Entropy-enthalpy transduction caused by conformational shifts can obscure the forces driving protein-ligand binding

The activity regulation of lipase from Aspergillus fumigatus by ligand through allosteric exploration

Lipase activity from Aspergillus fumigatus (AFL) were modulated by an effector (HMD) that was discovered for an allosteric site on the bioactive macromolecule. Experimental evaluation and computational modeling of allosteric effects revealed alterations in the structure of AFL. It was found that AFL's activity in HMD solution increased by approximately 46 % due to mainly enhanced lid flexibility. HMD-AFL interaction was driven by enthalpy and entropy. However, when AFL was coupled to HMD-modified microspheres (PS-HMD-p), its hydrolysis activity decreased by ∼14.3 % due to reduced lid flexibility. After immobilization, AFL's ester-synthesis activity also decreased, due to changes in the conformational dynamics and the geometric characteristics of active site. Investigating the structural dynamics of allosteric regulation of the lipase not only reveals its structural changes underlying the functional variation but also enhances the understanding of the allosteric property that is underappreciated in exoenzymes.

Elucidating the binding mechanisms of GABA and Muscimol as an avenue to discover novel GABA-mimetic small molecules

Gamma-aminobutyric acid (GABA) signaling is the principal inhibitory pathway in the central nervous system. It is critical in neuronal cell proliferation and fate determination. Any aberration in GABA inhibition results in psychiatric and neurological diseases. Thus, modulating GABAergic neurotransmission has become the basis of drug therapy for psychiatric and several neurological diseases. Though GABA and muscimol are classical inhibitors of GABA receptors, the search for novel inhibitors continues unabated. In this study, the binding mechanism of GABA and muscimol was elucidated and applied in the search for small molecule GABAergic inhibitors using comprehensive computational techniques. It was revealed that a high-affinity binding of GABA and muscimol was mediated by a water molecule involving α1Thr129 and then stabilized by strong interactions including salt bridges with β2Glu155 and α1Arg66 amidst hydrogen bonds, π-π stacking, and π -cation interactions with other residues. The binding of GABA and muscimol was also characterized by stability and deeper penetration into the hydrophobic core of the protein which resulted in conformational changes of the binding pocket and domain, by inducing correlated motions of the residues. Thermodynamics analysis showed GABA and muscimol exhibited total binding free energies of -19.85 ± 8.83 Kcal/mol and -26.55 ± 3.42 Kcal/mol, respectively. A pharmacophore model search, based on the energy contributions of implicating binding residues, resulted in the identification of ZINC68604167, ZINC19735138, ZINC04202466, ZINC00901626, and ZINC01532854 as potential GABA-mimetic compounds from metabolites and natural products libraries. This study has elucidated the binding mechanisms of GABA and muscimol and successfully applied in the identification of GABA-mimetic compounds.Communicated by Ramaswamy H. Sarma.

Host-guest systems for the SAMPL9 blinded prediction challenge: phenothiazine as a privileged scaffold for binding to cyclodextrins

Model systems are widely used in biology and chemistry to gain insight into more complex systems. In the field of computational chemistry, researchers use host-guest systems, relatively simple exemplars of noncovalent binding, to train and test the computational methods used in drug discovery. Indeed, host-guest systems have been developed to support the community-wide blinded SAMPL prediction challenges for over a decade. While seeking new host-guest systems for the recent SAMPL9 binding prediction challenge, which is the focus of the present PCCP Themed Collection, we identified phenothiazine as a privileged scaffold for guests of β cyclodextrin (βCD) and its derivatives. Building on this observation, we used calorimetry and NMR spectroscopy to characterize the noncovalent association of native βCD and three methylated derivatives of βCD with five phenothiazine drugs. The strongest association observed, that of thioridazine and one of the methyl derivatives, exceeds the well-known high affinity of rimantidine with βCD. Intriguingly, however, methylation of βCD at the 3 position abolished detectible binding for all of the drugs studied. The dataset has a clear pattern of entropy-enthalpy compensation. The NMR data show that all of the drugs position at least one aromatic proton at the secondary face of the CD, and most also show evidence of deep penetration of the binding site. The results of this study were used in the SAMPL9 blinded binding affinity-prediction challenge, which are detailed in accompanying papers of the present Themed Collection. These data also open the phenothiazines and, potentially, chemically similar drugs, such as the tricyclic antidepressants, as relatively potent binders of βCD, setting the stage for future SAMPL challenge datasets and for possible applications as drug reversal agents.

Coenzyme-binding pathway on glutamate dehydrogenase suggested from multiple-binding sites visualized by cryo-electron microscopy

The structure of hexameric glutamate dehydrogenase (GDH) in the presence of the coenzyme nicotinamide adenine dinucleotide phosphate (NADP) was visualized using cryogenic transmission electron microscopy to investigate the ligand-binding pathways to the active site of the enzyme. Each subunit of GDH comprises one hexamer-forming core domain and one nucleotide-binding domain (NAD domain), which spontaneously opens and closes the active-site cleft situated between the two domains. In the presence of NADP, the potential map of GDH hexamer, assuming D3 symmetry, was determined at a resolution of 2.4 Å, but the NAD domain was blurred due to the conformational variety. After focused classification with respect to the NAD domain, the potential maps interpreted as NADP molecules appeared at five different sites in the active-site cleft. The subunits associated with NADP molecules were close to one of the four metastable conformations in the unliganded state. Three of the five binding sites suggested a pathway of NADP molecules to approach the active-site cleft for initiating the enzymatic reaction. The other two binding modes may rarely appear in the presence of glutamate, as demonstrated by the reaction kinetics. Based on the visualized structures and the results from the enzymatic kinetics, we discussed the binding modes of NADP to GDH in the absence and presence of glutamate.

Sialic Acid 4-N-Piperazine and Piperidine Derivatives Bind with High Affinity to the P. mirabilis Sialic Acid Sodium Solute Symporter

In search for novel antibacterial compounds, bacterial sialic acid uptake inhibition represents a promising strategy. Sialic acid plays a critical role for growth and colonisation of several pathogenic bacteria, and its uptake inhibition in bacteria was recently demonstrated to be a viable strategy by targeting the SiaT sodium solute symporters from Proteus mirabilis and Staphylococcus aureus. Here we report the design, synthesis and evaluation of potential sialic acid uptake inhibitors bearing 4-N-piperidine and piperazine moieties. The 4-N-derivatives were obtained via 4-N-functionalization with piperidine and piperazine nucleophiles in an efficient direct substitution of the 4-O-acetate of Neu5Ac. Evaluation for binding to bacterial transport proteins with nanoDSF and ITC revealed compounds possessing nanomolar affinity for the P. mirabilis SiaT symporter. Computational analyses indicate the engagement of a previously untargeted portion of the binding site.

