Computational study of the "DFG-flip" conformational transition in c-Abl and c-Src tyrosine kinases

Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular, cancers. The ubiquitousness and structural similarities of kinases make specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved Asp-Phe-Gly (DFG) motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticeably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learned order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations (Meller, A.; Bhakat, S.; Solieva, S.; Bowman, G. R. Accelerating Cryptic Pocket Discovery Using AlphaFold. J. Chem. Theory Comput. 2023, 19, 4355-4363).

Classifying protein kinase conformations with machine learning

Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG-in, DFG-out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large-scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases.

Perspectives on Computational Enzyme Modeling: From Mechanisms to Design and Drug Development

Understanding enzyme mechanisms is essential for unraveling the complex molecular machinery of life. In this review, we survey the field of computational enzymology, highlighting key principles governing enzyme mechanisms and discussing ongoing challenges and promising advances. Over the years, computer simulations have become indispensable in the study of enzyme mechanisms, with the integration of experimental and computational exploration now established as a holistic approach to gain deep insights into enzymatic catalysis. Numerous studies have demonstrated the power of computer simulations in characterizing reaction pathways, transition states, substrate selectivity, product distribution, and dynamic conformational changes for various enzymes. Nevertheless, significant challenges remain in investigating the mechanisms of complex multistep reactions, large-scale conformational changes, and allosteric regulation. Beyond mechanistic studies, computational enzyme modeling has emerged as an essential tool for computer-aided enzyme design and the rational discovery of covalent drugs for targeted therapies. Overall, enzyme design/engineering and covalent drug development can greatly benefit from our understanding of the detailed mechanisms of enzymes, such as protein dynamics, entropy contributions, and allostery, as revealed by computational studies. Such a convergence of different research approaches is expected to continue, creating synergies in enzyme research. This review, by outlining the ever-expanding field of enzyme research, aims to provide guidance for future research directions and facilitate new developments in this important and evolving field.

Exploring kinase DFG loop conformational stability with AlphaFold2-RAVE

Kinases compose one of the largest fractions of the human proteome, and their misfunction is implicated in many diseases, in particular cancers. The ubiquitousness and structural similarities of kinases makes specific and effective drug design difficult. In particular, conformational variability due to the evolutionarily conserved DFG motif adopting in and out conformations and the relative stabilities thereof are key in structure-based drug design for ATP competitive drugs. These relative conformational stabilities are extremely sensitive to small changes in sequence, and provide an important problem for sampling method development. Since the invention of AlphaFold2, the world of structure-based drug design has noticably changed. In spite of it being limited to crystal-like structure prediction, several methods have also leveraged its underlying architecture to improve dynamics and enhanced sampling of conformational ensembles, including AlphaFold2-RAVE. Here, we extend AlphaFold2-RAVE and apply it to a set of kinases: the wild type DDR1 sequence and three mutants with single point mutations that are known to behave drastically differently. We show that AlphaFold2-RAVE is able to efficiently recover the changes in relative stability using transferable learnt order parameters and potentials, thereby supplementing AlphaFold2 as a tool for exploration of Boltzmann-weighted protein conformations.

Molecular Simulations of Conformational Transitions within the Insulin Receptor Kinase Reveal Consensus Features in a Multistep Activation Pathway

Modulating the transitions between active and inactive conformations of protein kinases is the primary means of regulating their catalytic activity, achieved by phosphorylation of the activation loop (A-loop). To elucidate the mechanism of this conformational activation, we applied the string method to determine the conformational transition path of insulin receptor kinase between the active and inactive conformations and the corresponding free-energy profiles with and without A-loop phosphorylation. The conformational change was found to proceed in three sequential steps: first, the flipping of the DFG motif of the active site; second, rotation of the A-loop; finally, the inward movement of the αC helix. The main energetic bottleneck corresponds to the conformational change in the A-loop, while changes in the DFG motif and αC helix occur before and after A-loop conformational change, respectively. In accordance with this, two intermediate states are identified, the first state just after the DFG flipping and the second state after the A-loop rotation. These intermediates exhibit structural features characteristic of the corresponding inactive and active conformations of other protein kinases. To understand the impact of A-loop phosphorylation on kinase conformation, the free energies of A-loop phosphorylation were determined at several states along the conformational transition path using the free-energy perturbation simulations. The calculated free energies reveal that while the unphosphorylated kinase interconverts between the inactive and active conformations, A-loop phosphorylation restricts access to the inactive conformation, thereby increasing the active conformation population. Overall, this study suggests a consensus mechanism of conformational activation between different protein kinases.

Efficient Interrogation of the Kinetic Barriers Demarcating Catalytic States of a Tyrosine Kinase with Optimal Physical Descriptors and Mixture Models

Computer simulations are increasingly used to access thermo-kinetic information underlying structural transformation of protein kinases. Such information are necessary to probe their roles in disease progression and interactions with drug targets. However, the investigations are frequently challenged by forbiddingly high computational expense, and by the lack of standard protocols for the design of low dimensional physical descriptors that encode system features important for transitions. Here, we consider the demarcating characteristics of the different states of Abelson tyrosine kinase associated with distinct catalytic activity to construct a set of physically meaningful, orthogonal collective variables that preserve the slow modes of the system. Independent sampling of each metastable state is followed by the estimation of global partition function along the appropriate physical descriptors using the modified Expectation Maximized Molecular Dynamics method. The resultant free energy barriers are in excellent agreement with experimentally known rate-limiting dynamics and activation energy computed with conventional enhanced sampling methods. We discuss possible directions for further development and applications.

Allosteric Boost by TAB1 on the TAK1 Kinase Favorably Sculpts the Thermodynamic Landscape of Activation

The intricate mechanisms of allosteric regulation in kinases are of general interest to the scientific community for potential therapeutic implications. However, the diversity among kinases and their regulatory routes requires a case-by-case study to widen the repertoire of known mechanisms. The present study achieves this by understanding TAK1 kinase activation by TAB1 as a model phenomenon for the first time. Despite the known capacity of TAK1 to switch between its inactive ("DFG-out") and active-like ("DFG-in") conformations, the questionable role of TAB1 in offering an energetic favor to this has been addressed here using sequential combination of enhanced sampling methods like targeted molecular dynamics (TMD) and Gaussian accelerated molecular dynamics (GaMD). It reveals how a minimal domain of TAB1 sufficiently acts like a "catalytic gear" by favorably sculpting TAK1's thermodynamic landscape (potential of mean force in 2D) that accelerates "in"-"out" conformational switching of the conserved DFG motif. Standard molecular dynamics simulations (∼5 μs) reveal that TAB1 fascinatingly exploits the "lever-like" αF helix of TAK1 kinase domain to remotely propel the DFG motif via subtle helical "unfolding-folding" modifications within the kinase activation loop. The presence of two charged residues on terminal poles of αF helix imparts it, with this unique "lever-like" utility, and this turns out to be one important signature of co-evolution between TAK1 and TAB1. The entire mechanism of TAB1's impact transduction, which is found to be analogous to the moves in the popular "Chinese checker" game, gives a clear proof of the "dynamics-driven allostery" concept in kinases. The findings further benchmark TAK1's known autophosphorylation capacity. A novel insight into kinase allostery is thus provided, which potentiates investigation of similar capacities in other kinases.

