Identification of GDC-1971 (RLY-1971), a SHP2 Inhibitor Designed for the Treatment of Solid Tumors.

Protein tyrosine phosphatase SHP2 mediates RAS-driven MAPK signaling and has emerged in recent years as a target of interest in oncology, both for treating with a single agent and in combination with a KRAS inhibitor. We were drawn to the pharmacological potential of SHP2 inhibition, especially following the initial observation that drug-like compounds could bind an allosteric site and enforce a closed, inactive state of the enzyme. Here, we describe the identification and characterization of GDC-1971 (formerly RLY-1971), a SHP2 inhibitor currently in clinical trials in combination with KRAS G12C inhibitor divarasib (GDC-6036) for the treatment of solid tumors driven by a KRAS G12C mutation.

A Conserved Local Structural Motif Controls the Kinetics of PTP1B Catalysis.

Protein tyrosine phosphatase 1B (PTP1B) is a negative regulator of the insulin and leptin signaling pathways, making it a highly attractive target for the treatment of type II diabetes. For PTP1B to perform its enzymatic function, a loop referred to as the "WPD loop" must transition between open (catalytically incompetent) and closed (catalytically competent) conformations, which have both been resolved by X-ray crystallography. Although prior studies have established this transition as the rate-limiting step for catalysis, the transition mechanism for PTP1B and other PTPs has been unclear. Here we present an atomically detailed model of WPD loop transitions in PTP1B based on unbiased, long-timescale molecular dynamics simulations and weighted ensemble simulations. We found that a specific WPD loop region─the PDFG motif─acted as the key conformational switch, with structural changes to the motif being necessary and sufficient for transitions between long-lived open and closed states of the loop. Simulations starting from the closed state repeatedly visited open states of the loop that quickly closed again unless the infrequent conformational switching of the motif stabilized the open state. The functional importance of the PDFG motif is supported by the fact that it is well conserved across PTPs. Bioinformatic analysis shows that the PDFG motif is also conserved, and adopts two distinct conformations, in deiminases, and the related DFG motif is known to function as a conformational switch in many kinases, suggesting that PDFG-like motifs may control transitions between structurally distinct, long-lived conformational states in multiple protein families.

Discovery and Validation of the Binding Poses of Allosteric Fragment Hits to Protein Tyrosine Phosphatase 1b: From Molecular Dynamics Simulations to X-ray Crystallography.

Fragment-based drug discovery has led to six approved drugs, but the small sizes of the chemical fragments used in such methods typically result in only weak interactions between the fragment and its target molecule, which makes it challenging to experimentally determine the three-dimensional poses fragments assume in the bound state. One computational approach that could help address this difficulty is long-timescale molecular dynamics (MD) simulations, which have been used in retrospective studies to recover experimentally known binding poses of fragments. Here, we present the results of long-timescale MD simulations that we used to prospectively discover binding poses for two series of fragments in allosteric pockets on a difficult and important pharmaceutical target, protein tyrosine phosphatase 1b (PTP1b). Our simulations reversibly sampled the fragment association and dissociation process. One of the binding pockets found in the simulations has not to our knowledge been previously observed with a bound fragment, and the other pocket adopted a very rare conformation. We subsequently obtained high-resolution crystal structures of members of each fragment series bound to PTP1b, and the experimentally observed poses confirmed the simulation results. To the best of our knowledge, our findings provide the first demonstration that MD simulations can be used prospectively to determine fragment binding poses to previously unidentified pockets.

A Deep-Learning View of Chemical Space Designed to Facilitate Drug Discovery.

Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug discovery goal. The use of deep-learning technologies could augment the typical practice of using human intuition in the design cycle, and thereby expedite drug discovery projects. Here, we present DESMILES, a deep neural network model that advances the state of the art in machine learning approaches to molecular design. We applied DESMILES to a previously published benchmark that assesses the ability of a method to modify input molecules to inhibit the dopamine receptor D2, and DESMILES yielded a 77% lower failure rate compared to state-of-the-art models. To explain the ability of DESMILES to hone molecular properties, we visualize a layer of the DESMILES network, and further demonstrate this ability by using DESMILES to tailor the same molecules used in the D2 benchmark test to dock more potently against seven different receptors.