General Theory of Specific Binding: Insights from a Genetic-Mechano-Chemical Protein Model

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. However, despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics, and genetics and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination-by varying degrees of flexibility and shape/chemistry complementarity-but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces, and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

Decoding molecular recognition of inhibitors targeting HDAC2 via molecular dynamics simulations and configurational entropy estimation

Molecular recognition by enzymes is a complicated process involving thermodynamic energies governing protein-ligand interactions. In order to aid the estimation of inhibitory activity of compounds targeting an enzyme, several computational methods can be employed to dissect this intermolecular contact. Herein, we report a structural dynamics investigation of an epigenetic enzyme HDAC2 in differentiating its binding to various inhibitors within the sub-sites of its active site. Molecular dynamics (MD) simulation was employed to elucidate the intermolecular interactions as well as the dynamics behavior of ligand binding. MD trajectories of five distinct HDAC2-inhibitor complexes reveal that compounds lacking adequate contacts with the opening rim of the active site possess high fluctuation along the cap portion, thus weakening the overall affinity. Key intermolecular interactions determining the effective binding of inhibitors include hydrogen bonds with Gly154, Asp181, and Tyr308; hydrophobic interactions between Phe155/Phe210 and the linker region; and a pi-stacking with Arg39 at the foot pocket. Decomposition of the binding free energy calculated per-residue by MM/PBSA also indicates that the interactions within the internal foot pocket, especially with residues Met35, Leu144, Gly305, and Gly306, can contribute significantly to the ligand binding. Additionally, configurational entropy of the binding was estimated and compared to the scale of the binding free energy in order to assess its contribution to the binding and to differentiate various ligand partners. It was found that the levels of entropic contribution are comparable among a set of structurally similar carbamide ligands, while it is greatly different for the set of unrelated ligands, ranging from 2.75 to 16.38 kcal/mol for the five inhibitors examined. These findings exemplify the importance of assessing molecular dynamics as well as estimating the entropic contribution in evaluating the ligand binding mechanism.

Comparative genomics, evolutionary epidemiology, and RBD-hACE2 receptor binding pattern in B.1.1.7 (Alpha) and B.1.617.2 (Delta) related to their pandemic response in UK and India

Background

The massive increase in COVID-19 infection had generated a second wave in India during May–June 2021 with a critical pandemic situation. The Delta variant (B.1.617.2) was a significant factor during the second wave. Conversely, the UK had passed through the crucial phase of the pandemic from November to December 2020 due to B.1.1.7. The study tried to comprehend the pandemic response in the UK and India to the spread of the B.1.1.7 (Alpha, UK) variant and B.1.617.2 (Delta, India) variant.

Methods

This study was performed in three directions to understand the pandemic response of the two emerging variants. First, we served comparative genomics, such as genome sequence submission patterns, mutational landscapes, and structural landscapes of significant mutations (N501Y, D614G, L452R, E484Q, and P681R). Second, we performed evolutionary epidemiology using molecular phylogenetics, scatter plots of the cluster evaluation, country-wise transmission pattern, and frequency pattern. Third, the receptor binding pattern was analyzed using the Wuhan reference strain and the other two variants.

Results

The study analyzed the country-wise and region-wise genome sequences and their submission pattern, molecular phylogenetics, scatter plot of the cluster evaluation, country-wise geographical distribution and transmission pattern, frequency pattern, entropy diversity, and mutational landscape of the two variants. The structural pattern was analyzed in the N501Y, D614G L452R, E484Q, and P681R mutations. The study found increased molecular interactivity between hACE2-RBD binding of B.1.1.7 and B.1.617.2 compared to the Wuhan reference strain. Our receptor binding analysis showed a similar indication pattern for hACE2-RBD of these two variants. However, B.1.617.2 offers slightly better stability in the hACE2-RBD binding pattern through MD simulation than B.1.1.7.

Conclusion

The increased hACE2-RBD binding pattern of B.1.1.7 and B.1.617.2 might help to increase the infectivity compared to the Wuhan reference strain.

Sialic Acid Derivatives Inhibit SiaT Transporters and Delay Bacterial Growth

Antibiotic resistance is a major worldwide concern, and new drugs with mechanistically novel modes of action are urgently needed. Here, we report the structure-based drug design, synthesis, and evaluation in vitro and in cellular systems of sialic acid derivatives able to inhibit the bacterial sialic acid symporter SiaT. We designed and synthesized 21 sialic acid derivatives and screened their affinity for SiaT by a thermal shift assay and elucidated the inhibitory mechanism through binding thermodynamics, computational methods, and inhibitory kinetic studies. The most potent compounds, which have a 180-fold higher affinity compared to the natural substrate, were tested in bacterial growth assays and indicate bacterial growth delay in methicillin-resistant Staphylococcus aureus. This study represents the first example and a promising lead in developing sialic acid uptake inhibitors as novel antibacterial agents.

Understanding p300-transcription factor interactions using sequence variation and hybridization

The hypoxic response is central to cell function and plays a significant role in the growth and survival of solid tumours. HIF-1 regulates the hypoxic response by activating over 100...

Sweet Drugs for Bad Bugs: A Glycomimetic Strategy against the DC-SIGN-Mediated Dissemination of SARS-CoV-2

The C-type lectin receptor DC-SIGN is a pattern recognition receptor expressed on macrophages and dendritic cells. It has been identified as a promiscuous entry receptor for many pathogens, including epidemic and pandemic viruses such as SARS-CoV-2, Ebola virus, and HIV-1. In the context of the recent SARS-CoV-2 pandemic, DC-SIGN-mediated virus dissemination and stimulation of innate immune responses has been implicated as a potential factor in the development of severe COVID-19. Inhibition of virus binding to DC-SIGN, thus, represents an attractive host-directed strategy to attenuate overshooting innate immune responses and prevent the progression of the disease. In this study, we report on the discovery of a new class of potent glycomimetic DC-SIGN antagonists from a focused library of triazole-based mannose analogues. Structure-based optimization of an initial screening hit yielded a glycomimetic ligand with a more than 100-fold improved binding affinity compared to methyl α-d-mannopyranoside. Analysis of binding thermodynamics revealed an enthalpy-driven improvement of binding affinity that was enabled by hydrophobic interactions with a loop region adjacent to the binding site and displacement of a conserved water molecule. The identified ligand was employed for the synthesis of multivalent glycopolymers that were able to inhibit SARS-CoV-2 spike glycoprotein binding to DC-SIGN-expressing cells, as well as DC-SIGN-mediated trans-infection of ACE2⁺ cells by SARS-CoV-2 spike protein-expressing viruses, in nanomolar concentrations. The identified glycomimetic ligands reported here open promising perspectives for the development of highly potent and fully selective DC-SIGN-targeted therapeutics for a broad spectrum of viral infections.

