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Deep generative modeling for biological sequences presents a unique challengein reconciling the bias-variance trade-off between explicit biological insightand model flexibility. The deep manifold sampler was recently proposed as ameans to iteratively sample variable-length protein sequences by exploiting thegradients from a function predictor. We introduce an alternative approach tothis guided sampling procedure, multi-segment preserving sampling, that enablesthe direct inclusion of domain-specific knowledge by designating preserved andnon-preserved segments along the input sequence, thereby restricting variationto only select regions. We present its effectiveness in the context of antibodydesign by training two models: a deep manifold sampler and a GPT-2 languagemodel on nearly six million heavy chain sequences annotated with the IGHV1-18gene. During sampling, we restrict variation to only thecomplementarity-determining region 3 (CDR3) of the input. We obtain logprobability scores from a GPT-2 model for each sampled CDR3 and demonstratethat multi-segment preserving sampling generates reasonable designs whilemaintaining the desired, preserved regions.

Multi-segment preserving sampling for deep manifold sampler