Cryptic pockets, that is, sites on protein targets that only become apparent when drugs bind, provide a promising alternative to classical binding sites for drug development. Here, we investigate the ...nature and dynamical properties of cryptic sites in four pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. We find that the studied cryptic sites do not correspond to local minima in the computed conformational free energy landscape of the unliganded proteins. They thus promptly close in all of the molecular dynamics simulations performed, irrespective of the force-field used. Temperature-based enhanced sampling approaches, such as Parallel Tempering, do not improve the situation, as the entropic term does not help in the opening of the sites. The use of fragment probes helps, as in long simulations occasionally it leads to the opening and binding to the cryptic sites. Our observed mechanism of cryptic site formation is suggestive of an interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, we developed a novel Hamiltonian Replica Exchange-based method “SWISH” (Sampling Water Interfaces through Scaled Hamiltonians), which combined with probes resulted in a promising general approach for cryptic site discovery. We also addressed the issue of “false-positives” and propose a simple approach to distinguish them from druggable cryptic pockets. Our simulations, whose cumulative sampling time was more than 200 μs, help in clarifying the molecular mechanism of pocket formation, providing a solid basis for the choice of an efficient computational method.
Conspectus This Account highlights recent advances and discusses major challenges in investigations of cryptic (hidden) binding sites by molecular simulations. Cryptic binding sites are not visible ...in protein targets crystallized without a ligand and only become visible crystallographically upon binding events. These sites have been shown to be druggable and might provide a rare opportunity to target difficult proteins. However, due to their hidden nature, they are difficult to find through experimental screening. Computational methods based on atomistic molecular simulations remain one of the best approaches to identify and characterize cryptic binding sites. However, not all methods are equally efficient. Some are more apt at quickly probing protein dynamics but do not provide thermodynamic or druggability information, while others that are able to provide such data are demanding in terms of time and resources. Here, we review the recent contributions of mixed-solvent simulations, metadynamics, Markov state models, and other enhanced sampling methods to the field of cryptic site identification and characterization. We discuss how these methods were able to provide precious information on the nature of the site opening mechanisms, to predict previously unknown sites which were used to design new ligands, and to compute the free energy landscapes and kinetics associated with the opening of the sites and the binding of the ligands. We highlight the potential and the importance of such predictions in drug discovery, especially for difficult (“undruggable”) targets. We also discuss the major challenges in the field and their possible solutions.
Conspectus This Account highlights recent advances and discusses major challenges in the field of drug-target recognition, binding, and unbinding studied using metadynamics-based approaches, with ...particular emphasis on their role in structure-based design. Computational chemistry has significantly contributed to drug design and optimization in an extremely broad range of areas, including prediction of target druggability and drug likeness, de novo design, fragment screening, ligand docking, estimation of binding affinity, and modulation of ADMET (absorption, distribution, metabolism, excretion, toxicity) properties. Computationally driven drug discovery must continuously adapt to keep pace with the evolving knowledge of the factors that modulate the pharmacological action of drugs. There is thus an urgent need for novel computational approaches that integrate the vast amount of complex information currently available for small (bio)organic compounds, biologically relevant targets and their complexes, while also accounting accurately for the thermodynamics and kinetics of drug-target association, the intrinsic dynamical behavior of biomolecular systems, and the complexity of protein–protein networks. Understanding the mechanism of drug binding to and unbinding from biological targets is fundamental for optimizing lead compounds and designing novel biologically active ones. One major challenge is the accurate description of the conformational complexity prior to and upon formation of drug–target complexes. Recently, enhanced sampling methods, including metadynamics and related approaches, have been successfully applied to investigate complex mechanisms of drugs binding to flexible targets. Metadynamics is a family of enhanced sampling techniques aimed at enhancing the rare events and reconstructing the underlying free energy landscape as a function of a set of order parameters, usually referred to as collective variables. Studies of drug binding mechanisms have predicted the most probable association and dissociation pathways and the related binding free energy profile. In addition, the availability of an efficient open-source implementation, running on cost-effective GPU (i.e., graphical processor unit) architectures, has considerably decreased the learning curve and the computational costs of the methods, and increased their adoption by the community. Here, we review the recent contributions of metadynamics and other enhanced sampling methods to the field of drug–target recognition and binding. We discuss how metadynamics has been used to search for transition states, to predict binding and unbinding paths, to treat conformational flexibility, and to compute free energy profiles. We highlight the importance of such predictions in drug discovery. Major challenges in the field and possible solutions will finally be discussed.