Definition of the immune evasion-replication interface of rabies virus P protein

Rabies virus phosphoprotein (P protein) is a multifunctional protein that plays key roles in replication as the polymerase cofactor that binds to the complex of viral genomic RNA and the nucleoprotein (N protein), and in evading the innate immune response by binding to STAT transcription factors. These interactions are mediated by the C-terminal domain of P (P_CTD). The colocation of these binding sites in the small globular P_CTD raises the question of how these interactions underlying replication and immune evasion, central to viral infection, are coordinated and, potentially, coregulated. While direct data on the binding interface of the P_CTD for STAT1 is available, the lack of direct structural data on the sites that bind N protein limits our understanding of this interaction hub. The P_CTD was proposed to bind via two sites to a flexible loop of N protein (N_pep) that is not visible in crystal structures, but no direct analysis of this interaction has been reported. Here we use Nuclear Magnetic Resonance, and molecular modelling to show N protein residues, Leu381, Asp383, Asp384 and phosphor-Ser389, are likely to bind to a ‘positive patch’ of the P_CTD formed by Lys211, Lys214 and Arg260. Furthermore, in contrast to previous predictions we identify a single site of interaction on the P_CTD by this N_pep. Intriguingly, this site is proximal to the defined STAT1 binding site that includes Ile201 to Phe209. However, cell-based assays indicate that STAT1 and N protein do not compete for P protein. Thus, it appears that interactions critical to replication and immune evasion can occur simultaneously with the same molecules of P protein so that the binding of P protein to activated STAT1 can potentially occur without interrupting interactions involved in replication. These data suggest that replication complexes might be directly involved in STAT1 antagonism.

For viruses to infect cells and generate progeny, they must be able to mediate replication, while simultaneously evading the innate immune system. Viruses with small genomes often achieve this through multifunctional proteins that have roles in both replication and immune evasion, such as the phosphoprotein (P protein) of rabies virus. P protein is an essential cofactor in genome replication and transcription, dependent on the well-folded C-terminal domain (P_CTD), which binds to the nucleoprotein (N protein) when complexed with RNA. The P_CTD can also bind and antagonize signal transducers and activators of transcription (STAT) proteins, that are essential for activating antiviral mechanisms. Here we show using Nuclear Magnetic Resonance spectroscopy and cell-based assays, that the STAT1-binding and N-binding interfaces are proximal but, nevertheless, it appears that the same molecule of P_CTD can simultaneously bind STAT1 and N protein. These data suggest that P-protein-STAT1 interaction, critical to immune evasion, can occur without interrupting interactions underlying replication, and so replication complexes might be directly involved in STAT1 antagonism.

Entropy-Entropy Compensation between the Protein, Ligand, and Solvent Degrees of Freedom Fine-Tunes Affinity in Ligand Binding to Galectin-3C

Molecular recognition is fundamental to biological signaling. A central question is how individual interactions between molecular moieties affect the thermodynamics of ligand binding to proteins and how these effects might propagate beyond the immediate neighborhood of the binding site. Here, we investigate this question by introducing minor changes in ligand structure and characterizing the effects of these on ligand affinity to the carbohydrate recognition domain of galectin-3, using a combination of isothermal titration calorimetry, X-ray crystallography, NMR relaxation, and computational approaches including molecular dynamics (MD) simulations and grid inhomogeneous solvation theory (GIST). We studied a congeneric series of ligands with a fluorophenyl-triazole moiety, where the fluorine substituent varies between the ortho, meta, and para positions (denoted O, M, and P). The M and P ligands have similar affinities, whereas the O ligand has 3-fold lower affinity, reflecting differences in binding enthalpy and entropy. The results reveal surprising differences in conformational and solvation entropy among the three complexes. NMR backbone order parameters show that the O-bound protein has reduced conformational entropy compared to the M and P complexes. By contrast, the bound ligand is more flexible in the O complex, as determined by ¹⁹F NMR relaxation, ensemble-refined X-ray diffraction data, and MD simulations. Furthermore, GIST calculations indicate that the O-bound complex has less unfavorable solvation entropy compared to the other two complexes. Thus, the results indicate compensatory effects from ligand conformational entropy and water entropy, on the one hand, and protein conformational entropy, on the other hand. Taken together, these different contributions amount to entropy-entropy compensation among the system components involved in ligand binding to a target protein.

Protein storytelling through physics

Every protein has a story-how it folds, what it binds, its biological actions, and how it misbehaves in aging or disease. Stories are often inferred from a protein's shape (i.e., its structure). But increasingly, stories are told using computational molecular physics (CMP). CMP is rooted in the principled physics of driving forces and reveals granular detail of conformational populations in space and time. Recent advances are accessing longer time scales, larger actions, and blind testing, enabling more of biology's stories to be told in the language of atomistic physics.

Application and Comprehensive Analysis of Neighbor Approximated Information Theoretic Configurational Entropy Methods to Protein-Ligand Binding Cases

The binding entropy is an important thermodynamic quantity which has numerous applications in studies of the biophysical process, and configurational entropy is often one of the major contributors in it. Therefore, its accurate estimation is important, though it is challenging mostly due to sampling limitations, anharmonicity, and multimodality of atomic fluctuations. The present work reports a Neighbor Approximated Maximum Information Spanning Tree (A-MIST) method for conformational entropy and presents its performance and computational advantage over conventional Mutual Information Expansion (MIE) and Maximum Information Spanning Tree (MIST) for two protein-ligand binding cases: indirubin-5-sulfonate to Plasmodium falciparum Protein Kinase 5 (PfPK5) and P. falciparum RON2-peptide to P. falciparum Apical Membrane Antigen 1 (PfAMA1). Important structural regions considering binding configurational entropy are identified, and physical origins for such are discussed. A thorough performance evaluation is done of a set of four entropy estimators (Maximum Likelihood (ML), Miller-Madow (MM), Chao-Shen (CS), and James and Stein shrinkage (JS)) with known varying degrees of sensitivity of the entropy estimate on the extent of sampling, each with two schemes for discretization of fluctuation data of Degrees of Freedom (DFs) to estimate Probability Density Functions (PDFs). Our comprehensive evaluation of influences of variations of parameters shows Neighbor Approximated MIE (A-MIE) outperforms MIE in terms of convergence and computational efficiency. In the case of A-MIE/MIE, results are sensitive to the choice of root atoms, graph search algorithm used for the Bond-Angle-Torsion (BAT) conversion, and entropy estimator, while A-MIST/MIST are not. A-MIST yields binding entropy within 0.5 kcal/mol of MIST with only 20-30% computation. Moreover, all these methods have been implemented in an OpenMP/MPI hybrid parallel C++11 code, and also a python package for data preprocessing and entropy contribution analysis is developed and made available. A comparative analysis of features of current implementation and existing tools is also presented.

