  Investigating conformational landscapes of proteins is a crucial way to
understand their biological functions and properties. AlphaFlow stands out as a
sequence-conditioned generative model that introduces flexibility into
structure prediction models by fine-tuning AlphaFold under the flow-matching
framework. Despite the advantages of efficient sampling afforded by
flow-matching, AlphaFlow still requires multiple runs of AlphaFold to finally
generate one single conformation. Due to the heavy consumption of AlphaFold,
its applicability is limited in sampling larger set of protein ensembles or the
longer chains within a constrained timeframe. In this work, we propose a
feature-conditioned generative model called AlphaFlow-Lit to realize efficient
protein ensembles generation. In contrast to the full fine-tuning on the entire
structure, we focus solely on the light-weight structure module to reconstruct
the conformation. AlphaFlow-Lit performs on-par with AlphaFlow and surpasses
its distilled version without pretraining, all while achieving a significant
sampling acceleration of around 47 times. The advancement in efficiency
showcases the potential of AlphaFlow-Lit in enabling faster and more scalable
generation of protein ensembles.
