Folding@home: Achievements from over 20 years of citizen science herald the exascale era.

Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over 20 years, the Folding@home distributed computing project has pioneered a massively parallel approach to biomolecular simulation, harnessing the resources of citizen scientists across the globe. Here, we summarize the scientific and technical advances this perspective has enabled. As the project's name implies, the early years of Folding@home focused on driving advances in our understanding of protein folding by developing statistical methods for capturing long-timescale processes and facilitating insight into complex dynamical processes. Success laid a foundation for broadening the scope of Folding@home to address other functionally relevant conformational changes, such as receptor signaling, enzyme dynamics, and ligand binding. Continued algorithmic advances, hardware developments such as graphics processing unit (GPU)-based computing, and the growing scale of Folding@home have enabled the project to focus on new areas where massively parallel sampling can be impactful. While previous work sought to expand toward larger proteins with slower conformational changes, new work focuses on large-scale comparative studies of different protein sequences and chemical compounds to better understand biology and inform the development of small-molecule drugs. Progress on these fronts enabled the community to pivot quickly in response to the COVID-19 pandemic, expanding to become the world's first exascale computer and deploying this massive resource to provide insight into the inner workings of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and aid the development of new antivirals. This success provides a glimpse of what is to come as exascale supercomputers come online and as Folding@home continues its work.

BICePs v2.0: Software for Ensemble Reweighting Using Bayesian Inference of Conformational Populations.

Bayesian Inference of Conformational Populations (BICePs) version 2.0 (v2.0) is a free, open-source Python package that reweights theoretical predictions of conformational state populations using sparse and/or noisy experimental measurements. In this article, we describe the implementation and usage of the latest version of BICePs (v2.0), a powerful, user-friendly and extensible package which makes several improvements upon the previous version. The algorithm now supports many experimental NMR observables (NOE distances, chemical shifts, J-coupling constants, and hydrogen-deuterium exchange protection factors), and enables convenient data preparation and processing. BICePs v2.0 can perform automatic analysis of the sampled posterior, including visualization, and evaluation of statistical significance and sampling convergence. We provide specific coding examples for these topics, and present a detailed example illustrating how to use BICePs v2.0 to reweight a theoretical ensemble using experimental measurements.

Expanded ensemble predictions of absolute binding free energies in the SAMPL9 host-guest challenge.

As part of the SAMPL9 community-wide blind host-guest challenge, we implemented an expanded ensemble workflow to predict absolute binding free energies for 13 small molecules against pillar[6]arene. Notable features of our protocol include consideration of a variety of protonation and enantiomeric states for both host and guests, optimization of alchemical intermediates, and analysis of free energy estimates and their uncertainty using large numbers of simulation replicates performed using distributed computing. Our predictions of absolute binding free energies resulted in a mean absolute error of 2.29 kcal mol-1 and an R2 of 0.54. Overall, results show that expanded ensemble calculations using all-atom molecular dynamics simulations are a valuable and efficient computational tool in predicting absolute binding free energies.

Oncogenic Mutations in the DNA-Binding Domain of FOXO1 that Disrupt Folding: Quantitative Insights from Experiments and Molecular Simulations.

FOXO1, a member of the family of winged-helix motif Forkhead box (FOX) transcription factors, is the most abundantly expressed FOXO member in mature B cells. Sequencing of diffuse large B-cell lymphoma (DLBCL) tumors and cell lines identified specific mutations in the forkhead domain linked to loss of function. Differential scanning calorimetry and thermal shift assays were used to characterize how eight of these mutations affect the stability of the FOX domain. Mutations L183P and L183R were found to be particularly destabilizing. Electrophoresis mobility shift assays show these same mutations also disrupt FOXO1 binding to their canonical DNA sequences, suggesting that the loss of function is due to destabilization of the folded structure. Computational modeling of the effect of mutations on FOXO1 folding was performed using alchemical free energy perturbation (FEP), and a Markov model of the entire folding reaction was constructed from massively parallel molecular simulations, which predicts folding pathways involving the late folding of helix α3. Although FEP can qualitatively predict the destabilization from L183 mutations, we find that a simple hydrophobic transfer model, combined with estimates of unfolded-state solvent-accessible surface areas from molecular simulations, is able to more accurately predict changes in folding free energies due to mutations. These results suggest that the atomic detail provided by simulations is important for the accurate prediction of mutational effects on folding stability. Corresponding disease-associated mutations in other FOX family members support further experimental and computational studies of the folding mechanism of FOX domains.

Estimation of binding rates and affinities from multiensemble Markov models and ligand decoupling.

Accurate and efficient simulation of the thermodynamics and kinetics of protein-ligand interactions is crucial for computational drug discovery. Multiensemble Markov Model (MEMM) estimators can provide estimates of both binding rates and affinities from collections of short trajectories but have not been systematically explored for situations when a ligand is decoupled through scaling of non-bonded interactions. In this work, we compare the performance of two MEMM approaches for estimating ligand binding affinities and rates: (1) the transition-based reweighting analysis method (TRAM) and (2) a Maximum Caliber (MaxCal) based method. As a test system, we construct a small host-guest system where the ligand is a single uncharged Lennard-Jones (LJ) particle, and the receptor is an 11-particle icosahedral pocket made from the same atom type. To realistically mimic a protein-ligand binding system, the LJ ϵ parameter was tuned, and the system was placed in a periodic box with 860 TIP3P water molecules. A benchmark was performed using over 80 µs of unbiased simulation, and an 18-state Markov state model was used to estimate reference binding affinities and rates. We then tested the performance of TRAM and MaxCal when challenged with limited data. Both TRAM and MaxCal approaches perform better than conventional Markov state models, with TRAM showing better convergence and accuracy. We find that subsampling of trajectories to remove time correlation improves the accuracy of both TRAM and MaxCal and that in most cases, only a single biased ensemble to enhance sampled transitions is required to make accurate estimates.

Effects of Salinity and Temperature on the Flexibility and Function of a Polyextremophilic Enzyme.

The polyextremophilic ß-galactosidase enzyme of the haloarchaeon Halorubrum lacusprofundi functions in extremely cold and hypersaline conditions. To better understand the basis of polyextremophilic activity, the enzyme was studied using steady-state kinetics and molecular dynamics at temperatures ranging from 10 °C to 50 °C and salt concentrations from 1 M to 4 M KCl. Kinetic analysis showed that while catalytic efficiency (kcat/Km) improves with increasing temperature and salinity, Km is reduced with decreasing temperatures and increasing salinity, consistent with improved substrate binding at low temperatures. In contrast, kcat was similar from 2-4 M KCl across the temperature range, with the calculated enthalpic and entropic components indicating a threshold of 2 M KCl to lower the activation barrier for catalysis. With molecular dynamics simulations, the increase in per-residue root-mean-square fluctuation (RMSF) was observed with higher temperature and salinity, with trends like those seen with the catalytic efficiency, consistent with the enzyme's function being related to its flexibility. Domain A had the smallest change in flexibility across the conditions tested, suggesting the adaptation to extreme conditions occurs via regions distant to the active site and surface accessible residues. Increased flexibility was most apparent in the distal active sites, indicating their importance in conferring salinity and temperature-dependent effects.

Metal-Binding Q-Proline Macrocycles.

We introduce the efficient Fmoc-SPPS and peptoid synthesis of Q-proline-based, metal-binding macrocycles (QPMs), which bind metal cations and display nine functional groups. Metal-free QPMs are disordered, evidenced by NMR and a crystal structure of QPM-3 obtained through racemic crystallization. Upon addition of metal cations, QPMs adopt ordered structures. Notably, the addition of a second functional group at the hydantoin amide position (R2) converts the proline ring from Cγ-endo to Cγ-exo, due to steric interactions.

Solution-State Preorganization of Cyclic ß-Hairpin Ligands Determines Binding Mechanism and Affinities for MDM2.

Understanding mechanisms of protein folding and binding is crucial to designing their molecular function. Molecular dynamics (MD) simulations and Markov state model (MSM) approaches provide a powerful way to understand complex conformational change that occurs over long time scales. Such dynamics are important for the design of therapeutic peptidomimetic ligands, whose affinity and binding mechanism are dictated by a combination of folding and binding. To examine the role of preorganization in peptide binding to protein targets, we performed massively parallel explicit-solvent MD simulations of cyclic ß-hairpin ligands designed to mimic the p53 transactivation domain and competitively bind mouse double minute 2 homologue (MDM2). Disrupting the MDM2-p53 interaction is a therapeutic strategy to prevent degradation of the p53 tumor suppressor in cancer cells. MSM analysis of over 3 ms of aggregate trajectory data enabled us to build a detailed mechanistic model of coupled folding and binding of four cyclic peptides which we compare to experimental binding affinities and rates. The results show a striking relationship between the relative preorganization of each ligand in solution and its affinity for MDM2. Specifically, changes in peptide conformational populations predicted by the MSMs suggest that entropy loss upon binding is the main factor influencing affinity. The MSMs also enable detailed examination of non-native interactions which lead to misfolded states and comparison of structural ensembles with experimental NMR measurements. In contrast to an MSM study of p53 transactivation domain (TAD) binding to MDM2, MSMs of cyclic ß-hairpin binding show a conformational selection mechanism. Finally, we make progress toward predicting accurate off rates of cyclic peptides using multiensemble Markov models (MEMMs) constructed from unbiased and biased simulated trajectories.

Metal Cation-Binding Mechanisms of Q-Proline Peptoid Macrocycles in Solution.

The rational design of foldable and functionalizable peptidomimetic scaffolds requires the concerted application of both computational and experimental methods. Recently, a new class of designed peptoid macrocycle incorporating spiroligomer proline mimics (Q-prolines) has been found to preorganize when bound by monovalent metal cations. To determine the solution-state structure of these cation-bound macrocycles, we employ a Bayesian inference method (BICePs) to reconcile enhanced-sampling molecular simulations with sparse ROESY correlations from experimental NMR studies to predict and design conformational and binding properties of macrocycles as functional scaffolds for peptidomimetics. Conformations predicted to be most populated in solution were then simulated in the presence of explicit cations to yield trajectories with observed binding events, revealing a highly preorganized all-trans amide conformation, whose formation is likely limited by the slow rate of cis/trans isomerization. Interestingly, this conformation differs from a racemic crystal structure solved in the absence of cation. Free energies of cation binding computed from distance-dependent potentials of mean force suggest Na+ has a higher affinity to the macrocycle than K+, with both cations binding much more strongly in acetonitrile than water. The simulated affinities are able to correctly rank the extent to which different macrocycle sequences exhibit preorganization in the presence of different metal cations and solvents, suggesting our approach is suitable for solution-state computational design.

Stacking Gaussian processes to improve [Formula: see text] predictions in the SAMPL7 challenge.

Accurate predictions of acid dissociation constants are essential to rational molecular design in the pharmaceutical industry and elsewhere. There has been much interest in developing new machine learning methods that can produce fast and accurate pKa predictions for arbitrary species, as well as estimates of prediction uncertainty. Previously, as part of the SAMPL6 community-wide blind challenge, Bannan et al. approached the problem of predicting [Formula: see text]s by using a Gaussian process regression to predict microscopic [Formula: see text]s, from which macroscopic [Formula: see text] values can be analytically computed (Bannan et al. in J Comput-Aided Mol Des 32:1165-1177). While this method can make reasonably quick and accurate predictions using a small training set, accuracy was limited by the lack of a sufficiently broad range of chemical space in the training set (e.g., the inclusion of polyprotic acids). Here, to address this issue, we construct a deep Gaussian Process (GP) model that can include more features without invoking the curse of dimensionality. We trained both a standard GP and a deep GP model using a database of approximately 3500 small molecules curated from public sources, filtered by similarity to targets. We tested the model on both the SAMPL6 and more recent SAMPL7 challenge, which introduced a similar lack of ionizable sites and/or environments found between the test set and the previous training set. The results show that while the deep GP model made only minor improvements over the standard GP model for SAMPL6 predictions, it made significant improvements over the standard GP model in SAMPL7 macroscopic predictions, achieving a MAE of 1.5 [Formula: see text].

Adaptive Markov state model estimation using short reseeding trajectories.

In the last decade, advances in molecular dynamics (MD) and Markov State Model (MSM) methodologies have made possible accurate and efficient estimation of kinetic rates and reactive pathways for complex biomolecular dynamics occurring on slow time scales. A promising approach to enhanced sampling of MSMs is to use "adaptive" methods, in which new MD trajectories are "seeded" preferentially from previously identified states. Here, we investigate the performance of various MSM estimators applied to reseeding trajectory data, for both a simple 1D free energy landscape and mini-protein folding MSMs of WW domain and NTL9(1-39). Our results reveal the practical challenges of reseeding simulations and suggest a simple way to reweight seeding trajectory data to better estimate both thermodynamic and kinetic quantities.

Microcanonical coarse-graining of the kinetic Ising model.

We propose a scheme for coarse-graining the dynamics of the 2-D kinetic Ising model onto the microcanonical ensemble. At subcritical temperatures, 2-D and higher-dimensional Ising lattices possess two basins of attraction separated by a free energy barrier. Projecting onto the microcanonical ensemble has the advantage that the dependence of the crossing rate constant on environmental conditions can be obtained from a single Monte Carlo trajectory. Using various numerical methods, we computed the forward rate constants of coarse-grained representations of the Ising model and compared them with the true value obtained from brute force simulation. While coarse-graining preserves detailed balance, the computed rate constants for barrier heights between 5 kT and 9 kT were consistently 50% larger than the true value. Markovianity testing revealed loss of dynamical memory, which we propose accounts for coarse-graining error. Committor analysis did not support the alternative hypothesis that microcanonical projection is incompatible with an optimal reaction coordinate. The correct crossing rate constant was obtained by spectrally decomposing the diffusion coefficient near the free energy barrier and selecting the slowest (reactive) component. The spectral method also yielded the correct rate constant in the 3-D Ising lattice, where coarse-graining error was 6% and memory effects were diminished. We conclude that microcanonical coarse-graining supplemented by spectral analysis of short-term barrier fluctuations provides a comprehensive kinetic description of barrier crossing in a non-inertial continuous-time jump process.

Simulations of the regulatory ACT domain of human phenylalanine hydroxylase (PAH) unveil its mechanism of phenylalanine binding.

Phenylalanine hydroxylase (PAH) regulates phenylalanine (Phe) levels in mammals to prevent neurotoxicity resulting from high Phe concentrations as observed in genetic disorders leading to hyperphenylalaninemia and phenylketonuria. PAH senses elevated Phe concentrations by transient allosteric Phe binding to a protein-protein interface between ACT domains of different subunits in a PAH tetramer. This interface is present in an activated PAH (A-PAH) tetramer and absent in a resting-state PAH (RS-PAH) tetramer. To investigate this allosteric sensing mechanism, here we used the GROMACS molecular dynamics simulation suite on the Folding@home computing platform to perform extensive molecular simulations and Markov state model (MSM) analysis of Phe binding to ACT domain dimers. These simulations strongly implicated a conformational selection mechanism for Phe association with ACT domain dimers and revealed protein motions that act as a gating mechanism for Phe binding. The MSMs also illuminate a highly mobile hairpin loop, consistent with experimental findings also presented here that the PAH variant L72W does not shift the PAH structural equilibrium toward the activated state. Finally, simulations of ACT domain monomers are presented, in which spontaneous transitions between resting-state and activated conformations are observed, also consistent with a mechanism of conformational selection. These mechanistic details provide detailed insight into the regulation of PAH activation and provide testable hypotheses for the development of new allosteric effectors to correct structural and functional defects in PAH.

Fluorinated Aromatic Monomers as Building Blocks To Control α-Peptoid Conformation and Structure.

Peptoids are peptidomimetics of interest in the fields of drug development and biomaterials. However, obtaining stable secondary structures is challenging, and designing these requires effective control of the peptoid tertiary amide cis/trans equilibrium. Herein, we report new fluorine-containing aromatic monomers that can control peptoid conformation. Specifically, we demonstrate that a fluoro-pyridine group can be used to circumvent the need for monomer chirality to control the cis/trans equilibrium. We also show that incorporation of a trifluoro-methyl group ( NCF3Rpe) rather than a methyl group ( NRpe) at the α-carbon of a monomer gives rise to a 5-fold increase in cis-isomer preference.

Site-Specific Immuno-PET Tracer to Image PD-L1.

The rapid ascension of immune checkpoint blockade treatments has placed an emphasis on the need for viable, robust, and noninvasive imaging methods for immune checkpoint proteins, which could be of diagnostic value. Immunoconjugate-based positron emission tomography (immuno-PET) allows for sensitive and quantitative imaging of target levels and has promising potential for the noninvasive evaluation of immune checkpoint proteins. However, the advancement of immuno-PET is currently limited by available imaging tools, which heavily rely on full-length IgGs with Fc-mediated effects and are heterogeneous mixtures upon random conjugation with chelators for imaging. Herein, we have developed a site-specific αPD-L1 Fab conjugate with the chelator 1,4,7-triazacyclononane- N, N', N″-triacetic acid (NOTA), enabling radiolabeling for PET imaging, using the amber suppression-mediated genetic incorporation of unnatural amino acid (UAA), p-azidophenylalanine. This Fab conjugate is homogeneous and demonstrated tight binding toward the PD-L1 antigen in vitro. The radiolabeled version, 64Cu-NOTA-αPD-L1, has been employed in PET imaging to allow for effective visualization and mapping of the biodistribution of PD-L1 in two normal mouse models, including the capturing of different PD-L1 expression levels in the spleens of the different mouse types. Follow-up in vivo blocking studies and ex vivo fluorescent staining further validated specific tissue uptakes of the imaging agent. This approach illustrates the utility of UAA-based site-specific Fab conjugation as a general strategy for making sensitive PET imaging probes, which could facilitate the elucidation of the roles of a wide variety of immune checkpoint proteins in immunotherapy.

Molecular simulations and free-energy calculations suggest conformation-dependent anion binding to a cytoplasmic site as a mechanism for Na+/K+-ATPase ion selectivity.

Na+/K+-ATPase transports Na+ and K+ ions across the cell membrane via an ion-binding site becoming alternatively accessible to the intra- and extracellular milieu by conformational transitions that confer marked changes in ion-binding stoichiometry and selectivity. To probe the mechanism of these changes, we used molecular simulation and free-energy perturbation approaches to identify probable protonation states of Na+- and K+-coordinating residues in E1P and E2P conformations of Na+/K+-ATPase. Analysis of these simulations revealed a molecular mechanism responsible for the change in protonation state: the conformation-dependent binding of an anion (a chloride ion in our simulations) to a previously unrecognized cytoplasmic site in the loop between transmembrane helices 8 and 9, which influences the electrostatic potential of the crucial Na+-coordinating residue Asp926 This mechanistic model is consistent with experimental observations and provides a molecular-level picture of how E1P to E2P enzyme conformational transitions are coupled to changes in ion-binding stoichiometry and selectivity.

Elucidating the inhibition of peptidoglycan biosynthesis in Staphylococcus aureus by albocycline, a macrolactone isolated from Streptomyces maizeus.

Antibiotic resistance is a serious threat to global public health, and methicillin-resistant Staphylococcus aureus (MRSA) is a poignant example. The macrolactone natural product albocycline, derived from various Streptomyces strains, was recently identified as a promising antibiotic candidate for the treatment of both MRSA and vancomycin-resistant S. aureus (VRSA), which is another clinically relevant and antibiotic resistant strain. Moreover, it was hypothesized that albocycline's antimicrobial activity was derived from the inhibition of peptidoglycan (i.e., bacterial cell wall) biosynthesis. Herein, preliminary mechanistic studies are performed to test the hypothesis that albocycline inhibits MurA, the enzyme that catalyzes the first step of peptidoglycan biosynthesis, using a combination of biological assays alongside molecular modeling and simulation studies. Computational modeling suggests albocycline exists as two conformations in solution, and computational docking of these conformations to an ensemble of simulated receptor structures correctly predicted preferential binding to S. aureus MurA-the enzyme that catalyzes the first step of peptidoglycan biosynthesis-over Escherichia coli (E. coli) MurA. Albocycline isolated from the producing organism (Streptomyces maizeus) weakly inhibited S. aureus MurA (IC50 of 480 µM) but did not inhibit E. coli MurA. The antimicrobial activity of albocycline against resistant S. aureus strains was superior to that of vancomycin, preferentially inhibiting Gram-positive organisms. Albocycline was not toxic to human HepG2 cells in MTT assays. While these studies demonstrate that albocycline is a promising lead candidate against resistant S. aureus, taken together they suggest that MurA is not the primary target, and further work is necessary to identify the major biological target.

Bridging Microscopic and Macroscopic Mechanisms of p53-MDM2 Binding with Kinetic Network Models.

Under normal cellular conditions, the tumor suppressor protein p53 is kept at low levels in part due to ubiquitination by MDM2, a process initiated by binding of MDM2 to the intrinsically disordered transactivation domain (TAD) of p53. Many experimental and simulation studies suggest that disordered domains such as p53 TAD bind their targets nonspecifically before folding to a tightly associated conformation, but the microscopic details are unclear. Toward a detailed prediction of binding mechanisms, pathways, and rates, we have performed large-scale unbiased all-atom simulations of p53-MDM2 binding. Markov state models (MSMs) constructed from the trajectory data predict p53 TAD binding pathways and on-rates in good agreement with experiment. The MSM reveals that two key bound intermediates, each with a nonnative arrangement of hydrophobic residues in the MDM2 binding cleft, control the overall on-rate. Using microscopic rate information from the MSM, we parameterize a simple four-state kinetic model to 1) determine that induced-fit pathways dominate the binding flux over a large range of concentrations, and 2) predict how modulation of residual p53 helicity affects binding, in good agreement with experiment. These results suggest new ways in which microscopic models of peptide binding, coupled with simple few-state binding flux models, can be used to understand biological function in physiological contexts.

Diverted Total Synthesis of Carolacton-Inspired Analogs Yields Three Distinct Phenotypes in Streptococcus mutans Biofilms.

The oral microbiome is a dynamic environment inhabited by both commensals and pathogens. Among these is Streptococcus mutans, the causative agent of dental caries, the most prevalent childhood disease. Carolacton has remarkably specific activity against S. mutans, causing acid-mediated cell death during biofilm formation; however, its complex structure limits its utility. Herein, we report the diverted total synthesis and biological evaluation of a rationally designed library of simplified analogs that unveiled three unique biofilm phenotypes further validating the role of natural product synthesis in the discovery of new biological phenomena.

Control of porphyrin interactions via structural changes of a peptoid scaffold.

Nature utilizes optimally organized pigments in light-harvesting complexes. To mimic the natural photosynthetic proteins, effective control over inter-pigment interactions is necessary to attain the desired photophysical properties. Previously, we developed porphyrin-peptoid conjugates (PPCamide) and displayed two porphyrins at defined positions on an α-helical peptoid using a flexible n-butyl linker. Herein, we synthesized new porphyrin-peptoid conjugates (PPCC-C), where porphyrins are conjugated through a rigid C-C linkage to the helical peptoid via the Suzuki-Miyaura cross-coupling reaction. With PPCC-C, we studied the effects of backbone conformation, inter-porphyrin distance, and the linker flexibility on porphyrin interactions. When the rigid C-C linkage was used, conformational homogeneity of the PPC increased, providing more effective intramolecular excitonic couplings between the porphyrins; however, the intermolecular porphyrin J-aggregation decreased. In PPCC-C with a nonameric peptoid backbone, the formation of a threaded loop conformation was observed, which could be switched back to a helical conformation by N-terminal acetylation or by the addition of a protic solvent. This threaded loop-to-helix conversion restored the intramolecular porphyrin interactions. Our results suggest that PPCs represent an excellent system for control over porphyrin interactions and therefore are useful as a model system to elucidate pigment interactions in nature or as a molecular construct with switchable photophysical properties.