Search

Virtual screening is a widely used tool for drug discovery, but its predictive power can vary dramatically depending on how much structural data is available. In the best case, crystal structures of a ligand-bound protein can help find more potent ligands. However, virtual screens tend to be less predictive when only ligand-free crystal structures are available, and even less predictive if a homology model or other predicted structure must be used. Here, we explore the possibility that this situation can be improved by better accounting for protein dynamics, as simulations started from a single structure have a reasonable chance of sampling nearby structures that are more compatible with ligand binding. As a specific example, we consider the cancer drug target PPM1D/Wip1 phosphatase, a protein that lacks crystal structures. High-throughput screens have led to the discovery of several allosteric inhibitors of PPM1D, but their binding mode remains unknown. To enable further drug discovery efforts, we assessed the predictive power of an AlphaFold-predicted structure of PPM1D and a Markov state model (MSM) built from molecular dynamics simulations initiated from that structure. Our simulations reveal a cryptic pocket at the interface between two important structural elements, the flap and hinge regions. Using deep learning to predict the pose quality of each docked compound for the active site and cryptic pocket suggests that the inhibitors strongly prefer binding to the cryptic pocket, consistent with their allosteric effect. The predicted affinities for the dynamically uncovered cryptic pocket also recapitulate the relative potencies of the compounds (τb=0.70) better than the predicted affinities for the static AlphaFold-predicted structure (τb=0.42). Taken together, these results suggest that targeting the cryptic pocket is a good strategy for drugging PPM1D and, more generally, that conformations selected from simulation can improve virtual screening when limited structural data is available.

A protein is a dynamic shape-shifter whose function is determined by the set of structures it adopts. Unfortunately, atomically-detailed structures are only available for a few conformations of any given protein, and these structures have limited explanatory and predictive power. Here, we provide a brief historical perspective on protein dynamics and introduce recent advances in computational and experimental methods that are providing unprecedented access to protein shape-shifting. Next, we focus on how these tools are revealing the mechanism of allosteric communication and features like cryptic pockets, both of which present new therapeutic opportunities. A major theme is the importance of considering the relative probabilities of different structures and the control one can exert over protein function by modulating this balance.

Understanding the structural determinants of a protein’s biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of β-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding., Comparing and contrasting structural ensembles of different protein variants helps connect specific structural features to a protein’s biochemical properties. Here, the authors propose DiffNets, a self-supervised, deep learning method that streamlines this process.

Coronaviruses have caused multiple epidemics in the past two decades, in addition to the current COVID-19 pandemic that is severely damaging global health and the economy. Coronaviruses employ between twenty and thirty proteins to carry out their viral replication cycle including infection, immune evasion, and replication. Among these, nonstructural protein 16 (Nsp16), a 2’-O-methyltransferase, plays an essential role in immune evasion. Nsp16 achieves this by mimicking its human homolog, CMTr1, which methylates mRNA to enhance translation efficiency and distinguish self from other. Unlike human CMTr1, Nsp16 requires a binding partner, Nsp10, to activate its enzymatic activity. The requirement of this binding partner presents two questions that we investigate in this manuscript. First, how does Nsp10 activate Nsp16? While experimentally-derived structures of the active Nsp16/Nsp10 complex exist, structures of inactive, monomeric Nsp16 have yet to be solved. Therefore, it is unclear how Nsp10 activates Nsp16. Using over one millisecond of molecular dynamics simulations of both Nsp16 and its complex with Nsp10, we investigate how the presence of Nsp10 shifts Nsp16’s conformational ensemble in order to activate it. Second, guided by this activation mechanism and Markov state models (MSMs), we investigate if Nsp16 adopts inactive structures with cryptic pockets that, if targeted with a small molecule, could inhibit Nsp16 by stabilizing its inactive state. After identifying such a pocket in SARS-CoV-2 Nsp16, we show that this cryptic pocket also opens in SARS-CoV-1 and MERS, but not in human CMTr1. Therefore, it may be possible to develop pan-coronavirus antivirals that target this cryptic pocket.Statement of SignificanceCoronaviruses are a major threat to human health. These viruses employ molecular machines, called proteins, to infect host cells and replicate. Characterizing the structure and dynamics of these proteins could provide a basis for designing small molecule antivirals. In this work, we use computer simulations to understand the moving parts of an essential SARS-CoV-2 protein, understand how a binding partner turns it on and off, and identify a novel pocket that antivirals could target to shut this protein off. The pocket is also present in other coronaviruses but not in the related human protein, so it could be a valuable target for pan-coronavirus antivirals.

SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression, and replication, which depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate an unprecedented 0.1 seconds of the viral proteome. Our simulations capture dramatic opening of the apo Spike complex, far beyond that seen experimentally, which explains and successfully predicts the existence of ‘cryptic’ epitopes. Different Spike homologues modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also observe dramatic conformational changes across the proteome, which reveal over 50 ‘cryptic’ pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas.

The SARS-CoV-2 nucleocapsid (N) protein is an abundant RNA-binding protein critical for viral genome packaging, yet the molecular details that underlie this process are poorly understood. Here we combine single-molecule spectroscopy with all-atom simulations to uncover the molecular details that contribute to N protein function. N protein contains three dynamic disordered regions that house putative transiently-helical binding motifs. The two folded domains interact minimally such that full-length N protein is a flexible and multivalent RNA-binding protein. N protein also undergoes liquid-liquid phase separation when mixed with RNA, and polymer theory predicts that the same multivalent interactions that drive phase separation also engender RNA compaction. We offer a simple symmetry-breaking model that provides a plausible route through which single-genome condensation preferentially occurs over phase separation, suggesting that phase separation offers a convenient macroscopic readout of a key nanoscopic interaction., SARS-CoV-2 nucleocapsid (N) protein is responsible for viral genome packaging. Here the authors employ single-molecule spectroscopy with all-atom simulations to provide the molecular details of N protein and show that it undergoes phase separation with RNA.

This Account highlights recent advances and discusses major challenges in investigations of cryptic (hidden) binding sites by molecular simulations. Cryptic binding sites are not visible in protein targets crystallized without a ligand and only become visible crystallographically upon binding events. These sites have been shown to be druggable and might provide a rare opportunity to target difficult proteins. However, due to their hidden nature, they are difficult to find through experimental screening. Computational methods based on atomistic molecular simulations remain one of the best approaches to identify and characterize cryptic binding sites. However, not all methods are equally efficient. Some are more apt at quickly probing protein dynamics but do not provide thermodynamic or druggability information, while others that are able to provide such data are demanding in terms of time and resources. Here, we review the recent contributions of mixed-solvent simulations, metadynamics, Markov state models, and other enhanced sampling methods to the field of cryptic site identification and characterization. We discuss how these methods were able to provide precious information on the nature of the site opening mechanisms, to predict previously unknown sites which were used to design new ligands, and to compute the free energy landscapes and kinetics associated with the opening of the sites and the binding of the ligands. We highlight the potential and the importance of such predictions in drug discovery, especially for difficult ("undruggable") targets. We also discuss the major challenges in the field and their possible solutions.

Allosteric (i.e., long-range) communication within proteins is crucial for many biological processes, such as the activation of signaling cascades in response to specific stimuli. However, the physical basis for this communication remains unclear. Existing computational methods for identifying allostery focus on the role of concerted structural changes, but recent experimental work demonstrates that disorder is also an important factor. Here, we introduce the Correlation of All Rotameric and Dynamical States (CARDS) framework for quantifying correlations between both the structure and disorder of different regions of a protein. To quantify disorder, we draw inspiration from methods for quantifying "dynamic heterogeneity" from chemical physics to classify segments of a dihedral's time evolution as being in either ordered or disordered regimes. To demonstrate the utility of this approach, we apply CARDS to the Catabolite Activator Protein (CAP), a transcriptional activator that is regulated by Cyclic Adenosine MonoPhosphate (cAMP) binding. We find that CARDS captures allosteric communication between the two cAMP-Binding Domains (CBDs). Importantly, CARDS reveals that this coupling is dominated by disorder-mediated correlations, consistent with NMR experiments that establish allosteric coupling between the CBDs occurs without a concerted structural change. CARDS also recapitulates an enhanced role for disorder in the communication between the DNA-Binding Domains (DBDs) and CBDs in the S62F variant of CAP. Finally, we demonstrate that using CARDS to find communication hotspots identifies regions of CAP that are in allosteric communication without foreknowledge of their identities. Therefore, we expect CARDS to be of great utility for both understanding and predicting allostery.

