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Accelerating crystal structure determination with iterative AlphaFold prediction.

Acta crystallographica. Section D, Structural biology
Experimental structure determination can be accelerated with artificial intelligence (AI)-based structure-prediction methods such as AlphaFold. Here, an automatic procedure requiring only sequence information and crystallographic data is presented th...

Real-to-bin conversion for protein residue distances.

Computational biology and chemistry
Protein Structure Prediction (PSP) has achieved significant progress lately. Prediction of inter-residue distances by machine learning and their exploitation during the conformational search is largely among the critical factors behind the progress. ...

GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features.

Molecular informatics
In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructin...

DeepPRObind: Modular Deep Learner that Accurately Predicts Structure and Disorder-Annotated Protein Binding Residues.

Journal of molecular biology
Current sequence-based predictors of protein-binding residues (PBRs) belong to two distinct categories: structure-trained vs. intrinsic disorder-trained. Since disordered PBRs differ from structured PBRs in several ways, including ability to bind mul...

Using deep-learning predictions of inter-residue distances for model validation.

Acta crystallographica. Section D, Structural biology
Determination of protein structures typically entails building a model that satisfies the collected experimental observations and its deposition in the Protein Data Bank. Experimental limitations can lead to unavoidable uncertainties during the proce...

Fast and accurate Ab Initio Protein structure prediction using deep learning potentials.

PLoS computational biology
Despite the immense progress recently witnessed in protein structure prediction, the modeling accuracy for proteins that lack sequence and/or structure homologs remains to be improved. We developed an open-source program, DeepFold, which integrates s...

Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).

The protein journal
Three-dimensional protein structure prediction is one of the major challenges in bioinformatics. According to recent research findings, real-valued distance prediction plays a vital role in determining the unique three-dimensional protein structure. ...

IDRBP-PPCT: Identifying Nucleic Acid-Binding Proteins Based on Position-Specific Score Matrix and Position-Specific Frequency Matrix Cross Transformation.

IEEE/ACM transactions on computational biology and bioinformatics
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two important nucleic acid-binding proteins (NABPs), which play important roles in biological processes such as replication, translation and transcription of genetic material. Some prote...

I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.

Nature protocols
Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-d...

Structural Bioinformatics and Deep Learning of Metalloproteins: Recent Advances and Applications.

International journal of molecular sciences
All living organisms require metal ions for their energy production and metabolic and biosynthetic processes. Within cells, the metal ions involved in the formation of adducts interact with metabolites and macromolecules (proteins and nucleic acids)....