Biology subjects

Sora, V.

Publications and source records attributed to Sora, V..

6 recordsLinked to original sources

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

N-of-one differential gene expression without control samples using a deep generative model

Differential gene expression analysis of bulk RNA sequencing data plays a major role in the diagnosis, prognosis, and understanding of disease. Such analyses are often challenging due to a lack of good controls and the heterogeneous nature of the samples. Here, we present a deep generative model that can replace control samples. The model is trained on RNA-seq data from healthy tissues and learns a low-dimensional representation that clusters tissues very well without supervision. When applied to cancer samples, the model accurately identifies representations close to the tissue of origin. We interpret these inferred representations as the closest normal to the disease samples and use the resulting count distributions to perform differential expression analysis of single cancer samples without control samples. In a detailed analysis of breast cancer, we demonstrate how our approach finds subtype-specific cancer driver and marker genes with high specificity and greatly outperforms the state-of-the-art method in detecting differentially expressed genes, DESeq2. We further show that the significant genes found using the model are highly enriched within cancer-specific driver genes across different cancer types. Our results show that the in silico closest normal provides a more favorable comparison than control samples.

bioinformatics↗

bioRxiv2022

psntools - a Python package for protein structure network analysis

The application of network theory to investigate protein structures and conformational ensembles through Protein Structure Networks (PSNs) has proven particularly insightful to study protein dynamics, the potentially disruptive effects of disease-related mutations, and allosteric mechanisms. Here, we present psntools, a novel Python package for downstream analysis of PSNs. psntools is completely PSN-agnostic, in contrast with several available tools in the community. psntools relies only on a few Python dependencies, most notably MDAnalysis and NetworkX, works without external software, and can be incorporated into Python-based analysis pipelines. We also present an example of the usage of psntools on a case of study of biological interest, which helped produce novel insights on the structural details of the interaction between BCL-xL and the BH3 motif of BECLIN-1. The psntools package and the data associated with the case study are available at https://github.com/ELELAB/psntools.

bioinformatics↗

bioRxiv2020

PyInteraph2 and PyInKnife2 to analyze networks in protein structural ensembles

MotivationProtein dynamic is essential for cellular functions. Due to the complex nature of non-covalent interactions and their long-range effects, the analysis of protein conformations using network theory can be enlightening. Protein Structure Networks (PSNs) rely on different philosophies, and the currently available tools suffer from limitations in terms of input formats, supported network models, and version control. Another issue is the precise definition of cutoffs for the network calculations and the assessment of the stability of the parameters, which ultimately affect the outcome of the analyses. ResultsWe provide two open-source software packages, i.e., PyInteraph2 and PyInKnife2, to implement and analyze PSNs in a harmonized, reproducible, and documented manner. PyInteraph2 interfaces with multiple formats for protein ensembles and calculates a diverse range of network models with the possibility to integrate them into a macro-network and perform further downstream graph analyses. PyInKnife2 is a standalone package that supports the network models implemented in PyInteraph2. It employs a jackknife resampling approach to estimate the convergence of network properties and streamline the selection of distance cutoffs. Several functionalities are based on MDAnalysis and NetworkX, including parallelization, and are available for Python 3.7. PyInteraph2 underwent a massive restructuring in terms of setup, installation, and test support compared to the original PyInteraph software. ConclusionsWe foresee that the modular structure of the code and the version control system of GitHub will promote the transition to a community-driven effort, boost reproducibility, and establish harmonized protocols in the PSN field. As developers, we will guarantee the introduction of new functionalities, assistance, training of new contributors, and maintenance of the package. AvailabilityThe packages are available at https://github.com/ELELAB/pyinteraph2 and https://github.com/ELELAB/PyInKnife2 with guides provided within the packages.

bioinformatics↗

bioRxiv2019

The conformational and mutational landscape of the ubiquitin-like marker for the autophagosome formation in cancer

Autophagy is a cellular process to recycle damaged cellular components and its modulation can be exploited for disease treatments. A key autophagy player is a ubiquitin-like protein, LC3B. Compelling evidence attests the role of autophagy and LC3B in different cancer types. Many LC3B structures have been solved, but a comprehensive study, including dynamics, has not been yet undertaken. To address this knowledge gap, we assessed ten physical models for molecular dynamics for their capabilities to describe the structural ensemble of LC3B in solution using different metrics and comparison with NMR data. With the resulting LC3B ensembles, we characterized the impact of 26 missense mutations from Pan-Cancer studies with different approaches. Our findings shed light on driver or neutral mutations in LC3B, providing an atlas of its modifications in cancer. Our framework could be used to assess the pathogenicity of mutations by accounting for the different aspects of protein structure and function altered by mutational events.

cancer biology↗

bioRxiv2019

Bcl-xL dynamics and cancer-associated mutations under the lens of protein structure networks and biomolecular simulations

Understanding the finely orchestrated interactions leading to or preventing programmed cell death (apoptosis) is of utmost importance in cancer research since the failure of these systems could eventually lead to the onset of the disease. In this regard, the maintenance of a delicate balance between promoters and inhibitors of mitochondrial apoptosis is crucial, as demonstrated by the interplay among the Bcl-2 family members. Particularly, Bcl-xL is a target of interest due to its forefront role of its dysfunctions in cancer development. Bcl-xL prevents apoptosis by binding both the pro-apoptotic BH3-only proteins, as PUMA, and noncanonical partners such as p53 at different sites. An allosteric communication between the BH3-only proteins binding pocket and the p53 binding site has been postulated and supported by NMR and other biophysical data, mediating the release of p53 from Bcl-xL upon PUMA binding. The molecular details, especially at the residue level, of this mechanism remain unclear. In this work, we investigated the distal communication between these two sites in both Bcl-xL in its free state and bound to PUMA, and we evaluated how missense mutations of Bcl-xL found in cancer samples might impair the communication and thus the allosteric mechanism. We employed all-atom explicit solvent microsecond molecular dynamics simulations analyzed through a Protein Structure Network approach and integrated with calculations of changes in free energies upon cancer-related mutations identified by genomics studies. We found a subset of candidate residues responsible for both maintaining protein stability and for conveying structural information between the two binding sites and hypothesized possible communication routes between specific residues at both sites.

bioinformatics↗