Enhancing the enthalpic contribution of hydrogen bonds by solvent shielding

In biological systems, polar interactions are heavily burdened by high desolvation penalties resulting from strong solute-solvent interactions. As a consequence thereof, enthalpic contributions of hydrogen bonds to the free energy of binding are severely diminished. However, this effect is strongly attenuated for interactions within solvent-shielded areas of proteins. In microcalorimetric experiments, we show that the bacterial lectin FimH utilizes conformational adaptions to effectively shield its binding site from solvent. The transition into a lower dielectric environment results in an enthalpic benefit of approximately -13 kJ mol-1 for mannoside binding. However, this effect can be abrogated, if the hydrogen bond network within the binding site is disturbed by deoxygenation of the ligand. Conformational adaption leading to reduced local dielectric constants could represent a general mechanism for proteins to enable enthalpy-driven recognition of polar ligands.

Effect of disulphide loop length on mechanochemical structural stability of macromolecules

Polymer chains folded with a single disulphide loop are shown to present distinct rates of mechanochemical fragmentation.

A Fragmenting Protocol with Explicit Hydration for Calculation of Binding Enthalpies of Target-Ligand Complexes at a Quantum Mechanical Level

Optimization of the enthalpy component of binding thermodynamics of drug candidates is a successful pathway of rational molecular design. However, the large size and missing hydration structure of target-ligand complexes often hinder such optimizations with quantum mechanical (QM) methods. At the same time, QM calculations are often necessitated for proper handling of electronic effects. To overcome the above problems, and help the QM design of new drugs, a protocol is introduced for atomic level determination of hydration structure and extraction of structures of target-ligand complex interfaces. The protocol is a combination of a previously published program MobyWat, an engine for assigning explicit water positions, and Fragmenter, a new tool for optimal fragmentation of protein targets. The protocol fostered a series of fast calculations of ligand binding enthalpies at the semi-empirical QM level. Ligands of diverse chemistry ranging from small aromatic compounds up to a large peptide helix of a molecular weight of 3000 targeting a leukemia protein were selected for systematic investigations. Comparison of various combinations of implicit and explicit water models demonstrated that the presence of accurately predicted explicit water molecules in the complex interface considerably improved the agreement with experimental results. A single scaling factor was derived for conversion of QM reaction heats into binding enthalpy values. The factor links molecular structure with binding thermodynamics via QM calculations. The new protocol and scaling factor will help automated optimization of binding enthalpy in future molecular design projects.

Broad-scale analysis of thermodynamic signatures in medicinal chemistry: are enthalpy-favored binders the better development option?

Thermodynamic profiles of ligand binding, particularly enthalpically favored binding signatures, have been suggested as a criterion to support the decision-making process around which compounds to select for further optimization in drug development. The concept was enthusiastically taken up, but turned out to be too superficial, either because many aspects determining thermodynamic profiles are insufficiently appreciated or because it is difficult to compare such data on a global scale. The impact of water, changes in protonation states, along with buffer dependencies and incompatible measurement conditions that are far from standard conditions hamper such broad-scale comparisons. However, thermodynamic signatures can make us aware of the impact of these aspects and provide important hints for improving our understanding of the binding process and defining criteria for drug optimization.

Interplay between Conformational Entropy and Solvation Entropy in Protein-Ligand Binding

Understanding the driving forces underlying molecular recognition is of fundamental importance in chemistry and biology. The challenge is to unravel the binding thermodynamics into separate contributions and to interpret these in molecular terms. Entropic contributions to the free energy of binding are particularly difficult to assess in this regard. Here we pinpoint the molecular determinants underlying differences in ligand affinity to the carbohydrate recognition domain of galectin-3, using a combination of isothermal titration calorimetry, X-ray crystallography, NMR relaxation, and molecular dynamics simulations followed by conformational entropy and grid inhomogeneous solvation theory (GIST) analyses. Using a pair of diastereomeric ligands that have essentially identical chemical potential in the unbound state, we reduced the problem of dissecting the thermodynamics to a comparison of the two protein-ligand complexes. While the free energies of binding are nearly equal for the R and S diastereomers, greater differences are observed for the enthalpy and entropy, which consequently exhibit compensatory behavior, ΔΔ H°(R - S) = -5 ± 1 kJ/mol and - TΔΔ S°(R - S) = 3 ± 1 kJ/mol. NMR relaxation experiments and molecular dynamics simulations indicate that the protein in complex with the S-stereoisomer has greater conformational entropy than in the R-complex. GIST calculations reveal additional, but smaller, contributions from solvation entropy, again in favor of the S-complex. Thus, conformational entropy apparently dominates over solvation entropy in dictating the difference in the overall entropy of binding. This case highlights an interplay between conformational entropy and solvation entropy, pointing to both opportunities and challenges in drug design.

β-Branched Amino Acids Stabilize Specific Conformations of Cyclic Hexapeptides

Cyclic peptides (CPs) are a promising class of molecules for drug development, particularly as inhibitors of protein-protein interactions. Predicting low-energy structures and global structural ensembles of individual CPs is critical for the design of bioactive molecules, but these are challenging to predict and difficult to verify experimentally. In our previous work, we used explicit-solvent molecular dynamics simulations with enhanced sampling methods to predict the global structural ensembles of cyclic hexapeptides containing different permutations of glycine, alanine, and valine. One peptide, cyclo-(VVGGVG) or P7, was predicted to be unusually well structured. In this work, we synthesized P7, along with a less well-structured control peptide, cyclo-(VVGVGG) or P6, and characterized their global structural ensembles in water using NMR spectroscopy. The NMR data revealed a structural ensemble similar to the prediction for P7 and showed that P6 was indeed much less well-structured than P7. We then simulated and experimentally characterized the global structural ensembles of several P7 analogs and discovered that β-branching at one critical position within P7 is important for overall structural stability. The simulations allowed deconvolution of thermodynamic factors that underlie this structural stabilization. Overall, the excellent correlation between simulation and experimental data indicates that our simulation platform will be a promising approach for designing well-structured CPs and also for understanding the complex interactions that control the conformations of constrained peptides and other macrocycles.

Conformational entropy of a single peptide controlled under force governs protease recognition and catalysis

An immense repertoire of protein chemical modifications catalyzed by enzymes is available as proteomics data. Quantifying the impact of the conformational dynamics of the modified peptide remains challenging to understand the decisive kinetics and amino acid sequence specificity of these enzymatic reactions in vivo, because the target peptide must be disordered to accommodate the specific enzyme-binding site. Here, we were able to control the conformation of a single-molecule peptide chain by applying mechanical force to activate and monitor its specific cleavage by a model protease. We found that the conformational entropy impacts the reaction in two distinct ways. First, the flexibility and accessibility of the substrate peptide greatly increase upon mechanical unfolding. Second, the conformational sampling of the disordered peptide drives the specific recognition, revealing force-dependent reaction kinetics. These results support a mechanism of peptide recognition based on conformational selection from an ensemble that we were able to quantify with a torsional free-energy model. Our approach can be used to predict how entropy affects site-specific modifications of proteins and prompts conformational and mechanical selectivity.