p38α Kinase Auto-Activation through Its Conformational Transition Induced by Tyr323 Phosphorylation

p38α is a key serine/threonine kinase that can enable atypical auto-activation through Zap70 phosphorylation and initiate T cell receptor signaling. The auto-activation plays an important role in autoimmune diseases. Although the classical activation mechanism of p38α has been studied in-depth, the atypical activation mechanism of Y323 phosphorylation-induced p38α auto-activation remains largely unexplained, especially the regulatory effects of phosphorylation on different sites (Y323 vs T180). From the X-ray experimental data, we identified the inactive and active states of p38α using principal component analysis. To understand the auto-activation process and the internal driving mechanism, a computational paradigm that couples the targeted molecular dynamics simulations, the String Method, and the umbrella sampling strategy were employed to generate the conformational landscape of p38α, including p38α T180-Y323, p38α T180-pY323, and p38α pT180-pY323 systems (pT180/pY323: phosphorylated T180/Y323). We explored that pY323 could change the conformational distribution and promote the conformational transition of p38α from the inactive state to the active state. Auto-activation of p38α is regulated by pY323 through destabilization of the hydrophobic core structure and aided by R173. This study will further explain the conformational transition of p38α induced by Y323 phosphorylation and provide insights into the universal molecular auto-activation mechanism of the p38 subfamily at the atomic level.

The Conformational Transition Pathways and Hidden Intermediates in DFG-Flip Process of c-Met Kinase Revealed by Metadynamics Simulations

Protein kinases intrinsically translate their conformations between active and inactive states, which is key to their enzymatic activities. The conformational flipping of the three-residue conservative motif, Asp-Phe-Gly (DFG), is crucial for many kinases' biological functions. Obtaining a detailed demonstration of the DFG flipping process and its corresponding dynamical and thermodynamical features could broaden our understanding of kinases' conformation-activity relationship. In this study, we employed metadynamics simulation, a widely used enhanced sampling technique, to analyze the conformational transition pathways of the DFG flipping for the c-Met kinase. The corresponding free energy landscape suggested two distinct transition pathways between the "DFG-in" and "DFG-out" states of the DFG-flip from c-Met. On the basis of the orientation direction of the F1223 residue, we correspondingly named the two pathways the "DFG-up" path, featuring forming a commonly discovered "DFG-up" transition state, and the "DFG-down" path, a unique transition pathway with F1223 rotating along the opposite direction away from the hydrophobic cavity. The free energies along the two pathways were then calculated using the Path Collective Variable (PCV) metadynamics simulation. The simulation results showed that, though having similar free energy barriers, the free energy cuve for the DFG-down path suggested a two-step conformational transition mechanism, while that for the DFG-up path showed the one-step transition feature. The c-Met DFG flipping mechanism and the new intermediate state discovered in this work could provide a deeper understanding of the conformation-activity relationship for c-Met and, possibly, reveal a new conformational state as the drug target for c-Met and other similar kinases.

Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset of Resistance Mutations in Kinases

Understanding how mutations render a drug ineffective is a problem of immense relevance. Often the mechanism through which mutations cause drug resistance can be explained purely through thermodynamics. However, the more perplexing situation is when two proteins have the same drug binding affinities but different residence times. In this work, we demonstrate how all-atom molecular dynamics simulations using recent developments grounded in statistical mechanics can provide a detailed mechanistic rationale for such variances. We discover dissociation mechanisms for the anti-cancer drug Imatinib (Gleevec) against wild-type and the N368S mutant of Abl kinase. We show how this point mutation triggers far-reaching changes in the protein's flexibility and leads to a different, much faster, drug dissociation pathway. We believe that this work marks an efficient and scalable approach to obtain mechanistic insight into resistance mutations in biomolecular receptors that are hard to explain using a structural perspective.

Allosteric Enhancement of the BCR-Abl1 Kinase Inhibition Activity of Nilotinib by Co-Binding of Asciminib

Inhibitors that bind competitively to the ATP binding pocket in the kinase domain of the oncogenic fusion protein BCR–Abl1 are used successfully in targeted therapy of chronic myeloid leukemia (CML). Such inhibitors provided the first proof of concept that kinase inhibition can succeed in a clinical setting. However, emergence of drug resistance and dose-dependent toxicities limit the effectiveness of these drugs. Therefore, treatment with a combination of drugs without overlapping resistance mechanisms appears to be an appropriate strategy. In the present work, we explore the effectiveness of combination therapies of the recently developed allosteric inhibitor asciminib with the ATP-competitive inhibitors nilotinib and dasatinib in inhibiting the BCR–Abl1 kinase activity in CML cell lines. Through these experiments, we demonstrate that asciminib significantly enhances the inhibition activity of nilotinib, but not of dasatinib. Exploring molecular mechanisms for such allosteric enhancement via systematic computational investigation incorporating molecular dynamics, metadynamics simulations, and density functional theory calculations, we found two distinct contributions. First, binding of asciminib triggers conformational changes in the inactive state of the protein, thereby making the activation process less favorable by ∼4 kcal/mol. Second, the binding of asciminib decreases the binding free energies of nilotinib by ∼3 and ∼7 kcal/mol for the wildtype and T315I-mutated protein, respectively, suggesting the possibility of reducing nilotinib dosage and lowering risk of developing resistance in the treatment of CML.

Practical Protocols for Efficient Sampling of Kinase-Inhibitor Binding Pathways Using Two-Dimensional Replica-Exchange Molecular Dynamics

Molecular dynamics (MD) simulations are increasingly used to study various biological processes such as protein folding, conformational changes, and ligand binding. These processes generally involve slow dynamics that occur on the millisecond or longer timescale, which are difficult to simulate by conventional atomistic MD. Recently, we applied a two-dimensional (2D) replica-exchange MD (REMD) method, which combines the generalized replica exchange with solute tempering (gREST) with the replica-exchange umbrella sampling (REUS) in kinase-inhibitor binding simulations, and successfully observed multiple ligand binding/unbinding events. To efficiently apply the gREST/REUS method to other kinase-inhibitor systems, we establish modified, practical protocols with non-trivial simulation parameter tuning. The current gREST/REUS simulation protocols are tested for three kinase-inhibitor systems: c-Src kinase with PP1, c-Src kinase with Dasatinib, and c-Abl kinase with Imatinib. We optimized the definition of kinase-ligand distance as a collective variable (CV), the solute temperatures in gREST, and replica distributions and umbrella forces in the REUS simulations. Also, the initial structures of each replica in the 2D replica space were prepared carefully by pulling each ligand from and toward the protein binding sites for keeping stable kinase conformations. These optimizations were carried out individually in multiple short MD simulations. The current gREST/REUS simulation protocol ensures good random walks in 2D replica spaces, which are required for enhanced sampling of inhibitor dynamics around a target kinase.

