3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

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We study the glass transition by exploring a broad class of kinetic rules that can significantly modify the normal dynamics of supercooled liquids while maintaining thermal equilibrium. Beyond the usual dynamics of liquids, this class includes dynamics in ...

Post-translational modifications (PTMs) play a pivotal role in regulating protein structure, interaction, and function. Aberrant PTM patterns are associated with diseases. Moreover, individual PTMs have a complex interaction with each other, known as PTM c ...

We study the statistical mechanics and the equilibrium dynamics of a system of classical Heisenberg spins with frustrated interactions on a d -dimensional simple hypercubic lattice, in the limit of infinite dimensionality d -> infinity . In the analysis we ...

The field of protein design has made remarkable progress over the past decade. Historically, the low reliability of purely structure-based design methods limited their application, but recent strategies that combine structure-based and sequence-based calcu ...

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 ...

Recently, single-particle cryo-electron microscopy emerged as a technique capable of determining protein structures at near-atomic resolution and resolving protein dynamics with a temporal resolution ranging from second to milliseconds. This thesis describ ...

Translation elongation plays an important role in regulating protein concentrations in the cell, and dysregulation of this process has been linked to several human diseases. In this study, we use data from ribo-seq experiments to model ribosome densities, ...

In this thesis, we concentrate on advancing high-level behavioral control policies for robotic systems within the framework of Dynamical Systems (DS). Throughout the course of this research, a unifying thread weaving through diverse fields emerges, and tha ...

In recent years, there has been a surge of interest in predicting computed activation barriers, to enable the acceleration of the automated exploration of reaction networks. Consequently, various predictive approaches have emerged, ranging from graph-based ...

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 ...

Hyperbolic lattices are a new type of synthetic materials based on regular tessellations in non-Euclidean spaces with constant negative curvature. While so far, there has been several theoretical investigations of hyperbolic topological media, experimental ...

Transmembrane protein 16 F (TMEM16F) is a Ca2+-activated homodimer which functions as an ion channel and a phospholipid scramblase. Despite the availability of several TMEM16F cryogenic electron microscopy (cryo-EM) structures, the mechanism of activation ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

Cartilage homeostasis, crucial for musculoskeletal function, is orchestrated by interconnected biophysical cues. In healthy cartilage, repetitive compressive loading not only elicits a range of mechanical stimuli but also induces a gradual transient temper ...

In this paper, we set the mathematical foundations of the Dynamical Low-Rank Approximation (DLRA) method for stochastic differential equations (SDEs). DLRA aims at approximating the solution as a linear combination of a small number of basis vectors with r ...

The rapid progress in the field of deep learning has had a significant impact on protein design. Deep learning methods have recently produced a breakthrough in protein structure prediction, leading to the availability of high-quality models for millions of ...

Computational models starting from large ensembles of evolutionarily related protein sequences capture a representation of protein families and learn constraints associated to protein structure and function. They thus open the possibility for generating no ...

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in comp ...

We generalize the class vectors found in neural networks to linear subspaces (i.e., points in the Grassmann manifold) and show that the Grassmann Class Representation (GCR) enables simultaneous improvement in accuracy and feature transferability. In GCR, e ...

3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data

Chattez avec Graph Search

Testing Theories of the Glass Transition with the Same Liquid but Many Kinetic Rules

Investigating the intra-molecular and inter-molecular effects of post-translational modifications on intrinsically disordered protein regions and structured protein regions

Frustrated magnets in the limit of infinite dimensions: Dynamics and disorder-free glass transition

Opportunities and challenges in design and optimization of protein function

Predicting protein interactions using geometric deep learning on protein surfaces

Microsecond Time-Resolved Cryo-Electron Microscopy

Towards improving full-length ribosome density prediction by bridging sequence and graph-based representations

Geometric Learning: Leveraging differential geometry for learning and control

Benchmarking machine-readable vectors of chemical reactions on computed activation barriers

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

Anomalous and Chern topological waves in hyperbolic networks

Structural heterogeneity of the ion and lipid channel TMEM16F

Towards Trustworthy Deep Learning for Image Reconstruction

Synergetic Support of Cartilage Homeostasis via Coupled Thermal-Hydrostatic Pressure Stimuli

Dynamical Low-Rank Approximation for Stochastic Differential Equations

A new age in protein design empowered by deep learning

Generative power of a protein language model trained on multiple sequence alignments

Density estimation in RKHS with application to Korobov spaces in high dimensions

De novo protein design by inversion of the AlphaFold structure prediction network

Get the Best of Both Worlds: Improving Accuracy and Transferability by Grassmann Class Representation