Molecular Dynamics simulations involve integrating Newton's laws of motion for a set of atoms. Briefly, given a set of n atomic coordinates X = { X → 1 , . . . , X → n : X → i ∈ ℝ 3 } and the corresponding velocity vectors V = { V → 1 , . . . , V → n : V → i ∈ ℝ 3 } , MD updates the positions and velocities of each atom according to an energy potential. The updates are performed via numerical integration, resulting in a conformational trajectory. The size of the time step for the numerical integration is normally on the order of a 1-2 femtoseconds (fs = 10^-15 sec), meaning that a 1 microsecond simulation requires one billion integration steps. In most circumstances, every 1000th to 10000th conformation is written to disc as an ordered series of frames.

Traditional methods for analyzing MD data either monitor the dynamics of global statistics (e.g., the radius of gyration, total energy, etc), or else identify sub-states via a clustering the frames 151617 or through Principal Components Analysis (PCA) and closely related methods (e.g., 1819202122). Clustering based methods do not produce generative models and generally rely on pairwise comparisons between frames and thus run in quadratic time with respect to the number of frames in the trajectory. Our algorithms produce generative models and only perform linear work in the number of frames. This complexity difference is especially important for long timescale simulations. PCA-based methods implicitly assume that the data are drawn from a multivariate Gaussian distribution. Our method makes the same assumption but differs from PCA in two important ways. First, PCA projects the data onto an orthogonal basis. Our method involves no change of basis, making the resulting model easier to interpret. Second, we employ L1 regularization when learning the parameters of our model. Regularization is a common strategy for reducing the tendency to over-fit data by, informally, penalizing overly complicated models. We use L1 regularization because it has desirable statistical properties. Specifically, it leads to consistent models (that is, given enough data our algorithm learns the true model) while while enjoying high efficiency (that is, the number of samples needed to achieve the true model is small).

More recently, Lange and Grubmüller introduced full correlation analysis 23, which can capture both linear and non-linear correlated motions from MD simulations. The algorithms in this paper are limited to linear models, but we note that they can be easily extended to more complex forms by using non-Gaussian random variables (e.g., 2425). Our final algorithm produces models that resemble Markov State Models (MSMs) 26 but are different in that they are fully generative.

A Markov Random Field M = ( G , Θ ) consists of an undirected graph G over a set of random variables X = {X₁, ..., X_n} and a set of functions Θ over the nodes and edges of G. Together, they define the joint distribution P(X). The topology of the graph determines the set of conditional independencies between the variables. In particular, the ith random variable is conditionally independent of the remaining variables, given its neighbors in the graph. Informally, if variables X_i and X_j are not connected by an edge in the graph, then any correlation between them is indirect. By 'indirect' we mean that the correlation between X_i and X_j (if any) can be explained in terms of a pathway of correlations (e.g., X_i → X_k → ··· → X_j). Conversely, if X_i and X_j are connected by an edge, then the correlation is direct. Our algorithm automatically detects these conditional independencies and learns the sparsest possible model, subject to fitting the data.

A Gaussian Graphical Model (GGM) or Gaussian Markov Random Field is simply a MRF where each variable is normally distributed. Thus, a GGM encodes a multivariate Gaussian distribution. A GGM has parameters M = ( h → , ∑ - 1 ) where Σ^-1 is an n × n matrix (known as the precision matrix) and h → is a n × 1 vector. The non-zero elements of Σ^-1 reveal the edges in the MRF. The inverse of the precision matrix, denoted by Σ, is the covariance matrix for a multivariate Gaussian distribution with mean μ → = h → T ∑ .

Gaussian distributions have a number of desirable properties including the availability of analytic expressions for a variety of quantities. For example, the probability of observing x → = ( x 1 , . . . , x n ) is:

P ( x ) = 1 Z exp { - 1 2 ( x → - μ → ) T ∑ - 1 ( x → - μ → ) } ,

where Z = ( 2 π ) n ∣ ∑ ∣ is the partition function and |Σ| denotes the determinant of Σ. Other quantities of interest can be computed as well, such as the free energy of the model, - ln Z, its differential entropy:

1 2 ln [ ( 2 π e ) n ] ∣ ∑ ∣ ]

or the KL-divergence between two different models:

K L ( M 0 ∥ M 1 ) = 1 ∕ 2 ( t r a c e ( ∑ 1 - 1 ∑ 0 ) + ( μ → 1 - μ → 0 ) T ∑ 1 - 1 ( μ → 1 - μ → 0 ) - ln ( ∣ ∑ 0 ∣ ∕ ∣ ∑ 1 ∣ ) - n ) .