A Companion Guide to the String Method with Swarms of Trajectories: Characterization, Performance, and Pitfalls

The string method with swarms of trajectories (SMwST) is an algorithm that identifies a physically meaningful transition pathway─a one-dimensional curve, embedded within a high-dimensional space of selected collective variables. The SMwST algorithm leans on a series of short, unbiased molecular dynamics simulations spawned at different locations of the discretized path, from whence an average dynamic drift is determined to evolve the string toward an optimal pathway. However conceptually simple in both its theoretical formulation and practical implementation, the SMwST algorithm is computationally intensive and requires a careful choice of parameters for optimal cost-effectiveness in applications to challenging problems in chemistry and biology. In this contribution, the SMwST algorithm is presented in a self-contained manner, discussing with a critical eye its theoretical underpinnings, applicability, inherent limitations, and use in the context of path-following free-energy calculations and their possible extension to kinetics modeling. Through multiple simulations of a prototypical polypeptide, combining the search of the transition pathway and the computation of the potential of mean force along it, several practical aspects of the methodology are examined with the objective of optimizing the computational effort, yet without sacrificing accuracy. In light of the results reported here, we propose some general guidelines aimed at improving the efficiency and reliability of the computed pathways and free-energy profiles underlying the conformational transitions at hand.

Allosteric regulation of autoinhibition and activation of c-Abl

c-Abl, a non-receptor tyrosine kinase, regulates cell growth and survival in healthy cells and causes chronic myeloid leukemia (CML) when fused by Bcr. Its activity is blocked in the assembled inactive state, where the SH3 and SH2 domains dock into the kinase domain, reducing its conformational flexibility, resulting in the autoinhibited state. It is active in an extended ‘open’ conformation. Allostery governs the transitions between the autoinhibited and active states. Even though experiments revealed the structural hallmarks of the two states, a detailed grasp of the determinants of c-Abl autoinhibition and activation at the atomic level, which may help innovative drug discovery, is still lacking. Here, using extensive molecular dynamics simulations, we decipher exactly how these determinants regulate it. Our simulations confirm and extend experimental data that the myristoyl group serves as the switch for c-Abl inhibition/activation. Its dissociation from the kinase domain promotes the SH2-SH3 release, initiating c-Abl activation. We show that the precise SH2/N-lobe interaction is required for full activation of c-Abl. It stabilizes a catalysis-favored conformation, priming it for catalytic action. Bcr-Abl allosteric drugs elegantly mimic the endogenous myristoyl-mediated autoinhibition state of c-Abl 1b. Allosteric activating mutations shift the ensemble to the active state, blocking ATP-competitive drugs. Allosteric drugs alter the active-site conformation, shifting the ensemble to re-favor ATP-competitive drugs. Our work provides a complete mechanism of c-Abl activation and insights into critical parameters controlling at the atomic level c-Abl inactivation, leading us to propose possible strategies to counter reemergence of drug resistance.

Activation of Abl1 Kinase Explored Using Well-Tempered Metadynamics Simulations on an Essential Dynamics Sampled Path

Well-tempered metadynamics (wT-metaD) simulations using path collective variables (CVs) have been successfully applied in recent years to explore conformational transitions in protein kinases and other biomolecular systems. While this methodology has the advantage of describing the transitions with a limited number of predefined path CVs, it requires as an input a reference path connecting the initial and target states of the system. It is desirable to automate the path generation using approaches that do not rely on the choice of geometric CVs to describe the transition of interest. To this end, we developed an approach that couples essential dynamics sampling with wT-metaD simulations. We used this newly developed procedure to explore the activation mechanism of Abl1 kinase and compute the associated free energy barriers. Through these simulations, we identified a three-step mechanism for the activation that involved two metastable intermediates that possessed a partially open activation loop and differed primarily in the “in” or “out” conformation of the aspartate residue of the DFG motif. One of these states is similar to a conformation that was detected in previous spectroscopic studies of Abl1 kinase, albeit its mechanistic role in the activation was hitherto not well understood. The present study establishes its intermediary role in the activation and predicts a rate-determining free energy barrier of 13.8 kcal/mol that is in good agreement with previous experimental and computational estimates. Overall, our study demonstrates the usability of essential dynamics sampling as a path CV in wT-metaD to conveniently study conformational transitions and accurately calculate the associated barriers.

TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES

Classical molecular dynamics (MD) simulations based on atomic models play an increasingly important role in a wide range of applications in physics, biology, and chemistry. Nonetheless, generating genuine knowledge about biological systems using MD simulations remains challenging. Protein tyrosine kinases are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Due to the large conformational changes and long timescales involved in their function, these kinases present particularly challenging problems to modern computational and theoretical frameworks aimed at elucidating the dynamics of complex biomolecular systems. Markov state models have achieved limited success in tackling the broader conformational ensemble and biased methods are often employed to examine specific long timescale events. Recent advances in machine learning continue to push the limitations of current methodologies and provide notable improvements when integrated with the existing frameworks. A broad perspective is drawn from a critical review of recent studies.

Multiscale computational study of ligand binding pathways: Case of p38 MAP kinase and its inhibitors

Protein kinases are one of the most important drug targets in the past 10 years. Understanding the inhibitor association processes will profoundly impact new binder designs with preferred binding kinetics. However, after more than a decade of effort, a complete atomistic-level study of kinase inhibitor binding pathways is still lacking. As all kinases share a similar scaffold, we used p38 kinase as a model system to investigate the conformational dynamics and free energy transition of inhibitor binding toward kinases. Two major kinase conformations, Asp-Phe-Gly (DFG)-in and DFG-out, and three types of inhibitors, type I, II, and III, were thoroughly investigated in this work. We performed Brownian dynamics simulations and up to 340 μs Gaussian-accelerated molecular dynamics simulations to capture the inhibitor binding paths and a series of conformational transitions of the p38 kinase from its apo to inhibitor-bound form. Eighteen successful binding trajectories, including all types of inhibitors, are reported herein. Our simulations suggest a mechanism of inhibitor recruitment, a faster ligand association step to a pre-existing DFG-in/DFG-out p38 protein, followed by a slower molecular rearrangement step to adjust the protein-ligand conformation followed by a shift in the energy landscape to reach the final bound state. The ligand association processes also reflect the energetic favor of type I and type II/III inhibitor binding through ATP and allosteric channels, respectively. These different binding routes are directly responsible for the fast (type I binders) and slow (type II/III binders) kinetics of different types of p38 inhibitors. Our findings also echo the recent study of p38 inhibitor dissociation, implying that ligand unbinding could undergo a reverse path of binding, and both processes share similar metastates. This study deepens the understanding of molecular and energetic features of kinase inhibitor-binding processes and will inspire future drug development from a kinetic point of view.

