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Databases, Protein

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ResCNNT-fold: Combining residual convolutional neural network and Transformer for protein fold recognition from language model embeddings.

Computers in biology and medicine
A comprehensive understanding of protein functions holds significant promise for disease research and drug development, and proteins with analogous tertiary structures tend to exhibit similar functions. Protein fold recognition stands as a classical ...

Uncovering new families and folds in the natural protein universe.

Nature
We are now entering a new era in protein sequence and structure annotation, with hundreds of millions of predicted protein structures made available through the AlphaFold database. These models cover nearly all proteins that are known, including thos...

Protein remote homology detection and structural alignment using deep learning.

Nature biotechnology
Exploiting sequence-structure-function relationships in biotechnology requires improved methods for aligning proteins that have low sequence similarity to previously annotated proteins. We develop two deep learning methods to address this gap, TM-Vec...

Facing the phase problem.

IUCrJ
The marvel of X-ray crystallography is the beauty and precision of the atomic structures deduced from diffraction patterns. Since these patterns record only amplitudes, phases for the diffracted waves must also be evaluated for systematic structure d...

MSBooster: improving peptide identification rates using deep learning-based features.

Nature communications
Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFrag...

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples.

Journal of chemical information and modeling
Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI d...

A deep learning solution for crystallographic structure determination.

IUCrJ
The general de novo solution of the crystallographic phase problem is difficult and only possible under certain conditions. This paper develops an initial pathway to a deep learning neural network approach for the phase problem in protein crystallogr...

A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.

Journal of chemical information and modeling
Over the past few years, many machine learning-based scoring functions for predicting the binding of small molecules to proteins have been developed. Their objective is to approximate the distribution which takes two molecules as input and outputs th...

Prediction of transport proteins from sequence information with the deep learning approach.

Computers in biology and medicine
Transport proteins (TPs) are vital to the growth and life of all living things, especially in fields of microbial pathogenesis and drug resistance of tumor cells. Accurately identifying potential TPs remains an important challenge for the advancement...

Machine learning for evolutionary-based and physics-inspired protein design: Current and future synergies.

Current opinion in structural biology
Computational protein design facilitates the discovery of novel proteins with prescribed structure and functionality. Exciting designs were recently reported using novel data-driven methodologies that can be roughly divided into two categories: evolu...