Let us consider the conventions on names used in the theoretical derivation of Metropolis-Hastings Monte Carlo as outlined here, for the sake of common nomenclature.
What we are building is a step-by-step Markov Chain Monte Carlo (MCMC) algorithm to describe the evolution of a system in an initial state towards a final state distributed according to a desired probability distribution $P(x)$. This final sentence is to be read in the sense that repeated iterations of the algorithm, on distinct initial states, lead to an ensemble of states distributed according to a desired distribution $P(x)$.
For each of these iterations, during each step, given an initial state $x$ for the system and a final state $x'$, the probability of the system moving from $x$ to $x'$ is factorized into the proposal probability $g(x'|x)$ and the acceptance probability $A(x'|x)$ -- i.e. $P(x'|x) = g(x'|x) A(x'|x)$.
The meaning of the proposal probability is that of being the probability associated to proposing the next state to be $x'$ if we start from the state $x$. That of the acceptance probability is the probability of accepting the state $x'$ if we start from the initial state $x$ and derives from the final desired distribution of the states $P(x)$ -- aside from physically justified fluctuations, the most probable states are accepted and the least ones rejected.
All that is stated above is valid for any MCMC method.
In a common scenario, the specific case of the Metropolis process -- which is a particular MCMC method -- we choose $g(x'|x)$ to be symmetrical. But beside it being so, we generally pose no further constraints on this choice.
It left me with some open questions: how does the choice of the form of the proposal distribution influence the MCMC algorithm? Does it depend on the system in analysis? Specifically, is there any physical meaning behind the choice of the proposal distribution $g(x'|x)$?