Molecular Docking

Laninamivir out-binds oseltamivir against N1, not N2

CourseworkAutoDock Vina, AutoDockTools

Laninamivir binds N1 more tightly than oseltamivir (−7.11 vs. −6.76 kcal/mol)Oseltamivir binds N2 more tightly than laninamivir (−5.73 vs. −5.59 kcal/mol)Designed — not run — a 3-box-size control to test whether that conclusion holds independent of simulation setup

Context

A computational-lab course on analytical techniques in human disease introduced molecular docking — computationally testing how well a small drug molecule fits into a protein's binding pocket — applied to neuraminidase, the flu-virus enzyme that lets new viral particles escape an infected cell. Drugs like Tamiflu (oseltamivir) work by jamming that pocket; different flu strains carry mutations there that change how well the same drug binds.

Question

Does one neuraminidase-inhibiting drug bind more tightly than another, and does that comparison hold across subtypes — or does the answer depend on which subtype you test?

Approach & rationale

The guided lab used AutoDock Vina to dock oseltamivir (Tamiflu) and laninamivir (LVO) against two neuraminidase subtypes, N1 and N2, each from a real PDB crystal structure, scoring the top poses by predicted binding energy.

Beyond the guided comparison, I also designed — without running — a follow-up study for a specific real-world case: the S247N mutation, which resists two neuraminidase inhibitors but not a third (peramivir). That design would test peramivir, oseltamivir and zanamivir against S247N at three box sizes each, specifically to check whether an apparent binding advantage is real or an artifact of how the simulation box was set up.

Methodology & iteration

Attempt — one fixed search box for every pair

TriedAll runs used a 40×40×40 Å search box, the same for all four ligand-subtype pairs.

AssumedThat box size is a setting rather than a variable that can change the answer.

Fell shortBox size is a real tradeoff. Too large, and the added conformational space makes results less precise and slower to compute; too small, and the true binding pocket risks being cut out of the box entirely, or the program's ability to explore conformations gets artificially restricted — though a smaller box does run faster.

Held upThe proposed S247N follow-up exists specifically to test whether conclusions about which drug wins survive a change in that setting.

In short

Top binding-affinity hits (kcal/mol, more negative = more stable): N1 + oseltamivir −6.76, N2 + oseltamivir −5.73, N1 + laninamivir −7.11, N2 + laninamivir −5.59..

Results

Top binding-affinity hits (kcal/mol, more negative = more stable): N1 + oseltamivir −6.76, N2 + oseltamivir −5.73, N1 + laninamivir −7.11, N2 + laninamivir −5.59.

Laninamivir binds N1 more tightly than oseltamivir does; for N2 the ranking flips and oseltamivir binds slightly more tightly. Which drug "wins" depends on which neuraminidase subtype you are asking about.

Significance

Coursework — not presented or published beyond the course. The S247N follow-up design remains proposed, not run.

Reflection

This course translated something I'd been learning to model in flat, 2D terms — neighboring points in time, or along a chromosome — into something physically three-dimensional: how a drug molecule fits a binding pocket based on atomic distance, energy, and molecular constraint. Docking made it concrete that a single mutation can be the difference between a drug working and a drug failing — computation here wasn't abstract, it was simulating the actual physics of why a treatment might stop working.

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