Molecular Simulations with Solvent Competition Quantify Water Displaceability and Provide Accurate Interaction Maps of Protein Binding Sites

Binding sites present well-defined interaction patterns that putative ligands must meet. Knowing them is essential to guide structure-based drug discovery projects. However, complex aspects of molecular recognitionsuch as protein flexibility or the effect of aqueous solvationhinder accurate predictions. This is particularly true for polar contacts, which are heavily influenced by the local environment and the behavior of discrete water molecules. Here we present and validate MDmix (Molecular Dynamics simulations with mixed solvents) as a method that provides much more accurate interaction maps than ordinary potentials (e.g., GRID). Additionally, MDmix also affords water displaceability predictions, with advantages over methods that use pure water as solvent (e.g., inhomogeneous fluid solvation theory). With current MD software and hardware solutions, predictions can be obtained in a matter of hours and visualized in a very intuitive manner. Thus, MDmix is an ideal complement in everyday structure-based drug discovery projects

Relationship between Protein Flexibility and Binding: Lessons for Structure-Based Drug Design

Conceptually, the simplistic lock and key model has been superseded by more realistic views of molecular recognition that take into account the intrinsic dynamics of biological macromolecules. However, it is still common for structure-based drug discovery methods to represent the receptor as static structures. The practical advantages of this approximation, the notable success attained over the past few decades with such simple models and the absence of clear guidelines for weighing the pros and cons of accounting for flexibility may prompt some investigators to stretch the rigid model beyond its scope. Here, we investigate the relationship between protein flexibility and binding free energy and present some useful hints for understanding when, and to what extent, flexibility should be considered. Using molecular dynamics simulations of hen egg-white lysozyme (HEWL) with explicit aqueous/organic solvent mixtures and a range of restraint conditions, we find out how artificially restricted mobility affects binding hot spots. Barring sampling errors or an inappropriate choice of reference structure, we find that decreased mobility (measured as B-factors) leads to artifactually more negative binding free energies, but a logarithmic relationship between both terms attenuates the errors. Consequently, ignoring flexibility may be an acceptable approximation for intrinsically rigid regions (such as the active site of enzymes) but may lead to larger errors elsewhere. For the same reason, local conformational sampling yields very accurate predictions and, owing to its practical advantages, may be preferable to full conformational sampling for many applications

Shielded Hydrogen Bonds as Structural Determinants of Binding Kinetics: Application in Drug Design

Time scale control of molecular interactions is an essential part of biochemical systems, but very little is known about the structural factors governing the kinetics of molecular recognition. In drug design, the lifetime of drug–target complexes is a major determinant of pharmacological effects but the absence of structure–kinetic relationships precludes rational optimization of this property. Here we show that almost buried polar atomsa common feature on protein binding sitestend to form hydrogen bonds that are shielded from water. Formation and rupture of this type of hydrogen bonds involves an energetically penalized transition state because it occurs asynchronously with dehydration/rehydration. In consequence, water-shielded hydrogen bonds are exchanged at slower rates. Occurrence of this phenomenon can be anticipated from simple structural analysis, affording a novel tool to interpret and predict structure–kinetics relationships. The validity of this principle has been investigated on two pairs of Hsp90 inhibitors for which detailed thermodynamic and kinetic data has been experimentally determined. The agreement between macroscopic observables and molecular simulations confirms the role of water-shielded hydrogen bonds as kinetic traps and illustrates how our finding could be used as an aid in structure-based drug discovery

Amino acid sequence alignment of PA2077 and PA2078 proteins, obtained by ClustalO.

<p>Significant amino acid motifs are highlighted in squares and the functionally identified amino acids are shown in <b>bold</b>. Conserved heme sequences (CXXCH) are shown in red. The predicted motif for ferrous ion union is depicted in green (EGR or EYD). The signature of oxidases containing the essential histidine like in MauG is shown in blue. Predicted tyrosyl radical (YRQH in PA2077) appears in grey. P450 motifs (EXXR) are in yellow and the distal histidines are highlighted in light green. Peroxidase signature (GXHXCLPHD) is shown in pink, with the peroxidase motif (HD) in orange. A red star indicates the position of the mutated residues (underlined).</p

(A) Effect of gene <i>PA2077</i> overexpression on cell viability of mutant ∆PA2078, measured as soluble protein and culture cell density.

<p>Non IPTG-induced (right tube; 1.21 mg mL^-1 soluble protein) and IPTG-induced (left tube; 0.8 mg mL^-1 soluble protein) cultures of mutant ∆PA2078 carrying gene <i>PA2077</i>, shown as example of cell lysis caused by overexpression of gene <i>PA2077</i>. An overall 66% decrease in soluble protein was found in mutants overexpressing gene <i>PA2077</i>, suggesting a toxic effect of the products released by the encoded functional 10(<i>S</i>)-DOX. (B) Schematic representation of the <i>PA2078-PA2077</i> operon showing the 57 Kbp DNA insertion affecting the operon architecture. (C) Thin layer chromatography analysis showing lack of oleic acid conversion by KK strains (1,2: KK1; 3,4: KK14; 5,6: KK72) incubated for 2 [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131462#pone.0131462.ref001" target="_blank">1</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131462#pone.0131462.ref003" target="_blank">3</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131462#pone.0131462.ref005" target="_blank">5</a>] or 4 hours [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131462#pone.0131462.ref002" target="_blank">2</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131462#pone.0131462.ref004" target="_blank">4</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131462#pone.0131462.ref006" target="_blank">6</a>] with oleic acid. Oleic acid and 10-H(P)OME are shown as control markers.</p

Products released from oleic acid by wild type PA2077 (A) and mutant PA2077 H130Q (B).

<p>Hydroperoxide 10-H(P)OME (RT = 11) could only be detected for wild type PA2077, whereas no conversion of oleic acid occurred when both mutants, PA2077 H130Q and PA2077 H375Q, were assayed.</p

Heme environment hydrophobicity is shown for RoxA (A), PA2077 (B) and PA2078 (C).

<p>Polar amino acids are shown as blue circles and hydrophobic amino acids appear in red.</p

A, B, C, Homology models of surrounding amino acids of the low potential heme environment in PA2077 and PA2078, obtained using rubber oxidase RoxA protein (pdb <i>4b2n</i>) as a template (C₁₉₁XXC₁₉₄H₁₉₅).

<p>RoxA structure is shown in grey, PA2077 in green and PA2078 in pink. <b>D,</b> Amino acid relationships between both heme-binding motifs (H_<b>195</b>, H_<b>641</b>) in RoxA compared to those of PA2077 and PA2078. <b>E,</b> Heme environment changes observed in PA2077 (green) and PA2078 (pink/yellow) in comparison with those of RoxA (grey).</p

Features and attributes of PA2077 and PA20778 nucleotide and amino acid sequences.

<p>^a: Numbering includes signal peptide.</p><p>Features and attributes of PA2077 and PA20778 nucleotide and amino acid sequences.</p

Detecting similar binding pockets to enable systems polypharmacology

<div><p>In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.</p></div