At our company, we offer advanced services for protein three-dimensional (3D) structure prediction, leveraging a wide range of computational tools. These tools combine classic homology modeling methods with novel Deep Learning (AI) algorithms
Our goal is to provide accurate and reliable models for applications such as drug design, enzyme engineering, protein function modification, and molecular performance analysis
AlphaFold2 / AlphaFold-Multimer (Developed by DeepMind): The most accurate current tool for predicting the atomic structure of proteins and complexes, especially suitable for unknown proteins without existing templates
RoseTTAFold: A powerful deep learning system for predicting the structure of single and complex proteins
ESMFold: A transformer-based method, developed by Meta AI, offering fast performance and suitable for large-scale predictions
SWISS-MODEL: An accurate and established tool for modeling based on structural templates available in the PDB database
Modeller: A flexible, Python-based platform for constructing homology models through sequence alignment
Phyre2: A suitable tool for identifying structures with distant similarity, using fold recognition algorithms
I-TASSER: Combines threading algorithms, ab initio modeling, and structural assembly for complex targets
QUARK: Suitable for small proteins, employing a fragment assembly strategy without requiring a template
RaptorX: Highly useful for proteins lacking known structural similarity
YASARA: A hybrid tool for homology modeling, coupled with energy optimization and structural refinement
DMFold: Suitable for predicting the three-dimensional structure of proteins, especially multi-chain complexes
We are ready to help you overcome the challenges of producing and developing innovative biotechnological products and advancing scientific research
The Ramachandran Plot examines the spatial configuration of the φ (phi) and ψ (psi) dihedral angles in the main chain of proteins. The Ramachandran Plot is a graphical tool used to assess the presence of residues within permissible regions. The placement of over 90% of the amino acids in the allowed region indicates high modeling accuracy. We use this analysis alongside tools such as MolProbity and QMEAN for the final validation of structures
Our company provides a comprehensive qualitative assessment of protein models by performing Z-score analysis using platforms like ProSA-web.
The Z-score serves as an overall energy indicator, showing how closely the free energy of the modeled structure aligns with validated experimental structures in databases. If the model’s Z-score falls outside the typical range for homologous proteins, it may indicate flaws in the modeled structure that require refinement. This analysis is particularly effective in ensuring the accuracy of predicted structures during the initial design phases.
Our company provides clients with a quantitative assessment of 3D structure quality by relying on the QMEAN algorithm.
QMEAN combines statistical and structural metrics to calculate both a global score (for the entire model) and a local score (for each residue). The outputs of this tool are displayed as visual plots, offering valuable guidance for identifying potential error-prone regions within the model. QMEAN can be applied to analyze models generated through homology modeling, de novo modeling, and AlphaFold.
Our company uses the VERIFY3D tool to assess the agreement between the three-dimensional (3D) structure and the linear amino acid sequence
This tool checks whether the spatial environment of each amino acid is compatible with its expected chemical characteristics in its 3D position. VERIFY3D is typically used to confirm homology-based models or refined structures, and it plays a crucial role in identifying sequence-structure inconsistencies
Our company utilizes IDDT analysis, and specifically the pLDDT (Predicted Local Distance Difference Test) index from AlphaFold, to accurately assess the confidence level of the predicted protein structure at the residue level. This method allows us to identify regions of the structure that are likely to have high accuracy, as well as pinpoint areas that may be less reliable. This approach is a crucial tool for refining and validating AlphaFold predictions
We are ready to partner with you in overcoming production challenges, developing innovative biotechnological products, and advancing scientific research
In our company’s specialized assessments, the Local Model Quality is meticulously evaluated using tools such as QMEAN, ProSA, and YASARA.
Instead of focusing on the overall structure, this assessment examines each individual residue for its spatial position, energy stability, and compatibility with the structural environment. This level of detail helps us to identify weak or unstable regions of the structure and optimize the overall model quality by proposing structural refinements or redesigning those specific areas.
By performing precise analyses with the ERRAT tool, our company evaluates protein models from the perspective of atomic bonding patterns and non-covalent behavior
ERRAT analyzes the behavior of heavy atoms within the protein context, identifying regions that deviate from standard patterns. The tool determines the overall quality level of the structure by providing a graph showing the percentage of residues that fall within the acceptable range. Models with a score above 85% are considered reliable structures
+98 912 836 0916
+98 930 144 1004
nimanezhad86@gmail.com
Custom-optimized rational enzyme engineering for your process requirements
©۲۰۲5 .neoenzyme all rights reserved