Protein-ligand binding enthalpies from near-millisecond simulations: Analysis of a preorganization paradox

Calorimetric studies of protein-ligand binding sometimes yield thermodynamic data that are difficult to understand. Today, molecular simulations can be used to seek insight into such calorimetric puzzles, and, when simulations and experiments diverge, the results can usefully motivate further improvements in computational methods. Here, we apply near-millisecond duration simulations to estimate the relative binding enthalpies of four peptidic ligands with the Grb2 SH2 domain. The ligands fall into matched pairs, where one member of each pair has an added bond that preorganizes the ligand for binding and thus may be expected to favor binding entropically, due to a smaller loss in configurational entropy. Calorimetric studies have shown that the constrained ligands do in fact bind the SH2 domain more tightly than the flexible ones, but, paradoxically, the improvement in affinity for the constrained ligands is enthalpic, rather than entropic. The present enthalpy calculations yield the opposite trend, as they suggest that the flexible ligands bind more exothermically. Additionally, the small relative binding enthalpies are found to be balances of large differences in the energies of structural components such as ligand and the binding site residues. As a consequence, the deviations from experiment in the relative binding enthalpies represent small differences between these large numbers and hence may be particularly susceptible to error, due, for example, to approximations in the force field. We also computed first-order estimates of changes in configurational entropy on binding. These too are, arguably, paradoxical, as they tend to favor binding of the flexible ligands. The paradox is explained in part by the fact that the more rigid constrained ligands reduce the entropy of binding site residues more than their flexible analogs do, at least in the simulations. This result offers a rather general counterargument to the expectation that preorganized ligands should be associated with more favorable binding entropies, other things being equal.

Testing the mutual information expansion of entropy with multivariate Gaussian distributions

The mutual information expansion (MIE) represents an approximation of the configurational entropy in terms of low-dimensional integrals. It is frequently employed to compute entropies from simulation data of large systems, such as macromolecules, for which brute-force evaluation of the full configurational integral is intractable. Here, we test the validity of MIE for systems consisting of more than m = 100 degrees of freedom (dofs). The dofs are distributed according to multivariate Gaussian distributions which were generated from protein structures using a variant of the anisotropic network model. For the Gaussian distributions, we have semi-analytical access to the configurational entropy as well as to all contributions of MIE. This allows us to accurately assess the validity of MIE for different situations. We find that MIE diverges for systems containing long-range correlations which means that the error of consecutive MIE approximations grows with the truncation order n for all tractable n ≪ m. This fact implies severe limitations on the applicability of MIE, which are discussed in the article. For systems with correlations that decay exponentially with distance, MIE represents an asymptotic expansion of entropy, where the first successive MIE approximations approach the exact entropy, while MIE also diverges for larger orders. In this case, MIE serves as a useful entropy expansion when truncated up to a specific truncation order which depends on the correlation length of the system.

Self-Consistent Framework Connecting Experimental Proxies of Protein Dynamics with Configurational Entropy

The recently developed NMR techniques enable estimation of protein configurational entropy change from the change in the average methyl order parameters. This experimental observable, however, does not directly measure the contribution of intramolecular couplings, protein main-chain motions, or angular dynamics. Here, we carry out a self-consistent computational analysis of the impact of these missing contributions on an extensive set of molecular dynamics simulations of different proteins undergoing binding. Specifically, we compare the configurational entropy change in protein complex formation as obtained by the maximum information spanning tree approximation (MIST), which treats the above entropy contributions directly, and the change in the average NMR methyl and NH order parameters. Our parallel implementation of MIST allows us to treat hard angular degrees of freedom as well as couplings up to full pairwise order explicitly, while still involving a high degree of sampling and tackling molecules of biologically relevant sizes. First, we demonstrate a remarkably strong linear relationship between the total configurational entropy change and the average change in both methyl and backbone-NH order parameters. Second, in contrast to canonical assumptions, we show that the main-chain and angular terms contribute significantly to the overall configurational entropy change and also scale linearly with it. Consequently, linear models starting from the average methyl order parameters are able to capture the contribution of main-chain and angular terms well. After applying the quantum-mechanical harmonic oscillator entropy formalism, we establish a similarly strong linear relationship for X-ray crystallographic B-factors. Finally, we demonstrate that the observed linear relationships remain robust against drastic undersampling and argue that they reflect an intrinsic property of compact proteins. Despite their remarkable strength, however, the above linear relationships yield estimates of configurational entropy change whose accuracy appears to be sufficient for qualitative applications only.

The Molecular Origin of Enthalpy/Entropy Compensation in Biomolecular Recognition

Biomolecular recognition can be stubborn; changes in the structures of associating molecules, or the environments in which they associate, often yield compensating changes in enthalpies and entropies of binding and no net change in affinities. This phenomenon—termed enthalpy/entropy (H/S) compensation—hinders efforts in biomolecular design, and its incidence—often a surprise to experimentalists—makes interactions between biomolecules difficult to predict. Although characterizing H/S compensation requires experimental care, it is unquestionably a real phenomenon that has, from an engineering perspective, useful physical origins. Studying H/S compensation can help illuminate the still-murky roles of water and dynamics in biomolecular recognition and self-assembly. This review summarizes known sources of H/ S compensation (real and perceived) and lays out a conceptual framework for understanding and dissecting—and, perhaps, avoiding or exploiting—this phenomenon in biophysical systems.

Dynamical origins of heat capacity changes in enzyme-catalysed reactions

Heat capacity changes are emerging as essential for explaining the temperature dependence of enzyme-catalysed reaction rates. This has important implications for enzyme kinetics, thermoadaptation and evolution, but the physical basis of these heat capacity changes is unknown. Here we show by a combination of experiment and simulation, for two quite distinct enzymes (dimeric ketosteroid isomerase and monomeric alpha-glucosidase), that the activation heat capacity change for the catalysed reaction can be predicted through atomistic molecular dynamics simulations. The simulations reveal subtle and surprising underlying dynamical changes: tightening of loops around the active site is observed, along with changes in energetic fluctuations across the whole enzyme including important contributions from oligomeric neighbours and domains distal to the active site. This has general implications for understanding enzyme catalysis and demonstrating a direct connection between functionally important microscopic dynamics and macroscopically measurable quantities.

Heat capacity changes affect the temperature dependence of enzyme catalysis, with implications for thermoadaptation, however their physical basis is unknown. Here the authors show that heat capacity changes are calculable by simulation, revealing distinct dynamical contributions from regions remote from the active site.