Stimulation of receptor tyrosine kinases (RTK) such as EGF locally increase reactive oxygen species (ROS) levels at the plasma membrane that oxidize cysteines in proteins to enhance downstream signaling. Spatial confinement of ROS is an important regulatory mechanism to redox signaling, but it remains unknown why stimulation of different receptor tyrosine kinases (RTKs) at the plasma membrane target distinct sets of downstream proteins. To uncover additional mechanisms specifying which cysteines are redox regulated by EGF stimulation, we performed time-resolved quantification of the oxidation of 4,200 cysteine sites subsequent to EGF stimulation in A431 cells. EGF induces three distinct spatiotemporal patterns of cysteine oxidation in functionally organized protein networks, consistent with the spatial confinement model. Unexpectedly, protein crystal structure analysis and molecular dynamic simulation indicate widespread redox regulation of cryptic cysteines that are only solvent exposed upon changes in protein conformation. Phosphorylation and increased flux of nucleotide substrates serve as two distinct modes by which EGF specifies which cryptic cysteines become solvent exposed and redox regulated. Since proteins structurally regulated by different RTKs or cellular perturbations are largely unique, solvent exposure and redox regulation of cryptic cysteines is an important mechanism contextually delineating redox signaling networks.Significance StatementCellular redox processes are interconnected, but are not in equilibrium. Thus, understanding the redox biology of cells requires a systems-level, rather than reductionist, approach. Factors specifying which cysteines are redox regulated by a stimulus remain poorly characterized but are critical to understanding the fundamental properties of redox signaling networks. Here, we show that EGF stimulation induces oxidation of specific cysteines in 3 distinct spatiotemporal patterns. Redox regulated proteins include many proteins in the EGF pathway as well as many cysteines with known functional importance. Many redox regulated cysteines are cryptic and solvent exposed by changes in protein structure that were induced by EGF treatment. The novel finding that cryptic cysteines are redox regulated has important implications for how redox signaling networks are specified and regulated to minimize crosstalk. In addition, this time-resolved dataset of the redox kinetics of 4,200 cysteine sites is an important resource for others and is an important technological achievement towards systems-level understanding of cellular redox biology.

SARS-CoV-2 has intricate mechanisms for initiating infection, immune evasion/suppression, and replication, which depend on the structure and dynamics of its constituent proteins. Many protein structures have been solved, but far less is known about their relevant conformational changes. To address this challenge, over a million citizen scientists banded together through the Folding@home distributed computing project to create the first exascale computer and simulate an unprecedented 0.1 seconds of the viral proteome. Our simulations capture dramatic opening of the apo Spike complex, far beyond that seen experimentally, which explains and successfully predicts the existence of ‘cryptic’ epitopes. Different Spike homologues modulate the probabilities of open versus closed structures, balancing receptor binding and immune evasion. We also observe dramatic conformational changes across the proteome, which reveal over 50 ‘cryptic’ pockets that expand targeting options for the design of antivirals. All data and models are freely available online, providing a quantitative structural atlas.

Apoptosis-inducing factor (AIF) is critical for mitochondrial respiratory complex biogenesis and for mediating necroptotic parthanatos; these functions are seemingly regulated by enigmatic allosteric switching driven by NADH charge-transfer complex (CTC) formation. Here, we define molecular pathways linking AIF's active site to allosteric switching regions by characterizing dimer-permissive mutants using small-angle X-ray scattering (SAXS) and crystallography and by probing AIF-CTC communication networks using molecular dynamics simulations. Collective results identify two pathways propagating allostery from the CTC active site: (1) active-site H454 links to S480 of AIF's central β-strand to modulate a hydrophobic border at the dimerization interface, and (2) an interaction network links AIF's FAD cofactor, central β-strand, and Cβ-clasp whereby R529 reorientation initiates C-loop release during CTC formation. This knowledge of AIF allostery and its flavoswitch mechanism provides a foundation for biologically understanding and biomedically controlling its participation in mitochondrial homeostasis and cell death.

The immune checkpoint receptor PD-1 and its ligand, PD-L1, have emerged as key regulators of anti-tumor immunity in humans. Recently, we reported an ultra-high-affinity PD-1 mutant, termed high-affinity consensus (HAC) PD-1, which shows superior therapeutic efficacy in mice compared with antibodies. However, the molecular details underlying the action of this agent remain incompletely understood, and a molecular view of PD-1/PD-L1 interactions in general is only beginning to emerge. Here, we report the structure of HAC PD-1 in complex with PD-L1, showing that it binds PD-L1 using a unique set of polar interactions. Biophysical studies and long-timescale molecular dynamics experiments reveal the mechanisms by which ten point mutations confer a 35,000-fold enhancement in binding affinity, and offer atomic-scale views of the role of conformational dynamics in PD-1/PD-L1 interactions. Finally, we show that the HAC PD-1 exhibits pH-dependent affinity, with pseudo-irreversible binding in a low pH setting akin to the tumor microenvironment.

Ebola virus nucleoprotein (eNP) assembles into higher-ordered structures that form the viral nucleocapsid (NC) and serve as the scaffold for viral RNA synthesis. However, molecular insights into the NCassembly process are lacking. Using a hybrid approach, we characterized the NC-like assembly of eNP, identified novel regulatory elements, and described how these elements impact function. We generated a three-dimensional structure of the eNP NC-like assembly at 5.8Å using electron cryo-microscopy and identified a new regulatory role foreNP helices α22-α23. Biochemical, biophysical, and mutational analyses revealed that inter-eNP contacts within α22-α23 are critical for viral NC assembly and regulate viral RNA synthesis. These observations suggest that the N terminus and α22-α23 of eNP function as context-dependent regulatory modules (CDRMs). Our current study provides a framework for a structural mechanism for NC-like assembly and a new therapeutic target.

Interest in atomically detailed simulations has grown significantly with recent advances in computational hardware and Markov state modeling (MSM) methods, yet outstanding questions remain that hinder their widespread adoption. Namely, how do alternative sampling strategies explore conformational space and how might this influence predictions generated from the data? Here, we seek to answer these questions for four commonly used sampling methods: (1) a single long simulation, (2) many short simulations run in parallel, (3) adaptive sampling, and (4) our recently developed goal-oriented sampling algorithm, FAST. We first develop a theoretical framework for analytically calculating the probability of discovering select states on simple landscapes, where we uncover the drastic effects of varying the number and length of simulations. We then use kinetic Monte Carlo simulations on a variety of physically inspired landscapes to characterize the probability of discovering particular states and transition pathways for each of the four methods. Consistently, we find that FAST simulations discover each target state with the highest probability, while traversing realistic pathways. Furthermore, we uncover the potential pathology that short parallel simulations sometimes predict an incorrect transition pathway by crossing large energy barriers that long simulations would typically circumnavigate. We refer to this pathology as "pathway tunneling". To protect against this phenomenon when using adaptive-sampling and FAST simulations, we introduce the FAST-string method. This method enhances sampling along the highest-flux transition paths to refine an MSMs transition probabilities and discriminate between competing pathways. Additionally, we compare the performance of a variety of MSM estimators in describing accurate thermodynamics and kinetics. For adaptive sampling, we recommend simply normalizing the transition counts out of each state after adding small pseudocounts to avoid creating sources or sinks. Lastly, we evaluate whether our insights from simple landscapes hold for all-atom molecular dynamics simulations of the folding of the λ-repressor protein. Remarkably, we find that FAST-contacts predicts the same folding pathway as a set of long simulations but with orders of magnitude less simulation time.

The retinoid X receptor α (RXRA) has been implicated in diverse hematological processes. To identify natural ligands of RXRA that are present in hematopoietic cells, we adapted an upstream activation sequence-green fluorescent protein (UAS-GFP) reporter mouse to detect natural RXRA ligands in vivo. We observed reporter activity in diverse types of hematopoietic cells in vivo. Reporter activity increased during granulocyte colony-stimulating factor (G-CSF)-induced granulopoiesis and after phenylhydrazine (PHZ)-induced anemia, suggesting the presence of dynamically regulated natural RXRA ligands in hematopoietic cells. Mouse plasma activated Gal4-UAS reporter cells in vitro, and plasma from mice treated with G-CSF or PHZ recapitulated the patterns of reporter activation that we observed in vivo. Plasma from mice with dietary vitamin A deficiency only mildly reduced RXRA reporter activity, whereas plasma from mice on a fatty acid restriction diet reduced reporter activity, implicating fatty acids as plasma RXRA ligands. Through differential extraction coupled with mass spectrometry, we identified the long-chain fatty acid C24:5 as a natural RXRA ligand that was greatly increased in abundance in response to hematopoietic stress. Together, these data suggest that natural RXRA ligands are present and dynamically increased in abundance in mouse hematopoietic cells in vivo. We thank the Alvin J. Siteman Cancer Center at Washington University School of Medicine and Barnes-Jewish Hospital in St. Louis, MO. for the use of the Flow Cytometry Core. The Siteman Cancer Center is supported in part by an NCI Cancer Center Support Grant P30 CA91842. We thank High-Throughput Screening Center at Washington University School of Medicine in St. Louis, MO. We thank Deborah Laflamme for technical assistance and Feng Gao for statistical assistance. This work was supported by NIH R01 HL128447 (JS Welch), NIH P50 CA171963 (Project 1, JS Welch), and by grants from the Spanish Ministry of Economy and Competitiveness (SAF2015-64287R, SAF2015-71878-REDT) (M Ricote). The mass spectrometry facility at Washington University is supported by NIH P30 DK020579, Daniel Ory. J.S.W., H.N. and M.R. designed experiments, performed experiments, and wrote the manuscript. H.F., O.M., G.H., M.P.M, T.E.F., G.R.B. designed and performed experiments. Sí

Tabtoxinine-β-lactam (TβL), also known as wildfire toxin, is a time- and ATP-dependent inhibitor of glutamine synthetase produced by plant pathogenic strains of Pseudomonas syringae. Here we demonstrate that recombinant glutamine synthetase from Escherichia coli phosphorylates the C3-hydroxyl group of the TβL 3-(S)-hydroxy-β-lactam (3-HβL) warhead. Phosphorylation of TβL generates a stable, noncovalent enzyme–ADP–inhibitor complex that resembles the glutamine synthetase tetrahedral transition state. The TβL β-lactam ring remains intact during enzyme inhibition, making TβL mechanistically distinct from traditional β-lactam antibiotics such as penicillin. Our findings could enable the design of new 3-HβL transition state inhibitors targeting enzymes in the ATP-dependent carboxylate-amine ligase superfamily with broad therapeutic potential in many disease areas.

SUMMARY Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne virus with 12%−30% case mortality rates and is related to the Heartland virus (HRTV) identified in the United States. Together, SFTSV and HRTV are emerging segmented, negative-sense RNA viral (sNSV) pathogens with potential global health impact. Here, we characterize the amino-terminal cap-snatching endonuclease domain of SFTSV polymerase (L) and solve a 2.4-Å X-ray crystal structure. While the overall structure is similar to those of other cap-snatching sNSV endonucleases, differences near the C terminus of the SFTSV endonuclease suggest divergence in regulation. Influenza virus endonuclease inhibitors, including the US Food and Drug Administration (FDA) approved Baloxavir (BXA), inhibit the endonuclease activity in in vitro enzymatic assays and in cell-based studies. BXA displays potent activity with a half maximal inhibitory concentration (IC50) of ~100 nM in enzyme inhibition and an EC50 value of ~250 nM against SFTSV and HRTV in plaque assays. Together, our data support sNSV endonucleases as an antiviral target., In Brief Wang et al. solve the X-ray crystal structure of SFTSV L endonuclease domain and investigate the characteristics of SFTSV and HRTV endonuclease function. Resulting data support a mechanism for regulation. Baloxavir effectively inhibits the endonuclease activity of SFTSV and HRTV., Graphical Abstract

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 β2AR. 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.

Although warfarin is the most widely used anticoagulant worldwide, the mechanism by which warfarin inhibits its target, human vitamin K epoxide reductase (hVKOR), remains unclear. Here we show that warfarin blocks a dynamic electron-transfer process in hVKOR. A major fraction of cellular hVKOR is at an intermediate redox state of this process containing a Cys51-Cys132 disulfide, a characteristic accommodated by a four-transmembrane-helix structure of hVKOR. Warfarin selectively inhibits this major cellular form of hVKOR, whereas disruption of the Cys51-Cys132 disulfide impairs warfarin binding and causes warfarin resistance. Relying on binding interactions identified by cysteine alkylation footprinting and mass spectrometry coupled with mutagenesis analysis, we are able to conduct structure simulations to reveal a closed warfarin-binding pocket stabilized by the Cys51-Cys132 linkage. Understanding the selective warfarin inhibition of a specific redox state of hVKOR should enable the rational design of drugs that exploit the redox chemistry and associated conformational changes in hVKOR.

TEM β-lactamase confers bacteria with resistance to many antibiotics and rapidly evolves activity against new drugs. However, functional changes are not easily explained by differences in crystal structures. We employ Markov state models to identify hidden conformations and explore their role in determining TEM’s specificity. We integrate these models with existing drug-design tools to create a new technique, called Boltzmann docking, which better predicts TEM specificity by accounting for conformational heterogeneity. Using our MSMs, we identify hidden states whose populations correlate with activity against cefotaxime. To experimentally detect our predicted hidden states, we use rapid mass spectrometric footprinting and confirm our models’ prediction that increased cefotaxime activity correlates with reduced Ω-loop flexibility. Finally, we design novel variants to stabilize the hidden cefotaximase states, and find their populations predict activity against cefotaxime in vitro and in vivo. Therefore, we expect this framework to have numerous applications in drug and protein design., Expression of TEM β-lactamase is a predominant mechanism underlying antibiotic resistance in pathogenic Gram-negative bacteria. Here, the authors use Markov state models to reveal and experimentally confirm hidden conformations that determine TEM substrate specificity.

Interleukin 15 (IL-15) and IL-2 have distinct immunological functions even though both signal through the receptor subunit IL-2Rβ and the common γ-chain (γ(c)). Here we found that in the structure of the IL-15-IL-15Rα-IL-2Rβ-γ(c) quaternary complex, IL-15 binds to IL-2Rβ and γ(c) in a heterodimer nearly indistinguishable from that of the IL-2-IL-2Rα-IL-2Rβ-γ(c) complex, despite their different receptor-binding chemistries. IL-15Rα substantially increased the affinity of IL-15 for IL-2Rβ, and this allostery was required for IL-15 trans signaling. Consistent with their identical IL-2Rβ-γ(c) dimer geometries, IL-2 and IL-15 showed similar signaling properties in lymphocytes, with any differences resulting from disparate receptor affinities. Thus, IL-15 and IL-2 induced similar signals, and the cytokine specificity of IL-2Rα versus IL-15Rα determined cellular responsiveness. Our results provide new insights for the development of specific immunotherapeutics based on IL-15 or IL-2.

Protein folding is a fundamental process in biology, key to understanding many human diseases. Experimentally, proteins often appear to fold via simple two- or three-state mechanisms involving mainly native-state interactions, yet recent network models built from atomistic simulations of small proteins suggest the existence of many possible metastable states and folding pathways. We reconcile these two pictures in a combined experimental and simulation study of acyl-coenzyme A-binding protein (ACBP), a two-state folder (folding time ~10 ms) exhibiting residual unfolded-state structure, and a putative early folding intermediate. Using single-molecule FRET in conjunction with side-chain mutagenises, we first demonstrate that the denatured state of ACBP at near-zero denaturant is unusually compact and enriched in long-range structure that can be perturbed by discrete hydrophobic core mutations. We then employ ultrafast laminar-flow mixing experiments to study the folding kinetics of ACBP on the microsecond timescale. These studies, along with with Trp-Cys quenching measurements of unfolded-state dynamics, suggest that unfolded-state structure forms on a surprisingly slow (~100 µs) timescale, and that sequence mutations strikingly perturb both time-resolved and equilibrium smFRET measurements in a similar way. A Markov State Model (MSM) of the ACBP folding reaction, constructed from over 30 milliseconds of molecular dynamics trajectory data, predicts a complex network of metastable stables, residual unfolded-state structure and kinetics consistent with experiment, but no well-defined intermediate preceding the main folding barrier. Taken together, these experimental and simulation results suggest that the previously characterized fast kinetic phase is not due to formation of a barrier-limited intermediate, but rather a more heterogeneous and slow acquisition of unfolded-state structure.

Two strategies have been recently employed to push molecular simulation to long, biologically relevant timescales: projection-based analysis of results from specialized hardware producing a small number of ultra-long trajectories and the statistical interpretation of massive parallel sampling performed with Markov state models (MSMs). Here, we assess the MSM as an analysis method by constructing a Markov model from ultra-long trajectories, specifically two previously reported 100 μs trajectories of the FiP35 WW domain (Shaw et. al. (2010) Science, 330: 341–346). We find that the MSM approach yields novel insights. It discovers new statistically significant folding pathways, in which either beta-hairpin of the WW domain can form first. The rates of this process approach experimental values in a direct quantitative comparison (timescales of 5.0 μs and 100 ns), within a factor of ~2. Finally, the hub-like topology of the MSM and identification of a holo conformation predicts how WW domains may function through a conformational selection mechanism.

Markov State Models provide a framework for understanding the fundamental states and rates in the conformational dynamics of biomolecules. We describe an improved protocol for constructing Markov State Models from molecular dynamics simulations. The new protocol includes advances in clustering, data preparation, and model estimation; these improvements lead to significant increases in model accuracy, as assessed by the ability to recapitulate equilibrium and kinetic properties of reference systems. A high-performance implementation of this protocol, provided in MSMBuilder2, is validated on dynamics ranging from picoseconds to milliseconds.

Simulating protein folding has been a challenging problem for decades due to the long timescales involved (compared with what is possible to simulate) and the challenges of gaining insight from the complex nature of the resulting simulation data. Markov State Models (MSMs) present a means to tackle both of these challenges, yielding simulations on experimentally relevant timescales, statistical significance, and coarse grained representations that are readily humanly understandable. Here, we review this method with the intended audience of non-experts, in order to introduce the method to a broader audience. We review the motivations, methods, and caveats of MSMs, as well as some recent highlights of applications of the method. We conclude by discussing how this approach is part of a paradigm shift in how one uses simulations, away from anecdotal single-trajectory approaches to a more comprehensive statistical approach.

