De Novo Protein Design

De novo protein design, often termed the inverse protein folding problem, enables the creation of entirely new proteins from scratch without relying on existing templates. By integrating computational modeling, artificial intelligence, and experimental validation, Creative BioMart provides a complete platform for designing novel proteins that fold into desired 3D topologies with defined structures and functions. Through rational and data-driven design, our de novo protein engineering service supports the development of proteins with customized stability, catalytic activity, and binding specificity—accelerating breakthroughs in biotechnology, therapeutics, and synthetic biology.

Understanding De Novo Protein Design

Figure 1. The strategy adapted for de novo designing of proteins. (Gulati and Poluri, 2016)

De novo protein design represents the frontier of rational protein engineering, where new proteins are designed atom by atom rather than modified from natural templates. The process addresses the inverse folding problem—identifying a sequence that folds into a predefined structural topology. Typically, this design involves three key steps:

1. Backbone Construction–assembling α-helices, β-strands, and loops into a stable 3D scaffold;
2. Sequence Optimization–generating and refining amino acid sequences for structural compatibility;
3. Computational and Experimental Validation–verifying folding, stability, and functionality.

Backbone construction relies on advanced algorithms and machine learning models capable of assembling secondary structure elements with precision. Subsequent sequence optimization employs energy-based or knowledge-based scoring functions—or hybrids of both—to identify the most stable and functional variants. Finally, candidate designs undergo rigorous computational simulations and laboratory validation to confirm correct folding and target performance.

Comprehensive De Novo Protein Design Services

Creative BioMart offers end-to-end de novo protein design solutions that bridge cutting-edge computational modeling with rigorous experimental validation. Our goal is to help researchers and industry partners design entirely new proteins—not just variants of existing ones—with tailored structural, functional, and biophysical properties.

Unlike traditional protein engineering approaches that modify known scaffolds, our de novo design workflow builds proteins from the ground up, defining their fold, topology, and amino acid sequence to achieve specific performance criteria such as enhanced stability, catalytic function, or novel binding interfaces.

Service Workflow

Our Core Capabilities

Backbone and Topology Design

Using AI-assisted modeling platforms, we define stable backbone topologies and secondary-structure arrangements that serve as the foundation for new proteins. This enables the generation of unique, designable folds that do not depend on natural templates.

Sequence Optimization

We apply molecular mechanics and knowledge-based energy functions to optimize amino acid sequences for stability, solubility, and desired functionality. Machine learning models are integrated to improve sequence–structure compatibility and predict folding accuracy.

Folding and Stability Prediction

Structure-based simulations and molecular dynamics are employed to assess thermodynamic stability, conformational flexibility, and folding kinetics, ensuring that each design is physically realistic and experimentally tractable.

Experimental Expression and Validation

Designed sequences are cloned and expressed in the most suitable host system (e.g., E. coli, yeast, or mammalian cells ). Purified proteins undergo experimental validation through biophysical assays and high-resolution techniques such as X-ray crystallography or NMR spectroscopy to confirm correct folding and stability.

Iterative Design Refinement

Computational redesign cycles are guided by experimental data to fine-tune stability, activity, or binding characteristics. This integrated loop ensures convergence toward the best-performing construct.

Optional and Specialized Services

For clients with advanced research or product development goals, Creative BioMart also offers:

• Machine learning–guided backbone generation for large-scale design of stable, novel protein architectures.
• Energy-based refinement using statistical potentials and atomistic simulations for improved folding accuracy.
• Automated sequence library generation to enable high-throughput in silico screening and variant exploration.
• Customized assay development for specific functional validations, including enzyme kinetics and ligand-binding analyses.

Why Partner with Creative BioMart

Comprehensive Design-to-Validation Workflow: From computational modeling to structural confirmation, all under one roof.

Expertise in AI-Driven Protein Design: Advanced algorithms ensure accurate backbone generation and fold prediction.

Customization and Flexibility: Tailored design strategies to meet unique structural or functional requirements.

High Validation Accuracy: Robust experimental validation through X-ray crystallography and NMR.

Proven Track Record: Successful delivery of stable, functional de novo proteins across diverse research applications.

Collaborative Scientific Support: Dedicated project managers and scientists ensure seamless communication and result interpretation.

Case Studies for De Novo Protein Design

Case 1: Discovery of entry inhibitors for HIV-1 via a new de novo protein design framework

Bellows et al., 2010. doi:10.1016/j.bpj.2010.09.050

A novel de novo protein design framework has been developed to identify new HIV-1 entry inhibitors using a ranking metric based on approximate binding affinity calculations. The method involves two stages: sequence selection, which generates ranked amino acid sequences via an integer programming model, and validation, which evaluates fold specificity and binding affinity. Designed 12-amino-acid peptides targeting the gp41 hydrophobic core were synthesized and tested in cell culture. All candidates showed inhibitory activity, with the top peptide achieving micromolar inhibition—a 3–15-fold improvement over the native sequence—and demonstrating equal potency against wild-type and Enfuvirtide-resistant HIV-1 strains.