Mapping the sequence-structure relationships of simple cyclic hexapeptides

Cyclic peptides are promising protein-protein interaction modulators with high binding affinities and specificities, as well as enhanced stabilities and oral availabilities over linear analogs. Despite their relatively small size and cyclic architecture, it is currently difficult to predict the favored conformation(s) of most classes of cyclic peptides. An improved understanding of the sequence-structure relationships for cyclic peptides will offer an avenue for the rational design of cyclic peptides as possible therapeutics. In this work, we systematically explored the sequence-structure relationships for two cyclic hexapeptide systems using molecular dynamics simulation techniques. Starting with an all-glycine cyclic hexapeptide, cyclo-G₆, we systematically replaced glycine residues with alanines and characterized the structural ensembles of different variants. The same process was repeated with valines to investigate the effects of larger side chains. An analysis of the origin of structure preferences was performed using thermodynamics decomposition and several general observations are reported.

Toward accurately modeling N-methylated cyclic peptides

Cyclic peptides have unique properties and can target protein surfaces specifically and potently. N-Methylation provides a promising way to further optimize the pharmacokinetic and structural profiles of cyclic peptides. The capability to accurately model structures adopted by N-methylated cyclic peptides would facilitate rational design of this interesting and useful class of molecules. We apply molecular dynamics simulations with advanced enhanced sampling methods to efficiently characterize the structural ensembles of N-methylated cyclic peptides, while simultaneously evaluating the overall performance of several simulation force fields. We find that one of the residue-specific force fields, RSFF2, is able to recapitulate experimental structures of the N-methylated cyclic peptide benchmarks tested here when the correct amide isomers are used as initial configurations and enforced during the simulations. Thus, using our simulation approach, it is possible to accurately and efficiently predict the structures of N-methylated cyclic peptides if sufficient information is available to determine the correct amide cis/trans configuration. Moreover, our results suggest that, upon further optimization of RSFF2 to more reliably predict cis/trans isomers, molecular dynamics simulations will be able to de novo predict N-methylated cyclic peptides in the near future, strongly motivating such continued optimization.

Molecular rigidity and enthalpy-entropy compensation in DNA melting

Enthalpy-entropy compensation (EEC) is observed in diverse molecular binding processes of importance to living systems and manufacturing applications, but this widely occurring phenomenon is not sufficiently understood from a molecular physics standpoint. To gain insight into this fundamental problem, we focus on the melting of double-stranded DNA (dsDNA) since measurements exhibiting EEC are extensive for nucleic acid complexes and existing coarse-grained models of DNA allow us to explore the influence of changes in molecular parameters on the energetic parameters by using molecular dynamics simulations. Previous experimental and computational studies have indicated a correlation between EEC and changes in molecular rigidity in certain binding-unbinding processes, and, correspondingly, we estimate measures of DNA molecular rigidity under a wide range of conditions, along with resultant changes in the enthalpy and entropy of binding. In particular, we consider variations in dsDNA rigidity that arise from changes of intrinsic molecular rigidity such as varying the associative interaction strength between the DNA bases, the length of the DNA chains, and the bending stiffness of the individual DNA chains. We also consider extrinsic changes of molecular rigidity arising from the addition of polymer additives and geometrical confinement of DNA between parallel plates. All our computations confirm EEC and indicate that this phenomenon is indeed highly correlated with changes in molecular rigidity. However, two distinct patterns relating to how DNA rigidity influences the entropy of association emerge from our analysis. Increasing the intrinsic DNA rigidity increases the entropy of binding, but increases in molecular rigidity from external constraints decreases the entropy of binding. EEC arises in numerous synthetic and biological binding processes and we suggest that changes in molecular rigidity might provide a common origin of this ubiquitous phenomenon in the mutual binding and unbinding of complex molecules.

Attach-Pull-Release Calculations of Ligand Binding and Conformational Changes on the First BRD4 Bromodomain

Bromodomains, protein domains involved in epigenetic regulation, are able to bind small molecules with high affinity. In the present study, we report free energy calculations for the binding of seven ligands to the first BRD4 bromodomain, using the attach-pull-release (APR) method to compute the reversible work of removing the ligands from the binding site and then allowing the protein to relax conformationally. We test three different water models, TIP3P, TIP4PEw, and SPC/E, as well as the GAFF and GAFF2 parameter sets for the ligands. Our simulations show that the apo crystal structure of BRD4 is only metastable, with a structural transition happening in the absence of the ligand typically after 20 ns of simulation. We compute the free energy change for this transition with a separate APR calculation on the free protein and include its contribution to the ligand binding free energies, which generally causes an underestimation of the affinities. By testing different water models and ligand parameters, we are also able to assess their influence in our results and determine which one produces the best agreement with the experimental data. Both free energies associated with the conformational change and ligand binding are affected by the choice of water model, with the two sets of ligand parameters affecting their binding free energies to a lesser degree. Across all six combinations of water model and ligand potential function, the Pearson correlation coefficients between calculated and experimental binding free energies range from 0.55 to 0.83, and the root-mean-square errors range from 1.4-3.2 kcal/mol. The current protocol also yields encouraging preliminary results when used to assess the relative stability of ligand poses generated by docking or other methods, as illustrated for two different ligands. Our method takes advantage of the high performance provided by graphics processing units and can readily be applied to other ligands as well as other protein systems.

Approaches for calculating solvation free energies and enthalpies demonstrated with an update of the FreeSolv database

Solvation free energies can now be calculated precisely from molecular simulations, providing a valuable test of the energy functions underlying these simulations. Here, we briefly review "alchemical" approaches for calculating the solvation free energies of small, neutral organic molecules from molecular simulations, and illustrate by applying them to calculate aqueous solvation free energies (hydration free energies). These approaches use a non-physical pathway to compute free energy differences from a simulation or set of simulations and appear to be a particularly robust and general-purpose approach for this task. We also present an update (version 0.5) to our FreeSolv database of experimental and calculated hydration free energies of neutral compounds and provide input files in formats for several simulation packages. This revision to FreeSolv provides calculated values generated with a single protocol and software version, rather than the heterogeneous protocols used in the prior version of the database. We also further update the database to provide calculated enthalpies and entropies of hydration and some experimental enthalpies and entropies, as well as electrostatic and nonpolar components of solvation free energies.

