μ-tempered metadynamics: Artifact independent convergence times for wide hills

Computational methods for exploring protein conformations

Proteins are dynamic molecules that can transition between a potentially wide range of structures comprising their conformational ensemble. The nature of these conformations and their relative probabilities are described by a high-dimensional free energy landscape. While computer simulation techniques such as molecular dynamics simulations allow characterisation of the metastable conformational states and the transitions between them, and thus free energy landscapes, to be characterised, the barriers between states can be high, precluding efficient sampling without substantial computational resources. Over the past decades, a dizzying array of methods have emerged for enhancing conformational sampling, and for projecting the free energy landscape onto a reduced set of dimensions that allow conformational states to be distinguished, known as collective variables (CVs), along which sampling may be directed. Here, a brief description of what biomolecular simulation entails is followed by a more detailed exposition of the nature of CVs and methods for determining these, and, lastly, an overview of the myriad different approaches for enhancing conformational sampling, most of which rely upon CVs, including new advances in both CV determination and conformational sampling due to machine learning.

Protein Conformational Transitions from All-Atom Adaptively Biased Path Optimization

Simulation methods are valuable for elucidating protein conformational transitions between functionally diverse states given that transition pathways are difficult to capture experimentally. Nonetheless, specific computational algorithms are required because of the high free energy barriers between these different protein conformational states. Adaptively biased path optimization (ABPO) is an unrestrained, transition-path optimization method that works in a reduced-variable space to construct an adaptive biasing potential to aid convergence. ABPO was previously applied using a coarse-grained Go̅-model to study conformational activation of Lyn, a Src family tyrosine kinase. How effectively ABPO can be applied at the higher resolution of an all-atom model to explore protein conformational transitions is not yet known. Here, we report the all-atom conformational transition paths of three protein systems constructed using the ABPO methodology. Two systems, triose phosphate isomerase and dihydrofolate reductase, undergo local flipping of a short loop that promotes ligand binding. The third system, estrogen receptor α ligand binding domain, has a helix that adopts different conformations when the protein is bound to an agonist or an antagonist. For each protein, distance-based or torsion-angle reduced variables were identified from unbiased trajectories. ABPO was computed in this reduced variable space to obtain the transition path between the two states. The all-atom ABPO is shown to successfully converge an optimal transition path for each of the three systems.

Overfill Protection and Hyperdynamics in Adaptively Biased Simulations

Two problems associated with adaptively biased simulations are considered: overfilling and time scale estimation. First, a simple and computationally efficient procedure for limiting the bias fill-depth of any adaptive biasing potential is introduced. The resulting bias potential floods only up to a specified level and avoids bias accumulation in higher regions of free energy. Second, hyperdynamics can be invoked in combination with this depth-limited bias potential to estimate time scales. We argue that allowing the bias to equilibrate to a set depth is one crux in efficient hyperdynamics application. In both simple (alanine dipeptide) and complex (ligand residence) test cases, the useable boost factors are 6-8 times larger when the bias fill depth is directly controlled, as opposed to controlling the update frequency. Update frequency has emerged as a proxy for controlling the fill height of adaptive biases, with infrequent metadynamics being an example. The Kolmogorov-Smirnov test is shown to be insufficient for determining the trustworthiness of a hyperdynamics simulation, and a more robust strategy is described. Overfill limiting and hyperdynamics have been added to the fABMACS project.

A fast, open source implementation of adaptive biasing potentials uncovers a ligand design strategy for the chromatin regulator BRD4

In this communication we introduce an efficient implementation of adaptive biasing that greatly improves the speed of free energy computation in molecular dynamics simulations. We investigated the use of accelerated simulations to inform on compound design using a recently reported and clinically relevant inhibitor of the chromatin regulator BRD4 (bromodomain-containing protein 4). Benchmarking on our local compute cluster, our implementation achieves up to 2.5 times more force calls per day than plumed2. Results of five 1 μs-long simulations are presented, which reveal a conformational switch in the BRD4 inhibitor between a binding competent and incompetent state. Stabilization of the switch led to a -3 kcal/mol improvement of absolute binding free energy. These studies suggest an unexplored ligand design principle and offer new actionable hypotheses for medicinal chemistry efforts against this druggable epigenetic target class.

The structural basis of the dominant negative phenotype of the Gαi1β1γ2 G203A/A326S heterotrimer

Aim:

Dominant negative mutant G proteins have provided critical insight into the mechanisms of G protein-coupled receptor (GPCR) signaling, but the mechanisms underlying the dominant negative characteristics are not completely understood. The aim of this study was to determine the structure of the dominant negative Gα_i1β₁γ₂ G203A/A326S complex (Gi-DN) and to reveal the structural basis of the mutation-induced phenotype of Gα_i1β₁γ₂.

Methods:

The three subunits of the Gi-DN complex were co-expressed with a baculovirus expression system. The Gi-DN heterotrimer was purified, and the structure of its complex with GDP was determined through X-ray crystallography.

Results:

The Gi-DN heterotrimer structure revealed a dual mechanism underlying the dominant negative characteristics. The mutations weakened the hydrogen bonding network between GDP/GTP and the binding pocket residues, and increased the interactions in the Gα-Gβγ interface. Concomitantly, the Gi-DN heterotrimer adopted a conformation, in which the C-terminus of Gα_i and the N-termini of both the Gβ and Gγ subunits were more similar to the GPCR-bound state compared with the wild type complex. From these structural observations, two additional mutations (T48F and D272F) were designed that completely abolish the GDP binding of the Gi-DN heterotrimer.

Conclusion:

Overall, the results suggest that the mutations impede guanine nucleotide binding and Gα-Gβγ protein dissociation and favor the formation of the G protein/GPCR complex, thus blocking signal propagation. In addition, the structure provides a rationale for the design of other mutations that cause dominant negative effects in the G protein, as exemplified by the T48F and D272F mutations.