Protein Structure Modeling
Background
Overview of Protein Structure Modeling
Understanding the 3D structure of proteins is key to figuring out their biological roles and is a cornerstone in drug discovery and development. Proteins do their thing, like catalyzing reactions or sending signals, through their unique three-dimensional shapes. These shapes are closely linked to what they do because a protein's activity is largely defined by its 3D structure. This shape also dictates how proteins interact with other molecules, like binding with other proteins, DNA, RNA, or small molecules.
Now, while techniques like X-ray crystallography, NMR spectroscopy, and cryo-EM can deliver detailed protein structures, they tend to be pricey and time-consuming. Especially with some big or flexible proteins, like membrane proteins, these experimental routes can hit a wall. That's why researchers are leaning more toward computational methods and machine learning to predict protein 3D shapes. These approaches can churn out a bunch of different structural possibilities and refine them through simulations to boost prediction accuracy.
Fig1. Representation of the protein structure prediction methods: (a) homology-based approach; (b) threading approach; (c) ab initio approach. (Hasan MR, et al., Molecules. 2022)
Applications of Protein Structure Modeling
Protein structure modeling is super versatile, reaching across many fields and research directions. Here are a few key areas where it's majorly applied:
Drug Design and Development
Protein modeling is key for finding new drugs. By figuring out proteins' 3D shapes, scientists can spot where new drugs might work and design molecules to fit those spots perfectly. Using methods like cryo-EM with AI makes the whole process faster and more accurate.
Disease Research and Diagnosis
Modeling lets researchers see how disease-related proteins go off track. By looking at structural changes in proteins linked to conditions like cancer and neurodegenerative diseases, scientists can discover the molecular details and work on new therapies.
Bioinformatics and Genomics
In genomics, protein modeling helps us grasp what gene-coded proteins actually do in cells. Tools like the UniProt database are treasure troves, offering loads of data that take bioinformatics research to the next level.
Enzyme Engineering and Metabolic Pathway Studies
Modeling optimizes enzymes for better catalytic efficiency and stability. By simulating 3D structures, researchers can craft more effective enzyme variants for industrial or bio-catalysis use.
Protein Interaction Network Analysis
Structure modeling sheds light on how proteins interact within cells, helping to map out complex biological processes.
Evolutionary and Phylogenetic Studies
In evolutionary biology, comparing structures of homologous proteins across species helps trace evolutionary links and study protein function changes over time.
Experimental Validation and Simulation
While methods like X-ray crystallography and NMR give precise structure data, they're often pricey and slow. Computational modeling steps in as a handy supplement to validate results or predict tough-to-determine structures.
Fig2. The vertical axis indicates the different ranges of applicability of comparative protein structure modeling, the corresponding accuracy of protein structure models, and their sample applications. (Webb B, et al., Curr Protoc Bioinformatics. 2016)
Service Procedure
Pipeline
At Profacgen, we're proud to be at the forefront of computational protein structure modeling services. Our commitment is all about providing top-notch, reliable structural models that cater to a wide range of research and development goals. We know how crucial quality and accuracy are in this field, and that's why we work hard to ensure our models meet your unique scientific needs, helping drive your projects forward with confidence.
Core Services
| Services Section | Homology Modeling | Membrane Protein Modeling | Antibody Modeling | Fusion Protein Modeling | Post-translational Modification | Fold Recognition |
| Overview | Homology modeling predicts a protein's 3D structure using known structures of similar proteins. | Models membrane proteins by considering their unique environment and interactions with lipid bilayers. | Predicts the 3D structure of antibodies, focusing on their antigen-binding sites. | Evaluates how fusion proteins interact and are organized to maintain their stability and functionality. | Predicts the structural impact of chemical modifications on proteins. | Identifies a protein's fold by comparing its sequence with known structures. |
| Applications |
|
|
|
|
|
|
Our Advantages
- Expertise and Experience. Highlight our team's years of skill in computational protein modeling, with successful projects and collaborations with top academic and industry players keeping us at the forefront.
