Plastic pollution is one of the most persistent environmental threats of our time. At SMCS-Psi Data Analytics Pvt. Ltd., we are advancing the frontier of plastic biodegradation using a powerful fusion of bioinformatics, structural modeling, and machine learning. Our latest innovation focuses on generating and validating novel plastic-degrading enzymes with the potential to revolutionize sustainable waste management.
The Challenge: Biodegrading Plastic with Precision
Most plastics, especially polyethylene terephthalate (PET), are resistant to natural degradation, accumulating in ecosystems for decades. Although a few bacterial species are known to degrade plastics, their enzymes often lack the efficiency or stability required for real-world applications.
Our goal: Design a new enzyme with enhanced plastic-degrading activity, identify its microbial source, and confirm its structure-function integrity using computational structural biology tools.
Sequence Discovery Using HMM
We began by mining known PETase-like enzymes using Blast search and later using Hidden Markov Models (HMMs) to capture conserved sequence motifs associated with plastic degradation. Using this HMM profile, we scanned microbial sequence databases to generate and identify novel enzyme sequences that share evolutionary signatures with known plastic-degrading enzymes.
Discovery Highlight
Among the sequences generated, we discovered a novel bacterial species previously uncharacterized in this context. This organism encoded a candidate enzyme with significant HMM alignment to the PETase family, hinting at a functional plastic-degrading mechanism.
Structural Validation: Ramachandran Plot & PROCHECK
After generating the protein’s 3D structure using homology modeling, we performed comprehensive structural validation:
Ramachandran Plot Analysis
> Over 90% of residues fell within the most favored regions, confirming a well-folded and stereochemically acceptable protein structure.
PROCHECK Residue Geometry Assessment
> Using PROCHECK, we evaluated dihedral angles, bond lengths, and residue planarity. The model showed excellent stereochemical quality, comparable to high-resolution crystal structures.
These validations ensured that our predicted enzyme was structurally sound and suitable for downstream functional evaluation.
Molecular Dynamics (MD) Simulation
To explore the structural stability and dynamics of the enzyme in a realistic environment, we ran Molecular Dynamics simulations over 100 ns.
Key insights from MD analysis:
The enzyme maintained a stable backbone RMSD, indicating overall structural stability.
Active site residues remained conformationally intact, suggesting functional reliability during substrate binding. Temperature and pressure equilibration parameters remained within optimal thresholds, reinforcing the enzyme’s thermodynamic viability.
From Sequence to Sustainability: Our Vision
This study exemplifies how AI-driven sequence mining, when paired with advanced structural biology, can lead to tangible breakthroughs in bioremediation and green technology. The novel enzyme we’ve discovered is now a candidate for:
In vitro PET plastic degradation testing
Directed evolution to improve kinetics and substrate specificity
Integration into bioengineered microbial consortia for waste treatment plants
Join Our Mission for a Greener Future
At SMCS-Psi, we are committed to transforming environmental challenges into opportunities using science, computation, and innovation. If you are a researcher, biotech startup, or environmental policymaker, we welcome collaborations to scale and commercialize such enzyme-based solutions.
