PROTEIN STRUCTURE PREDICTION
Protein structure prediction workflows in Protean 3D provide access to advanced AI-based modeling methods for predicting monomer and multimer protein structures. Using AlphaFold-Multimer and Boltz, researchers can generate highly accurate structural models for downstream visualization, analysis, and interaction studies.
A streamlined protein prediction wizard simplifies setup for all types of protein modeling workflows, providing a centralized, easy-to-use interface for configuring predictions. Guided steps help users enter sequence data, select modeling methods, and optimize prediction settings, making advanced AI-based protein structure prediction more accessible and efficient.
Predictions are available through NovaCloud using bundled prediction tokens included with Lasergene Protein or through NovaLocal for locally hosted and high-throughput prediction workflows.
Supported Prediction Methods
AlphaFold-Multimer
Predict multimer protein complexes using DeepMind’s AlphaFold-Multimer algorithm, designed for modeling interactions between multiple protein chains.
Boltz
Predict biomolecular complexes using the Boltz algorithm, which supports proteins, nucleic acids, and small molecules.
Protein structure prediction in 4 simple steps
Step 1
Select your protein sequence or region of interest
Step 2
Run the prediction using AlphaFold-Multimer or Boltz
Step 3
View model and analyze structural data, predicted binding sites, and protein function
Step 4
Visualize, align, and analyze predicted structures in Protean 3D
Resources
Please see our resources below for more information on protein structure prediction.
Protein Structure Prediction with NovaFold AI and NovaFold AI-Multimer
Protein Structure Prediction with NovaFold AI-Multimer
Protein Structure Prediction with NovaFold AI
Use NovaFold AI to Predict a Protein Structure with a Cytosolic Domain
Tutorials
Watch one of our videos or check out our user guide to learn more about using AlphaFold-Multimer and Boltz for 3D structure prediction.
3D Structure Prediction of a Protein Using NovaFold AI
DNASTAR’s NovaFold AI application uses the award-winning AlphaFold 2 algorithm to predict the 3D structure of a protein based on its sequence.
Lasergene Protein Overview
This video gives an overview of Protean 3D, NovaFold Antibody, and NovaDock.
FAQs
How do I access protein structure prediction workflows?
Protein structure prediction workflows are available through NovaCloud using bundled prediction tokens included with Lasergene Protein or through NovaLocal for locally hosted modeling workflows and high-throughput prediction studies.
What protein structure prediction methods are supported?
Protein structure prediction workflows in Protean 3D currently support AlphaFold-Multimer and Boltz for AI-based monomer and multimer structure prediction.
What is AlphaFold-Multimer?
AlphaFold-Multimer is DeepMind’s extension of AlphaFold designed for predicting structures involving multiple interacting protein chains, also known as multimers.
What is Boltz?
Boltz is an AI-based structure prediction method that supports prediction of biomolecular complexes, including proteins, nucleic acids, and small molecules.
How accurate are protein folding predictions?
The CASP (Critical Assessment of Protein Structure Prediction) challenge is widely regarded as the benchmark for evaluating protein structure prediction methods. AlphaFold2 and AlphaFold-Multimer demonstrated state-of-the-art performance in CASP experiments, while Boltz has been validated against leading modern structure prediction methods using benchmark datasets and CASP15 assessments.
How do I visualize the predicted structures?
Predicted structures can be visualized and analyzed directly in Protean 3D, including structural inspection, comparison, annotation, and downstream analysis workflows.
What statistics are provided for analyzing 3D protein structure prediction results?
Structure predictions include statistics such as the template modeling score (Tm-score), root mean square deviation (RMSD), confidence score, cluster size, density score, and more. The Protean 3D User Guide provides information about each of these scores and how they are used to determine the “best fit” model predictions.
What file types can I import to predict structures?
Protean 3D supports standard protein and structure file formats for sequence import, structure visualization, and downstream analysis workflows, including .aa, .fap, .fas, .fasta, .gp, .gbk, .sbd, and .pro.
Can I export the predicted structures for publication?
Yes. Predicted structures can be exported from Protean 3D in standard structure and image formats for downstream analysis, collaboration, and publication.
Can predicted structures be used for downstream docking or interaction studies?
Yes. Structures generated using AlphaFold-Multimer or Boltz can be used with protein docking workflows in Protean 3D, including antibody-antigen and protein-protein interaction studies.
Citations
Structure/epitope analysis and IgE binding activities of three cyclophilin family proteins from Dermatophagoides pteronyssinus
Li, Y., Sun, X. & Yang, L (2023). Sci Rep 13, 13630. https://doi.org/10.1038/s41598-023-40720-6.
The role of Streptococcal cell-envelope proteases in bacterial evasion of the innate immune system
Sophie McKenna, Kristin Krohn Huse, Sean Giblin et al. (2022). J Innate Immun 4 April 2022; 14 (2): 69–88. https://doi.org/10.1159/000516956.
Kir7.1 disease mutant T153I within the inner pore affects K+ conduction
Katie M. Beverley, Pawan K. Shahi, Meha Kabra et al. (2022), American Journal of Physiology-Cell Physiology 323:1, C56-C68. https://doi.org/10.1152/ajpcell.00093.2022.
Both recombinant Bacillus subtilis expressing PCV2d Cap protein and PCV2d-VLPs can stimulate strong protective immune responses in mice
Zhang Y, Wu Y, Peng C et al. (2023). Heliyon Volume 9, Issue 12, E22941, Dec 2023. https://doi.org/10.1016/j.heliyon.2023.e22941.
Characterization of a virulence factor in Plasmodiophora brassicae, with molecular markers for identification
Sedaghatkish A, Gossen BD, McDonald MR (2023). PLoS ONE 18(9): e0289842. https://doi.org/10.1371/journal.pone.0289842.
Ligands exert biased activity to regulate sigma 1 receptor interactions with cationic TRPA1, TRPV1, and TRPM8 dhannels
Cortés-Montero E, Sánchez-Blázquez P, Onetti Y, Merlos M and Garzón J (2019) Front. Pharmacol. 10:634. https://doi.org/10.3389/fphar.2019.00634.
The axonal motor neuropathy-related HINT1 protein is a zinc- and calmodulin-regulated cysteine SUMO protease
Elsa Cortés-Montero, María Rodríguez-Muñoz, Pilar Sánchez-Blázquez, and Javier Garzón (2019). Antioxidants & Redox Signaling, Vol. 31, No. 7 Sep 2019.
A bifunctional cellulase–xylanase of a new Chryseobacterium strain isolated from the dung of a straw-fed cattle
Tan, Hao et al. (2018). Microbial Biotechnology 11(2), 381– 398. https://doi.org/10.1111/1751-7915.13034.