Picosecond to Millisecond Structural Dynamics in Human Ubiquitin.

Human ubiquitin has been extensively characterized using a variety of experimental and computational methods and has become an important model for studying protein dynamics. Nevertheless, it has proven difficult to characterize the microsecond time scale dynamics of this protein with atomistic resolution. Here we use an unbiased computer simulation to describe the structural dynamics of ubiquitin on the picosecond to millisecond time scale. In the simulation, ubiquitin interconverts between a small number of distinct states on the microsecond to millisecond time scale. We find that the conformations visited by free ubiquitin in solution are very similar to those found various crystal structures of ubiquitin in complex with other proteins, a finding in line with previous experimental studies. We also observe weak but statistically significant correlated motions throughout the protein, including long-range concerted movement across the entire ß sheet, consistent with recent experimental observations. We expect that the detailed atomistic description of ubiquitin dynamics provided by this unbiased simulation may be useful in interpreting current and future experiments on this protein.

Systematic validation of protein force fields against experimental data.

Molecular dynamics simulations provide a vehicle for capturing the structures, motions, and interactions of biological macromolecules in full atomic detail. The accuracy of such simulations, however, is critically dependent on the force field--the mathematical model used to approximate the atomic-level forces acting on the simulated molecular system. Here we present a systematic and extensive evaluation of eight different protein force fields based on comparisons of experimental data with molecular dynamics simulations that reach a previously inaccessible timescale. First, through extensive comparisons with experimental NMR data, we examined the force fields' abilities to describe the structure and fluctuations of folded proteins. Second, we quantified potential biases towards different secondary structure types by comparing experimental and simulation data for small peptides that preferentially populate either helical or sheet-like structures. Third, we tested the force fields' abilities to fold two small proteins--one α-helical, the other with ß-sheet structure. The results suggest that force fields have improved over time, and that the most recent versions, while not perfect, provide an accurate description of many structural and dynamical properties of proteins.

Structure and dynamics of an unfolded protein examined by molecular dynamics simulation.

The accurate characterization of the structure and dynamics of proteins in disordered states is a difficult problem at the frontier of structural biology whose solution promises to further our understanding of protein folding and intrinsically disordered proteins. Molecular dynamics (MD) simulations have added considerably to our understanding of folded proteins, but the accuracy with which the force fields used in such simulations can describe disordered proteins is unclear. In this work, using a modern force field, we performed a 200 µs unrestrained MD simulation of the acid-unfolded state of an experimentally well-characterized protein, ACBP, to explore the extent to which state-of-the-art simulation can describe the structural and dynamical features of a disordered protein. By comparing the simulation results with the results of NMR experiments, we demonstrate that the simulation successfully captures important aspects of both the local and global structure. Our simulation was ~2 orders of magnitude longer than those in previous studies of unfolded proteins, a length sufficient to observe repeated formation and breaking of helical structure, which we found to occur on a multimicrosecond time scale. We observed one structural feature that formed but did not break during the simulation, highlighting the difficulty in sampling disordered states. Overall, however, our simulation results are in reasonable agreement with the experimental data, demonstrating that MD simulations can already be useful in describing disordered proteins. Finally, our direct calculation of certain NMR observables from the simulation provides new insight into the general relationship between structural features of disordered proteins and experimental NMR relaxation properties.

Activation mechanism of the ß2-adrenergic receptor.

A third of marketed drugs act by binding to a G-protein-coupled receptor (GPCR) and either triggering or preventing receptor activation. Although recent crystal structures have provided snapshots of both active and inactive functional states of GPCRs, these structures do not reveal the mechanism by which GPCRs transition between these states. Here we propose an activation mechanism for the ß(2)-adrenergic receptor, a prototypical GPCR, based on atomic-level simulations in which an agonist-bound receptor transitions spontaneously from the active to the inactive crystallographically observed conformation. A loosely coupled allosteric network, comprising three regions that can each switch individually between multiple distinct conformations, links small perturbations at the extracellular drug-binding site to large conformational changes at the intracellular G-protein-binding site. Our simulations also exhibit an intermediate that may represent a receptor conformation to which a G protein binds during activation, and suggest that the first structural changes during receptor activation often take place on the intracellular side of the receptor, far from the drug-binding site. By capturing this fundamental signaling process in atomic detail, our results may provide a foundation for the design of drugs that control receptor signaling more precisely by stabilizing specific receptor conformations.

