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Sequence Alignment

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EvoRator: Prediction of Residue-level Evolutionary Rates from Protein Structures Using Machine Learning.

Journal of molecular biology
Measuring evolutionary rates at the residue level is indispensable for gaining structural and functional insights into proteins. State-of-the-art tools for estimating rates take as input a large set of homologous proteins, a probabilistic model of ev...

A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers.

Bioinformatics (Oxford, England)
MOTIVATION: Deep learning has revolutionized protein tertiary structure prediction recently. The cutting-edge deep learning methods such as AlphaFold can predict high-accuracy tertiary structures for most individual protein chains. However, the accur...

AF2Complex predicts direct physical interactions in multimeric proteins with deep learning.

Nature communications
Accurate descriptions of protein-protein interactions are essential for understanding biological systems. Remarkably accurate atomic structures have been recently computed for individual proteins by AlphaFold2 (AF2). Here, we demonstrate that the sam...

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 ...

Sequence-based drug-target affinity prediction using weighted graph neural networks.

BMC genomics
BACKGROUND: Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development. Sequence-based d...

Prior knowledge facilitates low homologous protein secondary structure prediction with DSM distillation.

Bioinformatics (Oxford, England)
MOTIVATION: Protein secondary structure prediction (PSSP) is one of the fundamental and challenging problems in the field of computational biology. Accurate PSSP relies on sufficient homologous protein sequences to build the multiple sequence alignme...

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...

Single-sequence protein structure prediction using a language model and deep learning.

Nature biotechnology
AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain...

LambdaPP: Fast and accessible protein-specific phenotype predictions.

Protein science : a publication of the Protein Society
The availability of accurate and fast artificial intelligence (AI) solutions predicting aspects of proteins are revolutionizing experimental and computational molecular biology. The webserver LambdaPP aspires to supersede PredictProtein, the first in...

Fungal names: a comprehensive nomenclatural repository and knowledge base for fungal taxonomy.

Nucleic acids research
Fungal taxonomy is a complex and rapidly changing subject, which makes proper naming of fungi challenging for taxonomists. A registration platform with a standardized and information-integrated database is a powerful tool for efficient research on fu...