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Unregulated activation of Src kinases leads to aberrant signalling, uncontrolled growth and differentiation of cancerous cells. Reaching a complete mechanistic understanding of large-scale conformational transformations underlying the activation of kinases could greatly help in the development of therapeutic drugs for the treatment of these pathologies. In principle, the nature of conformational transition could be modelled in silico via atomistic molecular dynamics simulations, although this is very challenging because of the long activation timescales. Here we employ a computational paradigm that couples transition pathway techniques and Markov state model-based massively distributed simulations for mapping the conformational landscape of c-src tyrosine kinase. The computations provide the thermodynamics and kinetics of kinase activation for the first time, and help identify key structural intermediates. Furthermore, the presence of a novel allosteric site in an intermediate state of c-src that could be potentially used for drug design is predicted. Activation of c-src kinase is associated with uncontrolled growth and metastasis of tumour cells. Shukla et al. model conformational changes in c-src during activation, and identify an allosteric site in an intermediate state that may provide a target for small molecule therapeutics.

OpenMM is a molecular dynamics simulation toolkit with a unique focus on extensibility. It allows users to easily add new features, including forces with novel functional forms, new integration algorithms, and new simulation protocols. Those features automatically work on all supported hardware types (including both CPUs and GPUs) and perform well on all of them. In many cases they require minimal coding, just a mathematical description of the desired function. They also require no modification to OpenMM itself and can be distributed independently of OpenMM. This makes it an ideal tool for researchers developing new simulation methods, and also allows those new methods to be immediately available to the larger community.

Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle cloud-computing platform to simulate two milliseconds of dynamics of a major drug target, the G-protein-coupled receptor β 2 AR. Markov state models aggregate independent simulations into a single statistical model that is validated by previous computational and experimental results. Moreover, our models provide an atomistic description of the activation of a G-protein-coupled receptor and reveal multiple activation pathways. Agonists and inverse agonists interact differentially with these pathways, with profound implications for drug design. Two milliseconds of molecular dynamics simulations of a major drug-target G-protein-coupled receptor (GPCR) has been carried out using Google's Exacycle cloud computing platform. Markov state models were used to aggregate independent simulations into a statistical model that provides an atomistic description of GPCR ligand-modulated activation pathways.

Kinases are ubiquitous enzymes involved in the regulation of critical cellular pathways. However, in silico modelling of the conformational ensembles of these enzymes is difficult due to inherent limitations and the cost of computational approaches. Recent algorithmic advances combined with homology modelling and parallel simulations have enabled researchers to address this computational sampling bottleneck. Here, we present the results of molecular dynamics studies for seven Src family kinase (SFK) members: Fyn, Lyn, Lck, Hck, Fgr, Yes and Blk. We present a sequence invariant extension to Markov state models, which allows us to quantitatively compare the structural ensembles of the seven kinases. Our findings indicate that in the absence of their regulatory partners, SFK members have similar in silico dynamics with active state populations ranging from 4 to 40% and activation timescales in the hundreds of microseconds. Furthermore, we observe several potentially druggable intermediate states, including a pocket next to the adenosine triphosphate binding site that could potentially be targeted via a small-molecule inhibitor.

Bryostatin is in clinical trials for Alzheimer’s disease, cancer, and HIV/AIDS eradication. It binds to protein kinase C competitively with diacylglycerol, the endogenous protein kinase C regulator, and plant-derived phorbol esters, but each ligand induces different activities. Determination of the structural origin for these differing activities by X-ray analysis has not succeeded due to difficulties in co-crystallizing protein kinase C with relevant ligands. More importantly, static, crystal-lattice bound complexes do not address the influence of the membrane on the structure and dynamics of membrane-associated proteins. To address this general problem, we performed long-timescale (400–500 µs aggregate) all-atom molecular dynamics simulations of protein kinase C–ligand–membrane complexes and observed that different protein kinase C activators differentially position the complex in the membrane due in part to their differing interactions with waters at the membrane inner leaf. These new findings enable new strategies for the design of simpler, more effective protein kinase C analogs and could also prove relevant to other peripheral protein complexes. Natural supplies of bryostatin, a compound in clinical trials for Alzheimer’s disease, cancer, and HIV, are scarce. Here, the authors perform molecular dynamics simulations to understand how bryostatin interacts with membrane-bound protein kinase C, offering insights for the design of bryostatin analogs.

