4.8 Article

Generative design of de novo proteins based on secondary-structure constraints using an attention-based diffusion model

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 Biochemistry & Molecular Biology

AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Mihaly Varadi et al.

Summary: AlphaFold DB is an openly accessible database with high-accuracy protein-structure predictions, powered by DeepMind's AlphaFold v2.0. It provides programmatic access to a vast number of predicted structures and is expanding to cover more sequences.

NUCLEIC ACIDS RESEARCH (2022)

Article Materials Science, Biomaterials

End-to-End Deep Learning Model to Predict and Design Secondary Structure Content of Structural Proteins

Chi-Hua Yu et al.

Summary: This article reports a deep learning model that predicts the secondary structure content of proteins from their primary sequences. The model, using convolutional and recurrent architectures, accurately predicts the content of alpha-helix and beta-sheet structures. The predictions show excellent agreement with newly identified protein structures and have the potential for rapidly designing proteins with specific secondary structures.

ACS BIOMATERIALS SCIENCE & ENGINEERING (2022)

Article Biochemical Research Methods

ColabFold: making protein folding accessible to all

Milot Mirdita et al.

Summary: ColabFold combines fast homology search and optimized model utilization to offer accelerated prediction of protein structures and complexes, with a processing speed that is 40-60 times faster. It serves as a free and accessible platform for protein folding, capable of predicting close to 1,000 structures per day.

NATURE METHODS (2022)

Article Biochemistry & Molecular Biology

NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning

Magnus Haraldson Hoie et al.

Summary: Recent advances in machine learning and natural language processing have enabled accurate prediction of protein structures and functions, with NetSurfP-3.0 standing out as a tool with drastically improved runtime and reliable prediction performance.

NUCLEIC ACIDS RESEARCH (2022)

Article Materials Science, Multidisciplinary

FieldPerceiver: Domain agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs

Markus J. Buehler

Summary: Attention-based transformer neural networks have a significant impact in modeling physical systems. The FieldPerceiver model, using a multi-headed self-attention approach, is capable of accurately predicting physical field data and material properties, demonstrating its broad generalization capacity. The model's advantages include scalability, no domain knowledge requirement, and the ability to capture crack problems in fracture mechanics.

MATERIALS TODAY (2022)

Article Engineering, Mechanical

PRESTO: Rapid protein mechanical strength prediction with an end-to-end deep learning model

Frank Y. C. Liu et al.

Summary: The article introduces a deep learning model called PRESTO, which can rapidly and accurately predict the mechanical strength of proteins, successfully identifying mutation locations that may influence protein strength. The model can be used to design new protein sequences and establishes a unique protein strength curve.

EXTREME MECHANICS LETTERS (2022)

Article Mechanics

Modeling Atomistic Dynamic Fracture Mechanisms Using a Progressive Transformer Diffusion Model

Markus J. Buehler

Summary: Dynamic fracture is a significant area of materials analysis, and a machine learning model derived from atomistic simulations can effectively describe the dynamics and key aspects of fracture. The model, trained on a small dataset, offers a rapid assessment of dynamic fracture mechanisms for complex geometries and performs well on various validation cases.

JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME (2022)

Article Multidisciplinary Sciences

Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation

Eesha Khare et al.

Summary: A general model using deep learning and genetic algorithm was developed to design collagen sequences with specific melting temperatures (Tm). Experimental and computational methods were used to verify the accuracy of the model in predicting Tm values. The study also identified the most frequently occurring collagen triplets and their correlation with triple-helical quality. This research is critical for the development of collagen sequences with specific Tm values for materials manufacturing and biomedical applications.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2022)

Article Multidisciplinary Sciences

Robust deep learning-based protein sequence design using ProteinMPNN

J. Dauparas et al.

Summary: This article introduces a deep learning-based protein sequence design method, ProteinMPNN, which has shown outstanding performance in both theoretical and experimental tests, suggesting its wide applicability.

SCIENCE (2022)

Article Materials Science, Biomaterials

CollagenTransformer: End-to-End Transformer Model to Predict Thermal Stability of Collagen Triple Helices Using an NLP Approach

Eesha Khare et al.

Summary: This study demonstrates the use of Transformer models to predict the thermal stability of collagen triple helices based on the primary amino acid sequence. The results show that a small Transformer model and a pretrained ProtBERT model have similar performance, with the small model requiring fewer parameters. Additionally, the study suggests the potential of this approach for predicting other biophysical properties.

ACS BIOMATERIALS SCIENCE & ENGINEERING (2022)

Article Computer Science, Artificial Intelligence

ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

Ahmed Elnaggar et al.

Summary: Computational biology and bioinformatics provide valuable data for the development of language models in natural language processing. In this study, six different models were trained on protein sequence data and the resulting embeddings were used for various protein structure prediction tasks, demonstrating their advantages over traditional methods.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2022)

Article Materials Science, Multidisciplinary

A deep learning approach to the inverse problem of modulus identification in elasticity

Bo Ni et al.

Summary: This study proposes a deep learning approach to address the inverse elasticity problem of identifying elastic modulus distribution based on measured displacement/strain fields. By constructing representative sampling spaces of shear modulus distribution and using a conditional generative adversarial net, the deep learning model can learn high-dimensional mapping between strain and modulus through training over a limited portion of the sampling space, bypassing the costly iterative solver in conventional methods and enabling rapid and accurate deployment.

MRS BULLETIN (2021)

Article Biochemical Research Methods

The trRosetta server for fast and accurate protein structure prediction

Zongyang Du et al.

Summary: The trRosetta server is a web-based platform for fast and accurate protein structure prediction using deep learning and Rosetta, which distinguishes itself from other similar servers in terms of rapid and accurate prediction of novel structures.

NATURE PROTOCOLS (2021)

Article Physics, Applied

A deep learning augmented genetic algorithm approach to polycrystalline 2D material fracture discovery and design

Andrew J. Lew et al.

Summary: The article discusses the investigation of polycrystalline 2D material fracture using computational methods such as machine learning models and physics simulations. The study reveals the crack branching mechanism responsible for high bicrystal toughness, as well as qualitative and quantitative trends related to fracture energy.

APPLIED PHYSICS REVIEWS (2021)

Article Chemistry, Physical

Generative adversarial networks for transition state geometry prediction

Malgorzata Z. Makos et al.

Summary: This study introduces a novel application of generative adversarial networks (GANs) for predicting starting geometries in transition state searches of chemical reactions. The TS-GAN efficiently maps the potential energy space between reactants and products to generate reliable TS guess structures, showing high accuracy and efficiency compared to classical approaches. The current TS-GAN can be extended to any dataset with sufficient chemical reaction data for training, and the software is freely available for training, experimentation, and prediction.

JOURNAL OF CHEMICAL PHYSICS (2021)

Article Materials Science, Multidisciplinary

End-to-end deep learning method to predict complete strain and stress tensors for complex hierarchical composite microstructures

Zhenze Yang et al.

Summary: This study introduces a model based on deep learning that can predict complete strain and stress tensors of composite materials, improving the efficiency of predicting the mechanical behavior of composites.

JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS (2021)

Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

Article Multidisciplinary Sciences

Accurate prediction of protein structures and interactions using a three-track neural network

Minkyung Baek et al.

Summary: Through the three-track network, we achieved accuracies approaching those of DeepMind in CASP14, enabling rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and providing insights into the functions of proteins with currently unknown structure.

SCIENCE (2021)

Article Computer Science, Hardware & Architecture

Generative Adversarial Networks

Ian Goodfellow et al.

COMMUNICATIONS OF THE ACM (2020)

Article Chemistry, Physical

Expanding Canonical Spider Silk Properties through a DNA Combinatorial Approach

Zaroug Jaleel et al.

MATERIALS (2020)

Review Biochemistry & Molecular Biology

Multiscale Modeling of Silk and Silk-Based Biomaterials-A Review

Diego Lopez Barreiro et al.

MACROMOLECULAR BIOSCIENCE (2019)

Article Cell & Tissue Engineering

Predicting rates of in vivo degradation of recombinant spider silk proteins

Nina Dinjaski et al.

JOURNAL OF TISSUE ENGINEERING AND REGENERATIVE MEDICINE (2018)

Article Multidisciplinary Sciences

Computational Protein Design with Deep Learning Neural Networks

Jingxue Wang et al.

SCIENTIFIC REPORTS (2018)

Review Nanoscience & Nanotechnology

Nanofibrils in nature and materials engineering

Shengjie Ling et al.

NATURE REVIEWS MATERIALS (2018)

Article Biochemical Research Methods

Prediction of 8-state protein secondary structures by a novel deep learning architecture

Buzhong Zhang et al.

BMC BIOINFORMATICS (2018)

Review Geochemistry & Geophysics

A Review of the Autoencoder and Its Variants A comparative perspective from target recognition in synthetic-aperture radar images

Ganggang Dong et al.

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE (2018)

Review Chemistry, Multidisciplinary

Protein design: from computer models to artificial intelligence

Antonella Paladino et al.

WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE (2017)

Article Chemistry, Multidisciplinary

Hierarchically Enhanced Impact Resistance of Bioinspired Composites

Grace X. Gu et al.

ADVANCED MATERIALS (2017)

Review Multidisciplinary Sciences

The coming of age of de novo protein design

Po-Ssu Huang et al.

NATURE (2016)

Review Nanoscience & Nanotechnology

Structure and mechanics of interfaces in biological materials

Francois Barthelat et al.

NATURE REVIEWS MATERIALS (2016)

Article Biochemical Research Methods

3Dmol.js: molecular visualization with WebGL

Nicholas Rego et al.

BIOINFORMATICS (2015)

Review Chemistry, Physical

Bioinspired structural materials

Ulrike G. K. Wegst et al.

NATURE MATERIALS (2015)

Article Chemistry, Multidisciplinary

Sequence-Structure-Property Relationships of Recombinant Spider Silk Proteins: Integration of Biopolymer Design, Processing, and Modeling

Sreevidhya Tarakkad Krishnaji et al.

ADVANCED FUNCTIONAL MATERIALS (2013)

Article Chemistry, Multidisciplinary

Biological and Bioinspired Composites with Spatially Tunable Heterogeneous Architectures

Andre R. Studart

ADVANCED FUNCTIONAL MATERIALS (2013)

Article Engineering, Biomedical

Tunable nanomechanics of protein disulfide bonds in redox microenvironments

Sinan Keten et al.

JOURNAL OF THE MECHANICAL BEHAVIOR OF BIOMEDICAL MATERIALS (2012)

Review Nanoscience & Nanotechnology

Nanomechanics of functional and pathological amyloid materials

Tuomas P. J. Knowles et al.

NATURE NANOTECHNOLOGY (2011)

Article Multidisciplinary Sciences

Category Theoretic Analysis of Hierarchical Protein Materials and Social Networks

David I. Spivak et al.

PLOS ONE (2011)

Article Multidisciplinary Sciences

Nanostructure and molecular mechanics of spider dragline silk protein assemblies

Sinan Keten et al.

JOURNAL OF THE ROYAL SOCIETY INTERFACE (2010)

Article Physics, Fluids & Plasmas

Mechanical energy transfer and dissipation in fibrous beta-sheet-rich proteins

Zhiping Xu et al.

PHYSICAL REVIEW E (2010)

Article Physics, Fluids & Plasmas

Cooperative deformation of hydrogen bonds in beta-strands and beta-sheet nanocrystals

Zhao Qin et al.

PHYSICAL REVIEW E (2010)

Article Cell & Tissue Engineering

Nanomechanical Characterization of the Triple β-Helix Domain in the Cell Puncture Needle of Bacteriophage T4 Virus

Sinan Keten et al.

CELLULAR AND MOLECULAR BIOENGINEERING (2009)

Article Physics, Condensed Matter

A multi-timescale strength model of alpha-helical protein domains

Theodor Ackbarow et al.

JOURNAL OF PHYSICS-CONDENSED MATTER (2009)

Review Chemistry, Physical

Deformation and failure of protein materials in physiologically extreme conditions and disease

Markus J. Buehler et al.

NATURE MATERIALS (2009)

Article Multidisciplinary Sciences

Alpha-Helical Protein Networks Are Self-Protective and Flaw-Tolerant

Theodor Ackbarow et al.

PLOS ONE (2009)

Article Physics, Multidisciplinary

Asymptotic strength limit of hydrogen-bond assemblies in proteins at vanishing pulling rates

Sinan Keten et al.

PHYSICAL REVIEW LETTERS (2008)

Article Multidisciplinary Sciences

Hierarchies, multiple energy barriers, and robustness govern the fracture mechanics of α-helical and β-sheet protein domains

Theodor Ackbarow et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2007)

Review Polymer Science

Silk as a biomaterial

Charu Vepari et al.

PROGRESS IN POLYMER SCIENCE (2007)

Article Biochemical Research Methods

Porter: a new, accurate server for protein secondary structure prediction

G Pollastri et al.

BIOINFORMATICS (2005)