Pathway and mechanism of drug binding to G-protein-coupled receptors.

How drugs bind to their receptors--from initial association, through drug entry into the binding pocket, to adoption of the final bound conformation, or "pose"--has remained unknown, even for G-protein-coupled receptor modulators, which constitute one-third of all marketed drugs. We captured this pharmaceutically critical process in atomic detail using the first unbiased molecular dynamics simulations in which drug molecules spontaneously associate with G-protein-coupled receptors to achieve final poses matching those determined crystallographically. We found that several beta blockers and a beta agonist all traverse the same well-defined, dominant pathway as they bind to the ß(1)- and ß(2)-adrenergic receptors, initially making contact with a vestibule on each receptor's extracellular surface. Surprisingly, association with this vestibule, at a distance of 15 Å from the binding pocket, often presents the largest energetic barrier to binding, despite the fact that subsequent entry into the binding pocket requires the receptor to deform and the drug to squeeze through a narrow passage. The early barrier appears to reflect the substantial dehydration that takes place as the drug associates with the vestibule. Our atomic-level description of the binding process suggests opportunities for allosteric modulation and provides a structural foundation for future optimization of drug-receptor binding and unbinding rates.

Atomic-level characterization of the structural dynamics of proteins.

Molecular dynamics (MD) simulations are widely used to study protein motions at an atomic level of detail, but they have been limited to time scales shorter than those of many biologically critical conformational changes. We examined two fundamental processes in protein dynamics--protein folding and conformational change within the folded state--by means of extremely long all-atom MD simulations conducted on a special-purpose machine. Equilibrium simulations of a WW protein domain captured multiple folding and unfolding events that consistently follow a well-defined folding pathway; separate simulations of the protein's constituent substructures shed light on possible determinants of this pathway. A 1-millisecond simulation of the folded protein BPTI reveals a small number of structurally distinct conformational states whose reversible interconversion is slower than local relaxations within those states by a factor of more than 1000.

Improved side-chain torsion potentials for the Amber ff99SB protein force field.

Recent advances in hardware and software have enabled increasingly long molecular dynamics (MD) simulations of biomolecules, exposing certain limitations in the accuracy of the force fields used for such simulations and spurring efforts to refine these force fields. Recent modifications to the Amber and CHARMM protein force fields, for example, have improved the backbone torsion potentials, remedying deficiencies in earlier versions. Here, we further advance simulation accuracy by improving the amino acid side-chain torsion potentials of the Amber ff99SB force field. First, we used simulations of model alpha-helical systems to identify the four residue types whose rotamer distribution differed the most from expectations based on Protein Data Bank statistics. Second, we optimized the side-chain torsion potentials of these residues to match new, high-level quantum-mechanical calculations. Finally, we used microsecond-timescale MD simulations in explicit solvent to validate the resulting force field against a large set of experimental NMR measurements that directly probe side-chain conformations. The new force field, which we have termed Amber ff99SB-ILDN, exhibits considerably better agreement with the NMR data.

Principles of conduction and hydrophobic gating in K+ channels.