Figure 2. Crystal structure of C14linkmid in complex with the hydrophobic core of gp41, PDB code: 1GZL. The diaminoalkane crosslinker and the hydrophobic core of gp41, consisting of residues Leu29, Leu30, Leu32, Thr33, Val34, Trp35, Gly36, Ile37, Lys38, Leu40, and Gln41are colored blue (the residues are numbered according to their position in 1GZL). (Bellows et al., 2010)

Case 2: An evolution-based approach to de novo protein design and case study on Mycobacterium tuberculosis

Mitra et al., 2013. doi:10.1371/journal.pcbi.1003298

A novel computational protein design method leverages evolutionary information from structurally related protein families to guide the creation of new sequences for a given target structure. Proteins with similar folds are identified from the PDB, and a structural profile is generated to direct amino acid selection while accounting for solvation, torsion angles, and secondary structure. Tested on 87 diverse proteins, this approach improved foldability and functional potential compared to traditional physics-based methods, achieving an average RMSD of 2.1 Å. Applied to redesign all 243 resolved proteins of Mycobacterium tuberculosis, experimental validation of five variants confirmed solubility, secondary structure, and well-ordered tertiary folding, demonstrating enhanced stability and biological functionality.

Figure 3. An overview of the evolution-based protein design method (EBM). The procedure consists of profile construction, Monte Carlo search, and design selection. (Mitra et al., 2013)

Client Experiences with Our De Novo Protein Design Services

"Creative BioMart helped us design a completely new enzyme scaffold to catalyze a non-natural reaction for industrial chemical synthesis. Their team used de novo backbone modeling and sequence optimization to create a functional protein from scratch. The final enzyme demonstrated a threefold increase in catalytic efficiency compared to our initial benchmarks, and the structural validation confirmed correct folding. Their integrated computational and experimental approach significantly accelerated our R&D timeline."

— Senior Scientist | Industrial Biotechnology Company

"We partnered with Creative BioMart to develop a novel protein binder for a challenging diagnostic target. Through de novo design, they created a helical scaffold optimized for high-affinity ligand binding. The resulting protein exhibited excellent thermostability and specificity, validated via NMR and binding assays. Their scientific expertise and iterative optimization process exceeded our expectations."

— Director of Protein Engineering | Diagnostic Biotech Firm

"Our lab needed a self-assembling protein cage for drug delivery applications. Creative BioMart designed a de novo protein with precise cavity geometry, enabling nanoparticle encapsulation. Experimental characterization confirmed proper folding and assembly, while the protein showed exceptional stability under physiological conditions. Their ability to integrate computational modeling with experimental validation was instrumental in achieving our project goals."

— Principal Investigator | Academic Synthetic Biology Lab

"Creative BioMart’s de novo protein design service allowed us to generate a completely new catalytic scaffold for a proprietary metabolic pathway. Using machine learning-assisted backbone design and energy-optimized sequence generation, they delivered proteins with superior solubility and folding efficiency. The project shortened our experimental trial-and-error phase significantly and provided a validated platform for downstream pathway engineering."

— R&D Director | Synthetic Biology Startup

FAQs About De Novo Protein Design

• Q: What is de novo protein design, and how does it differ from sequence- or structure-based design?

  A: De novo protein design is the process of creating entirely new proteins from scratch, rather than modifying or modeling existing ones. Unlike sequence- or structure-based design, which rely on known natural templates, de novo design starts with a theoretical backbone and predicts an amino acid sequence that will fold into a desired 3D shape. This approach enables the creation of novel proteins with customized stability, functionality, and molecular architecture.

• Q: What information do I need to provide to start a de novo protein design project?

  A: Typically, clients should define the desired structural topology, target function, and performance requirements (e.g., stability, binding affinity, or catalytic activity). Any known constraints—such as size limits, environmental conditions, or target interactions—can also guide our modeling. Our scientific team will discuss project details and tailor the design strategy accordingly.

• Q: How do you ensure that the designed proteins actually fold and function as intended?

  A: We combine computational folding simulations, energy function scoring, and molecular dynamics to predict folding behavior. Promising designs are then expressed and validated experimentally through X-ray crystallography, NMR spectroscopy, or biochemical assays. This dual validation process ensures both structural accuracy and functional reliability.

• Q: What computational tools and algorithms do you use?

  A: We employ a hybrid suite of AI-assisted modeling algorithms, molecular mechanics-based energy functions, and knowledge-based potentials. Depending on the project, we may use state-of-the-art tools for backbone generation, sequence optimization, and fold prediction, integrated into our proprietary computational platform for high-throughput screening and scoring.

• Q: Can you design de novo proteins for specific industrial or biomedical applications?

  A: Yes. Our de novo protein design platform supports a wide range of applications, including enzyme catalysis, therapeutic protein scaffolds, biosensors, nanomaterials, and biopharmaceutical formulation. Each project is customized to achieve desired physicochemical and biological properties suitable for the intended environment or use case.

• Q: How long does a typical de novo protein design project take?

  A: Project timelines vary depending on design complexity and experimental requirements. On average, a full cycle—including backbone design, sequence optimization, and experimental validation—takes 8 to 16 weeks. We provide a detailed project plan and progress updates throughout the process to ensure transparency and alignment with client expectations.

References:

1. Bellows ML, Taylor MS, Cole PA, et al. Discovery of entry inhibitors for HIV-1 via a new de novo protein design framework. Biophysical Journal. 2010;99(10):3445-3453. doi:10.1016/j.bpj.2010.09.050
2. Huang PS, Boyken SE, Baker D. The coming of age of de novo protein design. Nature. 2016;537(7620):320-327. doi:10.1038/nature19946
3. Mitra P, Shultis D, Brender JR, et al. An evolution-based approach to de novo protein design and case study on Mycobacterium tuberculosis. Shakhnovich EI, ed. PLoS Comput Biol. 2013;9(10):e1003298. doi:10.1371/journal.pcbi.1003298