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Amino Acid Sequence

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Do "Newly Born" orphan proteins resemble "Never Born" proteins? A study using three deep learning algorithms.

Proteins
"Newly Born" proteins, devoid of detectable homology to any other proteins, known as orphan proteins, occur in a single species or within a taxonomically restricted gene family. They are generated by the expression of novel open reading frames, and a...

Exploiting conformational dynamics to modulate the function of designed proteins.

Proceedings of the National Academy of Sciences of the United States of America
With the recent success in calculating protein structures from amino acid sequences using artificial intelligence-based algorithms, an important next step is to decipher how dynamics is encoded by the primary protein sequence so as to better predict ...

Novel machine learning method allerStat identifies statistically significant allergen-specific patterns in protein sequences.

The Journal of biological chemistry
Cutting-edge technologies such as genome editing and synthetic biology allow us to produce novel foods and functional proteins. However, their toxicity and allergenicity must be accurately evaluated. It is known that specific amino acid sequences in ...

Identification of DNA-binding proteins by Kernel Sparse Representation via L-matrix norm.

Computers in biology and medicine
An understanding of DNA-binding proteins is helpful in exploring the role that proteins play in cell biology. Furthermore, the prediction of DNA-binding proteins is essential for the chemical modification and structural composition of DNA, and is of ...

Comparison of deep learning models with simple method to assess the problem of antimicrobial peptides prediction.

Molecular informatics
Antibiotic-resistant strains are an emerging threat to public health. The usage of antimicrobial peptides (AMPs) is one of the promising approaches to solve this problem. For the development of new AMPs, it is necessary to have reliable prediction me...

Graph-BERT and language model-based framework for protein-protein interaction identification.

Scientific reports
Identification of protein-protein interactions (PPI) is among the critical problems in the domain of bioinformatics. Previous studies have utilized different AI-based models for PPI classification with advances in artificial intelligence (AI) techniq...

Effectively Identifying Compound-Protein Interaction Using Graph Neural Representation.

IEEE/ACM transactions on computational biology and bioinformatics
Effectively identifying compound-protein interactions (CPIs) is crucial for new drug design, which is an important step in silico drug discovery. Current machine learning methods for CPI prediction mainly use one-demensional (1D) compound/protein str...

A Deep Learning Framework for Predicting Protein Functions With Co-Occurrence of GO Terms.

IEEE/ACM transactions on computational biology and bioinformatics
The understanding of protein functions is critical to many biological problems such as the development of new drugs and new crops. To reduce the huge gap between the increase of protein sequences and annotations of protein functions, many methods hav...

AttentionDTA: Drug-Target Binding Affinity Prediction by Sequence-Based Deep Learning With Attention Mechanism.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of drug-target relations (DTRs) is substantial in drug development. A large number of methods treat DTRs as drug-target interactions (DTIs), a binary classification problem. The main drawback of these methods are the lack of reliab...

BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach.

PLoS computational biology
Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity. However, experimental methods highly rely...