We present the first atomic-resolution observations of permeation and gating in a K(+) channel, based on molecular dynamics simulations of the Kv1.2 pore domain. Analysis of hundreds of simulated permeation events revealed a detailed conduction mechanism, resembling the Hodgkin-Keynes "knock-on" model, in which translocation of two selectivity filter-bound ions is driven by a third ion; formation of this knock-on intermediate is rate determining. In addition, at reverse or zero voltages, we observed pore closure by a novel "hydrophobic gating" mechanism: A dewetting transition of the hydrophobic pore cavity-fastest when K(+) was not bound in selectivity filter sites nearest the cavity-caused the open, conducting pore to collapse into a closed, nonconducting conformation. Such pore closure corroborates the idea that voltage sensors can act to prevent pore collapse into the intrinsically more stable, closed conformation, and it further suggests that molecular-scale dewetting facilitates a specific biological function: K(+) channel gating. Existing experimental data support our hypothesis that hydrophobic gating may be a fundamental principle underlying the gating of voltage-sensitive K(+) channels. We suggest that hydrophobic gating explains, in part, why diverse ion channels conserve hydrophobic pore cavities, and we speculate that modulation of cavity hydration could enable structural determination of both open and closed channels.

Equipartition and the Calculation of Temperature in Biomolecular Simulations.

Since the behavior of biomolecules can be sensitive to temperature, the ability to accurately calculate and control the temperature in molecular dynamics (MD) simulations is important. Standard analysis of equilibrium MD simulations-even constant-energy simulations with negligible long-term energy drift-often yields different calculated temperatures for different motions, however, in apparent violation of the statistical mechanical principle of equipartition of energy. Although such analysis provides a valuable warning that other simulation artifacts may exist, it leaves the actual value of the temperature uncertain. We observe that Tolman's generalized equipartition theorem should hold for long stable simulations performed using velocity-Verlet or other symplectic integrators, because the simulated trajectory is thought to sample almost exactly from a continuous trajectory generated by a shadow Hamiltonian. From this we conclude that all motions should share a single simulation temperature, and we provide a new temperature estimator that we test numerically in simulations of a diatomic fluid and of a solvated protein. Apparent temperature variations between different motions observed using standard estimators do indeed disappear when using the new estimator. We use our estimator to better understand how thermostats and barostats can exacerbate integration errors. In particular, we find that with large (albeit widely used) time steps, the common practice of using two thermostats to remedy so-called hot solvent-cold solute problems can have the counterintuitive effect of causing temperature imbalances. Our results, moreover, highlight the utility of multiple-time step integrators for accurate and efficient simulation.

Gaussian-mixture umbrella sampling.

We introduce the Gaussian-mixture umbrella sampling method (GAMUS) , a biased molecular dynamics technique based on adaptive umbrella sampling that efficiently escapes free energy minima in multidimensional problems. The prior simulation data are reweighted with a maximum likelihood formulation, and the new approximate probability density is fit to a Gaussian-mixture model, augmented by information about the unsampled areas. The method can be used to identify free energy minima in multidimensional reaction coordinates. To illustrate GAMUS , we apply it to the alanine dipeptide (2D reaction coordinate) and tripeptide (4D reaction coordinate).

Automated Event Detection and Activity Monitoring in Long Molecular Dynamics Simulations.

Events of scientific interest in molecular dynamics (MD) simulations, including conformational changes, folding transitions, and translocations of ligands and reaction products, often correspond to high-level structural rearrangements that alter contacts between molecules or among different parts of a molecule. Due to advances in computer architecture and software, MD trajectories representing such structure-changing events have become easier to generate, but the length of these trajectories poses a challenge to scientific interpretation and analysis. In this paper, we present automated methods for the detection of potentially important structure-changing events in long MD trajectories. In contrast with traditional tools for the analysis of such trajectories, our methods provide a detailed report of broken and formed contacts that aids in the identification of specific time-dependent side-chain interactions. Our approach employs a coarse-grained representation of amino acid side chains, a contact metric based on higher order generalizations of Delaunay tetrahedralization, techniques for detecting significant shifts in the resulting contact time series, and a new kernel-based measure of contact alteration activity. The analysis methods we describe are incorporated in a newly developed package, called TimeScapes, which is freely available and compatible with trajectories generated by a variety of popular MD programs. Tests based on actual microsecond time scale simulations demonstrate that the package can be used to efficiently detect and characterize important conformational changes in realistic protein systems.

Bayesian estimates of free energies from nonequilibrium work data in the presence of instrument noise.

