Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling

Abstract Deep learning has transformed protein structure prediction, yet most systems return a single dominant conformation with little control over alternative functional states. We introduce pair representation scaling, an inference-time method that biases conformational sampling in diffusion-based structure predictors by multiplying the latent pair representation by a single scalar before the Pairformer trunk, without retraining, an auxiliary model, or a second forward pass. On 86 two-state targets spanning domain motions and membrane transporters, scaling broadens the conformational ensembles of both AlphaFold 3 and Boltz-2 and recovers alternative states that default inference misses, most strongly in AlphaFold 3, where the gains extend even to targets deposited after the training cutoff. It approaches the alternative-state recovery of alignment-based sampling methods, and the benefit persists even without a multiple sequence alignment. The predicted distance distributions show that scaling shifts the encoded two-state distribution toward the experimentally observed alternative state, a directed modulation rather than arbitrary perturbation. Pair representation scaling is an interpretable, low-cost handle on the conformational ensembles of deep learning structure predictors. Abstract Figure Pair representation scaling A query sequence and its multiple sequence alignment enter the Pairformer trunk, where the latent pair representation z is rescaled by a single global scalar to (1 + β ) z and the diffusion module then generates a structure. The alignment and the trained weights are unchanged, and the same operation applies in AlphaFold 3 and Boltz-2. The scalar β is a single explicit handle: sweeping it broadens the sampled ensemble toward alternative states, and its effect can be read out directly inside the network.