Computational studies of anaplastic lymphoma kinase mutations reveal common mechanisms of oncogenic activation

High-risk tumors are genomically heterogeneous, harboring gene amplifications and mutations. The activation status of mutated proteins in cancer can profoundly impact disease progression, patient response, and drug sensitivity. Yet, outside of a few hotspot mutations, functional studies of clinically observed mutations are not commonly pursued. We report a combined experimental profiling and computational analysis of the effects of clinically observed and “test” mutations in the kinase domain of anaplastic lymphoma kinase (ALK), a known oncogenic driver in pediatric neuroblastoma. We find that the activation status of the mutated protein is a good indicator of the transforming ability in NIH 3T3 cells. We also report biophysical as well as data-driven models with predictive power to profile these mutant kinases in silico.

Kinases play important roles in diverse cellular processes, including signaling, differentiation, proliferation, and metabolism. They are frequently mutated in cancer and are the targets of a large number of specific inhibitors. Surveys of cancer genome atlases reveal that kinase domains, which consist of 300 amino acids, can harbor numerous (150 to 200) single-point mutations across different patients in the same disease. This preponderance of mutations—some activating, some silent—in a known target protein make clinical decisions for enrolling patients in drug trials challenging since the relevance of the target and its drug sensitivity often depend on the mutational status in a given patient. We show through computational studies using molecular dynamics (MD) as well as enhanced sampling simulations that the experimentally determined activation status of a mutated kinase can be predicted effectively by identifying a hydrogen bonding fingerprint in the activation loop and the αC-helix regions, despite the fact that mutations in cancer patients occur throughout the kinase domain. In our study, we find that the predictive power of MD is superior to a purely data-driven machine learning model involving biochemical features that we implemented, even though MD utilized far fewer features (in fact, just one) in an unsupervised setting. Moreover, the MD results provide key insights into convergent mechanisms of activation, primarily involving differential stabilization of a hydrogen bond network that engages residues of the activation loop and αC-helix in the active-like conformation (in >70% of the mutations studied, regardless of the location of the mutation).

Reduced efficacy of a Src kinase inhibitor in crowded protein solution

The inside of a cell is highly crowded with proteins and other biomolecules. How proteins express their specific functions together with many off-target proteins in crowded cellular environments is largely unknown. Here, we investigate an inhibitor binding with c-Src kinase using atomistic molecular dynamics (MD) simulations in dilute as well as crowded protein solution. The populations of the inhibitor, 4-amino-5-(4-methylphenyl)-7-(t-butyl)pyrazolo[3,4-d]pyrimidine (PP1), in bulk solution and on the surface of c-Src kinase are reduced as the concentration of crowder bovine serum albumins (BSAs) increases. This observation is consistent with the reduced PP1 inhibitor efficacy in experimental c-Src kinase assays in addition with BSAs. The crowded environment changes the major binding pathway of PP1 toward c-Src kinase compared to that in dilute solution. This change is explained based on the population shift mechanism of local conformations near the inhibitor binding site in c-Src kinase.

Type II Binders Targeting the "GLR-Out" Conformation of the Pseudokinase STRADα

Pseudokinases play important roles in signal transduction and cellular processes similar to those of catalytically competent kinases. However, pseudokinase pharmacological tractability and conformational space accessibility are poorly understood. Pseudokinases have only recently been suggested to adopt "inactive" conformations or interact with conformation-specific kinase inhibitors (e.g., type II compounds). In this work, the heavily substituted pseudokinase STRADα, which possesses a DFG → GLR substitution in the catalytic site that permits nucleotide binding while impairing divalent cation coordination, is used as a test case to demonstrate the potential applicability of conformation-specific, type II compounds to pseudokinase pharmacology. Integrated structural modeling is employed to generate a "GLR-out" conformational ensemble. Likely interacting type II compounds are identified through virtual screening against this ensemble model. Biophysical validation of compound binding is demonstrated through protein thermal stabilization and ATP competition. Localization of a top-performing compound through surface methylation strongly suggests that STRADα can adopt the "GLR-out" conformation and interact with compounds that comply with the standard type II pharmacophore. These results suggest that, despite a loss of catalytic function, some pseudokinases, including STRADα, may retain the conformational switching properties of conventional protein kinases.

Increasing the Sampling Efficiency of Protein Conformational Change by Combining a Modified Replica Exchange Molecular Dynamics and Normal Mode Analysis

Understanding conformational change at an atomic level is significant when determining a protein functional mechanism. Replica exchange molecular dynamics (REMD) is a widely used enhanced sampling method to explore protein conformational space. However, REMD with an explicit solvent model requires huge computational resources, immensely limiting its application. In this study, a variation of parallel tempering metadynamics (PTMetaD) with the omission of solvent-solvent interactions in exchange attempts and the use of low-frequency modes calculated by normal-mode analysis (NMA) as collective variables (CVs), namely ossPTMetaD, is proposed with the aim to accelerate MD simulations simultaneously in temperature and geometrical spaces. For testing the performance of ossPTMetaD, five protein systems with diverse biological functions and motion patterns were selected, including large-scale domain motion (AdK), flap movement (HIV-1 protease and BACE1), and DFG-motif flip in kinases (p38α and c-Abl). The simulation results showed that ossPTMetaD requires much fewer numbers of replicas than temperature REMD (T-REMD) with a reduction of ∼70% to achieve a similar exchange ratio. Although it does not obey the detailed balance condition, ossPTMetaD provides consistent results with T-REMD and experimental data. The high accessibility of the large conformational change of protein systems by ossPTMetaD, especially in simulating the very challenging DFG-motif flip of protein kinases, demonstrated its high efficiency and robustness in the characterization of the large-scale protein conformational change pathway and associated free energy profile.

Methodological uncertainties in drug-receptor binding free energy predictions based on classical molecular dynamics

Computational approaches are becoming an essential tool in modern drug design and discovery, with fast compound triaging using a combination of machine learning and docking techniques followed by molecular dynamics binding free energies assessment using alchemical techniques. The traditional MD-based alchemical free energy perturbation (FEP) method faces severe sampling issues that may limits its reliability in automated workflows. Here we review the major sources of uncertainty in FEP protocols for drug discovery, showing how the sampling problem can be effectively tackled by switching to nonequilibrium alchemical techniques.

