Profiling charge complementarity and selectivity for binding at the protein surface

Tetrapeptide Ac-HAEE-NH2 Protects α4β2 nAChR from Inhibition by Aβ

The cholinergic deficit in Alzheimer’s disease (AD) may arise from selective loss of cholinergic neurons caused by the binding of Aβ peptide to nicotinic acetylcholine receptors (nAChRs). Thus, compounds preventing such an interaction are needed to address the cholinergic dysfunction. Recent findings suggest that the ¹¹EVHH¹⁴ site in Aβ peptide mediates its interaction with α4β2 nAChR. This site contains several charged amino acid residues, hence we hypothesized that the formation of Aβ-α4β2 nAChR complex is based on the interaction of ¹¹EVHH¹⁴ with its charge-complementary counterpart in α4β2 nAChR. Indeed, we discovered a ³⁵HAEE³⁸ site in α4β2 nAChR, which is charge-complementary to ¹¹EVHH¹⁴, and molecular modeling showed that a stable Aβ₄₂-α4β2 nAChR complex could be formed via the ¹¹EVHH¹⁴:³⁵HAEE³⁸ interface. Using surface plasmon resonance and bioinformatics approaches, we further showed that a corresponding tetrapeptide Ac-HAEE-NH₂ can bind to Aβ via ¹¹EVHH¹⁴ site. Finally, using two-electrode voltage clamp in Xenopus laevis oocytes, we showed that Ac-HAEE-NH₂ tetrapeptide completely abolishes the Aβ₄₂-induced inhibition of α4β2 nAChR. Thus, we suggest that ³⁵HAEE³⁸ is a potential binding site for Aβ on α4β2 nAChR and Ac-HAEE-NH₂ tetrapeptide corresponding to this site is a potential therapeutic for the treatment of α4β2 nAChR-dependent cholinergic dysfunction in AD.

Structure-Activity Relationships for the Affinity of Chaotropic Polyoxometalate Anions towards Proteins

The influence of the composition of chaotropic polyoxometalate (POM) anions on their affinity to biological systems was studied by means of atomistic molecular dynamics (MD) simulations. The variations in the affinity to hen egg-white lysozyme (HEWL) were analyzed along two series of POMs whereby the charge or the size and shape of the metal cluster are modified systematically. Our simulations revealed a quadratic relationship between the charge of the POM and its affinity to HEWL as a consequence of the parabolic growth of POM⋅⋅⋅water interaction with the charge. As the charge increases, POMs become less chaotropic (more kosmotropic) increasing the number and the strength of POM-water hydrogen bonds and structuring the solvation shell around the POM. This atomistic description explains the proportionally larger desolvation energies and less protein affinity for highly charged POMs, and consequently, the preference for moderate charge densities (q/M=0.33). Also, our simulations suggest that POM⋅⋅⋅protein interactions are size-specific. The cationic pockets of HEWL protein show a preference for Keggin-like structures, which display the optimal dimensions (≈1 nm). Finally, we developed a quantitative multidimensional model for protein affinity with predictive ability (r² =0.97; q² =0.88) using two molecular descriptors that account for the charge density (charge per metal atom ratio; q/M) and the size and shape (shape weighted-volume; V_S ).

Electrostatic recognition in substrate binding to serine proteases

Serine proteases of the Chymotrypsin family are structurally very similar but have very different substrate preferences. This study investigates a set of 9 different proteases of this family comprising proteases that prefer substrates containing positively charged amino acids, negatively charged amino acids, and uncharged amino acids with varying degree of specificity. Here, we show that differences in electrostatic substrate preferences can be predicted reliably by electrostatic molecular interaction fields employing customized GRID probes. Thus, we are able to directly link protease structures to their electrostatic substrate preferences. Additionally, we present a new metric that measures similarities in substrate preferences focusing only on electrostatics. It efficiently compares these electrostatic substrate preferences between different proteases. This new metric can be interpreted as the electrostatic part of our previously developed substrate similarity metric. Consequently, we suggest, that substrate recognition in terms of electrostatics and shape complementarity are rather orthogonal aspects of substrate recognition. This is in line with a 2‐step mechanism of protein‐protein recognition suggested in the literature.