- Quality Assurance. We enforce strict quality checks on our models to ensure they meet top standards, backed by our certifications that reflect our dedication to excellence.
- Tailored Solutions. We provide customized services tailored to the specific needs of each project, often adapting modeling strategies to fit unique research or experimental needs.
- Comprehensive Support. We offer extensive support throughout the modeling process, from initial talks to post-delivery help, along with responsive customer service and close collaboration with clients.
Case Study
Project
Protein Structure Modeling Using AlphaFold2
Background
This project aims to model four specific proteins, with light and heavy chain sequences provided by the client. We used AlphaFold2, a top-notch protein structure prediction tool, to build and deliver the models.
Methods and Materials
Software and Hardware: Protein modeling was done using AlphaFold2 on a local server equipped with dual-channel E5-2697 Intel 24-core processors, Nvidia A100 (40GB) GPU, 96GB RAM, and a 3TB hard drive, running Ubuntu 18.04.
Modeling Details: We used AlphaFold v2.2.3 for modeling, using the "monomer" AI model and referencing all gene databases utilized in CASP14.
Model Relaxation and Selection: The protein models were relaxed using openMM's Amber force field, and the model with the highest confidence was chosen based on pLDDT ranking.
Results
Global-pLDDT Values: Using AlphaFold v2.2.3, the global pLDDT scores were:
Table1. Global-pLDDT values.
| Model Number | Global-pLDDT Value |
| Rank 1 | 86.1 |
| Rank 2 | 85.9 |
| Rank 3 | 85.4 |
| Rank 4 | 85.4 |
| Rank 5 | 85.1 |
Prediction Confidence: Most residues fell within the highest accuracy range, reflecting strong confidence in our predictions.
Fig3. Predicted pLDDT per position.
Visualization: The models were imported into PyMOL 2.5, rendered in light gray, with different regions highlighted in colors. The background was transparent, dpi set at 500, and images exported and saved.
Fig4. Target protein Rank_1.
Conclusions and Discussions
We've successfully completed the modeling of four proteins, meeting the project goals. By using AlphaFold2, we delivered high-quality protein structures with solid confidence, enhanced through detailed visualization, laying a strong foundation for the client's further research. This project showcases our computational protein analysis and modeling services' reliability in the bioinformatics platform.
FAQs
- pLDDT Scores: We assess the model's local confidence-the higher the pLDDT score, the more reliable the model.
- Ramachandran Plot: This checks if the model's geometry makes sense, ensuring residues fall within acceptable areas.
- ERRAT Score: It evaluates overall model quality-the lower the score, the better the quality.
- MolProbity Analysis: Provides a comprehensive overview of the model's geometry and stereochemistry, along with a detailed validation report.
- Experimental Validation: We use experimental methods like X-ray crystallography, NMR spectroscopy, and Cryo-EM to further confirm the model's accuracy.
- Protein Structure Model Files: Provided in PDB format, detailing atomic coordinates and structure.
- Model Validation Report: Includes pLDDT scores, Ramachandran plot, ERRAT score, and MolProbity analysis to ensure high quality and confidence.
- Detailed Analysis Results: Information on structural features, key residues, and potential binding sites.
- Visualization Images: High-quality images from tools like PyMOL and Chimera for a clearer understanding of the model.
- Application Analysis Results: Outcomes from downstream analyses like drug docking and molecular dynamics simulations.
- Deliverables are in PDB files, PDF reports, and PNG/JPG images, among other formats based on client needs.
Resources
| Computational Protein Analysis | Custom Bioinformatics Software Development | Computer Aided Drug Design | Biomarker Discovery and Development Service | Protein Interaction Analysis Services |
References:
- Hasan MR.; et al. Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process. Molecules. 2022;27(13):4169.
- Webb B, Sali A. Comparative Protein Structure Modeling Using MODELLER. Curr Protoc Bioinformatics. 2016;54:5.6.1-5.6.37.