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Sequence Analysis, Protein

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PYTHIA: Deep Learning Approach for Local Protein Conformation Prediction.

International journal of molecular sciences
Protein Blocks (PBs) are a widely used structural alphabet describing local protein backbone conformation in terms of 16 possible conformational states, adopted by five consecutive amino acids. The representation of complex protein 3D structures as 1...

Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.

Proteins
This article reports and analyzes the results of protein contact and distance prediction by our methods in the 14th Critical Assessment of techniques for protein Structure Prediction (CASP14). A new deep learning-based contact/distance predictor was ...

Protein tertiary structure prediction and refinement using deep learning and Rosetta in CASP14.

Proteins
The trRosetta structure prediction method employs deep learning to generate predicted residue-residue distance and orientation distributions from which 3D models are built. We sought to improve the method by incorporating as inputs (in addition to se...

PARROT is a flexible recurrent neural network framework for analysis of large protein datasets.

eLife
The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to i...

When homologous sequences meet structural decoys: Accurate contact prediction by tFold in CASP14-(tFold for CASP14 contact prediction).

Proteins
In this paper, we report our tFold framework's performance on the inter-residue contact prediction task in the 14th Critical Assessment of protein Structure Prediction (CASP14). Our tFold framework seamlessly combines both homologous sequences and st...

Improved Protein Structure Prediction Using a New Multi-Scale Network and Homologous Templates.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The accuracy of de novo protein structure prediction has been improved considerably in recent years, mostly due to the introduction of deep learning techniques. In this work, trRosettaX, an improved version of trRosetta for protein structure predicti...

TwinCons: Conservation score for uncovering deep sequence similarity and divergence.

PLoS computational biology
We have developed the program TwinCons, to detect noisy signals of deep ancestry of proteins or nucleic acids. As input, the program uses a composite alignment containing pre-defined groups, and mathematically determines a 'cost' of transforming one ...

Protein embeddings and deep learning predict binding residues for various ligand classes.

Scientific reports
One important aspect of protein function is the binding of proteins to ligands, including small molecules, metal ions, and macromolecules such as DNA or RNA. Despite decades of experimental progress many binding sites remain obscure. Here, we propose...

Applying and improving AlphaFold at CASP14.

Proteins
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...

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...