Diversity of Long-Lived Intermediates along the Binding Pathway of Imatinib to Abl Kinase Revealed by MD Simulations

Imatinib, a drug used for the treatment of chronic myeloid leukemia and other cancers, works by blocking the catalytic site of pathological constitutively active Abl kinase. While the binding pose is known from X-ray crystallography, the different steps leading to the formation of the complex are not well understood. The results from extensive molecular dynamics simulations show that imatinib can primarily exit the known crystallographic binding pose through the cleft of the binding site or by sliding under the αC helix. Once displaced from the crystallographic binding pose, imatinib becomes trapped in intermediate states. These intermediates are characterized by a high diversity of ligand orientations and conformations, and relaxation timescales within this region may exceed 3-4 ms. Analysis indicates that the metastable intermediate states should be spectroscopically indistinguishable from the crystallographic binding pose, in agreement with tryptophan stopped-flow fluorescence experiments.

Conformational states dynamically populated by a kinase determine its function

Protein kinases intrinsically sample a number of conformational states with distinct catalytic and binding activities. We used nuclear magnetic resonance spectroscopy to describe in atomic-level detail how Abl kinase interconverts between an active and two discrete inactive structures. Extensive differences in key structural elements between the conformational states give rise to multiple intrinsic regulatory mechanisms. The findings explain how oncogenic mutants can counteract inhibitory mechanisms to constitutively activate the kinase. Energetic dissection revealed the contributions of the activation loop, the Asp-Phe-Gly (DFG) motif, the regulatory spine, and the gatekeeper residue to kinase regulation. Characterization of the transient conformation to which the drug imatinib binds enabled the elucidation of drug-resistance mechanisms. Structural insight into inactive states highlights how they can be leveraged for the design of selective inhibitors.

The transition between active and inactive conformations of Abl kinase studied by rock climbing and Milestoning

BACKGROUND

Kinases are a family of enzymes that catalyze the transfer of the ɤ-phosphate group from ATP to a protein's residue. Malfunctioning kinases are involved in many health problems such as cardiovascular diseases, diabetes, and cancer. Kinases transitions between multiple conformations of inactive to active forms attracted considerable interest.

METHOD

A reaction coordinate is computed for the transition between the active to inactive conformation in Abl kinase with a focus on the DFG-in to DFG-out flip. The method of Rock Climbing is used to construct a path locally, which is subsequently optimized using a functional of the entire path. The discrete coordinate sets along the reaction path are used in a Milestoning calculation of the free energy landscape and the rate of the transition.

RESULTS

The estimated transition times are between a few milliseconds and seconds, consistent with simulations of the kinetics and with indirect experimental data. The activation requires the transient dissociation of the salt bridge between Lys271 and Glu286. The salt bridge reforms once the DFG motif is stabilized by a locked conformation of Phe382. About ten residues are identified that contribute significantly to the process and are included as part of the reaction space.

CONCLUSIONS

The transition from DFG-in to DFG-out in Abl kinase was simulated using atomic resolution of a fully solvated protein yielding detailed description of the kinetics and the mechanism of the DFG flip. The results are consistent with other computational methods that simulate the kinetics and with some indirect experimental measurements.

GENERAL SIGNIFICANCE

The activation of kinases includes a conformational transition of the DFG motif that is important for enzyme activity but is not accessible to conventional Molecular Dynamics. We propose a detailed mechanism for the transition, at a timescale longer than conventional MD, using a combination of reaction path and Milestoning algorithms. The mechanism includes local structural adjustments near the binding site as well as collective interactions with more remote residues.

Identification of Druggable Kinase Target Conformations Using Markov Model Metastable States Analysis of apo-Abl

Kinases are important targets for drug development. However, accounting for the impact of possible structural rearrangements on the binding of kinase inhibitors is complicated by the extensive flexibility of their catalytic domain. The dynamic N-lobe contains four particular mobile structural elements: the Asp-Phe-Gly (DFG) motif, the phosphate (P) positioning loop, the activation (A) loop, and the αC helix. In our previous study [Meng et al. J. Chem. Theory Comput. 2018 14, 2721-2732], we combined various simulation techniques with Markov state modeling (MSM) to explore the free energy landscape of Abl kinase beyond conformations that are known from X-ray crystallography. Here we examine the resulting Markov model in greater detail by analyzing its metastable states. A characterization of the states in terms of their DFG state, P-loop, and αC conformations is presented and compared to existing classification schemes. Several metastable states are found to be structurally close to known crystal structures of different kinases in complex with a variety of inhibitors. These results suggest that the set of conformations accessible to tyrosine kinases may be shared within the entire family and that the conformational dynamics of one kinase in the absence of any ligand can provide meaningful information about possible target conformations for inhibitors of any member of the kinase family.

Structural Characterization of the Aurora Kinase B "DFG-flip" Using Metadynamics

Aurora kinase B (AKB), a Ser/Thr kinase that plays a crucial role in mitosis, is overexpressed in several cancers. Clinical inhibitors targeting AKB bind to the active DFG "in" conformation of the kinase. It would be beneficial, however, to understand if AKB is susceptible to type II kinase inhibitors that bind to the inactive, DFG "out" conformation, since type II inhibitors achieve higher kinome selectivity and higher potency in vivo. The DFG "out" conformation of AKB is not yet experimentally determined which makes the design of type II inhibitors exceedingly difficult. An alternate approach is to simulate the DFG "out" conformation from the experimentally determined DFG "in" conformation using atomistic molecular dynamics (MD) simulation. In this work, we employed metadynamics (MTD) approach to simulate the DFG "out" conformation of AKB by choosing the appropriate collective variables. We examined structural changes during the DFG-flip and determined the interactions crucial to stabilize the kinase in active and inactive states. Interestingly, the MTD approach also identified a unique transition state (DFG "up"), which can be targeted by small molecule inhibitors. Structural insights about these conformations is essential for structure-guided design of next-generation AKB inhibitors. This work also emphasizes the usefulness of MTD simulations in predicting macromolecular conformational changes at reduced computational costs.

How Electrostatic Coupling Enables Conformational Plasticity in a Tyrosine Kinase

Protein kinases are important cellular signaling molecules involved in cancer and a multitude of other diseases. It is well-known that inactive kinases display a remarkable conformational plasticity; however, the molecular mechanisms remain poorly understood. Conformational heterogeneity presents an opportunity but also a challenge in kinase drug discovery. The ability to predictively model various conformational states could accelerate selective inhibitor design. Here we performed a proton-coupled molecular dynamics study to explore the conformational landscape of a c-Src kinase. Starting from a completely inactive structure, the simulations captured all major types of conformational states without the use of a target structure, mutation, or bias. The simulations allowed us to test the experimental hypotheses regarding the mechanism of DFG flip, its coupling to the αC-helix movement, and the formation of regulatory spine. Perhaps the most significant finding is how key titratable residues, such as DFG-Asp, αC-Glu, and HRD-Asp, change protonation states dependent on the DFG, αC, and activation loop conformations. Our data offer direct evidence to support a long-standing hypothesis that protonation of Asp favors the DFG-out state and explain why DFG flip is also possible in simulations with deprotonated Asp. The simulations also revealed intermediate states, among which a unique DFG-out/α-C state formed as DFG-Asp is moved into a back pocket forming a salt bridge with catalytic Lys, which can be tested in selective inhibitor design. Our finding of how proton coupling enables the remarkable conformational plasticity may shift the paradigm of computational studies of kinases which assume fixed protonation states. Understanding proton-coupled conformational dynamics may hold a key to further innovation in kinase drug discovery.