Electronic Structures of LNA Phosphorothioate Oligonucleotides

Important oligonucleotides in anti-sense research have been investigated in silico and experimentally. This involves quantum mechanical (QM) calculations and chromatography experiments on locked nucleic acid (LNA) phosphorothioate (PS) oligonucleotides. iso-potential electrostatic surfaces are essential in this study and have been calculated from the wave functions derived from the QM calculations that provide binding information and other properties of these molecules. The QM calculations give details of the electronic structures in terms of e.g., energy and bonding, which make them distinguish or differentiate between the individual PS diastereoisomers determined by the position of sulfur atoms. Rules are derived from the electronic calculations of these molecules and include the effects of the phosphorothioate chirality and formation of electrostatic potential surfaces. Physical and electrochemical descriptors of the PS oligonucleotides are compared to the experiments in which chiral states on these molecules can be distinguished. The calculations demonstrate that electronic structure, electrostatic potential, and topology are highly sensitive to single PS configuration changes and can give a lead to understanding the activity of the molecules.

Sensitivity Analysis and Charge-Optimization for Flexible Ligands: Applicability to Lead Optimization

Sensitivity analysis and charge-optimization have been suggested as methods to guide the optimization of lead compounds in early-stage drug discovery. However, applications to date have been restricted by the simplifying assumption of a rigid ligand. The present study applies both formalisms to the case of a flexible ligand in a model application to an HIV-protease inhibitor. The results suggest that sensitivity analysis is a fast and robust method for guiding charge changes in both a rigid and a flexible ligand, although its accuracy is limited by the fact that it represents a linear approximation. The more complete quadratic analysis provided by charge-optimization produces unexpected results when the ligand is considered to be flexible. For example, it can yield atomic charges which powerfully stabilize the bound conformation of the ligand relative to the conformation assumed for the free state, thus markedly destabilizing the assumed free conformation. Such results are traceable to the fact that the energy matrix possesses negative eigenvalues. However, optimizing charges under the assumption that the ligand does not change conformation upon binding leads to a set of charges that robustly improve affinity, even when the free conformation is later allowed to vary. Thus, both sensitivity analysis and charge-optimization appear to be useful techniques.

Quantum mechanical studies of DNA and LNA

Quantum mechanical (QM) methodology has been employed to study the structure activity relations of DNA and locked nucleic acid (LNA). The QM calculations provide the basis for construction of molecular structure and electrostatic surface potentials from molecular orbitals. The topologies of the electrostatic potentials were compared among model oligonucleotides, and it was observed that small structural modifications induce global changes in the molecular structure and surface potentials. Since ligand structure and electrostatic potential complementarity with a receptor is a determinant for the bonding pattern between molecules, minor chemical modifications may have profound changes in the interaction profiles of oligonucleotides, possibly leading to changes in pharmacological properties. The QM modeling data can be used to understand earlier observations of antisense oligonucleotide properties, that is, the observation that small structural changes in oligonucleotide composition may lead to dramatic shifts in phenotypes. These observations should be taken into account in future oligonucleotide drug discovery, and by focusing more on non RNA target interactions it should be possible to utilize the exhibited property diversity of oligonucleotides to produce improved antisense drugs.

Charge Optimization Theory for Induced-Fit Ligands

The design of ligands with high affinity and specificity remains a fundamental challenge in understanding molecular recognition and developing therapeutic interventions. Charge optimization theory addresses this problem by determining ligand charge distributions that produce the most favorable electrostatic contribution to the binding free energy. The theory has been applied to the design of binding specificity as well. However, the formulations described only treat a rigid ligand-one that does not change conformation upon binding. Here, we extend the theory to treat induced-fit ligands for which the unbound ligand conformation may differ from the bound conformation. We develop a thermodynamic pathway analysis for binding contributions relevant to the theory, and we illustrate application of the theory using HIV-1 protease with our previously designed and validated subnanomolar inhibitor. Direct application of rigid charge optimization approaches to nonrigid cases leads to very favorable intramolecular electrostatic interactions that are physically unreasonable, and analysis shows the ligand charge distribution massively stabilizes the preconformed (bound) conformation over the unbound. After analyzing this case, we provide a treatment for the induced-fit ligand charge optimization problem that produces physically realistic results. The key factor is introducing the constraint that the free energy of the unbound ligand conformation be lower or equal to that of the preconformed ligand structure, which corresponds to the notion that the unbound structure is the ground unbound state. Results not only demonstrate the applicability of this methodology to discovering optimized charge distributions in an induced-fit model, but also provide some insights into the energetic consequences of ligand conformational change on binding. Specifically, the results show that, from an electrostatic perspective, induced-fit binding is not an adaptation designed to enhance binding affinity; at best, it can only achieve the same affinity as optimized rigid binding.

