Abstract: Sequential Monte Carlo (SMC) methods are widely used to draw samples from intractable target distributions. Weight degeneracy can hinder the use of SMC when the target distribution is highly constrained. 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. A simulation study demonstrates the efficacy of the method for tackling the protein sampling problem, compared to existing SMC methods. As a real data example, we estimate the number of atomic contacts for a key segment of the SARS-CoV-2 viral spike protein.

Key words and phrases: Monte Carlo methods, particle filter, protein structure analysis, SARS-CoV-2.