many parameters of choosing an experimentally determined model. Yet, the experimentally determined structure still has much to offer, why it should be of interest to the community to ease the choice of experimentally determined models. We provide an open-source software package, PDBminer, to mine both the AlphaFold Database (AlphaFoldDB) and the PDB based on search criteria set by the user. This tool provides an up-to-date, quality-ranked table of structures applicable for further research. PDBminer provides an overview of the available protein structures to one or more input proteins, parallelizing the runs if multiple cores are specified. The output table reports the coverage of the protein structures aligned to the UniProt sequence, overcoming numbering differences in PDB structures, and providing information regarding model quality, protein complexes, ligands, and nucleotide binding. The PDBminer2coverage and PDBminer2network tools assist in visualizing the results. We suggest that PDBminer can be applied to overcome the tedious task of choosing a PDB structure without losing the wealth of additional information available in the PDB. As developers, we will guarantee the introduction of new functionalities, assistance, training of new contributors, and package maintenance. The package is available at http://github.com/ELELAB/PDBminer.

bioinformatics↗

bioRxiv2023

Comparison of force fields to study the zinc-finger containing protein NPL4, a target for Antabuse in cancer therapy

All-atom molecular dynamics (MD) simulations are a powerful approach to study the structure and dynamics of proteins related to health and disease. Advances in the MD field allow modeling proteins with high accuracy. However, modeling metal ions and their interactions with proteins is still challenging for MD simulations. Over one-third of known protein structures bind metal ions and have various cellular functions, such as structural stability, catalysis, and regulation. NPL4 is a zinc-binding protein and works as a cofactor for p97, and together they regulate protein homeostasis. NPL4 is also of biomedical importance and has been proposed as the target of Antabuse, a drug recently repurposed for cancer treatment. Recent experimental studies have proposed that the Antabuse metabolites, bis- (diethyldithiocarbamate)-copper (CuET) and cupric ions released from CuET, induce NPL4 misfolding and consequent aggregation. However, the molecular details of the mechanisms of interactions of Antabuse metabolites with NPL4 and the consequent structural effects are still elusive. In this context, biomolecular simulations can help to shed light on the related structural details. To apply MD simulations to NPL4 and its interaction with copper or Antabuse metabolites the first important step is identifying a suitable force field to describe the protein in its zinc-bound states. We first examined different sets of non-bonded parameters, because we want to study the misfolding mechanism and cannot rule out that the zinc ion may detach from the protein structure during the process and copper replaces it in the metal binding site. We investigated the force-field ability to model the coordination geometry of the metal ions by comparing the results from MD simulations with optimized geometries from quantum mechanics (QM) calculations using model systems of the zinc coordination site for NPL4. Furthermore, we investigated the performance of a MD force field including bonded parameters to treat copper ions and metal-coordinating atoms in NPL4 that we obtained based on QM calculations.

bioinformatics↗

bioRxiv2022

TRAP1 S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins.

S-nitrosylation is a post-translational modification in which nitric oxide (NO) binds to the thiol group of cysteine, generating an S-nitrosothiol (SNO) adduct. S-nitrosylation has different physiological roles, and its alteration has also been linked to a growing list of pathologies, including cancer. SNO can affect the function and stability of different proteins, such as the mitochondrial chaperone TRAP1. Interestingly, the SNO site (C501) of TRAP1 is in the proximity of another cysteine (C527). This feature suggests that the S-nitrosylated C501 could engage in a disulfide bridge with C527 in TRAP1, resembling the well-known ability of S-nitrosylated cysteines to resolve in disulfide bridge with vicinal cysteines. We used enhanced sampling simulations and in-vitro biochemical assays to address the structural mechanisms induced by TRAP1 S-nitrosylation. We showed that the SNO site induces conformational changes in the proximal cysteine and favors conformations suitable for disulfide-bridge formation. We explored 4172 known S-nitrosylated proteins using high-throughput structural analyses. Furthermore, we carried out coarse-grain simulations of 44 proteins to account for protein dynamics in the analyses. This resulted in the identification of up to 1248 examples of proximal cysteines which could sense the redox state of the SNO site, opening new perspectives on the biological effects of redox switches. In addition, we devised two bioinformatic workflows (https://github.com/ELELAB/SNO_investigation_pipelines) to identify proximal or vicinal cysteines for a SNO site with accompanying structural annotations. Finally, we analyzed mutations in tumor suppressor or oncogenes in connection with the conformational switch induced by S-nitrosylation. We classified the variants as neutral, stabilizing, or destabilizing with respect to the propensity to be S-nitrosylated and to undergo the population-shift mechanism. The methods applied here provide a comprehensive toolkit for future high-throughput studies of new protein candidates, variant classification, and a rich data source for the research community in the NO field.

biochemistry↗

bioRxiv2022

An Automatized Workflow to Study Mechanistic Indicators for Driver Gene Prediction with Moonlight

Prediction of tumor suppressors and oncogenes, also called driver genes, is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed changes in gene expression to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene or mutations in the regulator itself. In this work, we developed new functionalities and release Moonlight2, to provide the user with the mutation-based mechanistic indicator to streamline the analyses of this second layer of evidence. The function analyzes mutation information in a cancer cohort to classify them into driver and passenger mutations. Moreover, the function estimates the potential effect of a mutation on the transcriptional, translational, or protein structure/function level. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 and the newly developed function to a case study on Basal-like breast cancer subtype using data from The Cancer Genome Atlas. We found six oncogenes (SF3B4, EBNA1BP2, KRTCAP2, ZBTB8OS, RUNX2, and POLR2J) and ten tumor suppressor genes (KIF26B, NR5A2, ARHGAP25, EMCN, ARL15, PCOLCE, TPK1, TEK, KIR2DL4, and GMFG) containing a driver mutation in their promoter region, possibly explaining their deregulation. The Moonlight2R source code is available at https://github.com/ELELAB/Moonlight2R.

bioinformatics↗