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Sequence Analysis, Protein

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Effect of tokenization on transformers for biological sequences.

Bioinformatics (Oxford, England)
MOTIVATION: Deep-learning models are transforming biological research, including many bioinformatics and comparative genomics algorithms, such as sequence alignments, phylogenetic tree inference, and automatic classification of protein functions. Amo...

DeepSS2GO: protein function prediction from secondary structure.

Briefings in bioinformatics
Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation hav...

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.

Briefings in bioinformatics
Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific p...

MMSMAPlus: a multi-view multi-scale multi-attention embedding model for protein function prediction.

Briefings in bioinformatics
Protein is the most important component in organisms and plays an indispensable role in life activities. In recent years, a large number of intelligent methods have been proposed to predict protein function. These methods obtain different types of pr...

Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling.

Methods in molecular biology (Clifton, N.J.)
Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The thres...

DistilProtBert: a distilled protein language model used to distinguish between real proteins and their randomly shuffled counterparts.

Bioinformatics (Oxford, England)
SUMMARY: Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameter...

LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation.

Nucleic acids research
Deep learning techniques have significantly advanced the field of protein structure prediction. LOMETS3 (https://zhanglab.ccmb.med.umich.edu/LOMETS/) is a new generation meta-server approach to template-based protein structure prediction and function...

ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features.

Briefings in bioinformatics
MOTIVATION: The Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. As of CASP14, there are 79 global QA methods, and a minority of 39 residue-level QA methods with very few of them working on protein complex...

Organism-specific training improves performance of linear B-cell epitope prediction.

Bioinformatics (Oxford, England)
MOTIVATION: In silico identification of linear B-cell epitopes represents an important step in the development of diagnostic tests and vaccine candidates, by providing potential high-probability targets for experimental investigation. Current predict...

Comparative evaluation of shape retrieval methods on macromolecular surfaces: an application of computer vision methods in structural bioinformatics.

Bioinformatics (Oxford, England)
MOTIVATION: The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geo...