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

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

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 supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero. In this paper, we study a modification of the regularization path for infinite-width 2- ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learni ...

Cupredoxins are widely occurring copper-binding proteins with a typical Greek-key beta barrel fold. They are generally described as electron carriers that rely on a T1 copper centre coordinated by four ligands provided by the folded polypeptide. The discov ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

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 ...

Interferometric scattering (iSCAT) microscopy enables the label-free observation of biomolecules. Consequently, single-particle imaging and tracking with the iSCAT-based method known as mass photometry (MP) is a growing area of study. However, establishing ...

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

Recordings of the physiological history of cells provide insights into biological processes, yet obtaining such recordings is a challenge. To address this, we introduce a method to record transient cellular events for later analysis. We designed proteins t ...

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 ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Borrowing some quotes from Harper Lee's novel "To Kill A Mockingbird" to help frame our manuscript, we discuss methods to profile local proteomes. We initially focus on chemical biology regimens that function in live organisms and use reactive biotin speci ...

Over the past two decades, protein S-acylation (often referred to as S-palmitoylation) has emerged as an important regulator of vital signalling pathways. S-Acylation is a reversible post-translational modification that involves the attachment of a fatty a ...

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

The synthesis of complex sugars is a key aspect of microbial biology. Cyclic beta-1,2-glucan (C beta G) is a circular polysaccharide critical for host interactions of many bacteria, including major pathogens of humans (Brucella) and plants (Agrobacterium). ...

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

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

Chat with Graph Search

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

On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks

Random matrix methods for high-dimensional machine learning models

Computational drug development for membrane protein targets

Beyond the coupled distortion model: structural analysis of the single domain cupredoxin AcoP, a green mononuclear copper centre with original features

Residual-based attention in physics-informed neural networks

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

Surface passivation and functionalisation for mass photometry

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Recording physiological history of cells with chemical labeling

Structural heterogeneity of the ion and lipid channel TMEM16F

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Climbing into their Skin to Understand Contextual Protein-Protein Associations and Localizations: Functional Investigations in Transgenic Live Model Organisms

Mechanisms and functions of protein S-acylation

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Structure-function analysis of the cyclic β-1,2-glucan synthase from Agrobacterium tumefaciens

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination