COMBINE analysis of the specificity of binding of Ras proteins to their effectors

Prediction of the Drug-Target Binding Kinetics for Flexible Proteins by Comparative Binding Energy Analysis

There is growing consensus that the optimization of the kinetic parameters for drug-protein binding leads to improved drug efficacy. Therefore, computational methods have been developed to predict kinetic rates and to derive quantitative structure-kinetic relationships (QSKRs). Many of these methods are based on crystal structures of ligand-protein complexes. However, a drawback is that each ligand-protein complex is usually treated as having a single structure. Here, we present a modification of COMparative BINding Energy (COMBINE) analysis, which uses the structures of ligand-protein complexes to predict binding parameters. We introduce the option of using multiple structures to describe each ligand-protein complex in COMBINE analysis and apply this to study the effects of protein flexibility on the derivation of dissociation rate constants (k_off) for inhibitors of p38 mitogen-activated protein (MAP) kinase, which has a flexible binding site. Multiple structures were obtained for each ligand-protein complex by performing docking to an ensemble of protein configurations obtained from molecular dynamics simulations. Coefficients to scale ligand-protein interaction energies determined from energy-minimized structures of ligand-protein complexes were obtained by partial least squares regression, and they allowed for the computation of k_off values. The QSKR model obtained using single, energy-minimized crystal structures for each ligand-protein complex had higher predictive power than the QSKR model obtained with multiple structures from ensemble docking. However, incorporation of ligand-protein flexibility helped to highlight additional ligand-protein interactions that lead to longer residence times, such as interactions with residues Arg67 and Asp168, which are close to the ligand in many crystal structures. These results show that COMBINE analysis is a promising method to guide the design of compounds that bind to flexible proteins with improved binding kinetics.

A Protocol to Use Comparative Binding Energy Analysis to Estimate Drug-Target Residence Time

Comparative Binding Energy (COMBINE) analysis is an approach for deriving a target-specific scoring function to compute binding free energy, drug-binding kinetics, or a related property by exploiting the information contained in the three-dimensional structures of receptor-ligand complexes. Here, we describe the process of setting up and running COMBINE analysis to derive a Quantitative Structure-Kinetics Relationship (QSKR) for the dissociation rate constants (k_off) of inhibitors of a drug target. The derived QSKR model can be used to estimate residence times (τ, τ=1/k_off) for similar inhibitors binding to the same target, and it can also help to identify key receptor-ligand interactions that distinguish inhibitors with short and long residence times. Herein, we demonstrate the protocol for the application of COMBINE analysis on a dataset of 70 inhibitors of heat shock protein 90 (HSP90) belonging to 11 different chemical classes. The procedure is generally applicable to any drug target with known structural information on its complexes with inhibitors.

Leishmania donovani infection differentially regulates small G-proteins

Leishmania is a protozoan parasite that resides and replicates in macrophages and causes leishmaniasis. The parasite alters the signaling cascade in host macrophages and evades the host machinery. Small G-proteins are GTPases, grouped in 5 different families that play a crucial role in the regulation of cell proliferation, cell survival, apoptosis, intracellular trafficking, and transport. In particular, the Ras family of small G-proteins has been identified to play a significant role in the cellular functions mentioned before. Here, we studied the differential expression of the most important small G-proteins during Leishmania infection. We found major changes in the expression of different isoforms of Ras, mainly in N-Ras. We observed that Leishmania donovani infection led to enhanced N-Ras expression, whereas it inhibited K-Ras and H-Ras expression. Furthermore, an active N-Ras pull-down assay showed enhanced N-Ras activity. L donovani infection also increased extracellular signal-regulated kinase 1/2 phosphorylation and simultaneously decreased p38 phosphorylation. In contrast, pharmacological inhibition of Ras led to reduction in the phosphorylation of extracellular signal-regulated kinase 1/2 and enhanced the phosphorylation of p38 in Leishmania-infected cells, which could lead to increased interleukin-12 expression and decreased interleukin-10 expression. Indeed, farnesylthiosalicyclic acid (a Ras inhibitor), when used at the effective level in L donovani-infected macrophages, reduced amastigotes in the host macrophages. Thus, upregulated N-Ras expression during L donovani infection could be a novel immune evasion strategy of Leishmania and would be a potential target for antileishmanial immunotherapy.

Specific Conformational States of Ras GTPase upon Effector Binding

To uncover the structural and dynamical determinants involved in the highly specific binding of Ras GTPase to its effectors, the conformational states of Ras in uncomplexed form and complexed to the downstream effectors Byr2, PI3Kγ, PLCε, and RalGDS were investigated using molecular dynamics and cross-comparison of the trajectories. The subtle changes in the dynamics and conformations of Ras upon effector binding require an analysis that targets local changes independent of global motions. Using a structural alphabet, a computational procedure is proposed to quantify local conformational changes. Positions detected by this approach were characterized as either specific for a particular effector, specific for an effector domain type, or as effector unspecific. A set of nine structurally connected residues (Ras residues 5–8, 32–35, 39–42, 55–59, 73–78, and 161–165), which link the effector binding site to the distant C-terminus, changed dynamics upon effector binding, indicating a potential effector-unspecific signaling route within the Ras structure. Additional conformational changes were detected along the N-terminus of the central β-sheet. Besides the Ras residues at the effector interface (e.g., D33, E37, D38, and Y40), which adopt effector-specific local conformations, the binding signal propagates from the interface to distant hot-spot residues, in particular to Y5 and D57. The results of this study reveal possible conformational mechanisms for the stabilization of the active state of Ras upon downstream effector binding and for the structural determinants responsible for effector specificity.

QSAR analysis of antitumor active amides and quinolones from thiophene series

QSAR models for predicting antitumor activity of heterocyclic amides and quinolones from benzo[b]thiophene-, thieno[3,2-b]thiophene- and thieno[2,3-b], thiophene series against MiaPaCa-2 and MCF-7 cells were built. Complete dataset consisted of 59 compounds and several QSAR models with different predictive ability were derived. Beside standard approaches for building QSAR models, the approach based on a small dataset of 10 compounds selected regarding the results of principal component analysis was tested. The latter approach was shown as successful and can be useful for planning future experiments in order to speed up and simplify the search for new drug candidates. Based on the derived QSAR models, the most important properties for compound's antitumor activity against MiaPaCa-2 and MCF-7 cells were identified. Volume, sum of the hydrophobic surfaces and presence of the group that can be easily ionized in the pH range from 4 to 9, were found to be highly important for successful antitumor activity of the examined heterocyclic amides and quinolones. New compounds, with potentially higher biological activity against MiaPaCa-2 and MCF-7 cells, were proposed. Their activities were predicted using the derived QSAR models and the proposed compounds were shown as promising antitumor candidates.

Computational tools for designing and engineering biocatalysts

Current computational tools to assist experimentalists for the design and engineering of proteins with desired catalytic properties are reviewed. The applications of these tools for de novo design of protein active sites, optimization of substrate access and product exit pathways, redesign of protein-protein interfaces, identification of neutral/advantageous/deleterious mutations in the libraries from directed evolution and stabilization of protein structures are described. Remarkable progress is seen in de novo design of enzymes catalyzing a chemical reaction for which a natural biocatalyst does not exist. Yet, constructed biocatalysts do not match natural enzymes in their efficiency, suggesting that more research is needed to capture all the important features of natural biocatalysts in theoretical designs.

Fast predictions of thermodynamics and kinetics of protein-protein recognition from structures: from molecular design to systems biology

The increasing call for an overall picture of the interactions between the components of a biological system that give rise to the observed function is often summarized by the expression systems biology. Both the interpretative and predictive capabilities of holistic models of biochemical systems, however, depend to a large extent on the level of physico-chemical knowledge of the individual molecular interactions making up the network. This review is focused on the structure-based quantitative characterization of protein-protein interactions, ubiquitous in any biochemical pathway. Recently developed, fast and effective computational methods are reviewed, which allow the assessment of kinetic and thermodynamic features of the association-dissociation processes of protein complexes, both in water soluble and membrane environments. The performance and the accuracy of fast and semi-empirical structure-based methods have reached comparable levels with respect to the classical and more elegant molecular simulations. Nevertheless, the broad accessibility and lower computational cost provide the former methods with the advantageous possibility to perform systems-level analyses including extensive in silico mutagenesis screenings and large-scale structural predictions of multiprotein complexes.