The ensemble folding of two 21-residue α-helical peptides has been studied using all-atom simulations under several variants of the AMBER potential in explicit solvent using a global distributed computing network. Our extensive sampling, orders of magnitude greater than the experimental folding time, results in complete convergence to ensemble equilibrium. This allows for a quantitative assessment of these potentials, including a new variant of the AMBER-99 force field, denoted AMBER-99 ϕ, which shows improved agreement with experimental kinetic and thermodynamic measurements. From bulk analysis of the simulated AMBER-99 ϕ equilibrium, we find that the folding landscape is pseudo-two-state, with complexity arising from the broad, shallow character of the “native” and “unfolded” regions of the phase space. Each of these macrostates allows for configurational diffusion among a diverse ensemble of conformational microstates with greatly varying helical content and molecular size. Indeed, the observed structural dynamics are better represented as a conformational diffusion than as a simple exponential process, and equilibrium transition rates spanning several orders of magnitude are reported. After multiple nucleation steps, on average, helix formation proceeds via a kinetic “alignment” phase in which two or more short, low-entropy helical segments form a more ideal, single-helix structure.

Calmodulin (CaM) is a ubiquitous Ca 2+ sensor and a crucial signalling hub in many pathways aberrantly activated in disease. However, the mechanistic basis of its ability to bind diverse signalling molecules including G-protein-coupled receptors, ion channels and kinases remains poorly understood. Here we harness the high resolution of molecular dynamics simulations and the analytical power of Markov state models to dissect the molecular underpinnings of CaM binding diversity. Our computational model indicates that in the absence of Ca 2+ , sub-states in the folded ensemble of CaM’s C-terminal domain present chemically and sterically distinct topologies that may facilitate conformational selection. Furthermore, we find that local unfolding is off-pathway for the exchange process relevant for peptide binding, in contrast to prior hypotheses that unfolding might account for binding diversity. Finally, our model predicts a novel binding interface that is well-populated in the Ca 2+ -bound regime and, thus, a candidate for pharmacological intervention. Calmodulin is an important calcium sensor that can recognise and interact with a large range of proteins. Here, the authors use molecular dynamics simulations and Markov state models to show that the range of binding conformations adopted without substrate can help explain its diverse and specific binding.

Understanding molecular kinetics, and particularly protein folding, is a classic grand challenge in molecular biophysics. Network models, such as Markov state models (MSMs), are one potential solution to this problem. MSMs have recently yielded quantitative agreement with experimentally derived structures and folding rates for specific systems, leaving them positioned to potentially provide a deeper understanding of molecular kinetics that can lead to experimentally testable hypotheses. Here we use existing MSMs for the villin headpiece and NTL9, which were constructed from atomistic simulations, to accomplish this goal. In addition, we provide simpler, humanly comprehensible networks that capture the essence of molecular kinetics and reproduce qualitative phenomena like the apparent two-state folding often seen in experiments. Together, these models show that protein dynamics are dominated by stochastic jumps between numerous metastable states and that proteins have heterogeneous unfolded states (many unfolded basins that interconvert more rapidly with the native state than with one another) yet often still appear two-state. Most importantly, we find that protein native states are hubs that can be reached quickly from any other state. However, metastability and a web of nonnative states slow the average folding rate. Experimental tests for these findings and their implications for other fields, like protein design, are also discussed.

We use reinforcement learning to train an agent for computational RNA design: given a target secondary structure, design a sequence that folds to that structure in silico. Our agent uses a novel graph convolutional architecture allowing a single model to be applied to arbitrary target structures of any length. After training it on randomly generated targets, we test it on the Eterna100 benchmark and find it outperforms all previous algorithms. Analysis of its solutions shows it has successfully learned some advanced strategies identified by players of the game Eterna, allowing it to solve some very difficult structures. On the other hand, it has failed to learn other strategies, possibly because they were not required for the targets in the training set. This suggests the possibility that future improvements to the training protocol may yield further gains in performance.

The dynamic motions of protein structural elements, particularly flexible loops, are intimately linked with diverse aspects of enzyme catalysis. Engineering of these loop regions can alter protein stability, substrate binding and even dramatically impact enzyme function. When these flexible regions are unresolvable structurally, computational reconstruction in combination with large-scale molecular dynamics simulations can be used to guide the engineering strategy. Here we present a collaborative approach that consists of both experiment and computation and led to the discovery of a single mutation in the F/G loop of the nitrating cytochrome P450 TxtE that simultaneously controls loop dynamics and completely shifts the enzyme's regioselectivity from the C4 to the C5 position of L -tryptophan. Furthermore, we find that this loop mutation is naturally present in a subset of homologous nitrating P450s and confirm that these uncharacterized enzymes exclusively produce 5-nitro- L -tryptophan, a previously unknown biosynthetic intermediate. A collaborative approach between experiment and simulation has revealed a single mutation in the F/G loop of the newly described nitrating cytochrome P450 TxtE that controls loop dynamics and, more surprisingly, the regioselectivity of the reaction. This mutation is present in a subset of homologous nitrating P450s that produce a previously unidentified biosynthetic intermediate, 5-nitro- L -tryptophan.