Engineered capsids take time to build. Spyndle is for computationally deciding which ones are worth building.

Computational triage for Gene Therapy Capsids

Spyndle App

Serotypes and mutants as a panel: pore geometry, gating dynamics, and lining chemistry, before a production run or a wet-lab cycle.

The pore in view

Open Spyndle App

Spyndle dashboard for capsid loop design — **Dashboard**
A workspace for structure-guided capsid decisions.

Spyndle run analysis for a capsid design — **Run analysis**
Geometry and dynamics you can inspect before you build.

Who it's for

Two decisions, one pore

The same structure-derived read serves manufacturing yield and capsid engineering, before material is committed.

Manufacturing & CDMO

Pore geometry and packaging risk before a production run
A failed titer is a late place to learn the constriction
Serotype and mutant panels on the same measurement

Capsid & delivery R&D

Rank mutant libraries on gate stability and occlusion
See the candidate before a construct hits the bench
Commission a deeper dynamics run when the profile justifies it

Place the peptide, read the insertion, then simulate it. The sequence has been trialled against more than 100,000 AAV9 vectors.

Three passes

i. Insert

The peptide is placed into the capsid, so the candidate is a structure you can inspect, not only a sequence on a list.

ii. Profile

That insertion is read across pH, topology stability, and the conformations the insert can take.

iii. Simulate

Physics simulations of the peptide show how the insert behaves once it is in place.

The shell

Why Spyndle?

Spyndle Bio is a computational platform for gene therapy capsids. It resolves the five-fold pore of a serotype or mutant so the decision to build, screen, or scale rests on structure.

Spyndle Bio

The people building Spyndle

Computational biochemistry, biophysics, and agentic systems applied to AAV capsid decisions.

Juliyan Gunasinghe

Chief Scientific Officer

Cheminformatics and medicinal chemistry researcher pursuing a PhD at the University of Melbourne (MSc Biotechnology, Swinburne). Published in mechanism-encoded SAR, chemical space topology, and ligand optimisation across Cambridge, NUS, MIPS, and Melbourne collaborations.

Manith Marapperuma

Co-Founder · Lead Research Engineer

Foundational AI researcher with expertise in AI agents, computational biochemistry, and quantum computing. Builds AI-driven systems and data-intensive applications for biotech and drug discovery, including knowledge graphs and molecular dynamics.

Kavindi Hettiarachchi

Lead Biochemist

Computational chemistry graduate with experience in protein dynamics, machine learning, structural biology, and drug design. Applies computational approaches to complex biological systems.

Kavindu Eranda

Lead Biophysicist

Computational mathematics graduate from the University of Colombo with experience in quantum computing, molecular simulations, protein–ligand modeling, and scientific computing.