Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep DOI Creative Commons
Konstantina Tzavella, Adrián Díaz, Catharina Olsen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 17, 2023

Abstract The mutations driving cancer are being increasingly exposed through tumor-specific genomic data. However, differentiating between cancer-causing driver and random passenger remains challenging. State-of-the-art homology-based predictors contain built-in biases often ill-suited to the intricacies of biology. Protein Language Models have successfully addressed various biological problems but not yet been tested on challenging task mutation prediction at large scale. Additionally, they fail offer result interpretation, hindering their effective use in clinical settings. AI-based D2Deep method we introduce here addresses these challenges by combining two powerful elements: i) a non-specialized protein language model that captures makeup all sequences ii) protein-specific evolutionary information encompasses functional requirements for particular protein. relies exclusively sequence information, outperforms state-of-the-art intricate epistatic changes throughout caused mutations. These correlate with known setting can be used interpretation results. is trained balanced, somatic training set so effectively mitigates related hotspot compared techniques. versatility illustrated its performance non-cancer prediction, where most variants still lack consequences. predictions confidence scores available via https://tumorscope.be/d2deep help prioritization.

Language: Английский

Saturation genome editing-based clinical classification of BRCA2 variants DOI
Sounak Sahu, Mélissa Galloux,

Eileen Southon

et al.

Nature, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 8, 2025

Language: Английский

Citations

2
RosettaDDGPrediction for high‐throughput mutational scans: From stability to binding DOI Creative Commons

Valentina Sora,

Adrian Otamendi Laspiur, Kristine Degn

et al.

Protein Science, Journal Year: 2022, Volume and Issue: 32(1)

Published: Dec. 3, 2022

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks multiplex techniques. On the other hand, genomics initiatives provide a large amount data disease-related variants that can benefit from analyses with structure-based methods. Therefore, computational field should keep same pace new tools for fast accurate ΔΔG calculations. In this context, Rosetta modeling suite implements effective approaches predict folding/unfolding ΔΔGs monomer calculate binding complexes. However, application be challenging users without extensive experience Rosetta. Furthermore, protocols are designed considering one variant at time, making setup screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, customizable Python wrapper run calculations set using little intervention user. Moreover, RosettaDDGPrediction assists checking completed runs aggregates raw multiple variants, as well generates publication-ready graphics. We showed potential tool four case studies, including uncertain significance childhood cancer, proteins known unfolding values, interactions between target disordered motifs, phosphomimetics. available, charge under GNU General Public License v3.0, https://github.com/ELELAB/RosettaDDGPrediction.

Language: Английский

Citations

41
ASM variants in the spotlight: A structure-based atlas for unraveling pathogenic mechanisms in lysosomal acid sphingomyelinase DOI
Simone Scrima, Matteo Lambrughi, Matteo Tiberti

et al.

Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, Journal Year: 2024, Volume and Issue: 1870(7), P. 167260 - 167260

Published: May 21, 2024

Language: Английский

Citations

5
Predicting the structure-altering mechanisms of disease variants DOI Creative Commons

Matteo Arnaudi,

Mattia Utichi, Matteo Tiberti

et al.

Current Opinion in Structural Biology, Journal Year: 2025, Volume and Issue: 91, P. 102994 - 102994

Published: Feb. 27, 2025

Missense variants can affect the severity of disease, choice treatment, and treatment outcomes. While number known has been increasing at a rapid pace, available evidence their clinical effect lagging behind, constituting challenge for clinicians researchers. Multiplexed assays variant effects (MAVEs) are important to close gap; nonetheless, computational predictions pathogenicity still often only data scoring variants. Such methods not designed provide mechanistic explanation amino acid substitutions. To this purpose, we propose structure-based frameworks as ensemble methodologies, with each method tailored predict different aspect among those exerted by substitutions link predicted indicators. We review frameworks, well advancements in underlying that on several protein features, such stability, biomolecular interactions, allostery, post-translational modifications, more.

Language: Английский

Citations

0
Point mutations of the mitochondrial chaperone TRAP1 affect its functions and pro-neoplastic activity DOI Creative Commons
Claudio Laquatra,

Albert M. Magro,

Federica Guarra

et al.

Cell Death and Disease, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 12, 2025

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

Language: Английский

Citations

0
TRAP1 S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins DOI Creative Commons
Elena Papaleo, Matteo Tiberti,

Matteo Arnaudi

et al.

Cell Death and Disease, Journal Year: 2023, Volume and Issue: 14(4)

Published: April 21, 2023

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

Language: Английский

Citations

7
Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci DOI Creative Commons

Francesca Maselli,

Salvatore D’Antona, Mattia Utichi

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2023, Volume and Issue: 21, P. 5395 - 5407

Published: Jan. 1, 2023

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process neuronal cell death.A computational approach to investigate shared genetic specific loci was applied 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's (PD), Multiple (MS), Lewy body dementia (LBD).The datasets were analyzed separately, then we compared obtained results.For this purpose, correlation analysis genome-wide association revealed correlations with several human traits diseases.In addition, clumping carried out identify SNPs genetically associated each disease.We found 27 in AD, 6 ALS, 10 PD, 17 MS, 3 LBD.Most them located non-coding regions, exception on which protein structure stability prediction performed verify their impact disease.Furthermore, an differentially expressed miRNAs examined pathologies reveal regulatory mechanisms could involve genes selected SNPs.In conclusion, results constitute important step toward discovery diagnostic biomarkers better understanding diseases.

Language: Английский

Citations

4
PDBminer to Find and Annotate Protein Structures for Computational Analysis DOI Creative Commons
Kristine Degn, Ludovica Beltrame, Matteo Tiberti

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: May 7, 2023

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

Language: Английский

Citations

3
PDBminer to Find and Annotate Protein Structures for Computational Analysis DOI
Kristine Degn, Ludovica Beltrame, Matteo Tiberti

et al.

Journal of Chemical Information and Modeling, Journal Year: 2023, Volume and Issue: 63(23), P. 7274 - 7281

Published: Nov. 17, 2023

Computational methods relying on protein structure strongly depend the selected for investigation. Typical sources of structures include experimental available at Protein Data Bank (PDB) and high-quality in silico model structures, such as those AlphaFold Structure Database. Either option has significant advantages drawbacks, exploring wealth to identify most suitable ones specific applications can be a daunting task. We provide an open-source software package, PDBminer, with purpose making identification selection easier, faster, less error prone. PDBminer searches Database PDB interest provides up-to-date, quality-ranked table applicable further use. overview one or more input proteins, parallelizing runs if multiple cores are specified. The output reports coverage aligned UniProt sequence, overcoming numbering differences providing information regarding quality, complexes, ligands, nucleic acid chain binding. PDBminer2coverage PDBminer2network tools assist visualizing results. applied overcome tedious task choosing without losing additional PDB. Here, we showcase main functionalities package p53 tumor suppressor protein. is http://github.com/ELELAB/PDBminer.

Language: Английский

Citations

2
ASM Variants in the Spotlight: A Structure-Based Atlas for Unraveling Pathogenic Mechanisms in Lysosomal Acid Sphingomyelinase DOI Creative Commons
Simone Scrima, Matteo Lambrughi, Matteo Tiberti

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Nov. 24, 2023

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

Language: Английский

Citations

2