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Building a hidden markov model from synthetic protein designs

Hello , I'm working on a idea of building a hidden markov model from synthetic de novo protein designs.

Particularly, I'm building a hidden markov model using hmmer3. The background is I made 200 synthetic structural designs of enzymes with RFDiffusion2 from a theozyme motif (5 aminoacids) previously described in a crystalized protein. From those protein designs of equal length (450aa), I ran ligandmnn to predict the aminoacidic sequence of my enzymatic designs, after corroborating appropriate folding with alphafold3, I got 25 self-consistent sequences in which the position of the 5 catalytic residues are in correct place (same as in the rfdiffusion2 models). So when I perform a multiple sequence alignment of these synthetic sequences with Clustal Omega, the 5 catalytic residues are not aligned in the same columns as I expect them to be. What workaround you recommend me to do to achieve/force the placement of these 5 essential aminoacids in the same positions in the multiple sequence alignment ?

By discussing with some colleges, one told me that neighbor aminoacids are important to consider in this approach. Any ideas?

structural-designs msa hmm proteins

The HMM will characterise the "transition" likelihoods - i.e. yes, the neighbour of an amino acid (in part) determines what the HMM will spit out as the most likely residue in that position.

See this question for example, where the HMM will even 'overrule' what the simple or otherwise 'obvious' consensus position call would be because its factoring in a lot of additional other probabilities:

Conflicting consensus sequences from different tools (bug in hmmemit?)

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