Here, we describe a rapid all-atom Monte Carlo simulation approach to simulate unfolding of the essential bacterial enzyme Dihydrofolate Reductase (DHFR) and all possible single point-mutants.

All possible single point mutations of DHFR (159 * 19 = 3,021) were simulated with the Monte Carlo protein unfolding simulation protocol.

Although the use of rapid Monte Carlo simulations reduces simulation time and allows for a greater number of replicates, our method to predict stability effects of mutations based on non-equilibrium unfolding simulations represents a general approach that could be modified for use with conventional MD simulations, especially given the current rate of improvement in simulation speed and accuracy.

Here, we use a Monte Carlo protein unfolding approach (MCPU) with an all-atom simulation method and knowledge-based potential developed earlier in our lab [16,30,31] to simulate unfolding and predict melting temperatures for all possible single point mutants of E. coli Dihydrofolate Reductase (DHFR).

As described in the Materials and Methods section, the Monte Carlo move set consists of rotations about torsional angles.

Monte Carlo protein unfolding simulation

Monte Carlo simulations

We employed an all-atom Monte Carlo simulation program incorporating a knowledge-based potential, described in previous publications [16,31,65].

An additional minimization step was carried out by running the Monte Carlo simulation program at low temperature (0.100 in simulation units) for 2,000,000 steps.

This is in fact the interface that is the first to dissociate in the Monte Carlo simulations (see Fig.

The RMSDs of DHFR wild type and mutants 11 15A and 1155T vs. Monte Carlo step at temperatures from 0.1 to 3.2.

To calculate a p-Value, a Monte Carlo sampling algorithm was implemented.

Empirical p-values for PEACS scores were determined by Monte Carlo sampling: for a given perturbation with n replicates, a null distribution was obtained by randomly sampling n expression profiles from the experimental data, calculating a PEACS score, and iterating this process 10,000 times to generate a PEACS score null distribution.

The empirical p-value was then determined by ranking the PEACS score for the given perturbation relative to the PEACS scores generated by this Monte Carlo procedure.

P-Values were obtained through Monte Carlo resampling of the PEACS scores, as described in the text.

In particular, we use spatial birth and death processes nested Within a Markov chain Monte Carlo simulation algorithm.

For Markov chain Monte Carlo (MCMC) iterations t: [1. .

The model parameters—including the number and locations of population centers and spatial variation at study and peak levels—were estimated by fitting the model to peak activation coordinates from our database using Markov Chain Monte Carlo (MCMC) sampling with a generative birth-and-death algorithm for population centers.