AI Medical Compendium Topic:
Amino Acid Sequence

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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...

Learning spatial structures of proteins improves protein-protein interaction prediction.

Briefings in bioinformatics
Spatial structures of proteins are closely related to protein functions. Integrating protein structures improves the performance of protein-protein interaction (PPI) prediction. However, the limited quantity of known protein structures restricts the ...

Heavy chain sequence-based classifier for the specificity of human antibodies.

Briefings in bioinformatics
Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, antibodies are powerful tools in research and diagnostics. High-throughput sequencing technologies have promoted comprehensive profiling of the immune rep...

A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction.

Briefings in bioinformatics
In this article, we review two challenging computational questions in protein science: neoantigen prediction and protein structure prediction. Both topics have seen significant leaps forward by deep learning within the past five years, which immediat...

mCNN-ETC: identifying electron transporters and their functional families by using multiple windows scanning techniques in convolutional neural networks with evolutionary information of protein sequences.

Briefings in bioinformatics
In the past decade, convolutional neural networks (CNNs) have been used as powerful tools by scientists to solve visual data tasks. However, many efforts of convolutional neural networks in solving protein function prediction and extracting useful in...

CoCoPRED: coiled-coil protein structural feature prediction from amino acid sequence using deep neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Coiled-coil is composed of two or more helices that are wound around each other. It widely exists in proteins and has been discovered to play a variety of critical roles in biology processes. Generally, there are three types of structural...

Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.

Methods in molecular biology (Clifton, N.J.)
Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. He...