Path Collective Variables (PCVs) are a set of path-like variables that have been successfully used to investigate complex chemical and biological processes and compute their associated free energy ...surfaces and kinetics. Their current implementation relies on general, but at times inefficient, metrics (such as RMSD or DRMSD) to evaluate the distance between the instantaneous conformational state during the simulation and the reference coordinates defining the path. In this work, we present a new algorithm to construct optimal PCVs metrics as linear combinations of different CVs weighted through a spectral gap optimization procedure. The method was tested first on a simple model, trialanine peptide, in vacuo and then on a more complex path of an anticancer inhibitor binding to its pharmacological target. We also compared the results to those obtained with other path-based algorithms. We find that not only our proposed approach is able to automatically select relevant CVs for the PCVs metric but also that the resulting PCVs allow for reconstructing the associated free energy very efficiently. What is more, at difference with other path-based methods, our algorithm is able to explore nonlocally the reaction path space.
Calculating absolute binding free energies is challenging and important. In this paper, we test some recently developed metadynamics-based methods and develop a new combination with a Hamiltonian ...replica-exchange approach. The methods were tested on 18 chemically diverse ligands with a wide range of different binding affinities to a complex target; namely, human soluble epoxide hydrolase. The results suggest that metadynamics with a funnel-shaped restraint can be used to calculate, in a computationally affordable and relatively accurate way, the absolute binding free energy for small fragments. When used in combination with an optimal pathlike variable obtained using machine learning or with the Hamiltonian replica-exchange algorithm SWISH, this method can achieve reasonably accurate results for increasingly complex ligands, with a good balance of computational cost and speed. An additional benefit of using the combination of metadynamics and SWISH is that it also provides useful information about the role of water in the binding mechanism.
Due to its inhibition of the Abl kinase domain in the BCR-ABL fusion protein, imatinib is strikingly effective in the initial stage of chronic myeloid leukemia with more than 90% of the patients ...showing complete remission. However, as in the case of most targeted anti-cancer therapies, the emergence of drug resistance is a serious concern. Several drug-resistant mutations affecting the catalytic domain of Abl and other tyrosine kinases are now known. But, despite their importance and the adverse effect that they have on the prognosis of the cancer patients harboring them, the molecular mechanism of these mutations is still debated. Here by using long molecular dynamics simulations and large-scale free energy calculations complemented by in vitro mutagenesis and microcalorimetry experiments, we model the effect of several widespread drug-resistant mutations of Abl. By comparing the conformational free energy landscape of the mutants with those of the wild-type tyrosine kinases we clarify their mode of action. It involves significant and complex changes in the inactive-to-active dynamics and entropy/enthalpy balance of two functional elements: the activation-loop and the conserved DFG motif. What is more the T315I gatekeeper mutant has a significant impact on the binding mechanism itself and on the binding kinetics.
