Publications related to Trametinib Induces the Stabilization of a Dual GNAQ p.Gly48Leu-and FGFR4 p.Cys172Gly-Mutated Uveal Melanoma. The Role of Molecular Modelling in Personalized Oncology

Related publications (32)

Machine learning-aided generative molecular design

Machine learning has provided a means to accelerate early-stage drug discovery by combining molecule generation and filtering steps in a single architecture that leverages the experience and design preferences of medicinal chemists. However, designing mach ...

Nature Portfolio2024

Embracing cancer complexity: Hallmarks of systemic disease

Johan Auwerx, Douglas Hanahan

The last 50 years have witnessed extraordinary developments in understanding mechanisms of carcinogenesis, synthesized as the hallmarks of cancer. Despite this logical framework, our understanding of the molecular basis of systemic manifestations and the u ...

Cell Press2024

The role of LRRK2 in lung adenocarcinoma

Aspasia Gkasti

Lung cancer is the leading cause of cancer-related deaths worldwide and the most common lung cancer subtype is lung adenocarcinoma (LUAD). Frequently mutated genes involve activating mutations in KRAS and loss of function mutations in TP53. LUADs primarily ...

EPFL2024

Efficient and insightful descriptors for representing molecular and material space

Alexander Jan Goscinski

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024

Exciton migration in two-dimensional materials

Giovanni Consalvo Cistaro

Excitons play an essential role in the optical response of two-dimensional materials. These are bound states showing up in the band gaps of many-body systems and are conceived as quasiparticles formed by an electron and a hole. By performing real-time simu ...

Nature Portfolio2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

From Sequence to Dynamics to Function: Computational Design of Allostery and Ligand Selectivity in G-Protein Coupled Receptors

Mahdi Hijazi

The phenomenon of allostery, a general property in proteins that has been heralded as "the second secret of life" remains elusive to our understanding and even more challenging to incorporate into protein design. One example of allosteric proteins with gre ...

EPFL2024

Solvation Free Energies from Machine Learning Molecular Dynamics

Nicola Marzari, Nicéphore Arthur François Bonnet

The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...

Amer Chemical Soc2024

Prolamins' 3D structure: A new insight into protein modeling using the language of numbers and shapes

Niloufar Sharif

This study combined protein modeling methods to generate the prolamins' fractions as precise as possible. Hence, gliadins, zeins, kafirins, hordeins, secalins, avenins and oryzins were generated based on their characteristics and disulfide mapping. Finding ...

Elsevier Sci Ltd2024

Patient-derived mini-colons enable long-term modeling of tumor–microenvironment complexity

Matthias Lütolf, Nicolas Antoine Vincent Broguiere, Jakob Josef Langer, Lucie Carrié Tillard

Existing organoid models fall short of fully capturing the complexity of cancer because they lack sufficient multicellular diversity, tissue-level organization, biological durability and experimental flexibility. Thus, many multifactorial cancer processes, ...

2024

Use of a novel microbiome modulator improves anticancer immunity in a murine model of malignant pleural mesothelioma

Paul Joseph Dyson

Objective: Malignant pleural mesothelioma is a fatal disease and a clinical challenge, as few effective treatment modalities are available. Previous evidence links the gut microbiome to the host immunoreactivity to tumors. We thus evaluated the impact of a ...

2024

Predicting protein interactions using geometric deep learning on protein surfaces

Freyr Sverrisson

In the domain of computational structural biology, predicting protein interactions based on molecular structure remains a pivotal challenge. This thesis delves into this challenge through a series of interconnected studies. The first chapter introduces th ...

EPFL2024

2023 Roadmap on molecular modelling of electrochemical energy materials

Alfredo Pasquarello, Chunmin Zhang, Assil Bouzid

New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as em ...

Iop Publishing Ltd2023

Rational design of small-molecule responsive protein switches

Bruno Emanuel Ferreira De Sousa Correia, Leo Scheller, Sailan Shui, Stephen Michael Buckley

Small-molecule responsive protein switches are powerful tools for controlling cellular processes. These switches are designed to respond rapidly and specifically to their inducer. They have been used in numerous applications, including the regulation of ge ...

2023

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

Sebastian Martin Waszak

Background: Cancer genome sequencing enables accurate classification of tumours and tumour subtypes. However, prediction performance is still limited using exome-only sequencing and for tumour types with low somatic mutation burden such as many paediatri ...

2023

Computational Studies for the Design of Metalloenzyme Mimics

Guido Frisari

At present, there is no general standard automated method for engineering metalloenzymes, industrially-relevant systems able to catalyze environmentally friendly reactions. One of the most studied natural metalloenzymes is the second isoform of human carbo ...

EPFL2023

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Justin Villard

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

EPFL2023

A Geometric Transformer for Structural Biology: Development and Applications of the Protein Structure Transformer

Lucien Fabrice Krapp

Proteins, the central building blocks of life, play pivotal roles in nearly every biological function. To do so, these macromolecular structures interact with their surrounding environment in complex ways, leading to diverse functional behaviors. The predi ...

EPFL2023

The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

Francesco Aquilante

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments sp ...

Washington2023

Systems and methods for de novo design of protein interactions with learned surface fingerprints

Pablo Gainza Cirauqui, Michael Bronstein, Zander Harteveld, Andreas Scheck, Sarah Wehrle, Anthony Marchand, Alexandra Krina Van Hall-Beauvais, Freyr Sverrisson

The present application relates to a computer-implemented systems and methods for protein interaction design using surface fingerprints. The method comprises predicting at least one target interface site with high binding propensity, wherein, optionally, t ...

2023