Network-based modelling and percolation analysis of conformational dynamics and activation in the CDK2 and CDK4 proteins: dynamic and energetic polarization of the kinase lobes may determine divergence of the regulatory mechanisms.

The overarching goal of delineating molecular principles underlying differentiation of the activation mechanisms in cyclin-dependent kinases (CDKs) is important for understanding regulatory divergences among closely related kinases which can be exploited in drug discovery of targeted and allosteric inhibitors. To systematically characterize dynamic, energetic and network signatures of the activation mechanisms, we combined atomistic simulations and elastic network modeling with the analysis of the residue interaction networks and rigidity decomposition of the CDK2-cyclin A and CDK4-cyclin D1/D3 complexes. The results of this study show that divergences in the activation mechanisms of CDK2 and CDK4 may be determined by differences in stabilization and allosteric cooperativity of the regulatory regions. We show that differential stabilization of the kinase lobes in the CDK4-cyclin D complexes caused by the elevated mobility of the N-lobe residues can weaken allosteric interactions between regulatory regions and compromise cooperativity of the inter-lobe motions that is required to trigger activating transitions. Network modelling and percolation analysis were used to emulate thermal unfolding and perform decomposition of rigid and flexible regions in the CDK2 and CDK4 complexes. These simulations showed that the percolation phase transition in the CDK2-cyclin A complexes is highly cooperative and driven by allosteric coupling between functional regions from both kinase lobes. In contrast, the imbalances in the distribution of rigid and flexible regions for the CDK4-cyclin D complexes, which are manifested by the intrinsic instability of the N-lobe, may weaken allosteric interactions and preclude productive activation. The results of this integrative computational study offer a simple and robust network-based model that explains regulatory divergences between CDK2 and CDK4 kinases.

Molecular dynamics simulations and modelling of the residue interaction networks in the BRAF kinase complexes with small molecule inhibitors: probing the allosteric effects of ligand-induced kinase dimerization and paradoxical activation.

Protein kinases are central to proper functioning of cellular networks and are an integral part of many signal transduction pathways. The family of protein kinases represents by far the largest and most important class of therapeutic targets in oncology. Dimerization-induced activation has emerged as a common mechanism of allosteric regulation in BRAF kinases, which play an important role in growth factor signalling and human diseases. Recent studies have revealed that most of the BRAF inhibitors can induce dimerization and paradoxically stimulate enzyme transactivation by conferring an active conformation in the second monomer of the kinase dimer. The emerging connections between inhibitor binding and BRAF kinase domain dimerization have suggested a molecular basis of the activation mechanism in which BRAF inhibitors may allosterically modulate the stability of the dimerization interface and affect the organization of residue interaction networks in BRAF kinase dimers. In this work, we integrated structural bioinformatics analysis, molecular dynamics and binding free energy simulations with the protein structure network analysis of the BRAF crystal structures to determine dynamic signatures of BRAF conformations in complexes with different types of inhibitors and probe the mechanisms of the inhibitor-induced dimerization and paradoxical activation. The results of this study highlight previously unexplored relationships between types of BRAF inhibitors, inhibitor-induced changes in the residue interaction networks and allosteric modulation of the kinase activity. This study suggests a mechanism by which BRAF inhibitors could promote or interfere with the paradoxical activation of BRAF kinases, which may be useful in informing discovery efforts to minimize the unanticipated adverse biological consequences of these therapeutic agents.

Small-world networks of residue interactions in the Abl kinase complexes with cancer drugs: topology of allosteric communication pathways can determine drug resistance effects.

The human protein kinases play a fundamental regulatory role in orchestrating functional processes in complex cellular networks. Understanding how conformational equilibrium between functional kinase states can be modulated by ligand binding or mutations is critical for quantifying molecular basis of allosteric regulation and drug resistance. In this work, molecular dynamics simulations of the Abl kinase complexes with cancer drugs (Imatinib and Dasatinib) were combined with structure-based network modeling to characterize dynamics of the residue interaction networks in these systems. The results have demonstrated that structural architecture of kinase complexes can produce a small-world topology of the interaction networks. Our data have indicated that specific Imatinib binding to a small number of highly connected residues could lead to network-bridging effects and allow for efficient allosteric communication, which is mediated by a dominant pathway sensitive to the unphosphorylated Abl state. In contrast, Dasatinib binding to the active kinase form may activate a broader ensemble of allosteric pathways that are less dependent on the phosphorylation status of Abl and provide a better balance between the efficiency and resilience of signaling routes. Our results have unveiled how differences in the residue interaction networks and allosteric communications of the Abl kinase complexes can be directly related to drug resistance effects. This study offers a plausible perspective on how efficiency and robustness of the residue interaction networks and allosteric pathways in kinase structures may be associated with protein responses to drug binding.

