AIMC Topic: Sequence Analysis, Protein
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LOMETS3: integrating deep learning and profile alignment for advanced protein template recognition and function annotation.
Nucleic acids research
Jul 5, 2022
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
Jan 17, 2022
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)
Dec 11, 2021
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)
Dec 7, 2021
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...
Applying and improving AlphaFold at CASP14.
Proteins
Dec 1, 2021
We describe the operation and improvement of AlphaFold, the system that was entered by the team AlphaFold2 to the "human" category in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The AlphaFold system entered in CASP14 is ent...
Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework.
Bioinformatics (Oxford, England)
Nov 5, 2021
MOTIVATION: Over the past 50 years, our ability to model protein sequences with evolutionary information has progressed in leaps and bounds. However, even with the latest deep learning methods, the modelling of a critically important class of protein...
nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.
Briefings in bioinformatics
Nov 5, 2021
Lysine crotonylation (Kcr) is a newly discovered type of protein post-translational modification and has been reported to be involved in various pathophysiological processes. High-resolution mass spectrometry is the primary approach for identificatio...
Locating transcription factor binding sites by fully convolutional neural network.
Briefings in bioinformatics
Sep 2, 2021
Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learnin...
ITP-Pred: an interpretable method for predicting, therapeutic peptides with fused features low-dimension representation.
Briefings in bioinformatics
Jul 20, 2021
The peptide therapeutics market is providing new opportunities for the biotechnology and pharmaceutical industries. Therefore, identifying therapeutic peptides and exploring their properties are important. Although several studies have proposed diffe...
Deep-Kcr: accurate detection of lysine crotonylation sites using deep learning method.
Briefings in bioinformatics
Jul 20, 2021
As a newly discovered protein posttranslational modification, histone lysine crotonylation (Kcr) involved in cellular regulation and human diseases. Various proteomics technologies have been developed to detect Kcr sites. However, experimental approa...
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