Biology subjects

Tiberti, M.

Publications and source records attributed to Tiberti, M..

21 records · Page 2Linked to original sources

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

MutateX: an automated pipeline for in-silico saturation mutagenesis of protein structures and structural ensembles

Mutations resulting in amino acid substitution influence the stability of proteins along with their binding to other biomolecules. A molecular understanding of the effects induced by protein mutations are both of biotechnological and medical relevance. The availability of empirical free energy functions that quickly estimate the free energy change upon mutation ({Delta}{Delta}G) can be exploited for systematic screenings of proteins and protein complexes. Indeed, in silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of already-known mutations at the atomic level. Often software such as FoldX, while fast and reliable, lack the necessary automation features to make them useful in high-throughput scenarios. Here we introduce MutateX, a software which aims to automate the prediction of {Delta}{Delta}Gs associated with the systematic mutation of each available residue within a protein or protein complex to all other possible residue types, by employing the FoldX energy function. MutateX also supports {Delta}{Delta}G calculations over protein ensembles and the estimation of the changes in free energy upon post-translational modifications. At the heart of MutateX lies an automated pipeline engine that handles input preparation, performs parallel runs with FoldX and outputs publication-ready figures. We here illustrate the MutateX protocol applied to the study of the mutational landscape of cancer-related proteins, industrial enzymes and protein-protein interfaces. The results of the high-throughput scan provided by our tools could help in different applications, such as the analysis of disease-associated mutations, or in the design of protein variants for experimental studies or industrial applications. MutateX is a collection of Python tools that relies on Open Source libraries and requires the FoldX software to be installed beforehand. It is available free of charge and under the GNU General Public License from https://github.com/ELELAB/mutatex.

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↗