Hierarchy of simulation models in predicting molecular recognition mechanisms from the binding energy landscapes: structural analysis of the peptide complexes with SH2 domains.

Computer simulations using the simplified energy function and simulated tempering dynamics have accurately determined the native structure of the pYVPML, SVLpYTAVQPNE, and SPGEpYVNIEF peptides in the complexes with SH2 domains. Structural and equilibrium aspects of the peptide binding with SH2 domains have been studied by generating temperature-dependent binding free energy landscapes. Once some native peptide-SH2 domain contacts are constrained, the underlying binding free energy profile has the funnel-like shape that leads to a rapid and consistent acquisition of the native structure. The dominant native topology of the peptide-SH2 domain complexes represents an extended peptide conformation with strong specific interactions in the phosphotyrosine pocket and hydrophobic interactions of the peptide residues C-terminal to the pTyr group. The topological features of the peptide-protein interface are primarily determined by the thermodynamically stable phosphotyrosyl group. A diversity of structurally different binding orientations has been observed for the amino-terminal residues to the phosphotyrosine. The dominant native topology for the peptide residues carboxy-terminal to the phosphotyrosine is tolerant to flexibility in this region of the peptide-SH2 domain interface observed in equilibrium simulations. The energy landscape analysis has revealed a broad, entropically favorable topology of the native binding mode for the bound peptides, which is robust to structural perturbations. This could provide an additional positive mechanism underlying tolerance of the SH2 domains to hydrophobic conservative substitutions in the peptide specificity region.

Deciphering common failures in molecular docking of ligand-protein complexes.

Common failures in predicting crystal structures of ligand-protein complexes are investigated for three ligand-protein systems by a combined thermodynamic and kinetic analysis of the binding energy landscapes. Misdocked predictions in ligand-protein docking are classified as 'soft' and 'hard' failures. While a soft failure arises when the search algorithm is unable to find the global energy minimum corresponding to the crystal structure, a hard failure results from a flaw of the energy function to qualify the crystal structure as the predicted lowest energy conformation in docking simulations. We find that neither the determination of a single structure with the lowest energy nor finding the most common binding mode is sufficient to predict crystal structures of the complexes, which belong to the category of hard failures. In a proposed hierarchical approach, structural similarity clustering of the conformations, generated from equilibrium simulations with the simplified energy function, is followed by energy refinement with the AMBER force field. This protocol, that involves a hierarchy of energy functions, resolves some common failures in ligand-protein docking and detects crystallographic binding modes that were not found during docking simulations.

Towards understanding the mechanisms of molecular recognition by computer simulations of ligand-protein interactions.

The thermodynamic and kinetic aspects of molecular recognition for the methotrexate (MTX)-dihydrofolate reductase (DHFR) ligand-protein system are investigated by the binding energy landscape approach. The impact of 'hot' and 'cold' errors in ligand mutations on the thermodynamic stability of the native MTX-DHFR complex is analyzed, and relationships between the molecular recognition mechanism and the degree of ligand optimization are discussed. The nature and relative stability of intermediates and thermodynamic phases on the ligand-protein association pathway are studied, providing new insights into connections between protein folding and molecular recognition mechanisms, and cooperativity of ligand-protein binding. The results of kinetic docking simulations are rationalized based on the thermodynamic properties determined from equilibrium simulations and the shape of the underlying binding energy landscape. We show how evolutionary ligand selection for a receptor active site can produce well-optimized ligand-protein systems such as MTX-DHFR complex with the thermodynamically stable native structure and a direct transition mechanism of binding from unbound conformations to the unique native structure.

Thermodynamics and kinetics of ligand-protein binding studied with the weighted histogram analysis method and simulated annealing.

The thermodynamics of ligand-protein molecular recognition is investigated by the energy landscape approach for two systems: methotrexate(MTX)--dihydrofolate reductase(DHFR) and biotin-streptavidin. The temperature-dependent binding free energy profile is determined using the weighted histogram analysis method. Two different force fields are employed in this study: a simplified model of ligand-protein interactions and the AMBER force field with a soft core smoothing component, used to soften the repulsive part of the potential. The results of multiple docking simulations are rationalized from the shape of the binding free energy profile that characterizes the thermodynamics of the binding process.

Predicting structural effects in HIV-1 protease mutant complexes with flexible ligand docking and protein side-chain optimization.

We present a computational approach for predicting structures of ligand-protein complexes and analyzing binding energy landscapes that combines Monte Carlo simulated annealing technique to determine the ligand bound conformation with the dead-end elimination algorithm for side-chain optimization of the protein active site residues. Flexible ligand docking and optimization of mobile protein side-chains have been performed to predict structural effects in the V32I/I47V/V82I HIV-1 protease mutant bound with the SB203386 ligand and in the V82A HIV-1 protease mutant bound with the A77003 ligand. The computational structure predictions are consistent with the crystal structures of these ligand-protein complexes. The emerging relationships between ligand docking and side-chain optimization of the active site residues are rationalized based on the analysis of the ligand-protein binding energy landscape.

Molecular anchors with large stability gaps ensure linear binding free energy relationships for hydrophobic substituents.

Ligand-protein docking simulations are employed to analyze the binding energy landscape of the pipecolinyl fragment that serves as a recognition core of the FK506 ligand in binding with the FKBP12 protein. This fragment acts as a molecular anchor that specifically binds within the protein active site in a unique binding mode, in harmony with the structure of the FK506-FKBP12 complex. Molecular anchors are characterized by a large stability gap, defined to be the free energy of a ligand bound in the native binding mode relative to the free energy of alternative binding modes. For ligands that share a common anchor fragment, a linear binding free energy relationship may be expected for hydrophobic substituents provided they do not abrogate the anchor binding mode. Changes in solvent-accessible surface area for these peripheral groups are used to rationalize the relative binding affinities of a series of FKBP12-ligand complexes which share the pipecolinyl anchor fragment. A series of benzene derivatives that bind to a mutant form of T4 lysozyme is also analyzed, and implications for structure-based drug design are discussed.

Mean field analysis of FKBP12 complexes with FK506 and rapamycin: implications for a role of crystallographic water molecules in molecular recognition and specificity.

Mean field analysis of FKBP12 complexes with FK506 and rapamycin has been performed by using structures obtained from molecular docking simulations on a simple, yet robust molecular recognition energy landscape. When crystallographic water molecules are included in the simulations as an extension of the FKBP12 protein surface, there is an appreciable stability gap between the energy of the native FKBP12-FK506 complex and energies of conformations with the "native-like" binding mode. By contrast, the energy spectrum of the FKBP12-rapamycin complex is dense regardless of the presence of the water molecules. The stability gap in the FKBP12-FK506 system is determined by two critical water molecules from the effector region that participate in a network of specific hydrogen bond interactions. This interaction pattern protects the integrity and precision of the composite ligand-protein effector surface in the binary FKBP12-FK506 complex and is preserved in the crystal structure of the FKBP12-FK506-calcineurin ternary complex. These features of the binding energy landscapes provide useful insights into specific and nonspecific aspects of FK506 and rapamycin recognition.

Structural consensus in ligand-protein docking identifies recognition peptide motifs that bind streptavidin.

Computational structure prediction of streptavidin-peptide complexes for known recognition sequences and a number of random di-, tri-, and tetrapeptides has been conducted, and mechanisms of peptide recognition with streptavidin have been investigated by a new computational protocol. The structural consensus criterion, which is computed from multiple docking simulations and measures the accessibility of the dominant binding mode, identifies recognition motifs from a set of random peptide sequences, whereas energetic analysis is less discriminatory. The predicted conformations of recognition tripeptide and tetrapeptide sequences are also in structural harmony and composed of peptide fragments that are individually unfrustrated in their bound conformation, resulting in a minimally frustrated energy landscape for recognition peptides.

Unraveling principles of lead discovery: from unfrustrated energy landscapes to novel molecular anchors.

The search for novel leads is a critical step in the drug discovery process. Computational approaches to identify new lead molecules have focused on discovering complete ligands by evaluating the binding affinity of a large number of candidates, a task of considerable complexity. A new computational method is introduced in this work based on the premise that the primary molecular recognition event in the protein binding site may be accomplished by small core fragments that serve as molecular anchors, providing a structurally stable platform that can be subsequently tailored into complete ligands. To fulfill its role, we show that an effective molecular anchor must meet both the thermodynamic requirement of relative energetic stability of a single binding mode and its consistent kinetic accessibility, which may be measured by the structural consensus of multiple docking simulations. From a large number of candidates, this technique is able to identify known core fragments responsible for primary recognition by the FK506 binding protein (FKBP-12), along with a diverse repertoire of novel molecular cores. By contrast, absolute energetic criteria for selecting molecular anchors are found to be promiscuous. A relationship between a minimum frustration principle of binding energy landscapes and receptor-specific molecular anchors in their role as "recognition nuclei" is established, thereby unraveling a mechanism of lead discovery and providing a practical route to receptor-biased computational combinatorial chemistry.

Exploring the energy landscapes of molecular recognition by a genetic algorithm: analysis of the requirements for robust docking of HIV-1 protease and FKBP-12 complexes.

