Publications by authors named "G Tiana"

Background: Non-small cell lung cancers (NSCLCs) with fusions are effectively treated with tyrosine kinase inhibitors (TKIs). The widespread use of next-generation sequencing (NGS) assays to study the molecular profile of NSCLCs, can identify rare fusion partners of . Therapy decisions are made without considering which fusion partner is present and its potential oncogenic properties.

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We present a computational scheme for predicting the ligands that bind to a pocket of a known structure. It is based on the generation of a general abstract representation of the molecules, which is invariant to rotations, translations, and permutations of atoms, and has some degree of isometry with the space of conformations. We use these representations to train a nondeep machine learning algorithm to classify the binding between pockets and molecule pairs and show that this approach has a better generalization capability than existing methods.

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Protein-mediated interactions are ubiquitous in the cellular environment, and particularly in the nucleus, where they are responsible for the structuring of chromatin. We show through molecular-dynamics simulations of a polymer surrounded by binders that the strength of the binder-polymer interaction separates an equilibrium from a nonequilibrium regime. In the equilibrium regime, the system can be efficiently described by an effective model in which the binders are traced out.

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Constitutive heterochromatin is essential for transcriptional silencing and genome integrity. The establishment of constitutive heterochromatin in early embryos and its role in early fruitfly development are unknown. Lysine 9 trimethylation of histone H3 (H3K9me3) and recruitment of its epigenetic reader, heterochromatin protein 1a (HP1a), are hallmarks of constitutive heterochromatin.

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Article Synopsis
  • Researchers are exploring the high-dimensional protein sequence space to understand how its geometric structure influences natural evolution and protein foldability.
  • Using advanced transformer models for structure prediction, they found that natural proteins are mostly located in wide, flat energy minima, which resembles optimization problems in machine learning.
  • Their specialized statistical mechanics algorithms outperform traditional methods by identifying high entropy valleys, showing that these areas may lead to sequences similar in stability and features to natural proteins.
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