Solvated interaction energy (SIE) for scoring protein-ligand binding affinities. 2. Benchmark in the CSAR-2010 scoring exercise

Solvated interaction energy (SIE) is an end-point physics-based scoring function for predicting binding affinities from force-field nonbonded interaction terms, continuum solvation, and configurational entropy linear compensation. We tested the SIE function in the Community Structure-Activity Resource (CSAR) scoring challenge consisting of high-resolution cocrystal structures for 343 protein-ligand complexes with high-quality binding affinity data and high diversity with respect to protein targets. Particular emphasis was placed on the sensitivity of SIE predictions to the assignment of protonation and tautomeric states in the complex and the treatment of metal ions near the protein-ligand interface. These were manually curated from an originally distributed CSAR-HiQ data set version, leading to the currently distributed CSAR-NRC-HiQ version. We found that this manual curation was a critical step for accurately testing the performance of the SIE function. The standard SIE parametrization, previously calibrated on an independent data set, predicted absolute binding affinities with a mean-unsigned-error (MUE) of 2.41 kcal/mol for the CSAR-HiQ version, which improved to 1.98 kcal/mol for the upgraded CSAR-NRC-HiQ version. Half-half retraining-testing of SIE parameters on two predefined subsets of CSAR-NRC-HiQ led to only marginal further improvements to an MUE of 1.83 kcal/mol. Hence, we do not recommend altering the current default parameters of SIE at this time. For a sample of SIE outliers, additional calculations by molecular dynamics-based SIE averaging with or without incorporation of ligand strain, by MM-PB(GB)/SA methods with or without entropic estimates, or even by the linear interaction energy (LIE) formalism with an explicit solvent model, did not further improve predictions.

Exploring inhibitor binding at the S' subsites of cathepsin L

We report a series of noncovalent, reversible inhibitors of cathepsin L that have been designed to explore additional binding interactions with the S' subsites. The design was based on our previously reported crystal structure that suggested the possibility of engineering increased interactions with the S' subsites ( Chowdhury et al. J. Med. Chem. 2002, 45, 5321-5329 ). A representative of these new inhibitors has been co-crystallized with mature cathepsin L, and the structure has been solved and refined at 2.2 A. The inhibitors described in this work extend farther into the S' subsites of cathepsins than any inhibitors reported in the literature thus far. These interactions appear to make use of a S3' subsite that can potentially be exploited for enhanced specificity and/or affinity.

Specificity in molecular design: a physical framework for probing the determinants of binding specificity and promiscuity in a biological environment

Binding specificity is an important consideration in drug design. An effective drug molecule often must bind with high specificity to its intended target in the body; lower specificity implies the possibility of significant binding to unintended partners, which could instigate deleterious side effects. However, if the target is a rapidly mutating agent, a drug that is too specific will quickly lose its efficacy by not binding well to functional mutants. Therefore, in molecular design, it is crucial to tailor the binding specificity of a drug to the problem at hand. In practice, specificity is often studied on a case-by-case basis, and it is difficult to create general understanding of the determinants of specificity from the union of such available cases. In this work, we undertook a comprehensive, general study of molecular binding with emphasis on understanding the determinants of specificity from a physical standpoint. By extending a theoretical framework grounded in continuum electrostatics and creating an abstracted lattice model that captures key physical aspects of binding interactions, we systematically explored the relationship between a molecule's physical characteristics and its binding specificity toward potential partners. The theory and simulated binding interactions suggested that charged molecules are more specific binders than their hydrophobic counterparts for several reasons. First, the biological spectrum of possible binding characteristics includes more partners that bind equally well to hydrophobic ligands than to charged ligands. Also, charged ligands, whose electrostatic potentials have strong orientational dependence, are more sensitive to shape complementarity than their hydrophobic counterparts. Ligand conformational and orientational flexibility can further influence a charged molecule's ability to bind specifically. Interestingly, we found that conformational flexibility can increase the specificity of polar and charged ligands, by allowing them to greatly lower the binding free energy to a select few partners relative to others. Additionally, factors such as a molecule's size and the ionic strength of the solution were found to predictably affect binding specificity. Taken together, these results, all of which stem from a unified theoretical framework, provide valuable physical insight into the general determinants of binding specificity and promiscuity in a biological environment. The general principles discussed here could prove useful in the design of molecules with tailored specificities, leading to more effective therapeutics.

