Francesco Trozzi
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Leveraging Machine Learning and AlphaFold2 Steering to Discover State-Specific Inhibitors Across the Kinome Open
Protein kinases are structurally dynamic proteins that control downstream signaling cascades by phosphorylating their substrates. Protein kinases regulate their function by adopting several conformational states in their active site determ…
Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures Open
Summary Protein kinases are a family of signaling proteins, crucial for maintaining cellular homeostasis. When dysregulated, kinases drive the pathogenesis of several diseases, and are thus one of the largest target categories for drug dis…
Investigating the conformational landscape of AlphaFold2-predicted protein kinase structures Open
Protein kinases are a family of signalling proteins, crucial for maintaining cellular homeostasis. When dysregulated, kinases drive the pathogenesis of several diseases, and are thus one of the largest target categories for drug discovery.…
Mechanistic Insights into Enzyme Catalysis from Explaining Machine-Learned Quantum Mechanical and Molecular Mechanical Minimum Energy Pathways Open
With the increasing popularity of machine learning (ML) applications, the demand for explainable artificial intelligence techniques to explain ML models developed for computational chemistry has also emerged. In this study, we present the …
Allosteric control of ACE2 peptidase domain dynamics Open
The analysis of molecular dynamics simulation of the angiotensin-converting enzyme 2 peptidase domain via targeted machine learning and REDAN model revealed how the functional motions of this protein can be allosterically controlled.
Explore Protein Conformational Space With Variational Autoencoder Open
Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amou…
Explore protein conformational space with variational autoencoder Open
Molecular dynamic (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amoun…
Explore protein conformational space with variational autoencoder Open
Molecular dynamic (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amoun…
Dimeric allostery mechanism of the plant circadian clock photoreceptor ZEITLUPE Open
In Arabidopsis thaliana , the Light-Oxygen-Voltage (LOV) domain containing protein ZEITLUPE (ZTL) integrates light quality, intensity, and duration into regulation of the circadian clock. Recent structural and biochemical studies of ZTL in…
UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study Open
Proteins are the molecular machines of life. The multitude of possible conformations that proteins can adopt determines their free-energy landscapes. However, the inherently high dimensionality of a protein free-energy landscape poses a ch…
Predicting Potential SARS-COV-2 Drugs—In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking Open
Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing d…
SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction Open
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have re…
QM/MM modeling of class A β-lactamases reveals distinct acylation pathways for ampicillin and cefalexin Open
QM/MM chain-of-states calculations of CTX-M-44 show distinct acylation profiles for ampicillin and cefalexin, the acylation resistance observed for cefalexin attributes to decreased proton affinity induced by the delocalized π-conjugation.
Deciphering the Allosteric Process of the <i>Phaeodactylum tricornutum</i> Aureochrome 1a LOV Domain Open
The conformational-driven allosteric protein diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a) differs from other light-oxygen-voltage (LOV) proteins for its uncommon structural topology. The mechanism of signaling transduction in t…
Deciphering the Allosteric Process of Phaeodactylum tricornutum Aureochrome 1a LOV Domain Open
The conformational-driven allosteric protein diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a) di ers from other light-oxygen-voltage (LOV) proteins for its uncommon structural topology. The mechanism of signaling transduction in Pt…
Deciphering the Allosteric Process of Phaeodactylum tricornutum Aureochrome 1a LOV Domain Open
The conformational-driven allosteric protein diatom Phaeodactylum tricornutum aureochrome 1a (PtAu1a) di ers from other light-oxygen-voltage (LOV) proteins for its uncommon structural topology. The mechanism of signaling transduction in Pt…
Predicting Potential SARS-COV-2 Drugs - In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking Open
Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. Currently, research labs around the world are looking for new pharmaceutical treatments by repurposing existing drugs, identifying potential antibody-base…
Predicting Potential SARS-COV-2 Drugs - In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking Open
Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing d…
SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction Open
Computational prediction of Protein-Ligand Interaction (PLI) is an important step in the modern drug discovery pipeline as it mitigates the cost, time, and resources required to screen novel therapeutics. Deep Neural Networks (DNN) have re…
View article: Engineering the Fullerene‐protein Interface by Computational Design: The Sum is More than its Parts
Engineering the Fullerene‐protein Interface by Computational Design: The Sum is More than its Parts Open
Of all the amino acids, the surface of π‐electron conjugated carbon nanoparticles has the largest affinity for tryptophan, followed by tyrosine, phenylalanine, and histidine. In order to increase the binding of a protein to a fullerene, it…