  Sequential Monte Carlo (SMC) methods are widely used to draw samples from
intractable target distributions. Particle degeneracy can hinder the use of SMC
when the target distribution is highly constrained or multimodal. As a
motivating application, we consider the problem of sampling protein structures
from the Boltzmann distribution. This paper proposes a general SMC method that
propagates multiple descendants for each particle, followed by resampling to
maintain the desired number of particles. Simulation studies demonstrate the
efficacy of the method for tackling the protein sampling problem. As a real
data example, we use our method to estimate the number of atomic contacts for a
key segment of the SARS-CoV-2 viral spike protein.
