Advances in DNA sequencing and machine learning are providing insights into protein sequences structures on an enormous scale1. However, the energetics driving folding invisible these remain largely unknown2. The hidden thermodynamics of can drive disease3,4, shape evolution5-7 guide engineering8-10, new approaches needed to reveal for every sequence structure. Here we present cDNA display proteolysis, a method measuring thermodynamic stability up 900,000 domains one-week experiment. From...

Abstract Advances in DNA sequencing and machine learning are illuminating protein sequences structures on an enormous scale. However, the energetics driving folding invisible these remain largely unknown. The hidden thermodynamics of can drive disease, shape evolution, guide engineering, new approaches needed to reveal for every sequence structure. We present cDNA display proteolysis, a method measuring thermodynamic stability up 900,000 domains one-week experiment. From 1.8 million...

The spectacular recent advances in protein and complex structure prediction hold promise for reconstructing interactomes at large-scale residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel impact sequence variations on strength association.In this work, we report Deep Local Analysis, a novel efficient deep learning framework that relies strikingly simple deconstruction interfaces into small locally oriented...

Abstract Motivation With the recent advances in protein 3D structure prediction, interactions are becoming more central than ever before. Here, we address problem of determining how proteins interact with one another. More specifically, investigate possibility discriminating near-native complex conformations from incorrect ones by exploiting local environments around interfacial residues. Results Deep Local Analysis (DLA)-Ranker is a deep learning framework applying convolutions to set...

Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in cell and what manner relies on partial, noisy, highly heterogeneous data. Thus, there is a need for methods comprehensively describing organizing such LEVELNET versatile interactive tool visualizing, exploring, comparing protein-protein interaction (PPI) networks inferred from different types evidence. helps break down complexity PPI by representing them as...

Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and improving medicine bioengineering. Over past years, a large body experimental data has been accumulated on who interacts whom in what manner. However, these are highly heterogeneous sometimes contradictory, noisy, biased. Ab initio methods provide means to "blind" protein-protein interaction network...

Abstract Grapheme to phoneme conversion is one of the main subsystems Text-to-Speech (TTS) systems. Converting sequence written words their corresponding sequences for Persian language more challenging than other languages; because in standard orthography this short vowels are omitted and pronunciation ofwords depends on positions a sentence. Common approaches used commercial TTS systems have several modules complicated models natural processing homograph disambiguation that make...

Abstract The spectacular advances in protein and complex structure prediction hold promises for the reconstruction of interactomes at large scale residue resolution. Beyond determining 3D arrangement interacting partners, modeling approaches should be able to sense impact sequence variations such as point mutations on strength association. In this work, we report DLA-mutation, a novel efficient deep learning framework accurately predicting mutation-induced binding affinity changes. It relies...

Font recognition is one of the pre-processing steps in optical character (OCR) systems that affects on their performance. In this paper two methods are proposed for Persian font recognition. first method, Gabor filter used feature extraction from images, then principle component analysis (PCA) applied to reduce dimensions and finally, a multi-layer Perceptron (MLP) neural network classification. second techniques, random forest utilized recognizing fonts. For evaluation, dataset includes 10...

A bstract With the recent advances in protein 3D structure prediction, interactions are becoming more central than ever before. Here, we address problem of determining how proteins interact with one another. More specifically, investigate possibility discriminating near-native complex conformations from incorrect ones by exploiting local environments around interfacial residues. Deep Local Analysis (DLA)-Ranker is a deep learning framework applying convolutions to set locally oriented cubes...

A bstract The spectacular recent advances in protein and complex structure prediction hold promise for reconstructing interactomes at large scale residue resolution. Beyond determining the 3D arrangement of interacting partners, modeling approaches should be able to unravel impact sequence variations on strength association. In this work, we report Deep Local Analysis (DLA), a novel efficient deep learning framework that relies strikingly simple deconstruction interfaces into small locally...

A bstract Physical interactions between proteins are central to all biological processes. Yet, the current knowledge of who interacts with whom in cell and what manner relies on partial, noisy, highly heterogeneous data. Thus, there is a need for methods comprehensively describing organising such LEVELNET versatile interactive tool visualising, exploring comparing protein-protein interaction (PPI) networks inferred from different types evidence. helps break down complexity PPI by...

Abstract Proteins ensure their biological functions by interacting with each other. Hence, characterising protein interactions is fundamental for our understanding of the cellular machinery, and improving medicine bioengineering. Over past years, a large body experimental data has been accumulated on who interacts whom in what manner. However, these are highly heterogeneous sometimes contradictory, noisy, biased. Ab initio methods provide means to “blind” protein-protein interaction network...

Abstract Proteins ensure their biological functions by interacting with each other, and other molecules. Determining the relative position orientation of protein partners in a complex remains challenging. Here, we address problem ranking candidate conformations toward identifying near-native conformations. We propose deep learning approach relying on local representation interface an explicit account its geometry. show that method is able to recognise certain pattern distributions specific...