Protein Fold Recognition Service

Proteins are essential for life, performing critical functions from catalyzing metabolic reactions to defending against pathogens. Their functionality depends on their 3D structures resulting from amino acid chain folding. Computational fold recognition uses algorithms to predict protein structures when experimental data is lacking. CD ComputaBio offers advanced protein fold recognition services, combining cutting-edge technology with deep expertise to deliver accurate, reliable structural models.

Introduction to Protein Fold Recognition

Fold recognition identifies structural templates from known protein folds (e.g., the Protein Data Bank, PDB) to predict the structure of a target sequence. Unlike homology modeling, which relies on high sequence similarity, fold recognition excels when evolutionary relationships are distant but structural motifs are conserved. It accurately identifies structural similarities even with low sequence homology. Based on the principle of evolutionary conservation of protein folding patterns, this method uses hidden Markov models (HMM), threading algorithms, and artificial intelligence (e.g., AlphaFold) to overcome limitations of experimental methods and efficiently analyze proteins difficult to crystallize or dynamically changing. This method is particularly valuable for proteins with no close homologs, enabling predictions where traditional methods fail.

Fig 1. The flowchart of the FoldRec-C2C predictor based on three re-ranking models, including seq-to-seq, seq-to-cluster, and cluster-to-cluster models. (Shao J, et al., 2021)

How It Works:

Template Identification - Scan databases for structural homologs using sequence-structure alignment algorithms.
Model Construction - Align the target sequence to the template's 3D structure, adjusting for gaps and loops.
Refinement - Optimize the model using energy minimization and molecular dynamics to ensure stability.

Applications of Protein Fold Recognition

Drug Target Identification

Fold recognition accelerates protein target identification by integrating threading algorithms with machine learning-powered residue contact predictions. By modeling disease-implicated proteins (e.g., cancer-associated kinases or amyloidogenic peptides in neurodegeneration), researchers can virtually screen multi-billion-entry compound libraries (e.g., ZINC20, Enamine REAL), optimizing lead molecules through free energy perturbation calculations with atomic-level precision.

Enzyme Structure Prediction

Fold recognition, a pivotal computational approach in enzyme structure prediction, enables the identification of structural templates for enzymes with low sequence homology (often <25%) by leveraging the evolutionary conservation of protein folds over amino acid sequences. For instance, in industrial cellulase development, the predicted structures resolve catalytic triads with 0.8Å accuracy, enabling computational mutagenesis to enhance thermostability while maintaining substrate binding efficiency.

CD ComputaBio provides expertise to help identify the correct structural folds in the known template protein structure of the target protein by combining sequence map alignments with multiple structural information. The method begins with the construction of a structure template database/library, and then gradually replaces the sequences of known protein structures in the library with query sequences of unknown structures.

Our Services

Fusion Protein Fold Recognition

Leveraging multi-template threading and co-evolutionary contact analysis, CD ComputaBio's fusion protein fold recognition identifies structural homologs for targets with <20% sequence identity. The method combines fragment assembly simulations with physicochemical constraint matching (e.g., β-sheet topology validation) to model fusion proteins achieving high accuracy in ligand-binding pocket prediction.

Membrane Protein Fold Recognition

CD ComputaBio's membrane protein fold recognition is a powerful bioinformatics method for predicting membrane protein 3D structures by accurately identifying their characteristic folds. This is crucial because membrane proteins are vital for numerous cellular processes (e.g., transport, signaling) and are key drug targets in therapeutic development.

Glycoprotein Fold Recognition

CD ComputaBio's glycoprotein fold recognition accurately elucidates the 3D structure of glycoproteins through a thorough analysis of their structural features and functions. Advanced deep learning models are employed for precise prediction of these complex structures, offering deeper insights into their functional roles, interactions, and biological significance.

Viral Protein Fold Recognition

CD ComputaBio utilizes a fold recognition method, incorporating neural networks and support vector machines (SVM), to predict the structure of viral proteins. This method exhibits superior accuracy when compared to traditional methods. Furthermore, deep learning-based methods are employed to refine fold recognition through the extraction of specific structural features, such as residue-residue interactions.

Peptide Fold Recognition

CD ComputaBio's peptide fold recognition service employs graph neural networks to map conformational landscapes of cyclic/linear peptides (5-50 residues). By analyzing hydrogen-bond propensities and Ramachandran plot outliers, it resolves bioactive conformations of peptides precision.

Enzyme Fold Recognition

CD ComputaBio's enzyme fold recognition is a computational method used to predict the three-dimensional structure of an enzyme based on its amino acid sequence. The process is critical in bioinformatics as it helps in understanding the functional properties of enzymes, guiding drug design, and revealing evolutionary relationships.

Our Advantages

Unmatched Accuracy - Hybrid algorithms (ML + physics-based) validated against experimental benchmarks.
Cutting-Edge Infrastructure - GPU-accelerated clusters and cloud computing ensure rapid processing.
Expert Team - PhD-level scientists with industry experience across therapeutic areas.
Speed & Scalability - Models dare elivered in days, not months.
Cost-Effectiveness - Slash R&D budgets by prioritizing in silico insights.

CD ComputaBio establishes a unique platform for folding identification, integrating state-of-the-art technologies, a multidisciplinary team of experts, and a client-centric service model. CD ComputaBio provides not only high-precision structural models but also in-depth data analysis to elucidate their biological significance, empowering clients in drug discovery, enzyme engineering, and beyond. Contact us today to learn more about how our services can empower your research.

Reference:

Shao J, Yan K, Liu B. FoldRec-C2C: protein fold recognition by combining cluster-to-cluster model and protein similarity network[J]. Briefings in bioinformatics, 2021, 22(3): bbaa144.

* For Research Use Only.

Related Services