4.7 Review

Deep Learning-Based Advances in Protein Structure Prediction

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Biochemical Research Methods

DeepDist: real-value inter-residue distance prediction with deep residual convolutional network

Tianqi Wu et al.

Summary: The study introduces a multi-task deep learning distance predictor (DeepDist) that can simultaneously predict real-value inter-residue distances and classify them into multiple distance intervals. Tested on 43 CASP13 hard domains, DeepDist achieves comparable performance in real-value distance prediction and multi-class distance prediction.

BMC BIOINFORMATICS (2021)

Article Biochemical Research Methods

DISTEVAL: a web server for evaluating predicted protein distances

Badri Adhikari et al.

Summary: The development of the web server DISTEVAL provides a useful tool for evaluating predicted inter-residue distances in protein structures. The server offers informative visualizations, including heatmaps, chord diagrams, and 3D models, as well as quantitative metrics like mean absolute error and contact precision, enabling comprehensive assessment of predictions even without a true 3D structure.

BMC BIOINFORMATICS (2021)

Article Biochemistry & Molecular Biology

UniProt: the universal protein knowledgebase in 2021

Alex Bateman et al.

Summary: The UniProt Knowledgebase aims to provide users with a comprehensive, high-quality set of protein sequences annotated with functional information. Updates over the past two years have increased the number of sequences to approximately 190 million, with new methods to assess proteome completeness and quality. UniProtKB has responded to the COVID-19 pandemic by expertly curating relevant entries and making them rapidly available through a dedicated portal.

NUCLEIC ACIDS RESEARCH (2021)

Article Multidisciplinary Sciences

DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes

Jonas Pfab et al.

Summary: DeepTracer is an automated deep learning-based method for fast de novo multichain protein complex structure determination from high-resolution cryo-EM maps. It shows improved residue coverage and rmsd values compared to current state-of-the-art methods, and demonstrates competitive accuracy and efficiency in structure modeling. With exceptionally fast computations, DeepTracer allows for tracing a large number of residues in a short amount of time.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)

Article Biophysics

Super Resolution Cryo-EM Maps with 3D Deep Generative Networks

Sai Raghavendra Maddhuri Venkata Subramaniya et al.

BIOPHYSICAL JOURNAL (2021)

Article Multidisciplinary Sciences

Improved protein structure refinement guided by deep learning based accuracy estimation

Naozumi Hiranuma et al.

Summary: DeepAccNet is a deep learning framework that estimates per-residue accuracy and residue-residue distance signed error in protein models, guiding Rosetta protein structure refinement and demonstrating improved accuracy prediction and refinement compared to other methods.

NATURE COMMUNICATIONS (2021)

Article Biology

CASSPER is a semantic segmentation-based particle picking algorithm for single-particle cryo-electron microscopy

Blesson George et al.

Summary: George, Assaiya et al. develop a deep learning tool, CASSPER, that automates the detection of protein particles in transmission microscope images. This algorithm uses semantic segmentation and visually prepared training samples to capture the differences in the transmission intensities of microscope images, enabling automation of data processing.

COMMUNICATIONS BIOLOGY (2021)

Article Computer Science, Artificial Intelligence

Extraction of protein dynamics information from cryo-EM maps using deep learning

Shigeyuki Matsumoto et al.

Summary: Cryo-electron microscopy (cryo-EM) is a powerful tool for determining protein structures at atomic-level resolution. The DEFMap deep learning approach can extract dynamics hidden in cryo-EM density maps, providing valuable insights into protein function and structural changes. This multidisciplinary approach combines experimental data, molecular dynamics simulations, and deep learning techniques to study protein dynamics.

NATURE MACHINE INTELLIGENCE (2021)

Article Biochemical Research Methods

Protein contact prediction using metagenome sequence data and residual neural networks

Qi Wu et al.

BIOINFORMATICS (2020)

Article Biochemical Research Methods

DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout

Badri Adhikari

BIOINFORMATICS (2020)

Article Biochemical Research Methods

DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment

Hiroyuki Fukuda et al.

BMC BIOINFORMATICS (2020)

Article Chemistry, Medicinal

Will Cryo-Electron Microscopy Shift the Current Paradigm in Protein Structure Prediction?