A GGM can also be used to manipulate a subset of variables and then then compute the marginal densities for the remaining variables. For example, let V ⊂ X be an arbitrary subset of variables X and let W be the complement set. We can condition the model by setting variables V to some particular value, v → . The marginal distribution over W given v→ is a multivariate Gaussian with parameters ( μ → w ∣ v → , ∑ W ) where

μ → W ∣ v → = μ → W + ∑ W T ∑ V V - 1 ( v → - μ → v )

∑ W = ∑ W W - ∑ W V T ∑ V V - 1 ∑ W V

Here, ∑ = ∑ W W ∑ W V ∑ W V T ∑ V V . Thus, inference can be performed analytically via matrix operations. In this way, users can predict the conformational changes induced by local perturbations or, more generally, study the couplings between arbitrarily chosen subsets of variables.

Output The first algorithm produces a Gaussian Graphical Model M = ( h → , ∑ - 1 ) . The first step is to compute the sample mean μ → = 1 ∕ t ∑ i = 1 t d → i . Then it computes the regularized precision matrix Σ^-1 (see below). Finally, h→ is computed as follows: h → = ∑ - 1 μ → .

The algorithm produces the sparsest precision matrix that still fits the data (see below). It also guarantees that Σ^-1 is positive-definite, which means it can be inverted to produce the regularized covariance matrix (as opposed to the sample covariance, which is trivial to compute). This is important because Eqs 1-3 require the covariance matrix, Σ. We further note that a sparse precision matrix does not imply that the corresponding covariance matrix is sparse, nor does a sparse covariance imply that the corresponding precision matrix is sparse. That is, our algorithm isn't equivalent to simply thresholding the sample covariance matrix, and then inverting.

The second algorithm is a straight-forward extension of the first. Instead of producing a time-averaged model, it produced time-varying model: M ( τ ) = ( h → ( τ ) , ∑ - 1 ( τ ) ) . Here, τ ≤ t indexes over sequentially ordered windows of frames in the trajectory. The width of the window, w, is a parameter and may be adjusted to learn time-varying models at a particular time-scale. Naturally, a separate time-averaged model could be learned for each window. Instead, the second algorithm applies a simple smoothing kernel so that the parameters of the τth window includes information from neighboring window too. In this way, the algorithm ensures that the parameters of the time-varying model evolve as smoothly as possible, subject to fitting the data.

Let D^(τ) ⊆ D denote the subset of frames in the MD trajectory that correspond to the τth window, 1 ≤ τ ≤ T. The second algorithm solves the following optimization problem for each 1 ≤ τ ≤ T:

∑ - 1 ( τ ) = arg max X ≻ 0 log ∣ X ∣ - t r a c e ( S ( τ ) X ) - λ ∥ X ∥ 1

Here, S(τ) is the weighted covariance matrix, and is calculated as follows:

S ( τ ) = ∑ k = τ - κ τ + κ w k ( D ( k ) - D ( k ) ) ( D ( k ) - D ( k ) ) T ∑ k = τ - κ τ + κ w k

where k indexes over windows τ - κ to τ + κ, κ is a user-specified kernel width, and the weights w_k are defined by a nonnegative kernel function. The choice of kernel function is specified by the user. In our experiments the kernel mixed the current window and the previous window with the current window having twice the weight of the previous. The time-varying model is then constructed by solving Eq. 9 for each S(τ). That is, the primary difference between the time-averaged and time-varying version of the algorithm is the kernel function.

The final algorithm builds on the second algorithm. Recall that the second algorithm learns T sequentially ordered models over windows of the trajectory. Moreover, recall that each model encodes a multivariate Gaussian (Eq. 1) and that the KL-divergence between multivariate Gaussians can be computed analytically via Eq. 3. The KL-divergence (also known as information gain or relative entropy) is a non-negative measure of the difference between two probability distributions. It is zero if and only if the two distributions are identical. It is not, however, a distance metric because it is not symmetric. That is D(P||Q) ≠ D(Q||P), in general. However, it is common to define a symmetric KL-divergence by simply summing KL_sym = D(P||Q)+D(Q||P). We can thus cluster the models using any standard clustering algorithm, such as k-means or a hierarchial approach. In our experiments we used complete linkage clustering, an agglomerative method that minimizes the maximum distance between elements when merging clusters.

Let S be the set of clusters returned by a clustering algorithm. Our final algorithm treats those clusters as states in a Markov Chain. The prior probability of being in each state can be estimated using free energy calculations 2930 for each cluster, or according to the relative sizes of each cluster. It then estimates the transition probabilities between states i and j by counting the number of times a model assigned to cluster i is followed by a model assigned to cluster j. This simple approach creates a model that can be used to generate new trajectories by first sampling states from the Markov Chain and then sampling conformations from the models associated with that state.