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

What Makes a Kinase Promiscuous for Inhibitors?

ATP-competitive kinase inhibitors often bind several kinases due to the high conservation of the ATP binding pocket. Through clustering analysis of a large kinome profiling dataset, we found a cluster of eight promiscuous kinases that on average bind more than five times more kinase inhibitors than the other 398 kinases in the dataset. To understand the structural basis of promiscuous inhibitor binding, we determined the co-crystal structure of the receptor tyrosine kinase DDR1 with the type I inhibitors dasatinib and VX-680. Surprisingly, we find that DDR1 binds these type I inhibitors in an inactive conformation typically reserved for type II inhibitors. Our computational and biochemical studies show that DDR1 is unusually stable in this inactive conformation, giving a mechanistic explanation for inhibitor promiscuity. This phenotypic clustering analysis provides a strategy to obtain functional insights not available by sequence comparison alone.

Dynamics of human protein kinase Aurora A linked to drug selectivity

Protein kinases are major drug targets, but the development of highly-selective inhibitors has been challenging due to the similarity of their active sites. The observation of distinct structural states of the fully-conserved Asp-Phe-Gly (DFG) loop has put the concept of conformational selection for the DFG-state at the center of kinase drug discovery. Recently, it was shown that Gleevec selectivity for the Tyr-kinase Abl was instead rooted in conformational changes after drug binding. Here, we investigate whether protein dynamics after binding is a more general paradigm for drug selectivity by characterizing the binding of several approved drugs to the Ser/Thr-kinase Aurora A. Using a combination of biophysical techniques, we propose a universal drug-binding mechanism, that rationalizes selectivity, affinity and long on-target residence time for kinase inhibitors. These new concepts, where protein dynamics in the drug-bound state plays the crucial role, can be applied to inhibitor design of targets outside the kinome.

Protein kinases are a family of enzymes found in all living organisms. These enzymes help to control many biological processes, including cell division. When particular protein kinases do not work correctly, cells may start to divide uncontrollably, which can lead to cancer. One example is the kinase Aurora A, which is over-active in many common human cancers. As a result, researchers are currently trying to design drugs that reduce the activity of Aurora A in the hope that these could form new anticancer treatments.

In general, drugs are designed to be as specific in their action as possible to reduce the risk of harmful side effects to the patient. Designing a drug that affects a single protein kinase, however, is difficult because there are hundreds of different kinases in the body, all with similar structures. Because drugs often work by binding to specific structural features, a drug that targets one protein kinase can often alter the activity of a large number of others too.

Gleevec is a successful anti-leukemia drug that specifically works on one target kinase, producing minimal side effects. It was recently discovered that the drug works through a phenomenon called ‘induced fit’. This means that after the drug binds it causes a change in the enzyme’s overall shape that alters the activity of the enzyme. The shape change is complex, and so even small structural differences can change the effect of a particular drug.

Do other drugs that target other protein kinases also produce induced fit effects? To find out, Pitsawong, Buosi, Otten, Agafonov et al. studied how three anti-cancer drugs interact with Aurora A: two drugs specifically designed to switch off Aurora A, and Gleevec (which does not target Aurora A).

The two drugs that specifically target Aurora A were thought to work by targeting one structural feature of the enzyme. However, the biochemical and biophysical experiments performed by Pitsawong et al. revealed that these drugs instead work through an induced fit effect. By contrast, Gleevec did not trigger an induced fit on Aurora A and so bound less tightly to it.

In light of these results, Pitsawong et al. suggest that future efforts to design drugs that target protein kinases should focus on exploiting the induced fit process. This will require more research into the structure of particular kinases.

Predicting the Conformational Variability of Abl Tyrosine Kinase using Molecular Dynamics Simulations and Markov State Models

Understanding protein conformational variability remains a challenge in drug discovery. The issue arises in protein kinases, whose multiple conformational states can affect the binding of small-molecule inhibitors. To overcome this challenge, we propose a comprehensive computational framework based on Markov state models (MSMs). Our framework integrates the information from explicit-solvent molecular dynamics simulations to accurately rank-order the accessible conformational variants of a target protein. We tested the methodology using Abl kinase with a reference and blind-test set. Only half of the Abl conformational variants discovered by our approach are present in the disclosed X-ray structures. The approach successfully identified a protein conformational state not previously observed in public structures but evident in a retrospective analysis of Lilly in-house structures: the X-ray structure of Abl with WHI-P154. Using a MSM-derived model, the free energy landscape and kinetic profile of Abl was analyzed in detail highlighting opportunities for targeting the unique metastable states.

A Catalytically Disabled Double Mutant of Src Tyrosine Kinase Can Be Stabilized into an Active-Like Conformation

Tyrosine kinases are enzymes playing a critical role in cellular signaling. Molecular dynamics umbrella sampling potential of mean force computations are used to quantify the impact of activating and inactivating mutations of c-Src kinase. The potential of mean force computations predict that a specific double mutant can stabilize c-Src kinase into an active-like conformation while disabling the binding of ATP in the catalytic active site. The active-like conformational equilibrium of this catalytically dead kinase is affected by a hydrophobic unit that connects to the hydrophobic spine network via the C-helix. The αC-helix plays a crucial role in integrating the hydrophobic residues, making it a hub for allosteric regulation of kinase activity and the active conformation. The computational free-energy landscapes reported here illustrate novel design principles focusing on the important role of the hydrophobic spines. The relative stability of the spines could be exploited in future efforts to artificially engineer active-like but catalytically dead forms of protein kinases.

Computational Exploration of Conformational Transitions in Protein Drug Targets

Protein drug targets vary from highly structured to completely disordered; either way dynamics governs function. Hence, understanding the dynamical aspects of how protein targets function can enable improved interventions with drug molecules. Computational approaches offer highly detailed structural models of protein dynamics which are becoming more predictive as model quality and sampling power improve. However, the most advanced and popular models still have errors owing to imperfect parameter sets and often cannot access longer timescales of many crucial biological processes. Experimental approaches offer more certainty but can struggle to detect and measure lightly populated conformations of target proteins and subtle allostery. An emerging solution is to integrate available experimental data into advanced molecular simulations. In the future, molecular simulation in combination with experimental data may be able to offer detailed models of important drug targets such that improved functional mechanisms or selectivity can be accessed.

