interestingly, the Boltz predictions on the Adaptyv Bio site are different to my colabfold predicted structures. (left to right) PDB of antibody, colabdold on my scFv & boltz on my scFv on the right.

I tried one other scaffold which is this APPI kunitz domain (orange PDB 3l3t) but the splice is incompatible with this cystein bridge + hydrogen bond network. AlphaFold predicts that splicing breaks the fold: and the loop doesn't end up in the right place for binding, not hopeful for that one

Here’s the original antibody 1e5 on the left and my designed scvf on the right. AlphaFold prediction is consistent with the original antibody so that means: send it.

First I found a scFv in the pdb that used g4s (PDB 2GHW left) whose structure spanned 33Å with a 16-res linker. the gap to fuse the two 1E5 chains is 26Å (right) but it needs to take a curved path: hopefully, 16-res is still sufficient

We start with the designed scaffold protein called Adhiron (left PDB 4N6T) and splice in the 13 residue loop from the antibody. Colabfold structure on the right shows spliced residues (red) binding in the same location as the antibody: send it.

Step 1 find known binders for this viral protein: we find an antibody called 1E5 (PDB 8XC4 left). We can align the viral-human complex with the viral antibody complex (right) and see that both the human receptor and antibody have a coil burried in the virus central cavity.

The target protein is Nipah glyco-protein G (left), and in complex with cognate human receptor ephrinB2 shown in white on the right. (PDB 2VSM)

Ok let’s go @adaptyv.bio binder design competition: this time designing proteins to neutralise the Nipah virus. Lots of great de novo ML binder design tools out there now, but this year I’m submitting an entry from TEAM HUMAN, seeing if pure rational design can win against the machines.

It was lovely to speak at the CATH 30 symposium, celebrating 30 years of the @cathgene3d.bsky.social protein structure classification database. I was presenting recent work on our new generative protein-family language model: preprint coming soon.

Thanks to everyone who came and talked with me about my poster at #PSB2025 : computational methods for predicting which mutations will cause drug inefficacy via protein-drug binding disruption