Constraining binding hot spots: NMR and molecular dynamics simulations provide a structural explanation for enthalpy-entropy compensation in SH2-ligand binding

Entropy-Enthalpy Compensation in Electron-Transfer Processes

Solvent reorganization energies, free energies, and entropies are obtained for photoexcitation of three molecules that exhibit strong solvatochromism [Nile red, 5-(dimethylamino)-5'-nitro-2,2-bisthiophene, and Reichardt's dye B30] by measuring their optical absorption spectra at temperatures between 150 and 300 K in solvents with a range of polarities. Energies, free energies, and entropies of solvent reorganization are also obtained from computer simulations of three intramolecular electron-transfer reactions (charge separation and recombination in Zn-porphyrin-quinone cyclophane and charge transfer in a bis-biphenylandrostane radical anion). Entropy-enthalpy compensation in the solvent reorganization free energy for photoexcitation or electron transfer is found to be essentially complete (having nearly equal and opposite contributions from entropic and enthalpic effects) for all of the processes with solvent reorganization energies less than about 0.1 eV. Compensation becomes less complete as the reorganization energy becomes larger. A semiclassical treatment of the solvent reorganization entropy can rationalize these results.

Insights into Modeling Approaches in Chemistry: Assessing Ligand-Protein Binding Thermodynamics Based on Rigid-Flexible Model Molecules

In the field of chemistry, model compounds find extensive use for investigating complex objects. One prime example of such object is the protein-ligand supramolecular interaction. Prediction the enthalpic and entropic contribution to the free energy associated with this process, as well as the structural and dynamic characteristics of protein-ligand complexes poses considerable challenges. This review exemplifies modeling approaches used to study protein-ligand binding (PLB) thermodynamics by employing pairs of conformationally constrained/flexible model molecules. Strategically designing the model molecules can reduce the number of variables that influence thermodynamic parameters. This enables scientists to gain deeper insights into the enthalpy and entropy of PLB, which is relevant for medicinal chemistry and drug design. The model studies reviewed here demonstrate that rigidifying ligands may induce compensating changes in the enthalpy and entropy of binding. Some "rules of thumb" have started to emerge on how to minimize entropy-enthalpy compensation and design efficient rigidified or flexible ligands.

BANΔIT: B'-Factor Analysis for Drug Design and Structural Biology

The analysis of B‐factor profiles from X‐ray protein structures can be utilized for structure‐based drug design since protein mobility changes have been associated with the quality of protein‐ligand interactions. With the BANΔIT (B’‐factor analysis and ΔB’ interpretation toolkit), we have developed a JavaScript‐based browser application that provides a graphical user interface for the normalization and analysis of B’‐factor profiles. To emphasize the usability for rational drug design applications, we have analyzed a selection of crystallographic protein‐ligand complexes and have given exemplary conclusions for further drug optimization including the development of a B’‐factor‐supported pharmacophore model for SARS CoV‐2 main protease inhibitors. BANΔIT is available online at https://bandit.uni‐mainz.de. The source code can be downloaded from https://github.com/FBarthels/BANDIT.

Structure-thermodynamics-relationships of hepatitis C viral NS3 protease inhibitors

Thermodynamic parameters were determined for structurally-related inhibitors of HCV NS3 protease to assess how binding entropies and enthalpies vary with incremental changes at the P2 and P3 inhibitor subsites. Changing the heterocyclic substituent at P2 from a pyridyl to a 7-methoxy-2-phenyl-4-quinolyl group leads to a 710-fold increase in affinity. Annelating a benzene ring onto a pyridine ring leads to quinoline-derived inhibitors having higher affinities, but the individual enthalpy and entropy contributions are markedly different for each ligand pair. Introducing a phenyl group at C2 of the heterocyclic ring at P2 uniformly leads to higher affinity analogs with more favorable binding entropies, while adding a methoxy group at C7 of the quinoline ring at P2 provides derivatives with more favorable binding enthalpies. Significant enthalpy/entropy compensation is observed for structural changes made to inhibitors lacking a 2-phenyl substituent, whereas favorable changes in both binding enthalpies and entropies accompany structural modifications when a 2-phenyl group is present. Overall, binding energetics of inhibitors having a 2-phenyl-4-quinolyl group at P2 are dominated by entropic effects, whereas binding of the corresponding norphenyl analogs are primarily enthalpy driven. Notably, the reversal from an entropy driven association to an enthalpy driven one for this set of inhibitors also correlates with alternate binding modes. When the steric bulk of the side chain at P3 is increased from a hydrogen atom to a tert-butyl group, there is a 770-fold improvement in affinity. The 30-fold increase resulting from the first methyl group is solely the consequence of a more favorable change in entropy, whereas subsequent additions of methyl groups leads to modest increases in affinity that arise primarily from incremental improvements in binding enthalpies accompanied with smaller favorable entropic contributions.

Entropy Hotspots for the Binding of Intrinsically Disordered Ligands to a Receptor Domain

Proline-rich motifs (PRMs) are widely used for mediating protein-protein interactions with weak binding affinities. Because they are intrinsically disordered when unbound, conformational entropy plays a significant role for the binding. However, residue-level differences of the entropic contribution in the binding of different ligands remain not well understood. We use all-atom molecular dynamics simulation and the maximal information spanning tree formalism to analyze conformational entropy associated with the binding of two PRMs, one from the Abl kinase and the other from the nonstructural protein 1 of the 1918 Spanish influenza A virus, to the N-terminal SH3 (nSH3) domain of the CrkII protein. Side chains of the stably folded nSH3 experience more entropy change upon ligand binding than the backbone, whereas PRMs involve comparable but heterogeneous entropy changes among the backbone and side chains. In nSH3, two conserved nonpolar residues forming contacts with the PRM experience the largest side-chain entropy loss. In contrast, the C-terminal charged residues of PRMs that form polar contacts with nSH3 experience the greatest side-chain entropy loss, although their "fuzzy" nature is attributable to the backbone that remains relatively flexible. Thus, residues that form high-occupancy contacts between nSH3 and PRM do not reciprocally contribute to entropy loss. Furthermore, certain surface residues of nSH3 distal to the interface with PRMs gain entropy, indicating a nonlocal effect of ligand binding. Comparing between the PRMs from cAbl and nonstructural protein 1, the latter involves a larger side-chain entropy loss and forms more contacts with nSH3. Consistent with experiments, this indicates stronger binding of the viral ligand at the expense of losing the flexibility of side chains, whereas the backbone experiences less entropy loss. The entropy "hotspots" as identified in this study will be important for tuning the binding affinity of various ligands to a receptor.

Entropic allostery dominates the phosphorylation-dependent regulation of Syk tyrosine kinase release from immunoreceptor tyrosine-based activation motifs

Spleen tyrosine kinase (Syk) is an essential player in immune signaling through its ability to couple multiple classes of membrane immunoreceptors to intracellular signaling pathways. Ligand binding leads to the recruitment of Syk to a phosphorylated cytoplasmic region of the receptors called ITAM. Syk binds to ITAM with high-affinity (nanomolar Kd ) via its tandem pair of SH2 domains. The affinity between Syk and ITAM is allosterically regulated by phosphorylation at Y130 in a linker connecting the tandem SH2 domains; when Y130 is phosphorylated, the binding affinity decreases (micromolar Kd ). Previous equilibrium binding studies attribute the increase in the binding free energy to an intra-molecular binding (isomerization) step of the tandem SH2 and ITAM, but a physical basis for the increased free energy is unknown. Here, we provide evidence that Y130 phosphorylation imposes an entropy penalty to isomerization, but surprisingly, has negligible effect on the SH2 binding interactions with ITAM and thus on the binding enthalpy. An analysis of NMR chemical shift differences characterized conformational effects of ITAM binding, and binding thermodynamics were measured from isothermal titration calorimetry. Together the data support a previously unknown mechanism for the basis of regulating protein-protein interactions through protein phosphorylation. The decreased affinity for Syk association with immune receptor ITAMs by Y130 phosphorylation is an allosteric mechanism driven by an increased entropy penalty, likely contributed by conformational disorder in the SH2-SH2 inter-domain structure, while SH2-ITAM binding contacts are not affected, and binding enthalpy is unchanged.

Natural Products and Their Mimics as Targets of Opportunity for Discovery

Diverse structural types of natural products and their mimics have served as targets of opportunity in our laboratory to inspire the discovery and development of new methods and strategies to assemble polyfunctional and polycyclic molecular architectures. Furthermore, our efforts toward identifying novel compounds having useful biological properties led to the creation of new targets, many of which posed synthetic challenges that required the invention of new methodology. In this Perspective, selected examples of how we have exploited a diverse range of natural products and their mimics to create, explore, and solve a variety of problems in chemistry and biology will be discussed. The journey was not without its twists and turns, but the unexpected often led to new revelations and insights. Indeed, in our recent excursion into applications of synthetic organic chemistry to neuroscience, avoiding the more-traveled paths was richly rewarding.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

Differential recognition of syk-binding sites by each of the two phosphotyrosine-binding pockets of the Vav SH2 domain

The association of spleen tyrosine kinase (Syk), a central tyrosine kinase in B cell signaling, with Vav SH2 domain is controlled by phosphorylation of two closely spaced tyrosines in Syk linker B: Y342 and Y346. Previous studies established both singly phosphorylated and doubly phosphorylated forms play a role in signaling. The structure of the doubly phosphorylated form identified a new recognition of phosphotyrosine whereby two phosphotyrosines bind simultaneously to the Vav SH2 domain, one in the canonical pTyr pocket and one in the specificity pocket on the opposite side of the central β-sheet. It is unknown if the specificity pocket can bind phosphotyrosine independent of phosphotyrosine binding the pTyr pocket. To address this gap in knowledge, we determined the structure of the complex between Vav1 SH2 and a peptide (SykLB-YpY) modeling the singly phosphorylated-Y346 form of Syk with unphosphorylated Y342. The nuclear magnetic resonance (NMR) data conclusively establish that recognition of phosphotyrosine is swapped between the two pockets; phosphorylated pY346 binds the specificity pocket of Vav1 SH2, and unphosphorylated Y342 occupies what is normally the pTyr binding pocket. Nearly identical changes in chemical shifts occurred upon binding all three forms of singly and doubly phosphorylated peptides; however, somewhat smaller shift perturbations for SykLB-YpY from residues in regions of high internal mobility suggest that internal motions are coupled to binding affinity. The differential recognition that includes this swapped binding of phosphotyrosine to the specificity pocket of Vav SH2 increases the repertoire of possible phosphotyrosine binding by SH2 domains in regulating protein-protein interactions in cellular signaling.

Configurational space discretization and free energy calculation in complex molecular systems

We sought to design a free energy calculation scheme with the hope of saving cost for generating dynamical information that is inherent in trajectories. We demonstrated that snapshots in a converged trajectory set are associated with implicit conformers that have invariant statistical weight distribution (ISWD). Since infinite number of sets of implicit conformers with ISWD may be created through independent converged trajectory sets, we hypothesized that explicit conformers with ISWD may be constructed for complex molecular systems through systematic increase of conformer fineness, and tested the hypothesis in lipid molecule palmitoyloleoylphosphatidylcholine (POPC). Furthermore, when explicit conformers with ISWD were utilized as basic states to define conformational entropy, change of which between two given macrostates was found to be equivalent to change of free energy except a mere difference of a negative temperature factor, and change of enthalpy essentially cancels corresponding change of average intra-conformer entropy. By implicitly taking advantage of entropy enthalpy compensation and forgoing all dynamical information, constructing explicit conformers with ISWD and counting thermally accessible number of which for interested end macrostates is likely to be an efficient and reliable alternative end point free energy calculation strategy.

Evaluating the dynamics and electrostatic interactions of folded proteins in implicit solvents

Three implicit solvent models, namely GBMVII, FACTS, and SCPISM, were evaluated for their abilities to emulate an explicit solvent environment by comparing the simulated conformational ensembles, dynamics, and electrostatic interactions of the Src SH2 domain and the Lyn kinase domain. This assessment in terms of structural features in folded proteins expands upon the use of hydration energy as a metric for comparison. All-against-all rms coordinate deviation, average positional fluctuations, and ion-pair distance distribution were used to compare the implicit solvent models with the TIP3P explicit solvent model. Our study shows that the Src SH2 domains solvated with TIP3P, GBMVII, and FACTS sample similar global conformations. Additionally, the Src SH2 ion-pair distance distributions of solvent-exposed side chains corresponding to TIP3P, GBMVII, and FACTS do not differ substantially, indicating that GBMVII and FACTS are capable of modeling these electrostatic interactions. The ion-pair distance distributions of SCPISM are distinct from others, demonstrating that these electrostatic interactions are not adequately reproduced with the SCPISM model. On the other hand, for the Lyn kinase domain, a non-globular protein with bilobal structure and a large concavity on the surface, implicit solvent does not accurately model solvation to faithfully reproduce partially buried electrostatic interactions and lobe-lobe conformations. Our work reveals that local structure and dynamics of small, globular proteins are modeled well using FACTS and GBMVII. Nonetheless, global conformations and electrostatic interactions in concavities of multi-lobal proteins resulting from simulations with implicit solvent models do not match those obtained from explicit water simulations.

Interolog interfaces in protein-protein docking

Proteins are essential elements of biological systems, and their function typically relies on their ability to successfully bind to specific partners. Recently, an emphasis of study into protein interactions has been on hot spots, or residues in the binding interface that make a significant contribution to the binding energetics. In this study, we investigate how conservation of hot spots can be used to guide docking prediction. We show that the use of evolutionary data combined with hot spot prediction highlights near‐native structures across a range of benchmark examples. Our approach explores various strategies for using hot spots and evolutionary data to score protein complexes, using both absolute and chemical definitions of conservation along with refinements to these strategies that look at windowed conservation and filtering to ensure a minimum number of hot spots in each binding partner. Finally, structure‐based models of orthologs were generated for comparison with sequence‐based scoring. Using two data sets of 22 and 85 examples, a high rate of top 10 and top 1 predictions are observed, with up to 82% of examples returning a top 10 hit and 35% returning top 1 hit depending on the data set and strategy applied; upon inclusion of the native structure among the decoys, up to 55% of examples yielded a top 1 hit. The 20 common examples between data sets show that more carefully curated interolog data yields better predictions, particularly in achieving top 1 hits. Proteins 2015; 83:1940–1946. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Relative Binding Enthalpies from Molecular Dynamics Simulations Using a Direct Method

The potential for reliably predicting relative binding enthalpies, ΔΔE, from a direct method utilizing molecular dynamics is examined for a system of three phosphotyrosyl peptides binding to a protein receptor, the Src SH2 domain. The binding enthalpies were calculated from the potential energy differences between the bound and the unbound end-states of each peptide from equilibrium simulations in explicit water. The statistical uncertainties in the ensemble-mean energy values from multiple, independent simulations were obtained using a bootstrap method. Simulations were initiated with different starting coordinates as well as different velocities. Statistical uncertainties in ΔΔE are 2 to 3 kcal/mol based on calculations from 40, 10 ns trajectories for each system (three SH2–peptide complexes or unbound peptides). Uncertainties in relative component energies, comprising solute–solute, solute–solvent and solvent–solvent interactions, are considerably larger. Energy values were estimated from an unweighted ensemble averaging of multiple trajectories with the a priori assumption that all trajectories are equally likely. Distributions in energy–rmsd space indicate that the trajectories sample the same basin and the difference in mean energy values between trajectories is due to sampling of alternative local regions of this superbasin. The direct estimate of relative binding enthalpies is concluded to be a reasonable approach for well-ordered systems with ΔΔE values greater than ∼3 kcal/mol, although the approach would benefit from future work to determine properly distributed starting points that would enable efficient sampling of conformational space using multiple trajectories.

Drug Resistance Mutations Alter Dynamics of Inhibitor-Bound HIV-1 Protease

Under the selective pressure of therapy, HIV-1 protease mutants resistant to inhibitors evolve to confer drug resistance. Such mutations can impact both the dynamics and structures of the bound and unbound forms of the enzyme. Flap+ is a multidrug-resistant variant of HIV-1 protease with a combination of primary and secondary resistance mutations (L10I, G48V, I54V, V82A) and a strikingly altered thermodynamic profile for darunavir (DRV) binding relative to the wild-type protease. We elucidated the impact of these mutations on protein dynamics in the DRV-bound state using molecular dynamics simulations and NMR relaxation experiments. Both methods concur in that the conformational ensemble and dynamics of protease are impacted by the drug resistance mutations in Flap+ variant. Surprisingly this change in ensemble dynamics is different from that observed in the unliganded form of the same variant (Cai, Y. et al. J. Chem. Theory Comput.2012, 8, 3452–3462). Our comparative analysis of both inhibitor-free and bound states presents a comprehensive picture of the altered dynamics in drug-resistant mutant HIV-1 protease and underlies the importance of incorporating dynamic analysis of the whole system, including the unliganded state, into revealing drug resistance mechanisms.

Druggable orthosteric and allosteric hot spots to target protein-protein interactions

Drug designing targeting protein-protein interactions is challenging. Because structural elucidation and computational analysis have revealed the importance of hot spot residues in stabilizing these interactions, there have been on-going efforts to develop drugs which bind the hot spots and out-compete the native protein partners. The question arises as to what are the key 'druggable' properties of hot spots in protein-protein interactions and whether these mimic the general hot spot definition. Identification of orthosteric (at the protein- protein interaction site) and allosteric (elsewhere) druggable hot spots is expected to help in discovering compounds that can more effectively modulate protein-protein interactions. For example, are there any other significant features beyond their location in pockets in the interface? The interactions of protein-protein hot spots are coupled with conformational dynamics of protein complexes. Currently increasing efforts focus on the allosteric drug discovery. Allosteric drugs bind away from the native binding site and can modulate the native interactions. We propose that identification of allosteric hot spots could similarly help in more effective allosteric drug discovery. While detection of allosteric hot spots is challenging, targeting drugs to these residues has the potential of greatly increasing the hot spot and protein druggability.

Correlating structure and energetics in protein-ligand interactions: paradigms and paradoxes

Predicting protein-binding affinities of small molecules, even closely related ones, is a formidable challenge in biomolecular recognition and medicinal chemistry. A thermodynamic approach to optimizing affinity in protein-ligand interactions requires knowledge and understanding of how altering the structure of a small molecule will be manifested in protein-binding enthalpy and entropy changes; however, there is a relative paucity of such detailed information. In this review, we examine two strategies commonly used to increase ligand potency. The first of these involves introducing a cyclic constraint to preorganize a small molecule in its biologically active conformation, and the second entails adding nonpolar groups to a molecule to increase the amount of hydrophobic surface that is buried upon binding. Both of these approaches are motivated by paradigms suggesting that protein-binding entropy changes should become more favorable, but paradoxes can emerge that defy conventional wisdom.

Protein-Ligand Interactions: Thermodynamic Effects Associated with Increasing the Length of an Alkyl Chain

Thermodynamic parameters were determined for complex formation between the Grb2 SH2 domain and tripeptides of the general form Ac-pTyr-Xaa-Asn in which the Xaa residue bears a linear alkyl chain varying in length from 1-5 carbon atoms. Binding affinity increases upon adding a methylene group to the Ala derivative, but further chain extension gives no extra enhancement in potency. The thermodynamic signatures of the ethyl and n-propyl derivatives are virtually identical as are those for the n-butyl and n-pentyl analogs. Crystallographic analysis of the complexes reveals a high degree of similarity in the structure of the domain and the bound ligands with the notable exception that there is a gauche interaction in the side chains in the bound conformations of ligands having n-propyl, n-butyl, and n-pentyl groups. However, eliminating this unfavorable interaction by introducing a Z-double bond into the side chain of the n-propyl analog does not result in an increase in affinity. Increases in the amount of nonpolar surface that is buried upon ligand binding correlate with favorable changes in ΔH°, but these are usually offset by corresponding unfavorable changes in -TΔS°; there is little correlation of ΔC_p with changes in the amount of buried nonpolar surface.

A prospective overview of the essential requirements in molecular modeling for nanomedicine design

Nanotechnology has presented many new challenges and opportunities in the area of nanomedicine design. The issues related to nanoconjugation, nanosystem-mediated targeted drug delivery, transitional stability of nanovehicles, the integrity of drug transport, drug-delivery mechanisms and chemical structural design require a pre-estimated and determined course of assumptive actions with property and characteristic estimations for optimal nanomedicine design. Molecular modeling in nanomedicine encompasses these pre-estimations and predictions of pertinent design data via interactive computographic software. Recently, an increasing amount of research has been reported where specialized software is being developed and employed in an attempt to bridge the gap between drug discovery, materials science and biology. This review provides an assimilative and concise incursion into the current and future strategies of molecular-modeling applications in nanomedicine design and aims to describe the utilization of molecular models and theoretical-chemistry computographic techniques for expansive nanomedicine design and development.

A simplified confinement method for calculating absolute free energies and free energy and entropy differences

A simple and robust formulation of the path-independent confinement method for the calculation of free energies is presented. The simplified confinement method (SCM) does not require matrix diagonalization or switching off the molecular force field, and has a simple convergence criterion. The method can be readily implemented in molecular dynamics programs with minimal or no code modifications. Because the confinement method is a special case of thermodynamic integration, it is trivially parallel over the integration variable. The accuracy of the method is demonstrated using a model diatomic molecule, for which exact results can be computed analytically. The method is then applied to the alanine dipeptide in vacuum, and to the α-helix ↔ β-sheet transition in a 16-residue peptide modeled in implicit solvent. The SCM requires less effort for the calculation of free energy differences than previous formulations because it does not require computing normal modes. The SCM has a diminished advantage for determining absolute free energy values, because it requires decreasing the MD integration step to obtain accurate results. An approximate confinement procedure is introduced, which can be used to estimate directly the configurational entropy difference between two macrostates, without the need for additional computation of the difference in the free energy or enthalpy. The approximation has convergence properties similar to those of the standard confinement method for the calculation of free energies. The use of the approximation requires about 5 times less wall-clock simulation time than that needed to compute enthalpy differences to similar precision from an MD trajectory. For the biomolecular systems considered in this study, the errors in the entropy approximation are under 10%. Practical applications of the methods to proteins are currently limited to implicit solvent simulations.

Entropy-enthalpy compensation: role and ramifications in biomolecular ligand recognition and design

Recent calorimetric studies of interactions between small molecules and biomolecular targets have generated renewed interest in the phenomenon of entropy-enthalpy compensation. In these studies, entropic and enthalpic contributions to binding are observed to vary substantially and in an opposing manner as the ligand or protein is modified, whereas the binding free energy varies little. In severe examples, engineered enthalpic gains can lead to completely compensating entropic penalties, frustrating ligand design. Here, we examine the evidence for compensation, as well as its potential origins, prevalence, severity, and ramifications for ligand engineering. We find the evidence for severe compensation to be weak in light of the large magnitude of and correlation between errors in experimental measurements of entropic and enthalpic contributions to binding, though a limited form of compensation may be common. Given the difficulty of predicting or measuring entropic and enthalpic changes to useful precision, or using this information in design, we recommend ligand engineering efforts instead focus on computational and experimental methodologies to directly assess changes in binding free energy.

High-throughput analysis of peptide-binding modules

Modular protein interaction domains (PIDs) that recognize linear peptide motifs are found in hundreds of proteins within the human genome. Some PIDs such as SH2, 14-3-3, Chromo, and Bromo domains serve to recognize posttranslational modification (PTM) of amino acids (such as phosphorylation, acetylation, methylation, etc.) and translate these into discrete cellular responses. Other modules such as SH3 and PSD-95/Discs-large/ZO-1 (PDZ) domains recognize linear peptide epitopes and serve to organize protein complexes based on localization and regions of elevated concentration. In both cases, the ability to nucleate-specific signaling complexes is in large part dependent on the selectivity of a given protein module for its cognate peptide ligand. High-throughput (HTP) analysis of peptide-binding domains by peptide or protein arrays, phage display, mass spectrometry, or other HTP techniques provides new insight into the potential protein-protein interactions prescribed by individual or even whole families of modules. Systems level analyses have also promoted a deeper understanding of the underlying principles that govern selective protein-protein interactions and how selectivity evolves. Lastly, there is a growing appreciation for the limitations and potential pitfalls associated with HTP analysis of protein-peptide interactomes. This review will examine some of the common approaches utilized for large-scale studies of PIDs and suggest a set of standards for the analysis and validation of datasets from large-scale studies of peptide-binding modules. We will also highlight how data from large-scale studies of modular interaction domain families can provide insight into systems level properties such as the linguistics of selective interactions.

Detection of long-range concerted motions in protein by a distance covariance

We asses the ability of a distance correlation coefficient (DiCC), calculated from distance covariance, for detecting long-range concerted motion in proteins. We establish a set of criteria for ideal correlation coefficient values based on the coefficient of determination in multi-dimension, R2. We compare in detail DiCC and conventional coefficients against these criteria. We demonstrate that in contrast to conventional correlation coefficients, which capture long-distance correlation adequately only with certain restrictions in multi-dimension, DiCC reflects appropriate correlation in both one- and multi-dimension. Finally we demonstrate the usefulness of DiCC for assessing long-distance correlated fluctuation in protein dynamics.