Millisecond dynamics of BTK reveal kinome-wide conformational plasticity within the apo kinase domain

Bruton tyrosine kinase (BTK) is a key enzyme in B-cell development whose improper regulation causes severe immunodeficiency diseases. Design of selective BTK therapeutics would benefit from improved, in-silico structural modeling of the kinase’s solution ensemble. However, this remains challenging due to the immense computational cost of sampling events on biological timescales. In this work, we combine multi-millisecond molecular dynamics (MD) simulations with Markov state models (MSMs) to report on the thermodynamics, kinetics, and accessible states of BTK’s kinase domain. Our conformational landscape links the active state to several inactive states, connected via a structurally diverse intermediate. Our calculations predict a kinome-wide conformational plasticity, and indicate the presence of several new potentially druggable BTK states. We further find that the population of these states and the kinetics of their inter-conversion are modulated by protonation of an aspartate residue, establishing the power of MD & MSMs in predicting effects of chemical perturbations.

Tyrosine Kinase Activation and Conformational Flexibility: Lessons from Src-Family Tyrosine Kinases

Protein kinases are enzymes that catalyze the covalent transfer of the γ-phosphate of an adenosine triphosphate (ATP) molecule onto a tyrosine, serine, threonine, or histidine residue in the substrate and thus send a chemical signal to networks of downstream proteins. They are important cellular signaling enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Unregulated protein kinase activity is often associated with a wide range of diseases, therefore making protein kinases major therapeutic targets. A prototypical system of central interest to understand the regulation of kinase activity is provided by tyrosine kinase c-Src, which belongs to the family of Src-related non-receptor tyrosine kinases (SFKs). Although the broad picture of autoinhibition via the regulatory domains and via the phosphorylation of the C-terminal tail is well characterized from a structural point of view, a detailed mechanistic understanding at the atomic-level is lacking. Advanced computational methods based on all-atom molecular dynamics (MD) simulations are employed to advance our understanding of tyrosine kinase activation. The computational studies suggest that the isolated kinase domain (KD) is energetically most favorable in the inactive conformation when the activation loop (A-loop) of the KD is not phosphorylated. The KD makes transient visits to a catalytically competent active-like conformation. The process of bimolecular trans-autophosphorylation of the A-loop eventually locks the KD in the active state. Activating point mutations may act by slightly increasing the population of the active-like conformation, enhancing the availability of the A-loop to be phosphorylated. The Src-homology 2 (SH2) and Src-homology 3 (SH3) regulatory domains, depending upon their configuration, either promote the inactive or the active state of the kinase domain. In addition to the roles played by the SH3, SH2, and KD, the Src-homology 4-Unique domain (SH4-U) region also serves as a key moderator of substrate specificity and kinase function. Thus, a fundamental understanding of the conformational propensity of the SH4-U region and how this affects the association to the membrane surface are likely to lead to the discovery of new intermediate states and alternate strategies for inhibition of kinase activity for drug discovery. The existence of a multitude of KD conformations poses a great challenge aimed at the design of specific inhibitors. One promising computational strategy to explore the conformational flexibility of the KD is to construct Markov state models from aggregated MD data.

The Activation of c-Src Tyrosine Kinase: Conformational Transition Pathway and Free Energy Landscape

Tyrosine kinases are important cellular signaling allosteric enzymes that regulate cell growth, proliferation, metabolism, differentiation, and migration. Their activity must be tightly controlled, and malfunction can lead to a variety of diseases, particularly cancer. The nonreceptor tyrosine kinase c-Src, a prototypical model system and a representative member of the Src-family, functions as complex multidomain allosteric molecular switches comprising SH2 and SH3 domains modulating the activity of the catalytic domain. The broad picture of self-inhibition of c-Src via the SH2 and SH3 regulatory domains is well characterized from a structural point of view, but a detailed molecular mechanism understanding is nonetheless still lacking. Here, we use advanced computational methods based on all-atom molecular dynamics simulations with explicit solvent to advance our understanding of kinase activation. To elucidate the mechanism of regulation and self-inhibition, we have computed the pathway and the free energy landscapes for the "inactive-to-active" conformational transition of c-Src for different configurations of the SH2 and SH3 domains. Using the isolated c-Src catalytic domain as a baseline for comparison, it is observed that the SH2 and SH3 domains, depending upon their bound orientation, promote either the inactive or active state of the catalytic domain. The regulatory structural information from the SH2-SH3 tandem is allosterically transmitted via the N-terminal linker of the catalytic domain. Analysis of the conformational transition pathways also illustrates the importance of the conserved tryptophan 260 in activating c-Src, and reveals a series of concerted events during the activation process.

Energetics and structural characterization of the “DFG-flip” conformational transition of B-RAF kinase: a SITS molecular dynamics study

A combination of a homology modeling technique and an enhanced sampling molecular dynamics simulation implemented using the SITS method is employed to compute a detailed map of the free-energy landscape and explore the conformational transition pathway of B-RAF kinase.

Relative Binding Free Energy Calculations Applied to Protein Homology Models

A significant challenge and potential high-value application of computer-aided drug design is the accurate prediction of protein-ligand binding affinities. Free energy perturbation (FEP) using molecular dynamics (MD) sampling is among the most suitable approaches to achieve accurate binding free energy predictions, due to the rigorous statistical framework of the methodology, correct representation of the energetics, and thorough treatment of the important degrees of freedom in the system (including explicit waters). Recent advances in sampling methods and force fields coupled with vast increases in computational resources have made FEP a viable technology to drive hit-to-lead and lead optimization, allowing for more efficient cycles of medicinal chemistry and the possibility to explore much larger chemical spaces. However, previous FEP applications have focused on systems with high-resolution crystal structures of the target as starting points-something that is not always available in drug discovery projects. As such, the ability to apply FEP on homology models would greatly expand the domain of applicability of FEP in drug discovery. In this work we apply a particular implementation of FEP, called FEP+, on congeneric ligand series binding to four diverse targets: a kinase (Tyk2), an epigenetic bromodomain (BRD4), a transmembrane GPCR (A2A), and a protein-protein interaction interface (BCL-2 family protein MCL-1). We apply FEP+ using both crystal structures and homology models as starting points and find that the performance using homology models is generally on a par with the results when using crystal structures. The robustness of the calculations to structural variations in the input models can likely be attributed to the conformational sampling in the molecular dynamics simulations, which allows the modeled receptor to adapt to the "real" conformation for each ligand in the series. This work exemplifies the advantages of using all-atom simulation methods with full system flexibility and offers promise for the general application of FEP to homology models, although additional validation studies should be performed to further understand the limitations of the method and the scenarios where FEP will work best.