Computer simulations can complement experiments by providing insight into molecular kinetics with atomic resolution. Unfortunately, even the most powerful supercomputers can only simulate small systems for short time scales, leaving modeling of most biologically relevant systems and time scales intractable. In this work, however, we show that molecular simulations driven by adaptive sampling of networks called Markov State Models (MSMs) can yield tremendous time and resource savings, allowing previously intractable calculations to be performed on a routine basis on existing hardware. We also introduce a distance metric (based on the relative entropy) for comparing MSMs. We primarily employ this metric to judge the convergence of various sampling schemes but it could also be employed to assess the effects of perturbations to a system (e.g., determining how changing the temperature or making a mutation changes a system’s dynamics).

Part of understanding a molecule’s conformational dynamics is mapping out the dominant metastable, or long lived, states that it occupies. Once identified, the rates for transitioning between these states may then be determined in order to create a complete model of the system’s conformational dynamics. Here we describe the use of the MSMBuilder package (now available at https://simtk.org/home/msmbuilder/) to build Markov State Models (MSMs) to identify the metastable states from Generalized Ensemble (GE) simulations, as well as other simulation datasets. Besides building MSMs, the code also includes tools for model evaluation and visualization.

Recently a temperature-jump FTIR study of a designed three-stranded sheet showing a fast relaxation time of approximately 140 +/- 20 ns was published. We performed massively parallel molecular dynamics simulations in explicit solvent to probe the structural events involved in this relaxation. While our simulations produce similar relaxation rates, the structural ensemble is broad. We observe the formation of turn structure, but only very weak interaction in the strand regions, which is consistent with the lack of strong backbone-backbone NOEs in previous structural NMR studies. These results suggest that either (D)P(D)P-II folds at time scales longer than 240 ns, or that (D)P(D)P-II is not a well-defined three-stranded beta-sheet. This work also provides an opportunity to compare the performance of several popular forcefield models against one another.

Basic principles of statistical mechanics require that proteins sample an ensemble of conformations at any nonzero temperature. However, it is still common to treat the crystallographic structure of a protein as the structure of its native state, largely because high-resolution structural characterization of protein flexibility remains a profound challenge. To assess the typical degree of conformational heterogeneity within folded proteins, we construct Markov state models describing the thermodynamics and kinetics of proteins ranging from 72 to 263 residues in length. Each of these models is built from hundreds of microseconds of atomically detailed molecular dynamics simulations. Examination of the side-chain degrees of freedom reveals that almost every residue visits at least two rotameric states over this time frame, with rotamer transition rates spanning a wide range of time scales (from nanoseconds to tens of microseconds). We also report substantial backbone dynamics on time scales longer than are typically addressed by experimental measures of protein flexibility, such as NMR order parameters. Finally, we demonstrate that these extensive rearrangements are consistent with NMR and crystallographic data, which supports the validity of our models. Altogether, these results depict the interior of proteins not as well-ordered solids, as is often imagined, but instead as dense fluids, which undergo substantial structural fluctuations despite their high packing fraction.

To date, the slowest-folding proteins folded ab initio by all-atom molecular dynamics simulations have had folding times in the range of nanoseconds to microseconds. We report simulations of several folding trajectories of NTL9(1-39), a protein which has a folding time of approximately 1.5 ms. Distributed molecular dynamics simulations in implicit solvent on GPU processors were used to generate ensembles of trajectories out to approximately 40 micros for several temperatures and starting states. At a temperature less than the melting point of the force field, we observe a small number of productive folding events, consistent with predictions from a model of parallel uncoupled two-state simulations. The posterior distribution of the folding rate predicted from the data agrees well with the experimental folding rate (approximately 640/s). Markov State Models (MSMs) built from the data show a gap in the implied time scales indicative of two-state folding and heterogeneous pathways connecting diffuse mesoscopic substates. Structural analysis of the 14 out of 2000 macrostates transited by the top 10 folding pathways reveals that native-like pairing between strands 1 and 2 only occurs for macrostates with p(fold)0.5, suggesting beta(12) hairpin formation may be rate-limiting. We believe that using simulation data such as these to seed adaptive resampling simulations will be a promising new method for achieving statistically converged descriptions of folding landscapes at longer time scales than ever before.