The Dynameomics Entropy Dictionary: A Large-Scale Assessment of Conformational Entropy across Protein Fold Space

Molecular dynamics (MD) simulations contain considerable information with regard to the motions and fluctuations of a protein, the magnitude of which can be used to estimate conformational entropy. Here we survey conformational entropy across protein fold space using the Dynameomics database, which represents the largest existing data set of protein MD simulations for representatives of essentially all known protein folds. We provide an overview of MD-derived entropies accounting for all possible degrees of dihedral freedom on an unprecedented scale. Although different side chains might be expected to impose varying restrictions on the conformational space that the backbone can sample, we found that the backbone entropy and side chain size are not strictly coupled. An outcome of these analyses is the Dynameomics Entropy Dictionary, the contents of which have been compared with entropies derived by other theoretical approaches and experiment. As might be expected, the conformational entropies scale linearly with the number of residues, demonstrating that conformational entropy is an extensive property of proteins. The calculated conformational entropies of folding agree well with previous estimates. Detailed analysis of specific cases identifies deviations in conformational entropy from the average values that highlight how conformational entropy varies with sequence, secondary structure, and tertiary fold. Notably, α-helices have lower entropy on average than do β-sheets, and both are lower than coil regions.

Soaking suggests "alternative facts": Only co-crystallization discloses major ligand-induced interface rearrangements of a homodimeric tRNA-binding protein indicating a novel mode-of-inhibition

For the efficient pathogenesis of Shigella, the causative agent of bacillary dysentery, full functionality of tRNA-guanine transglycosylase (TGT) is mandatory. TGT performs post-transcriptional modifications of tRNAs in the anticodon loop taking impact on virulence development. This suggests TGT as a putative target for selective anti-shigellosis drug therapy. Since bacterial TGT is only functional as homodimer, its activity can be inhibited either by blocking its active site or by preventing dimerization. Recently, we discovered that in some crystal structures obtained by soaking the full conformational adaptation most likely induced in solution upon ligand binding is not displayed. Thus, soaked structures may be misleading and suggest irrelevant binding modes. Accordingly, we re-investigated these complexes by co-crystallization. The obtained structures revealed large conformational rearrangements not visible in the soaked complexes. They result from spatial perturbations in the ribose-34/phosphate-35 recognition pocket and, consequently, an extended loop-helix motif required to prevent access of water molecules into the dimer interface loses its geometric integrity. Thermodynamic profiles of ligand binding in solution indicate favorable entropic contributions to complex formation when large conformational adaptations in the dimer interface are involved. Native MS titration experiments reveal the extent to which the homodimer is destabilized in the presence of each inhibitor. Unexpectedly, one ligand causes a complete rearrangement of subunit packing within the homodimer, never observed in any other TGT crystal structure before. Likely, this novel twisted dimer is catalytically inactive and, therefore, suggests that stabilizing this non-productive subunit arrangement may be used as a further strategy for TGT inhibition.

Detection of Side Chain Rearrangements Mediating the Motions of Transmembrane Helices in Molecular Dynamics Simulations of G Protein-Coupled Receptors

Structure and dynamics are essential elements of protein function. Protein structure is constantly fluctuating and undergoing conformational changes, which are captured by molecular dynamics (MD) simulations. We introduce a computational framework that provides a compact representation of the dynamic conformational space of biomolecular simulations. This method presents a systematic approach designed to reduce the large MD simulation spatiotemporal datasets into a manageable set in order to guide our understanding of how protein mechanics emerge from side chain organization and dynamic reorganization. We focus on the detection of side chain interactions that undergo rearrangements mediating global domain motions and vice versa. Side chain rearrangements are extracted from side chain interactions that undergo well-defined abrupt and persistent changes in distance time series using Gaussian mixture models, whereas global domain motions are detected using dynamic cross-correlation. Both side chain rearrangements and global domain motions represent the dynamic components of the protein MD simulation, and are both mapped into a network where they are connected based on their degree of coupling. This method allows for the study of allosteric communication in proteins by mapping out the protein dynamics into an intramolecular network to reduce the large simulation data into a manageable set of communities composed of coupled side chain rearrangements and global domain motions. This computational framework is suitable for the study of tightly packed proteins, such as G protein-coupled receptors, and we present an application on a seven microseconds MD trajectory of CC chemokine receptor 7 (CCR7) bound to its ligand CCL21.

Decoding the Interactions Regulating the Active State Mechanics of Eukaryotic Protein Kinases

Eukaryotic protein kinases regulate most cellular functions by phosphorylating targeted protein substrates through a highly conserved catalytic core. In the active state, the catalytic core oscillates between open, intermediate, and closed conformations. Currently, the intramolecular interactions that regulate the active state mechanics are not well understood. Here, using cAMP-dependent protein kinase as a representative model coupled with biochemical, biophysical, and computational techniques, we define a set of highly conserved electrostatic and hydrophobic interactions working harmoniously to regulate these mechanics. These include the previously identified salt bridge between a lysine from the β3-strand and a glutamate from the αC-helix as well as an electrostatic interaction between the phosphorylated activation loop and αC-helix and an ensemble of hydrophobic residues of the Regulatory spine and Shell. Moreover, for over three decades it was thought that the highly conserved β3-lysine was essential for phosphoryl transfer, but our findings show that the β3-lysine is not required for phosphoryl transfer but is essential for the active state mechanics.

Eukaryotic protein kinases (EPKs) regulate over a third of the human proteome by transferring the γ-phosphate from adenosine triphosphate (ATP) to a protein substrate in a process known as protein phosphorylation. Biochemical and biophysical studies have shown that EPKs undergo multiconformational rearrangements in which the catalytic core is oscillating between open, intermediate, and closed conformations when active. Presently, the intramolecular interactions that regulate this dynamic process are not well understood. In this paper, we show how a set of conserved electrostatic and hydrophobic interactions harmoniously regulate the active state mechanics. The electrostatic interactions involve the highly conserved salt bridge between the lysine from subdomain-II and glutamate from subdomain-III as well as an interaction between the activation loop and αC-helix. The hydrophobic interactions include the nonlinear motifs known as the Regulatory spine and Shell that traverse both lobes of the catalytic core. Furthermore, our findings show that the highly conserved “catalytic lysine” is not directly required for phosphoryl transfer but rather serves as a hub that aligns and positions the dynamic core elements required for catalysis.

Benzene Probes in Molecular Dynamics Simulations Reveal Novel Binding Sites for Ligand Design

Protein flexibility poses a major challenge in binding site identification. Several computational pocket detection methods that utilize small-molecule probes in molecular dynamics (MD) simulations have been developed to address this issue. Although they have proven hugely successful at reproducing experimental structural data, their ability to predict new binding sites that are yet to be identified and characterized has not been demonstrated. Here, we report the use of benzenes as probe molecules in ligand-mapping MD (LMMD) simulations to predict the existence of two novel binding sites on the surface of the oncoprotein MDM2. One of them was serendipitously confirmed by biophysical assays and X-ray crystallography to be important for the binding of a new family of hydrocarbon stapled peptides that were specifically designed to target the other putative site. These results highlight the predictive power of LMMD and suggest that predictions derived from LMMD simulations can serve as a reliable basis for the identification of novel ligand binding sites in structure-based drug design.