Effective Riemannian Diffusion Model for Conformational Dynamics of Biomolecular Systems

We present a Riemannian formalism for effective diffusion of biomolecules in collective variable spaces that provides a robust framework for conformational free energy calculation methods. Unlike their Euclidean counterparts, the Riemannian potential of mean force (PMF) and minimum free energy path (MFEP) are invariant under coordinate transformations. The presented formalism can be readily employed to modify the collective variable based enhanced sampling techniques, such as umbrella sampling (US) commonly used in biomolecular simulations, to take into account the role of intrinsic geometry of collective variable space. Although our model is mathematically equivalent to a Euclidean diffusion with a position-dependent diffusion tensor, the Riemannian formulation provides a more convenient framework for free energy calculation methods and path-finding algorithms aimed at characterizing the effective conformational dynamics of biomolecules. A simple three-dimensional toy model and a pentapeptide (met-enkephalin) simulated in an explicit solvent environment are used to illustrate the workings of the formalism and its implementation.

Sensitivity in Binding Free Energies Due to Protein Reorganization

Tremendous recent improvements in computer hardware, coupled with advances in sampling techniques and force fields, are now allowing protein-ligand binding free energy calculations to be routinely used to aid pharmaceutical drug discovery projects. However, despite these recent innovations, there are still needs for further improvement in sampling algorithms to more adequately sample protein motion relevant to protein-ligand binding. Here, we report our work identifying and studying such clear and remaining needs in the apolar cavity of T4 lysozyme L99A. In this study, we model recent experimental results that show the progressive opening of the binding pocket in response to a series of homologous ligands.1 Even while using enhanced sampling techniques, we demonstrate that the predicted relative binding free energies (RBFE) are sensitive to the initial protein conformational state. Particularly, we highlight the importance of sufficient sampling of protein conformational changes and demonstrate how inclusion of three key protein residues in the "hot" region of the FEP/REST simulation improves the sampling and resolves this sensitivity, given enough simulation time.

Molecular Dynamics Simulations and Classical Multidimensional Scaling Unveil New Metastable States in the Conformational Landscape of CDK2

Protein kinases are key regulatory nodes in cellular networks and their function has been shown to be intimately coupled with their structural flexibility. However, understanding the key structural mechanisms of large conformational transitions remains a difficult task. CDK2 is a crucial regulator of cell cycle. Its activity is finely tuned by Cyclin E/A and the catalytic segment phosphorylation, whereas its deregulation occurs in many types of cancer. ATP competitive inhibitors have failed to be approved for clinical use due to toxicity issues raised by a lack of selectivity. However, in the last few years type III allosteric inhibitors have emerged as an alternative strategy to selectively modulate CDK2 activity. In this study we have investigated the conformational variability of CDK2. A low dimensional conformational landscape of CDK2 was modeled using classical multidimensional scaling on a set of 255 crystal structures. Microsecond-scale plain and accelerated MD simulations were used to populate this landscape by using an out-of-sample extension of multidimensional scaling. CDK2 was simulated in the apo-form and in complex with the allosteric inhibitor 8-anilino-1-napthalenesulfonic acid (ANS). The apo-CDK2 landscape analysis showed a conformational equilibrium between an Src-like inactive conformation and an active-like form. These two states are separated by different metastable states that share hybrid structural features with both forms of the kinase. In contrast, the CDK2/ANS complex landscape is compatible with a conformational selection picture where the binding of ANS in proximity of the αC helix causes a population shift toward the inactive conformation. Interestingly, the new metastable states could enlarge the pool of candidate structures for the development of selective allosteric CDK2 inhibitors. The method here presented should not be limited to the CDK2 case but could be used to systematically unmask similar mechanisms throughout the human kinome.

Advanced molecular dynamics simulation methods for kinase drug discovery

Interest in the application of molecular dynamics (MD) simulations has increased in the field of protein kinase (PK) drug discovery. PKs belong to an important drug target class because they are directly involved in a number of diseases, including cancer. MD methods simulate dynamic biological and chemical events at an atomic level. This information can be combined with other in silico and experimental methods to efficiently target selected receptors. In this review, we present common and advanced methods of MD simulations and we focus on the recent applications of MD-based methodologies that provided significant insights into the elucidation of biological mechanisms involving PKs and into the discovery of novel kinase inhibitors.

Allosteric inhibition of c-Met kinase in sub-microsecond molecular dynamics simulations induced by its inhibitor, tivantinib

Molecular dynamics simulations showed that conformation transition of c-Met from DFG-in to DFG-out may accomplish rapidly in the presence of tivantinib. A unique binding mode of tivantinib was found to be critical for this “DFG-flip”.

Computational study of the W260A activating mutant of Src tyrosine kinase

Tyrosine kinases are enzymes playing a critical role in cellular signaling. Mutations causing increased in kinase activity are often associated with cancer and various pathologies. One example in Src tyrosine kinases is offered by the substitution of the highly conserved tryptophan 260 by an alanine (W260A), which has been shown to cause an increase in activity. Here, molecular dynamics simulations based on atomic models are carried out to characterize the conformational changes in the linker region and the catalytic (kinase) domain of Src kinase to elucidate the impact of the W260A mutation. Umbrella sampling calculations show that the conformation of the linker observed in the assembled down-regulated state of the kinase is most favored when the kinase domain is in the inactive state, whereas the conformation of the linker observed in the re-assembled up-regulated state of the kinase is favored when the kinase domain is in the unphosphorylated active-like state. The calculations further indicate that there are only small differences between the WT and W260A mutant. In both cases, the intermediates states are very similar and the down-regulated inactive conformation is the most stable state. However, the calculations also show that the free energy cost to reach the unphosphorylated active-like conformation is slightly smaller for the W260A mutant compared with WT. A simple kinetic model is developed and submitted to a Bayesian Monte Carlo analysis to illustrate how such small differences can contribute to accelerate the trans-autophosphorylation reaction and yield a large increase in the activity of the mutant as observed experimentally.

Evolution and intelligent design in drug development

Sophisticated protein kinase networks, empowering complexity in higher organisms, are also drivers of devastating diseases such as cancer. Accordingly, these enzymes have become major drug targets of the twenty-first century. However, the holy grail of designing specific kinase inhibitors aimed at specific cancers has not been found. Can new approaches in cancer drug design help win the battle with this multi-faced and quickly evolving enemy? In this perspective we discuss new strategies and ideas that were born out of a recent breakthrough in understanding the molecular basis underlying the clinical success of the cancer drug Gleevec. An "old" method, stopped-flow kinetics, combined with old enzymes, the ancestors dating back up to about billion years, provides an unexpected outlook for future intelligent design of drugs.