Energetics of Ortho-7 (oxime drug) translocation through the active-site gorge of tabun conjugated acetylcholinesterase

Oxime drugs translocate through the 20 Å active-site gorge of acetylcholinesterase in order to liberate the enzyme from organophosphorus compounds' (such as tabun) conjugation. Here we report bidirectional steered molecular dynamics simulations of oxime drug (Ortho-7) translocation through the gorge of tabun intoxicated enzyme, in which time dependent external forces accelerate the translocation event. The simulations reveal the participation of drug-enzyme hydrogen bonding, hydrophobic interactions and water bridges between them. Employing nonequilibrium theorems that recovers the free energy from irreversible work done, we reconstruct potential of mean force along the translocation pathway such that the desired quantity represents an unperturbed system. The potential locates the binding sites and barriers for the drug to translocate inside the gorge. Configurational entropic contribution of the protein-drug binding entity and the role of solvent translational mobility in the binding energetics is further assessed.

Limiting assumptions in structure-based design: binding entropy

In order to deal with the complexity of biological systems at the atomic level, limiting assumptions are often made which do not reflect the reality of the system under study. One example is the assumption that the entropy of binding of the macromolecule is not influenced significantly by the different ligands. Recent experimental data on ligands binding to HIV-1 protease challenge this assumption.

C-terminal substitution of MDM2 interacting peptides modulates binding affinity by distinctive mechanisms

The complex between the proteins MDM2 and p53 is a promising drug target for cancer therapy. The residues 19–26 of p53 have been biochemically and structurally demonstrated to be a most critical region to maintain the association of MDM2 and p53. Variation of the amino acid sequence in this range obviously alters the binding affinity. Surprisingly, suitable substitutions contiguous to this region of the p53 peptides can yield tightly binding peptides. The peptide variants may differ by a single residue that vary little in their structural conformations and yet are characterized by large differences in their binding affinities. In this study a systematic analysis into the role of single C-terminal mutations of a 12 residue fragment of the p53 transactivation domain (TD) and an equivalent phage optimized peptide (12/1) were undertaken to elucidate their mechanistic and thermodynamic differences in interacting with the N-terminal of MDM2. The experimental results together with atomistically detailed dynamics simulations provide insight into the principles that govern peptide design protocols with regard to protein-protein interactions and peptidomimetic design.

(E)-alkene and ethylene isosteres substantially alter the hydrogen-bonding network in class II MHC A(q)/glycopeptide complexes and affect T-cell recognition

The structural basis for antigen presentation by class II major histocompatibility complex (MHC) proteins to CD4(+) T-cells is important for understanding and possibly treating autoimmune diseases. In the work described in this paper, (E)-alkene and ethylene amide-bond isosteres were used to investigate the effect of removing hydrogen-bonding possibilities from the CII259-270 glycopeptide, which is bound by the arthritis-associated murine A(q) class II MHC protein. The isostere-modified glycopeptides showed varying and unexpectedly large losses of A(q) binding that could be linked to the dynamics of the system. Molecular dynamics (MD) simulations revealed that the backbone of CII259-270 and the A(q) protein are able to form up to 11 hydrogen bonds, but fewer than this number are present at any one time. Most of the strong hydrogen-bond interactions were formed by the N-terminal part of the glycopeptide, i.e., in the region where the isosteric replacements were made. The structural dynamics also revealed that hydrogen bonds were strongly coupled to each other; the loss of one hydrogen-bond interaction had a profound effect on the entire hydrogen-bonding network. The A(q) binding data revealed that an ethylene isostere glycopeptide unexpectedly bound more strongly to A(q) than the corresponding (E)-alkene, which is in contrast to the trend observed for the other isosteres. Analysis of the MD trajectories revealed that the complex conformation of this ethylene isostere was structurally different and had an altered molecular interaction pattern compared to the other A(q)/glycopeptide complexes. The introduced amide-bond isosteres also affected the interactions of the glycopeptide/A(q) complexes with T-cell receptors. The dynamic variation of the patterns and strengths of the hydrogen-bond interactions in the class II MHC system is of critical importance for the class II MHC/peptide/TCR signaling system.