How Molecular Conformational Changes Affect Changes in Free Energy

A simple quantitative relationship between the molecular conformational changes and the corresponding changes in the free energy is presented. The change in free energy is the sum of that part of the enthalpic change that is due to the externally applied work (perturbation) and of that part of the entropic change, termed dissipative entropy, that is related to the conformational changes. The dissipative entropy is equivalent to the relative entropy, a concept from information theory, between the distributions of the conformations in the initial and the final states. The remaining change in entropy (nondissipative) cancels exactly with the remaining enthalpic change. The calculation of the dissipative entropy is demonstrated to pose the main difficulty in free energy computation. The straightforward decomposition of the dissipative entropy into contributions from different parts of the system promises to improve the understanding of the role of conformational changes in biochemical reactions.

Impact of thymine glycol damage on DNA duplex energetics: Correlations with lesion-induced biochemical and structural consequences

The magnitude and nature of lesion-induced energetic perturbations empirically correlate with mutagenicity/cytotoxicity profiles and can be predictive of lesion outcomes during polymerase-mediated replication in vitro. In this study, we assess the sequence and counterbase-dependent energetic impact of the Thymine glycol (Tg) lesion on a family of deoxyoligonucleotide duplexes. Tg damage arises from thymine and methyl-cytosine exposure to oxidizing agents or radiation-generated free-radicals. The Tg lesion blocks polymerase-mediated DNA replication in vitro and the unrepaired site elicits cytotoxic lethal consequences in vivo. Our combined calorimetric and spectroscopic characterization correlates Tg -induced energetic perturbations with biological and structural properties. Specifically, we incorporate a 5R-Tg isomer centered within the tridecanucleotide sequence 5'-GCGTACXCATGCG-3' (X = Tg or T) which is hybridized with the corresponding complementary sequence 5'-CGCATGNGTACGC-3' (N = A, G, T, C) to generate families of Tg -damaged (Tg ·N) and lesion-free (T·N) duplexes. We demonstrate that the magnitude and nature of the Tg destabilizing impact is dependent on counterbase identity (i.e., A ∼ G < T < C). The observation that a Tg lesion is less destabilizing when positioned opposite purines suggests that favorable counterbase stacking interactions may partially compensate lesion-induced perturbations. Moreover, the destabilizing energies of Tg ·N duplexes parallel their respective lesion-free T·N mismatch counterparts (i.e., G < T < C). Elucidation of Tg-induced destabilization relative to the corresponding undamaged mismatch energetics allows resolution of lesion-specific and sequence-dependent impacts. The Tg-induced energetic perturbations are consistent with its replication blocking properties and may serve as differential recognition elements for discrimination by the cellular repair machinery.

Insights into Protein-Ligand Interactions: Mechanisms, Models, and Methods

Molecular recognition, which is the process of biological macromolecules interacting with each other or various small molecules with a high specificity and affinity to form a specific complex, constitutes the basis of all processes in living organisms. Proteins, an important class of biological macromolecules, realize their functions through binding to themselves or other molecules. A detailed understanding of the protein–ligand interactions is therefore central to understanding biology at the molecular level. Moreover, knowledge of the mechanisms responsible for the protein-ligand recognition and binding will also facilitate the discovery, design, and development of drugs. In the present review, first, the physicochemical mechanisms underlying protein–ligand binding, including the binding kinetics, thermodynamic concepts and relationships, and binding driving forces, are introduced and rationalized. Next, three currently existing protein-ligand binding models—the “lock-and-key”, “induced fit”, and “conformational selection”—are described and their underlying thermodynamic mechanisms are discussed. Finally, the methods available for investigating protein–ligand binding affinity, including experimental and theoretical/computational approaches, are introduced, and their advantages, disadvantages, and challenges are discussed.

Spatial Decomposition of Translational Water-Water Correlation Entropy in Binding Pockets

A number of computational tools available today compute the thermodynamic properties of water at surfaces and in binding pockets by using inhomogeneous solvation theory (IST) to analyze explicit-solvent simulations. Such methods enable qualitative spatial mappings of both energy and entropy around a solute of interest and can also be applied quantitatively. However, the entropy estimates of existing methods have, to date, been almost entirely limited to the first-order terms in the IST’s entropy expansion. These first-order terms account for localization and orientation of water molecules in the field of the solute but not for the modification of water–water correlations by the solute. Here, we present an extension of the Grid Inhomogeneous Solvation Theory (GIST) approach which accounts for water–water translational correlations. The method involves rewriting the two-point density of water in terms of a conditional density and utilizes the efficient nearest-neighbor entropy estimation approach. Spatial maps of this second order term, for water in and around the synthetic host cucurbit[7]uril and in the binding pocket of the enzyme Factor Xa, reveal mainly negative contributions, indicating solute-induced water–water correlations relative to bulk water; particularly strong signals are obtained for sites at the entrances of cavities or pockets. This second-order term thus enters with the same, negative, sign as the first order translational and orientational terms. Numerical and convergence properties of the methodology are examined.

Dancing through Life: Molecular Dynamics Simulations and Network-Centric Modeling of Allosteric Mechanisms in Hsp70 and Hsp110 Chaperone Proteins

Hsp70 and Hsp110 chaperones play an important role in regulating cellular processes that involve protein folding and stabilization, which are essential for the integrity of signaling networks. Although many aspects of allosteric regulatory mechanisms in Hsp70 and Hsp110 chaperones have been extensively studied and significantly advanced in recent experimental studies, the atomistic picture of signal propagation and energetics of dynamics-based communication still remain unresolved. In this work, we have combined molecular dynamics simulations and protein stability analysis of the chaperone structures with the network modeling of residue interaction networks to characterize molecular determinants of allosteric mechanisms. We have shown that allosteric mechanisms of Hsp70 and Hsp110 chaperones may be primarily determined by nucleotide-induced redistribution of local conformational ensembles in the inter-domain regions and the substrate binding domain. Conformational dynamics and energetics of the peptide substrate binding with the Hsp70 structures has been analyzed using free energy calculations, revealing allosteric hotspots that control negative cooperativity between regulatory sites. The results have indicated that cooperative interactions may promote a population-shift mechanism in Hsp70, in which functional residues are organized in a broad and robust allosteric network that can link the nucleotide-binding site and the substrate-binding regions. A smaller allosteric network in Hsp110 structures may elicit an entropy-driven allostery that occurs in the absence of global structural changes. We have found that global mediating residues with high network centrality may be organized in stable local communities that are indispensable for structural stability and efficient allosteric communications. The network-centric analysis of allosteric interactions has also established that centrality of functional residues could correlate with their sensitivity to mutations across diverse chaperone functions. This study reconciles a wide spectrum of structural and functional experiments by demonstrating how integration of molecular simulations and network-centric modeling may explain thermodynamic and mechanistic aspects of allosteric regulation in chaperones.