Energy landscapes of molecular recognition are explored by performing "semi-rigid" docking of FK-506 and rapamycin with the Fukisawa binding protein (FKBP-12), and flexible docking simulations of the Ro-31-8959 and AG-1284 inhibitors with HIV-1 protease by a genetic algorithm. The requirements of a molecular recognition model to meet thermodynamic and kinetic criteria of ligand-protein docking simultaneously are investigated using a family of simple molecular recognition energy functions. The critical factor that determines the success rate in predicting the structure of ligand-protein complexes is found to be the roughness of the binding energy landscape, in accordance with a minimal frustration principle. The results suggest that further progress in structure prediction of ligand-protein complexes can be achieved by designing molecular recognition energy functions that generate binding landscapes with reduced frustration.

A mean field model of ligand-protein interactions: implications for the structural assessment of human immunodeficiency virus type 1 protease complexes and receptor-specific binding.

We propose a general mean field model of ligand-protein interactions to determine the thermodynamic equilibrium of a system at finite temperature. The method is employed in structural assessments of two human immuno-deficiency virus type 1 protease complexes where the gross effects of protein flexibility are incorporated by utilizing a data base of crystal structures. Analysis of the energy spectra for these complexes has revealed that structural and thermo-dynamic aspects of molecular recognition can be rationalized on the basis of the extent of frustration in the binding energy landscape. In particular, the relationship between receptor-specific binding of these ligands to human immunodeficiency virus type 1 protease and a minimal frustration principle is analyzed.

Empirical free energy calculations of human immunodeficiency virus type 1 protease crystallographic complexes. II. Knowledge-based ligand-protein interaction potentials applied to thermodynamic analysis of hydrophobic mutations.

Empirical free energy calculations of HIV-1 protease crystallographic complexes based on the developed knowledge-based ligand-protein interaction potentials have enabled a detailed thermodynamic analysis. Binding free energies are estimated within an empirical model that postulates that hydrophobic effect, mean field ligand-protein interaction potentials and conformational entropy changes are the dominant forces that determine complex formation. To provide a quantitative framework of the binding thermodynamics contributions the derived knowledge-based potentials have been linked with the hydrophobicity and conformational entropy scales originally developed to explain protein stability. The comparative analysis of studied inhibitors provides reasonable estimates of distinctions in their binding affinity with HIV-1 protease and gives insight into the nature of the binding determinants. The binding free energy changes upon a simple hydrophobic mutation Ile -> Val in the JG-365, MVT-101 and U75875 inhibitors of HIV-1 protease have been evaluated within a model that includes the effects of solvation, cavity formation, conformational entropy and mean field ligand-protein interactions. In general, free energy changes associated with a particular perturbation of a system can not be rigorously decomposed into separate terms from first principles. We explored the relationships between the changes in hydrophobic contributions and mean field ligand-protein interaction energies in the context of a totally buried and dense area of the binding site. We assume, therefore, that these simple hydrophobic deletions would not induce noticeable conformational changes in the enzyme and can be interpreted with some confidence in the framework of the model. The analysis has revealed the decisive effect of the energetics of ligand-protein interactions on the estimated free energy changes.

Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally flexible docking by evolutionary programming.

BACKGROUND: An important prerequisite for computational structure-based drug design is prediction of the structures of ligand-protein complexes that have not yet been experimentally determined by X-ray crystallography or NMR. For this task, docking of rigid ligands is inadequate because it assumes knowledge of the conformation of the bound ligand. Docking of flexible ligands would be desirable, but requires one to search an enormous conformational space. We set out to develop a strategy for flexible docking by combining a simple model of ligand-protein interactions for molecular recognition with an evolutionary programming search technique. RESULTS: We have developed an intermolecular energy function that incorporates steric and hydrogen-bonding terms. The parameters in this function were obtained by docking in three different protein systems. The effectiveness of this method was demonstrated by conformationally flexible docking of the inhibitor AG-1343, a potential new drug against AIDS, into HIV-1 protease. For this molecule, which has nine rotatable bonds, the crystal structure was reproduced within 1.5 A root-mean-square deviation 34 times in 100 simulations, each requiring eight minutes on a Silicon Graphics R4400 workstation. The energy function correctly evaluates the crystal structure as the global energy minimum. CONCLUSIONS: We believe that a solution of the docking problem may be achieved by matching a simple model of molecular recognition with an efficient search procedure. The necessary ingredients of a molecular recognition model include only steric and hydrogen-bond interaction terms. Although these terms are not necessarily sufficient to predict binding affinity, they describe ligand-protein interactions faithfully enough to enable a docking program to predict the structure of the bound ligand. This docking strategy thus provides an important tool for the interdisciplinary field of rational drug design.