Coupled atomic charge selectivity for optimal ligand-charge distributions at protein binding sites

Charge optimization as a tool for both analyzing and enhancing binding electrostatics has become an attractive approach over the past few years. An interesting feature of this method for molecular design is that it provides not only the optimal charge magnitudes, but also the selectivity of a particular atomic center for its optimal charge. The current approach to compute the charge selectivity at a given atomic center of a ligand in a particular binding process is based on the binding-energy cost incurred upon the perturbation of the optimal charge distribution by a unit charge at the given atomic center, while keeping the other atomic partial charges at their optimal values. A limitation of this method is that it does not take into account the possible concerted changes in the other atomic charges that may incur a lower energetic cost than perturbing a single charge. Here, we describe a novel approach for characterizing charge selectivity in a concerted manner, taking into account the coupling between the ligand charge centers in the binding process. We apply this novel charge selectivity measure to the celecoxib molecule, a nonsteroidal anti-inflammatory agent binding to cyclooxygenase 2 (COX2), which has been recently shown to also exhibit cross-reactivity toward carbonic anhydrase II (CAII), to which it binds with nanomolar affinity. The uncoupled and coupled charge selectivity profiles over the atomic centers of the celecoxib ligand, binding independently to COX2 and CAII, are analyzed comparatively and rationalized with respect to available experimental data. Very different charge selectivity profiles are obtained for the uncoupled versus coupled selectivity calculations.

Optimal charges in lead progression: a structure-based neuraminidase case study

Collective experience in structure-based lead progression has found electrostatic interactions to be more difficult to optimize than shape-based ones. A major reason for this is that the net electrostatic contribution observed includes a significant nonintuitive desolvation component in addition to the more intuitive intermolecular interaction component. To investigate whether knowledge of the ligand optimal charge distribution can facilitate more intuitive design of electrostatic interactions, we took a series of small-molecule influenza neuraminidase inhibitors with known protein cocrystal structures and calculated the difference between the optimal and actual charge distributions. This difference from the electrostatic optimum correlates with the calculated electrostatic contribution to binding (r(2) = 0.94) despite small changes in binding modes caused by chemical substitutions, suggesting that the optimal charge distribution is a useful design goal. Furthermore, detailed suggestions for chemical modification generated by this approach are in many cases consistent with observed improvements in binding affinity, and the method appears to be useful despite discrete chemical constraints. Taken together, these results suggest that charge optimization is useful in facilitating generation of compound ideas in lead optimization. Our results also provide insight into design of neuraminidase inhibitors.

Application of new multi-resolution methods for the comparison of biomolecular electrostatic properties in the absence of global structural similarity

In this paper we present a method for the multi-resolution comparison of biomolecular electrostatic potentials without the need for global structural alignment of the biomolecules. The underlying computational geometry algorithm uses multi-resolution attributed contour trees (MACTs) to compare the topological features of volumetric scalar fields. We apply the MACTs to compute electrostatic similarity metrics for a large set of protein chains with varying degrees of sequence, structure, and function similarity. For calibration, we also compute similarity metrics for these chains by a more traditional approach based upon 3D structural alignment and analysis of Carbo similarity indices. Moreover, because the MACT approach does not rely upon pairwise structural alignment, its accuracy and efficiency promises to perform well on future large-scale classification efforts across groups of structurally-diverse proteins. The MACT method discriminates between protein chains at a level comparable to the Carbo similarity index method; i.e., it is able to accurately cluster proteins into functionally-relevant groups which demonstrate strong dependence on ligand binding sites. The results of the analyses are available from the linked web databases http://ccvweb.cres.utexas.edu/MolSignature/ and http://agave.wustl.edu/similarity/. The MACT analysis tools are available as part of the public domain library of the Topological Analysis and Quantitative Tools (TAQT) from the Center of Computational Visualization, at the University of Texas at Austin (http://ccvweb.csres.utexas.edu/software). The Carbo software is available for download with the open-source APBS software package at http://apbs.sf.net/.

Carboxy-monopeptidase substrate specificity of human cathepsin X

Cathepsin X is a papain-like cysteine protease with restricted positional specificity, acting primarily as a carboxy-monopeptidase. We mapped the specificities at the S2, S1, and S1' subsites of human cathepsin X by systematically and independently substituting the P2, P1, and P1' positions of the carboxy-monopeptidase substrate Abz-FRF(4NO(2)) with natural amino acids. Human cathepsin X has broad S2, S1, and S1' specificities within two orders of magnitude in k(cat)/K(M), excluding proline that is not tolerated at these subsites. Glycine is not favored in S2, but is among the preferred residues in S1 and S1', which highlights S2 as the affinity-determinant subsite. The presence of peculiar residues at several binding site positions (Asp76, His234, Asn75, and Glu72) does not translate into a markedly different sequence specificity profile relative to other human cathepsins. These findings suggest that a specific function of human cathepsin X is unlikely to result from sequence specificity, but rather from a combination of its unique positional specificity and the co-localization of enzyme and substrate in a specific cellular environment.