medical-6

Protein Structure Prediction

3260 papers • 126 benchmarks • 313 datasets

Image credit: FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in protein-structure-prediction-12

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Dataset

Best Model

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CASPSeq

CASPSimSeq

PaenSeq

Libraries

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Use these libraries to find protein-structure-prediction-12 models and implementations

PaddlePaddle/PaddleHelix

2 papers 783

Datasets

SidechainNet

Subtasks

Protein Interface Prediction Protein complex prediction

Most implemented papers

Highly accurate protein structure prediction with AlphaFold

O. Vinyals, A. Pritzel, Pushmeet Kohli, K. Kavukcuoglu, D. Hassabis, A. Senior, Richard Evans, J. Jumper, Tim Green, Augustin Žídek, Alex Bridgland, Stig Petersen, David Silver, Bernardino Romera-Paredes, Stanislav Nikolov, Clemens Meyer, T. Back, O. Ronneberger, Michael Figurnov, Anna Potapenko, Ellen Clancy, Martin Steinegger, Kathryn Tunyasuvunakool, Russ Bates, Simon A A Kohl, Andy Ballard, A. Cowie, Rishub Jain, J. Adler, D. Reiman, Michal Zielinski, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein•Wed Jul 14 2021

This work validated an entirely redesigned version of the neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods.

Content

UniProtSeq

32987 0

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MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction

Zeming Lin, Jack Lanchantin, Yanjun Qi•Thu Feb 11 2016

This work proposes a deep convolutional neural network architecture, MUST-CNN, that uses a novel multilayer shift-and-stitch technique to generate fully dense per-position predictions on protein sequences and beats the state-of-the-art performance on two large protein property prediction datasets.

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Accurate Protein Structure Prediction by Embeddings and Deep Learning Representations

Linyong Nan, Iddo Drori, D. Thaker, Arjun Srivatsa, Daniel Jeong, Yueqi Wang, Fan Wu, Dimitri Leggas, Jinhao Lei, Weiyi Lu, Wei Fu, Sashank Karri, A. Kannan, A. Moretti, Mohammed Alquraishi, C. Keasar, I. Pe’er•Fri Nov 08 2019

This work demonstrates state-of-the-art protein structure prediction (PSP) results using embeddings and deep learning models for prediction of backbone atom distance matrices and torsion angles, and creates a new gold standard dataset of proteins which is comprehensive and easy to use.

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Distribution-Free, Risk-Controlling Prediction Sets

Michael I. Jordan, J. Malik, Anastasios Nikolas Angelopoulos, Stephen Bates, Lihua Lei•Wed Jan 06 2021

This work shows how to generate set-valued predictions from a black-box predictor that controls the expected loss on future test points at a user-specified level, and provides explicit finite-sample guarantees for any dataset by using a holdout set to calibrate the size of the prediction sets.

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ProteinNet: a standardized data set for machine learning of protein structure

Mohammed Alquraishi•Thu Jan 31 2019

The ProteinNet series of data sets were created to provide a standardized mechanism for training and assessing data-driven models of protein sequence-structure relationships and to create validation sets distinct from the official CASP sets that faithfully mimic their difficulty.

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Iterative SE(3)-Transformers

I. Posner, F. Fuchs, E. Wagstaff, Justas Dauparas•Thu Feb 25 2021

An iterative version of the SE(3)-Transformer is implemented, an SE( 3)-equivariant attention-based model for graph data, to address the additional complications which arise when applying the SE (3)- Transformer in an iterative fashion and consider why a iterative model may be beneficial in some problem settings.

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ProteinBERT: a universal deep-learning model of protein sequence and function

Dan Ofer, N. Brandes, M. Linial, Nadav Rappoport, Yam Peleg•Mon May 24 2021

ProteinBERT is introduced, a deep language model specifically designed for proteins that obtains state-of-the-art performance on multiple benchmarks covering diverse protein properties, despite using a far smaller model than competing deep-learning methods.

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PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction

Lei Chen, Fan Yu, Sirui Liu, Jun Zhang, Haotian Chu, Min Wang, Boxin Xue, Ningxi Ni, Jialiang Yu, Yuhao Xie, Zhenyu Chen, Mengyun Chen, Yuan Liu, Piya Patra, Fan Xu, Jieping Chen, Zidong Wang, Lijiang Yang, Y. Gao•Thu Jun 23 2022

This work presents the first million-level protein structure prediction dataset with high coverage and diversity, named as PSP, and provides in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset.

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