The immunostimulatory cytokine interleukin-2 (IL-2) is a growth factor for a wide range of leukocytes, including T cells and natural killer (NK) cells. Considerable effort has been invested in using IL-2 as a therapeutic agent for a variety of immune disorders ranging from AIDS to cancer. However, adverse effects have limited its use in the clinic. On activated T cells, IL-2 signals through a quaternary 'high affinity' receptor complex consisting of IL-2, IL-2Rα (termed CD25), IL-2Rβ and IL-2Rγ. Naive T cells express only a low density of IL-2Rβ and IL-2Rγ, and are therefore relatively insensitive to IL-2, but acquire sensitivity after CD25 expression, which captures the cytokine and presents it to IL-2Rβ and IL-2Rγ. Here, using in vitro evolution, we eliminated the functional requirement of IL-2 for CD25 expression by engineering an IL-2 'superkine' (also called super-2) with increased binding affinity for IL-2Rβ. Crystal structures of the IL-2 superkine in free and receptor-bound forms showed that the evolved mutations are principally in the core of the cytokine, and molecular dynamics simulations indicated that the evolved mutations stabilized IL-2, reducing the flexibility of a helix in the IL-2Rβ binding site, into an optimized receptor-binding conformation resembling that when bound to CD25. The evolved mutations in the IL-2 superkine recapitulated the functional role of CD25 by eliciting potent phosphorylation of STAT5 and vigorous proliferation of T cells irrespective of CD25 expression. Compared to IL-2, the IL-2 superkine induced superior expansion of cytotoxic T cells, leading to improved antitumour responses in vivo, and elicited proportionally less expansion of T regulatory cells and reduced pulmonary oedema. Collectively, we show that in vitro evolution has mimicked the functional role of CD25 in enhancing IL-2 potency and regulating target cell specificity, which has implications for immunotherapy.

Protein folding is a classic grand challenge that is relevant to numerous human diseases, such as protein misfolding diseases like Alzheimer’s disease. Solving the folding problem will ultimately require a combination of theory, simulation, and experiment, with theory and simulation providing an atomically detailed picture of both the thermodynamics and kinetics of folding and experimental tests grounding these models in reality. However, theory and simulation generally fall orders of magnitude short of biologically relevant time scales. Here we report significant progress toward closing this gap: an atomistic model of the folding of an 80-residue fragment of the λ repressor protein with explicit solvent that captures dynamics on a 10 milliseconds time scale. In addition, we provide a number of predictions that warrant further experimental investigation. For example, our model’s native state is a kinetic hub, and biexponential kinetics arises from the presence of many free-energy basins separated by barriers of different heights rather than a single low barrier along one reaction coordinate (the previously proposed incipient downhill folding scenario).

Protein folding is an important problem in structural biology with significant medical implications, particularly for misfolding disorders like Alzheimer's disease. Solving the folding problem will ultimately require a combination of theory and experiment, with theoretical models providing a comprehensive view of folding and experiments grounding these models in reality. Here we review progress towards this goal over the past decade, with an emphasis on recent theoretical advances that are empowering chemically detailed models of folding and the new results these technologies are providing. In particular, we discuss new insights made possible by Markov state models (MSMs), including the role of non-native contacts and the hub-like character of protein folded states.

Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models (MSMs), are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex conformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer's disease.

Simulating biologically relevant timescales at atomic resolution is a challenging task since typical atomistic simulations are at least two orders of magnitude shorter. Markov State Models (MSMs) provide one means of overcoming this gap without sacrificing atomic resolution by extracting long time dynamics from short simulations. MSMs coarse grain space by dividing conformational space into long-lived, or metastable, states. This is equivalent to coarse graining time by integrating out fast motions within metastable states. By varying the degree of coarse graining one can vary the resolution of an MSM; therefore, MSMs are inherently multi-resolution. Here we introduce a new algorithm Super-level-set Hierarchical Clustering (SHC), to our knowledge, the first algorithm focused on constructing MSMs at multiple resolutions. The key insight of this algorithm is to generate a set of super levels covering different density regions of phase space, then cluster each super level separately, and finally recombine this information into a single MSM. SHC is able to produce MSMs at different resolutions using different super density level sets. To demonstrate the power of this algorithm we apply it to a small RNA hairpin, generating MSMs at four different resolutions. We validate these MSMs by showing that they are able to reproduce the original simulation data. Furthermore, long time folding dynamics are extracted from these models. The results show that there are no metastable on-pathway intermediate states. Instead, the folded state serves as a hub directly connected to multiple unfolded/misfolded states which are separated from each other by large free energy barriers.