AIMC Topic: Protein Domains

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E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

Journal of chemical information and modeling
With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and fur...

AlphaFold-multimer predicts ATG8 protein binding motifs crucial for autophagy research.

PLoS biology
In this issue of PLOS Biology, Ibrahim and colleagues demonstrate how AlphaFold-multimer, an artificial intelligence-based structure prediction tool, can be used to identify sequence motifs binding to the ATG8 family of proteins central to autophagy.

Neural Networks in the Design of Molecules with Affinity to Selected Protein Domains.

International journal of molecular sciences
Drug design with machine learning support can speed up new drug discoveries. While current databases of known compounds are smaller in magnitude (approximately 108), the number of small drug-like molecules is estimated to be between 1023 and 1060. Th...

Decoding CAR T cell phenotype using combinatorial signaling motif libraries and machine learning.

Science (New York, N.Y.)
Chimeric antigen receptor (CAR) costimulatory domains derived from native immune receptors steer the phenotypic output of therapeutic T cells. We constructed a library of CARs containing ~2300 synthetic costimulatory domains, built from combinations ...

Learning Proteome Domain Folding Using LSTMs in an Empirical Kernel Space.

Journal of molecular biology
The recognition of protein structural folds is the starting point for protein function inference and for many structural prediction tools. We previously introduced the idea of using empirical comparisons to create a data-augmented feature space calle...

Protein sequence design with a learned potential.

Nature communications
The task of protein sequence design is central to nearly all rational protein engineering problems, and enormous effort has gone into the development of energy functions to guide design. Here, we investigate the capability of a deep neural network mo...

Protein Design with Deep Learning.

International journal of molecular sciences
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using De...

iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach.

Computational and mathematical methods in medicine
Membrane protein is an important kind of proteins. It plays essential roles in several cellular processes. Based on the intramolecular arrangements and positions in a cell, membrane proteins can be divided into several types. It is reported that the ...

MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

Structure (London, England : 1993)
The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances...

A deep-learning framework for multi-level peptide-protein interaction prediction.

Nature communications
Peptide-protein interactions are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. Recently, a number of computational methods have been developed to predict peptide...