Luciano A. Abriata et al.

JOURNAL OF CHEMICAL INFORMATION AND MODELING (2020)

Review Biochemistry & Molecular Biology

Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Eman Alnabati et al.

MOLECULES (2020)

Article Multidisciplinary Sciences

Improved protein structure prediction using potentials from deep learning

Andrew W. Senior et al.

NATURE (2020)

Article Multidisciplinary Sciences

Improved protein structure prediction using predicted interresidue orientations

Jianyi Yang et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

Review Biochemistry & Molecular Biology

Deep learning methods in protein structure prediction

Mirko Torrisi et al.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2020)

Article Biochemical Research Methods

FUpred: detecting protein domains through deep-learning-based contact map prediction

Wei Zheng et al.

BIOINFORMATICS (2020)

Article Chemistry, Physical

Evolving data standards for cryo-EM structures

Catherine L. Lawson et al.

STRUCTURAL DYNAMICS-US (2020)

Article Multidisciplinary Sciences

Deep Learning to Predict Protein Backbone Structure from High-Resolution Cryo-EM Density Maps

Dong Si et al.

SCIENTIFIC REPORTS (2020)

Article Biotechnology & Applied Microbiology

Protein Contact Map Prediction Based on ResNet and DenseNet

Zhong Li et al.

BIOMED RESEARCH INTERNATIONAL (2020)

Review Biochemistry & Molecular Biology

Machine Learning Approaches for Quality Assessment of Protein Structures

Jiarui Chen et al.

BIOMOLECULES (2020)

Article Biochemical Research Methods

Geometric potentials from deep learning improve prediction of CDR H3 loop structures

Jeffrey A. Ruffolo et al.

BIOINFORMATICS (2020)

Article Chemistry, Multidisciplinary

Predicting the Real-Valued Inter-Residue Distances for Proteins

Wenze Ding et al.

ADVANCED SCIENCE (2020)

Article Biochemistry & Molecular Biology

MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning

Ruben Sanchez-Garcia et al.

JOURNAL OF STRUCTURAL BIOLOGY (2020)

Article Multidisciplinary Sciences

A fully open-source framework for deep learning protein real-valued distances

Badri Adhikari

SCIENTIFIC REPORTS (2020)

Review Computer Science, Artificial Intelligence

Deep Learning in Protein Structural Modeling and Design

Wenhao Gao et al.

PATTERNS (2020)

Article Biochemistry & Molecular Biology

End-to-End Differentiable Learning of Protein Structure

Mohammed AlQuraishi

CELL SYSTEMS (2019)

Article Biochemical Research Methods

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning

Sai Raghavendra Maddhuri Venkata Subramaniya et al.

NATURE METHODS (2019)

Article Biochemistry & Molecular Biology

Deep-learning contact-map guided protein structure prediction in CASP13

Wei Zheng et al.

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2019)

Article Biochemistry & Molecular Biology

Estimation of model accuracy in CASP13

Jianlin Chene et al.

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2019)

Review Cell Biology

Advances in protein structure prediction and design

Brian Kuhlman et al.

NATURE REVIEWS MOLECULAR CELL BIOLOGY (2019)

Article Multidisciplinary Sciences

Distance-based protein folding powered by deep learning

Jinbo Xu

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

Article Biochemistry & Molecular Biology

Evaluation of template-based modeling in CASP13

Tristan I. Croll et al.

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2019)

Article Biochemistry & Molecular Biology

Analysis of distance-based protein structure prediction by deep learning in CASP13

Jinbo Xu et al.

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2019)

Article Biochemical Research Methods

Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix

Dorothee Liebschner et al.

ACTA CRYSTALLOGRAPHICA SECTION D-STRUCTURAL BIOLOGY (2019)

Article Biochemistry & Molecular Biology

Modelling structures in cryo-EM maps

Sony Malhotra et al.

CURRENT OPINION IN STRUCTURAL BIOLOGY (2019)

Article Biochemical Research Methods

PconsC4: fast, accurate and hassle-free contact predictions

Mirco Michel et al.