The Jarzynski equality and the fluctuation theorem relate equilibrium free energy differences to nonequilibrium measurements of the work. These relations extend to single-molecule experiments that have probed the finite-time thermodynamics of proteins and nucleic acids. The effects of experimental error and instrument noise have not been considered previously. Here, we present a Bayesian formalism for estimating free energy changes from nonequilibrium work measurements that compensates for instrument noise and combines data from multiple driving protocols. We reanalyze a recent set of experiments in which a single RNA hairpin is unfolded and refolded using optical tweezers at three different rates. Interestingly, the fastest and farthest-from-equilibrium measurements contain the least instrumental noise and, therefore, provide a more accurate estimate of the free energies than a few slow, more noisy, near-equilibrium measurements. The methods we propose here will extend the scope of single-molecule experiments; they can be used in the analysis of data from measurements with atomic force microscopy, optical, and magnetic tweezers.

A differential fluctuation theorem.

We derive a nonequilibrium thermodynamics identity (the "differential fluctuation theorem") that connects forward and reverse joint probabilities of nonequilibrium work and of arbitrary generalized coordinates corresponding to states of interest. This identity allows us to estimate the free energy difference between domains of these states. Our results follow from a general symmetry relation between averages over nonequilibrium forward and backward path functions derived by Crooks [Crooks, G. E. Phys. Rev. E 2000, 61, 2361-2366]. We show how several existing nonequilibrium thermodynamic identities can be obtained directly from the differential fluctuation theorem. We devise an approach for measuring conformational free energy differences, and we demonstrate its applicability to the analysis of molecular dynamics simulations by estimating the free energy difference between two conformers of the alanine dipeptide model system. We anticipate that these developments can be applied to the analysis of laboratory experiments.

Microsecond molecular dynamics simulation shows effect of slow loop dynamics on backbone amide order parameters of proteins.

A molecular-level understanding of the function of a protein requires knowledge of both its structural and dynamic properties. NMR spectroscopy allows the measurement of generalized order parameters that provide an atomistic description of picosecond and nanosecond fluctuations in protein structure. Molecular dynamics (MD) simulation provides a complementary approach to the study of protein dynamics on similar time scales. Comparisons between NMR spectroscopy and MD simulations can be used to interpret experimental results and to improve the quality of simulation-related force fields and integration methods. However, apparent systematic discrepancies between order parameters extracted from simulations and experiments are common, particularly for elements of noncanonical secondary structure. In this paper, results from a 1.2 micros explicit solvent MD simulation of the protein ubiquitin are compared with previously determined backbone order parameters derived from NMR relaxation experiments [Tjandra, N.; Feller, S. E.; Pastor, R. W.; Bax, A. J. Am. Chem. Soc. 1995, 117, 12562-12566]. The simulation reveals fluctuations in three loop regions that occur on time scales comparable to or longer than that of the overall rotational diffusion of ubiquitin and whose effects would not be apparent in experimentally derived order parameters. A coupled analysis of internal and overall motion yields simulated order parameters substantially closer to the experimentally determined values than is the case for a conventional analysis of internal motion alone. Improved agreement between simulation and experiment also is encouraging from the viewpoint of assessing the accuracy of long MD simulations.

Determination of DNA-base orientation on carbon nanotubes through directional optical absorbance.

We develop an approach for determining the orientation of DNA bases attached to carbon nanotubes (CNTs), by combining ab initio time-dependent density functional theory and optical spectroscopy measurements. The structures we find are in good agreement with the geometry of nucleosides on a (10,0) CNT obtained from molecular simulations using empirical force fields. The results shed light into the complex interactions of the DNA-CNT system, a candidate for ultrafast DNA sequencing through electronic probes.

DNA nucleoside interaction and identification with carbon nanotubes.

We investigate the interaction of individual DNA nucleosides with a carbon nanotube (CNT) in vacuum and in the presence of external gate voltage. We propose a scheme to discriminate between nucleosides on CNTs based on measurement of electronic features through a local probe such as scanning tunneling spectroscopy. We demonstrate through quantum mechanical calculations that these measurements can achieve 100% efficiency in identifying DNA bases. Our results support the practicality of ultrafast DNA sequencing using electrical measurements.