Edoardo Milanetti

Microsecond molecular dynamics of SOD1 variants suggest a structural basis for divergent ALS clinical outcomes

The paper uses molecular dynamics simulations to analyze SOD1 variants and finds that structural stability and conformational rigidity correlate with clinical severity in ALS.

α-Synuclein and γ-Tubulin Cooperatively Regulate Activity-Evoked Presynaptic Microtubule Nucleation to Gate Dopamine Release

This study identifies a mechanism by which α-synuclein and γ-tubulin cooperatively regulate activity-evoked presynaptic microtubule nucleation to control dopamine release.

Preventing pathogenic dimerization in a misfolded antibody light chain through the design of an inhibitory peptide

This study designs an inhibitory peptide to prevent pathogenic dimerization of a misfolded antibody light chain by optimizing its sequence to maximize interface complementarity.

Towards universal models for collective interactions in biomolecular condensates

The paper proposes a model for collective interactions in biomolecular condensates that describes dynamic clusters and their material properties.

Molecular dynamics analysis of Superoxide Dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression

This study uses molecular dynamics and clinical data to show structural and dynamic differences between WTL and MBR SOD1 variants, linking these to distinct ALS clinical outcomes.

Structural analysis of SARS-CoV-2 genome and predictions of the human interactome

The paper analyzes the structural elements of the SARS-CoV-2 genome and predicts human protein interactions, identifying conserved regions and potential host factors involved in viral replication.

proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking

proABC-2 predicts antibody-antigen contacts using a convolutional neural network and integrates with HADDOCK for docking.

Insights on protein thermal stability: a graph representation of molecular interactions

The paper introduces a graph-theoretical framework to assess protein thermal stability using structural data, achieving 76% accuracy in distinguishing thermostable from mesostable proteins.