We applied the first algorithm to simulations of a complex (Figure 1-left) consisting of gp120 (a glycoprotein on the surface of the HIV envelope) and the CD4 receptor (a glycoprotein expressed on the surface of T helper cells). The binding of gp120 to CD4 receptors is among the first events involved in HIV's entry into helper T-Cells. We performed two simulations using namd 31. The first simulation was the gp120-CD4 complex in explicit solvent at 310 degrees Kelvin. The second simulation was the same complex bound to Ibalizumab (Figure 1-right), a humanized monoclonal antibody that binds to CD4 and inhibits the viral entry process 32. Each trajectory was each 2 ns long and contained 4500 frames.

Ibalizumab's mechanism of action is poorly understood. As can be seen in Figure 1, Ibalizumab does not prevent gp120 from binding to CD4, nor does it directly bind to gp120 itself, suggesting that its inhibitory action occurs via an allosteric mechanism. To investigate this phenomenon, we applied our first algorithm to the two trajectories and then compared the resulting models. The variables in the models corresponded to the positional fluctuations of the C-α atoms, relative to the initial frame of the simulation.

We applied the second algorithm to a simulation of the engrailed homeodomain (Figure 6), a 54-residue DNA binding domain. The DNA-binding domains of the homeotic proteins, called homeodomains (HD), play an important role in the development of all metazoans 34 and certain mutations to HDs are known to cause disease in humans 35. Homeodomains fold into a highly conserved structure consisting of three alpha-helices wherein the C-terminal helix makes sequence-specific contacts in the major groove of DNA 36. The Engrailed Homeodomain (En-HD) is an ultra-fast folding protein that is predicted to exhibit significant amounts of helical structure in the denatured state ensemble 37. Moreover, the experimentally determined unfolding rate is of 1.1E + 03/sec 38, which is also fast. Taken together, these observations suggest that the protein may exhibit substantial conformational fluctuations at equilibrium.

We performed three 50-microsecond simulations of the protein at 300, 330, and 350 degrees Kelvin. These simulations were performed on ANTON14, a special-purpose supercomputer designed to perform long-timescale simulations. Each simulation had more than 500,000 frames. In this paper, we learned a time-varying model of the first microsecond of the 300 degree trajectory, modeling the fluctuations of the alpha carbons. The window size was 2 ns, and a sawtooth smoothing kernel was applied such that the ith model is built from the data from windows i, i -1, and i - 2 such with kernel weights 0.57, 0.29, and 0.14, respectively. A total of 500 models were learned from the first microsecond of the trajectory.

Figure 7-A plots the differential entropy (Eq. 2) of the 500 models. We see that the curve has a variety of peaks and valleys that can be used to segment the trajectory into putative sub-states. Figures 7-B and 7-C illustrate the correlation networks obtained from the models with the smallest and largest differential entropies, respectively. As can be seen, the simulation visits sub-states that have radically different correlation structures.

Figure 8-A plots the average log-likelihood of the frames from the i + 1st window under the ith model. Sharp drops in the likelihood can also be used to segment the trajectory into possible sub-states and to pin-point the moment when the system transitions between them. Figure 8-B shows the log-likelihood of each of the frames under each of the 500 models. Figure 8-C shows the first 50 rows and the first 2,000 columns of Figure 8-B. The clear block-structure of the matrix more clearly illustrates the sub-states visited by the simulation.

Figure 9-A plots the symmetric version of the KL-divergence (Eq. 3) between sequential models. Once again, spikes in this curve can be used to segment the trajectory.

Using the 500 models learned in the previous section, we computed the symmetric KL-divergence between all pairs of models. Recall that the KL-divergence (Eq. 3) is a measure of the difference between distributions. Figure 9-B plots the pairwise KL divergences between the 500 models.

We then applied complete linkage clustering to the KL-divergence matrix. Complete linkage clustering minimizes the maximum distance between elements when merging clusters. We selected a total of 7 clusters based on the assumption that the number of sub-states visited by a sequence of m models proportional to the logarithm of m. The intuition behind this assumption is that different sub-states are separated by energy barriers and the probability of surmounting an energy barrier is exponentially small in the height of the barrier. Figure 10 shows two representative structures from the two largest clusters. As can be seen, the primary difference between the two structures is the N-terminal loop.

Finally, we estimated the parameters of a Markov chain over the 7 clusters by counting the number of times a model from the ith cluster was followed by a model from the jth cluster. The resulting state-transition matrix is shown in Figure 11. The matrix indicates that state 4 is the dominant state, but inter-converts with states 6 and 7. This state-transition matrix and the graphical models associated with each state encapsulate the statistics of the trajectory.