Rocco Moretti
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View article: CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2
CACHE Challenge #3: Targeting the Nsp3 Macrodomain of SARS-CoV-2 Open
The third Critical Assessment of Computational Hit-finding Experiments (CACHE) challenged computational teams to identify chemically novel ligands targeting the macrodomain 1 of SARS-CoV-2 Nsp3, a promising coronavirus drug target. Twenty-…
View article: Cache: Utilizing ultra-large library screening in Rosetta to identify novel binders of the WD-repeat domain of Leucine-Rich Repeat Kinase 2
Cache: Utilizing ultra-large library screening in Rosetta to identify novel binders of the WD-repeat domain of Leucine-Rich Repeat Kinase 2 Open
In this study, we present a pipeline for identifying novel ligands targeting the Tryptophan-Aspartate-Repeat domain 40 (WDR40) of Leucine-Rich Repeat Kinase 2 (LRRK2), a protein associated with Parkinson’s disease, as part of the first Cri…
View article: Better together: Elements of successful scientific software development in a distributed collaborative community
Better together: Elements of successful scientific software development in a distributed collaborative community Open
Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software en…
View article: CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13
CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13 Open
A critical assessment of computational hit-finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comp…
View article: RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms
RosettaAMRLD: A Reaction-Driven Approach for Structure-Based Drug Design from Combinatorial Libraries with Monte Carlo Metropolis Algorithms Open
The Rosetta automated Monte Carlo reaction-based ligand design (RosettaAMRLD) integrates a Monte Carlo Metropolis (MCM) algorithm and reaction-driven molecule proposal to enhance structure-based de novo drug discovery. By leveraging combin…
View article: Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors
Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors Open
The fusion of traditional chemical descriptors with graph neural networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from th…
View article: CACHE: Utilizing Ultra-Large Library Screening in Rosetta to Identify Novel Binders of the WD-Repeat Domain of Leucine-Rich Repeat Kinase 2
CACHE: Utilizing Ultra-Large Library Screening in Rosetta to Identify Novel Binders of the WD-Repeat Domain of Leucine-Rich Repeat Kinase 2 Open
In this study, we present a pipeline for identifying novel ligands targeting the Thryptophan-Aspartate-Repeat domain 40 (WDR40) of Leucine-Rich Repeat Kinase 2 (LRRK2), a protein associated with Parkinson's disease, as part of the first Cr…
View article: CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13
CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13 Open
A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comp…
View article: CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13
CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13 Open
A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comp…
View article: CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13
CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13 Open
A critical assessment of computational hit finding experiments (CACHE) challenge was conducted to predict ligands for the SARS-CoV-2 Nsp13 helicase RNA binding site, a highly conserved COVID-19 target. Twenty-three participating teams comp…
View article: Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants
Self-supervised machine learning methods for protein design improve sampling but not the identification of high-fitness variants Open
Machine learning (ML) is changing the world of computational protein design, with data-driven methods surpassing biophysical-based methods in experimental success. However, they are most often reported as case studies, lack integration and…
View article: RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data
RosettaHDX: Predicting antibody-antigen interaction from hydrogen-deuterium exchange mass spectrometry data Open
High-throughput characterization of antibody-antigen complexes at the atomic level is critical for understanding antibody function and enabling therapeutic development. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) enables rapid e…
View article: CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson’s Disease Associated Protein
CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson’s Disease Associated Protein Open
The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each select…
View article: Posttranslationally modified self-peptides promote hypertension in mouse models
Posttranslationally modified self-peptides promote hypertension in mouse models Open
Posttranslational modifications can enhance immunogenicity of self-proteins. In several conditions, including hypertension, systemic lupus erythematosus, and heart failure, isolevuglandins (IsoLGs) are formed by lipid peroxidation and cova…
View article: CACHE Challenge #1: targeting the WDR domain of LRRK2, a Parkinson’s Disease associated protein
CACHE Challenge #1: targeting the WDR domain of LRRK2, a Parkinson’s Disease associated protein Open
The CACHE challenges are a series of prospective benchmarking exercises meant to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE #1 challenge in which 23 computational teams ea…
View article: Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants
Self-supervised machine learning methods for protein design improve sampling, but not the identification of high-fitness variants Open
Machine learning (ML) is changing the world of computational protein design, with data- driven methods surpassing biophysical-based methods in experimental success rates. However, they are most often reported as case studies, lack integrat…
View article: Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd)
Ultra-large library screening with an evolutionary algorithm in Rosetta (REvoLd) Open
Ultra-large make-on-demand compound libraries now contain billions of readily available compounds. This represents a golden opportunity for in-silico drug discovery. One challenge, however, is the time and computational cost of an exhausti…
View article: Combining machine learning with structurebased protein design to predict and engineer post-Translational modifications of proteins
Combining machine learning with structurebased protein design to predict and engineer post-Translational modifications of proteins Open
Post-Translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, …
View article: Recent Advances in Automated Structure-Based De Novo Drug Design
Recent Advances in Automated Structure-Based De Novo Drug Design Open
As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. D…
View article: Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins
Combining machine learning with structure-based protein design to predict and engineer post-translational modifications of proteins Open
Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, …
View article: Discovery of post-translationally modified self-peptides that promote hypertension
Discovery of post-translationally modified self-peptides that promote hypertension Open
Post translational modifications can enhance immunogenicity of self-proteins. In several conditions including hypertension, systemic lupus, and heart failure, isolevuglandins (IsoLGs) are formed by lipid peroxidation and covalently bond wi…
View article: Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery
Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery Open
In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemi…
View article: Drugit: Crowd-sourcing molecular design of non-peptidic VHL binders
Drugit: Crowd-sourcing molecular design of non-peptidic VHL binders Open
Given the role of human intuition in current drug design efforts, crowd-sourced 'citizen scientist' games have the potential to greatly expand the pool of potential drug designers. Here, we introduce ‘Drugit', the small molecule design mod…
View article: Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors
Advancements in Ligand-Based Virtual Screening through the Synergistic Integration of Graph Neural Networks and Expert-Crafted Descriptors Open
The fusion of traditional chemical descriptors with Graph Neural Networks (GNNs) offers a compelling strategy for enhancing ligand-based virtual screening methodologies. A comprehensive evaluation revealed that the benefits derived from th…
View article: Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids
Rosetta FlexPepDock to predict peptide-MHC binding: An approach for non-canonical amino acids Open
Computation methods that predict the binding of peptides to MHC-I are important tools for screening and identifying immunogenic antigens and have the potential to accelerate vaccine and drug development. However, most available tools are s…
View article: Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery
Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery Open
In computer-aided drug discovery, quantitative structure activity relation models are trained to predict biological activity from chemical structure. Despite the recent success of applying graph neural network to this task, important chemi…
View article: Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks
Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks Open
Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflow…