Learning generative models for protein fold families

Cited by (202)

Items 101–200 of 202

1. Quantitative approaches for decoding the specificity of the human T cell repertoire
   Zahra S. Ghoreyshi, Jason Thomas George · 2023 · Frontiers in Immunology
2. Fundamentals to function: Quantitative and scalable approaches for measuring protein stability
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3. Structure and mechanism of TagA, a novel membrane-associated glycosyltransferase that produces wall teichoic acids in pathogenic bacteria
   Michele D. Kattke, Jason E. Gosschalk, Orlando E. Martinez, Garima Kumar, Robert T. Gale, Duilio Cascio, Michael R. Sawaya, Martin Philips, Eric D. Brown, Robert Clubb · 2019 · PLoS Pathogens
4. Correlated Electrostatic Mutations Provide a Reservoir of Stability in HIV Protease
   Omar Haq, Michael Andrec, Alexandre V. Morozov, Ronald M. Levy · 2012 · PLoS Computational Biology
5. Applications of sequence coevolution in membrane protein biochemistry
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6. CS-ROSETTA
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7. Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity
   Lorenzo Asti, Guido Uguzzoni, Paolo Marcatili, Andrea Pagnani · 2016 · PLoS Computational Biology
8. Synthetic protein alignments by CCMgen quantify noise in residue-residue contact prediction
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9. Regulatory inter-domain interactions influence Hsp70 recruitment to the DnaJB8 chaperone
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10. Contact Potential for Structure Prediction of Proteins and Protein Complexes from Potts Model
    Anishchenko, Ivan, Kundrotas, Petras J., Vakser, Ilya A. · 2018 · Biophysical Journal
11. Insights into the key determinants of membrane protein topology enable the identification of new monotopic folds
    Sonya Entova, Jean‐Marc Billod, Jean‐Marie Swiecicki, Sonsoles Martín‐Santamaría, Barbara Imperiali · 2018 · eLife
12. From systems to structure — using genetic data to model protein structures
    Hannes Braberg, Ignacia Echeverria, Robyn M. Kaake, Andrej Šali, Nevan J. Krogan · 2022 · Nature Reviews Genetics
13. I-COMS: Interprotein-COrrelated Mutations Server
    Javier Iserte, Franco L. Simonetti, Diego Javier Zea, Elin Teppa, Cristina Marino‐Buslje · 2015 · Nucleic Acids Research
14. Direct coevolutionary couplings reflect biophysical residue interactions in proteins
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15. Protein Structural Information and Evolutionary Landscape by In Vitro Evolution
    Marco Fantini, Simonetta Lisi, Paolo De Los Rios, Antonino Cattaneo, Annalisa Pastore · 2019 · Molecular Biology and Evolution
16. Single Layers of Attention Suffice to Predict Protein Contacts
    Nicholas Bhattacharya, Neil Thomas, Roshan Rao, Justas Dauparas, Peter K. Koo, David A. Baker, Yun S. Song, Sergey Ovchinnikov · 2020 · bioRxiv (Cold Spring Harbor Laboratory)
17. The role of coevolutionary signatures in protein interaction dynamics, complex inference, molecular recognition, and mutational landscapes
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18. Rosetta design with co-evolutionary information retains protein function
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19. Computational structure prediction provides a plausible mechanism for electron transfer by the outer membrane protein Cyc2 from Acidithiobacillus ferrooxidans
    Virginia Jiang, Sagar D. Khare, Scott A. Banta · 2021 · Protein Science
20. Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs
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21. Capturing coevolutionary signals inrepeat proteins
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22. Co-evolution techniques are reshaping the way we do structural bioinformatics
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23. Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling
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24. Protein structure prediction: making AWSEM AWSEM‐ER by adding evolutionary restraints
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25. De novo protein structure prediction using ultra-fast molecular dynamics simulation
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26. Classifying disease-associated variants using measures of protein activity and stability
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27. Protein language models can capture protein quaternary state
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28. On the Entropy of Protein Families
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29. SSEmb: A joint embedding of protein sequence and structure enables robust variant effect predictions
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30. Population-specific design of de-immunized protein biotherapeutics
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31. Statistical physics and representations in real and artificial neural networks
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32. Can Protein Structure Prediction Methods Capture Alternative Conformations of Membrane Transporters?
    Xie, Tengyu, Huang, Jing · 2024 · Journal of Chemical Information and Modeling
33. Auxiliary interfaces support the evolution of specific toxin–antitoxin pairing
    Grzegorz Jan Grabe, Rachel T. Giorgio, Alexander M. J. Hall, Rhodri M. L. Morgan, Laurent Dubois, Tyler A. Sisley, Julian Anthony Rycroft, S. Hare, Sophie Hélaine · 2021 · Nature Chemical Biology
34. In vivo functional phenotypes from a computational epistatic model of evolution
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35. Predicting protein β-sheet contacts using a maximum entropy-based correlated mutation measure
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36. Toward Inferring Potts Models for Phylogenetically Correlated Sequence Data
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37. AWSEM-Suite: a protein structure prediction server based on template-guided, coevolutionary-enhanced optimized folding landscapes
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38. Sparse generative modeling via parameter reduction of Boltzmann machines: Application to protein-sequence families
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39. Efficient generative modeling of protein sequences using simple autoregressive models
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40. Gleaning structural and functional information from correlations in protein multiple sequence alignments
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41. State-of-the-art web services for de novo protein structure prediction
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42. Recent advances in generative biology for biotherapeutic discovery
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43. Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction
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44. Interpreting Potts and Transformer Protein Models Through the Lens of Simplified Attention
    Bhattacharya, Nicholas, Thomas, Neil, Rao, Roshan, Dauparas, Justas, Koo, Peter K., Baker, David, Song, Yun S., Ovchinnikov, Sergey · 2021 · Biocomputing 2022
45. Deep learning geometrical potential for high-accuracy ab initio protein structure prediction
    Yang Li, Chengxin Zhang, Dong‐Jun Yu, Yang Zhang · 2022 · iScience
46. Distinguishing features of fold‐switching proteins
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47. Function-guided protein design by deep manifold sampling
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48. Fluid protein fold space and its implications
    Lauren L. Porter · 2023 · BioEssays
49. A generative model for protein contact networks
    Lorenzo Livi, Enrico Maiorino, Alessandro Giuliani, Antonello Rizzi, Alireza Sadeghian · 2015 · Journal of Biomolecular Structure and Dynamics
50. Insights into the Dynamics of the Human Zinc Transporter ZnT8 by MD Simulations
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51. Protein oligomer modeling guided by predicted interchain contacts in <scp>CASP14</scp>
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52. MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search
    Ke'ai Chen, Thomas Litfin, Jaswinder Singh, J. Zhan, Yao-Qi Zhou · 2024 · Genomics, Proteomics & Bioinformatics
53. New insights into substrate folding preference of plant OSCs
    Yongshuo Ma, Yuan Zhou, Sergey Ovchinnikov, Per Jr Greisen, Sanwen Huang, Yi Shang · 2016 · 中国科学通报：英文版
54. Template‐based modeling by ClusPro in CASP13 and the potential for using co‐evolutionary information in docking
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55. Allosteric Modulation of G Protein Coupled Receptors by Cytoplasmic, Transmembrane and Extracellular Ligands
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56. Recent progress and future challenges in structure-based protein-protein interaction prediction
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57. Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading
    Sutanu Bhattacharya, Rahmatullah Roche, Md Hossain Shuvo, Debswapna Bhattacharya · 2021 · Frontiers in Molecular Biosciences
58. Structure of the Bacterial Cytoskeleton Protein Bactofilin by NMR Chemical Shifts and Sequence Variation
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59. Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds
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60. Inferring repeat-protein energetics from evolutionary information
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61. Novel HARS2 missense variants identified in individuals with sensorineural hearing impairment and Perrault syndrome
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62. Artificial intelligence-based methods for protein structure prediction: a survey
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63. Multiplexed assays reveal effects of missense variants in MSH2 and cancer predisposition
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64. Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution
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65. Multidimensional mutual information methods for the analysis of covariation in multiple sequence alignments
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66. Foamy Viruses, Bet, and APOBEC3 Restriction
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67. Scaling down protein language modeling with MSA Pairformer
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68. Machine learning in biological physics: From biomolecular prediction to design
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69. CoCoNet—boosting RNA contact prediction by convolutional neural networks
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70. Unsupervisedly Prompting AlphaFold2 for Accurate Few-Shot Protein Structure Prediction
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71. Review of multimer protein–protein interaction complex topology and structure prediction*
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72. Global Mapping of Metalloproteomes
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73. Evolution of the Immunoglobulin Isotypes—Variations of Biophysical Properties among Animal Classes
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74. Field-theoretic density estimation for biological sequence space with applications to 5′ splice site diversity and aneuploidy in cancer
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75. DMFpred: Predicting protein disorder molecular functions based on protein cubic language model
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76. Undersampling and the inference of coevolution in proteins
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77. <scp>IFF</scp> : Identifying key residues in intrinsically disordered regions of proteins using machine learning
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78. Coevolutionary Analysis of Protein Sequences for Molecular Modeling
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79. Elucidating modes of activation and herbicide resistance by sequence assembly and molecular modelling of the Acetolactate synthase complex in sugarcane
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80. An information theoretic framework reveals a tunable allosteric network in group II chaperonins
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81. Computing the Pathogenicity of Wilson’s Disease ATP7B Mutations: Implications for Disease Prevalence
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82. Transfer of knowledge from model organisms to evolutionarily distant non-model organisms: The coral Pocillopora damicornis membrane signaling receptome
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83. Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?
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84. Random Forest Refinement of the KECSA2 Knowledge-Based Scoring Function for Protein Decoy Detection
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85. Unified framework for modeling multivariate distributions in biological sequences
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86. FilterDCA: Interpretable supervised contact prediction using inter-domain coevolution
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87. Correlated mutations via regularized multinomial regression
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88. Regularization and decimation pseudolikelihood approaches to statistical inference in XY spin models
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89. Improved inference of intermolecular contacts through protein–protein interaction prediction using coevolutionary analysis
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90. Limits to detecting epistasis in the fitness landscape of HIV
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91. Neural Potts Model
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92. Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
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93. Sequence-structure-function relationships in the microbial protein universe
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94. STAG-LLM: Predicting TCR-pHLA binding with protein language models and computationally generated 3D structures
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95. Contact prediction is hardest for the most informative contacts, but improves with the incorporation of contact potentials
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96. Zinc finger domain of the human DTX protein adopts a unique RING fold
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97. Statistical characteristics of amino acid covariance as possible descriptors of viral genomic complexity
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98. Human SIRT1 Multispecificity Is Modulated by Active-Site Vicinity Substitutions during Natural Evolution
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99. Coevolution Analysis of HIV-1 Envelope Glycoprotein Complex
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100. Structure-based Markov random field model for representing evolutionary constraints on functional sites
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