Protein-protein docking by simulating the process of association subject to biochemical constraints

We present a computational procedure for modeling protein-protein association and predicting the structures of protein-protein complexes. The initial sampling stage is based on an efficient Brownian dynamics algorithm that mimics the physical process of diffusional association. Relevant biochemical data can be directly incorporated as distance constraints at this stage. The docked configurations are then grouped with a hierarchical clustering algorithm into ensembles that represent potential protein-protein encounter complexes. Flexible refinement of selected representative structures is done by molecular dynamics simulation. The protein-protein docking procedure was thoroughly tested on 10 structurally and functionally diverse protein-protein complexes. Starting from X-ray crystal structures of the unbound proteins, in 9 out of 10 cases it yields structures of protein-protein complexes close to those determined experimentally with the percentage of correct contacts >30% and interface backbone RMSD <4 A. Detailed examination of all the docking cases gives insights into important determinants of the performance of the computational approach in modeling protein-protein association and predicting of protein-protein complex structures.

Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems

Drug resistance is a significant obstacle in the effective treatment of diseases with rapidly mutating targets, such as AIDS, malaria, and certain forms of cancer. Such targets are remarkably efficient at exploring the space of functional mutants and at evolving to evade drug binding while still maintaining their biological role. To overcome this challenge, drug regimens must be active against potential target variants. Such a goal may be accomplished by one drug molecule that recognizes multiple variants or by a drug "cocktail"--a small collection of drug molecules that collectively binds all desired variants. Ideally, one wants the smallest cocktail possible due to the potential for increased toxicity with each additional drug. Therefore, the task of designing a regimen for multiple target variants can be framed as an optimization problem--find the smallest collection of molecules that together "covers" the relevant target variants. In this work, we formulate and apply this optimization framework to theoretical model target ensembles. These results are analyzed to develop an understanding of how the physical properties of a target ensemble relate to the properties of the optimal cocktail. We focus on electrostatic variation within target ensembles, as it is one important mechanism by which drug resistance is achieved. Using integer programming, we systematically designed optimal cocktails to cover model target ensembles. We found that certain drug molecules covered much larger regions of target space than others, a phenomenon explained by theory grounded in continuum electrostatics. Molecules within optimal cocktails were often dissimilar, such that each drug was responsible for binding variants with a certain electrostatic property in common. On average, the number of molecules in the optimal cocktails correlated with the number of variants, the differences in the variants' electrostatic properties at the binding interface, and the level of binding affinity required. We also treated cases in which a subset of target variants was to be avoided, modeling the common challenge of closely related host molecules that may be implicated in drug toxicity. Such decoys generally increased the size of the required cocktail and more often resulted in infeasible optimizations. Taken together, this work provides practical optimization methods for the design of drug cocktails and a theoretical, physics-based framework through which useful insights can be achieved.

Choose your own path: specificity in Ras GTPase signaling

The Ras superfamily of small G proteins contributes importantly to numerous cellular and physiological processes (M. F. Olsen and R. Marais, Semin. Immunol., 2000, 12, 63). This family comprises a large class of proteins (more than 150) which all share a common enzymatic function: hydrolysis of the gamma-phosphate of guanosine triphosphate (GTP) to create the products guanosine diphosphate (GDP) and inorganic phosphate (Y. Takai, T. Sasaki and T. Matozaki, Physiol. Rev., 2001, 81, 153). For this reason Ras family proteins, which include the Ras, Rho, Arf/Sara, Ran and Rab subfamilies, are classified as GTPases (G. W. Reuther and C. J. Der, Curr. Opin. Cell Biol., 2000, 12, 157). Guanine nucleotide coupling is a key regulator of enzymatic function; thus, Ras family GTPases participate in signal transduction. Ras signaling depends on binding to effectors. Many of the known effectors can bind to multiple Ras isotypes, often leading to common cellular outcomes, but each Ras isotype also engages specific effector pathways to mediate unique functions. Further, each Ras isotype can propagate multiple signaling pathways, indicating the presence of cellular determinants which allow for promiscuity in Ras-effector interactions while also maintaining specificity. Small distinctions in sequence, structure, and/or cellular regulation contribute to these differences in Ras-effector binding and subsequent cellular effects. A major focus of investigation in the Ras signaling field is identifying the determinants of these individualized functions. This review will attempt to summarize the current state of understanding of this question (with a particular focus on the Ras subfamily) and the approaches being taken to address it, and will discuss prospective areas for future investigation.