BIOINFORMATICS (2019)

Article Biochemical Research Methods

PIXER: an automated particle-selection method based on segmentation using a deep neural network

Jingrong Zhang et al.

BMC BIOINFORMATICS (2019)

Article Biochemistry & Molecular Biology

Analysis of deep learning methods for blind protein contact prediction in CASP12

Sheng Wang et al.

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2018)

Review Biochemistry & Molecular Biology

Cryo-electron microscopy for structural analysis of dynamic biological macromolecules

Kazuyoshi Murata et al.

BIOCHIMICA ET BIOPHYSICA ACTA-GENERAL SUBJECTS (2018)

Article Multidisciplinary Sciences

De novo main-chain modeling for EM maps using MAINMAST

Genki Terashi et al.

NATURE COMMUNICATIONS (2018)

Article Biochemistry & Molecular Biology

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

Yang Liu et al.

CELL SYSTEMS (2018)

Article Chemistry, Multidisciplinary

Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy

Ruben Sanchez-Garcia et al.

Article Biochemical Research Methods

DNCON2: improved protein contact prediction using two-level deep convolutional neural networks

Badri Adhikari et al.

BIOINFORMATICS (2018)

Article Biochemical Research Methods

Recent advances in sequence-based protein structure prediction

B. K. C. Dukka

BRIEFINGS IN BIOINFORMATICS (2017)

Article Biochemical Research Methods

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Sheng Wang et al.

PLOS COMPUTATIONAL BIOLOGY (2017)

Article Biochemistry & Molecular Biology

Template-based protein structure prediction in CASP11 and retrospect of I-TASSER in the last decade

Jianyi Yang et al.

PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2016)

Article Biochemical Research Methods

ConEVA: a toolbox for comprehensive assessment of protein contacts

Badri Adhikari et al.

BMC BIOINFORMATICS (2016)

Article Biochemistry & Molecular Biology

DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM

Feng Wang et al.

JOURNAL OF STRUCTURAL BIOLOGY (2016)

Article Chemistry, Medicinal

Advances in Protein Contact Map Prediction Based on Machine Learning

Jiang Xie et al.

Medicinal Chemistry (2015)

Letter Biochemical Research Methods

The I-TASSER Suite: protein structure and function prediction

Jianyi Yang et al.

NATURE METHODS (2015)

Article Biochemical Research Methods

CCMpred-fast and precise prediction of protein residue-residue contacts from correlated mutations

Stefan Seemayer et al.

BIOINFORMATICS (2014)

Article Biochemical Research Methods

A study and benchmark of DNcon: a method for protein residue-residue contact prediction using deep networks

Jesse Eickholt et al.

BMC BIOINFORMATICS (2013)

Article Biochemistry & Molecular Biology

Computational methods for constructing protein structure models from 3D electron microscopy maps

Juan Esquivel-Rodriguez et al.

JOURNAL OF STRUCTURAL BIOLOGY (2013)

Article Biochemical Research Methods

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

Michael Remmert et al.

NATURE METHODS (2012)

Article Biochemical Research Methods

PyRosetta: a script-based interface for implementing molecular modeling algorithms using Rosetta

Sidhartha Chaudhury et al.

BIOINFORMATICS (2010)

Article Biochemical Research Methods

Hidden Markov model speed heuristic and iterative HMM search procedure

L. Steven Johnson et al.

BMC BIOINFORMATICS (2010)

Review Biochemistry & Molecular Biology

Macromolecular modeling with Rosetta

Rhiju Das et al.

ANNUAL REVIEW OF BIOCHEMISTRY (2008)

Article Biochemical Research Methods

Version 1.2 of the Crystallography and NMR system

Axel T. Brunger

NATURE PROTOCOLS (2007)

Article Biochemistry & Molecular Biology

EVAcon:: a protein contact prediction evaluation service

O Graña et al.

NUCLEIC ACIDS RESEARCH (2005)

Article Biochemical Research Methods

The PSIPRED protein structure prediction server

LJ McGuffin et al.

BIOINFORMATICS (2000)

Article Biochemistry & Molecular Biology

The Protein Data Bank

HM Berman et al.

NUCLEIC ACIDS RESEARCH (2000)