Allostery is a phenomenon observed in many proteins where binding of a macromolecular partner or a small-molecule ligand at one location leads to specific perturbations at a site not in direct ...contact with the region where the binding occurs. The list of proteins under allosteric regulation includes AGC protein kinases. AGC kinases have a conserved allosteric site, the phosphoinositide-dependent protein kinase 1 (PDK1)-interacting fragment (PIF) pocket, which regulates protein ATP-binding, activity, and interaction with substrates. In this study, we identify small molecules that bind to the ATP-binding site and affect the PIF pocket of AGC kinase family members, PDK1 and Aurora kinase. We describe the mechanistic details and show that although PDK1 and Aurora kinase inhibitors bind to the conserved ATP-binding site, they differentially modulate physiological interactions at the PIF-pocket site. Our work outlines a strategy for developing bidirectional small-molecule allosteric modulators of protein kinases and other signaling proteins.
•PS653 inhibits the binding of PIFtide by interaction with the ATP-binding site•Adenosine binds at the ATP-binding site and enhances the binding of PIFtide•Molecular dynamics simulations describe the conformational changes of PDK1•Potent drugs to the ATP site of PDK1 have different effects on the PIF pocket
Schulze et al. show that compounds binding to the ATP-binding site of a kinase can produce profound effects on a distant site, enhancing or inhibiting the interaction with allosteric regulators. The principle can be exploited in drug discovery and development.
Whether recent updates and new releases of atomistic force fields can model the structural and dynamical properties of proteins containing both folded and partially disordered domains is still ...unclear. To address this fundamental question, we tested eight recently released force fields against our set of nuclear magnetic resonance (NMR) observables for a complex and medically relevant system, the major factor VIII binding region on the von Willebrand factor. This biomedically important region comprises both a folded and a partially structured domain. By using an enhanced sampling technique (temperature replica-exchange molecular dynamics simulations), we find that some force fields indeed rise to the challenge and capture the structural and dynamical features of the NMR ensemble and, therefore, are the appropriate choice for simulations of proteins with partially structured domains. What is more, we show that only such force fields can qualitatively capture the effects of a pathogenic mutation on the structural ensemble.
In all living organisms, coenzyme A (CoA) is an essential cofactor with a unique design allowing it to function as an acyl group carrier and a carbonyl-activating group in diverse biochemical ...reactions. It is synthesized in a highly conserved process in prokaryotes and eukaryotes that requires pantothenic acid (vitamin B5), cysteine and ATP. CoA and its thioester derivatives are involved in major metabolic pathways, allosteric interactions and the regulation of gene expression. A novel unconventional function of CoA in redox regulation has been recently discovered in mammalian cells and termed protein CoAlation. Here, we report for the first time that protein CoAlation occurs at a background level in exponentially growing bacteria and is strongly induced in response to oxidizing agents and metabolic stress. Over 12% of
gene products were shown to be CoAlated in response to diamide-induced stress
CoAlation of
glyceraldehyde-3-phosphate dehydrogenase was found to inhibit its enzymatic activity and to protect the catalytic cysteine 151 from overoxidation by hydrogen peroxide. These findings suggest that in exponentially growing bacteria, CoA functions to generate metabolically active thioesters, while it also has the potential to act as a low-molecular-weight antioxidant in response to oxidative and metabolic stress.
Our current understanding of epidermal growth factor receptor (EGFR) autoinhibition is based on X-ray structural data of monomer and dimer receptor fragments and does not explain how mutations ...achieve ligand-independent phosphorylation. Using a repertoire of imaging technologies and simulations we reveal an extracellular head-to-head interaction through which ligand-free receptor polymer chains of various lengths assemble. The architecture of the head-to-head interaction prevents kinase-mediated dimerisation. The latter, afforded by mutation or intracellular treatments, splits the autoinhibited head-to-head polymers to form stalk-to-stalk flexible non-extended dimers structurally coupled across the plasma membrane to active asymmetric tyrosine kinase dimers, and extended dimers coupled to inactive symmetric kinase dimers. Contrary to the previously proposed main autoinhibitory function of the inactive symmetric kinase dimer, our data suggest that only dysregulated species bear populations of symmetric and asymmetric kinase dimers that coexist in equilibrium at the plasma membrane under the modulation of the C-terminal domain.
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