AIMC Topic: Amino Acid Sequence

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Homologues not needed: Structure prediction from a protein language model.

Structure (London, England : 1993)
Accurate protein structure predictors use clusters of homologues, which disregard sequence specific effects. In this issue of Structure, Weißenow and colleagues report a deep learning-based tool, EMBER2, that efficiently predicts the distances in a p...

NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning.

Nucleic acids research
Recent advances in machine learning and natural language processing have made it possible to profoundly advance our ability to accurately predict protein structures and their functions. While such improvements are significantly impacting the fields o...

AlphaFold accurately predicts distinct conformations based on the oligomeric state of a de novo designed protein.

Protein science : a publication of the Protein Society
Using the molecular modeling program Rosetta, we designed a de novo protein, called SEWN0.1, which binds the heterotrimeric G protein Gα The design is helical, well-folded, and primarily monomeric in solution at a concentration of 10 μM. However, whe...

Predicting protein-peptide binding residues via interpretable deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Identifying the protein-peptide binding residues is fundamentally important to understand the mechanisms of protein functions and explore drug discovery. Although several computational methods have been developed, most of them highly rely on...

Scoring protein sequence alignments using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: A high-quality sequence alignment (SA) is the most important input feature for accurate protein structure prediction. For a protein sequence, there are many methods to generate a SA. However, when given a choice of more than one SA for a ...

Do deep learning models make a difference in the identification of antimicrobial peptides?

Briefings in bioinformatics
In the last few decades, antimicrobial peptides (AMPs) have been explored as an alternative to classical antibiotics, which in turn motivated the development of machine learning models to predict antimicrobial activities in peptides. The first genera...

DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.

Bioinformatics (Oxford, England)
MOTIVATION: Drug discovery has witnessed intensive exploration of predictive modeling of drug-target physical interactions over two decades. However, a critical knowledge gap needs to be filled for correlating drug-target interactions with clinical o...

PIPENN: protein interface prediction from sequence with an ensemble of neural nets.

Bioinformatics (Oxford, England)
MOTIVATION: The interactions between proteins and other molecules are essential to many biological and cellular processes. Experimental identification of interface residues is a time-consuming, costly and challenging task, while protein sequence data...

ProteinBERT: a universal deep-learning model of protein sequence and function.

Bioinformatics (Oxford, England)
SUMMARY: Self-supervised deep language modeling has shown unprecedented success across natural language tasks, and has recently been repurposed to biological sequences. However, existing models and pretraining methods are designed and optimized for t...

ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.

Briefings in bioinformatics
Protein secretion has a pivotal role in many biological processes and is particularly important for intercellular communication, from the cytoplasm to the host or external environment. Gram-positive bacteria can secrete proteins through multiple secr...