Direct methods for computing single-molecule entropies from molecular simulations

Designing mimosine-containing peptides as efficient metal chelators: Insights from molecular dynamics and quantum calculations

Mimosine, a non-essential amino acid derived from plants, has a strong affinity for binding divalent and trivalent metal cations, including Zn2+, Ni2+, Fe2+/3+, and Al3+. This ability endows mimosine with significant antimicrobial and anti-cancer properties, making it a promising candidate for therapeutic applications. Previous research has demonstrated the effectiveness of mimosine-containing peptides as metal chelators, offering a safer alternative to conventional chelation agents. However, optimizing the design of these peptides necessitates a thorough understanding of their conformational ensembles in both free and metal-bound states. Here, we perform an in-depth analysis of mimosine-containing peptides using long-time MD simulations and quantum calculations to identify key factors critical for peptide design. Our results show that these peptides can achieve metal-binding affinities comparable to established aluminum chelators like deferiprone and citrate. Additionally, we underscore the crucial role of the peptide backbone in reducing the entropic penalty associated with metal binding. We propose strategies to modulate this entropic penalty-a challenging thermodynamic property to evaluate but essential in complexes between short peptides and metals-by incorporating proline residues and optimizing sequence length. These approaches offer promising pathways for developing efficient peptide chelators.

Fuentes L, Pimenta A, Nagem RAP, Chávez-Olórtegui C, Schneider FS, Molina F, Sanchez EF, Suárez D, Ferreira RS. Assessing the Interactions between Snake Venom Metalloproteinases and Hydroxamate Inhibitors Using Kinetic and ITC Assays, Molecular Dynamics Simulations and MM/PBSA-Based Scoring Functions

Bothrops species are the main cause of snake bites in rural communities of tropical developing countries of Central and South America. Envenomation by Bothrops snakes is characterized by prominent local inflammation, hemorrhage and necrosis as well as systemic hemostatic disturbances. These pathological effects are mainly caused by the major toxins of the viperidae venoms, the snake venom metalloproteinases (SVMPs). Despite the antivenom therapy efficiency to block the main toxic effects on bite victims, this treatment shows limited efficacy to prevent tissue necrosis. Thus, drug-like inhibitors of these toxins have the potential to aid serum therapy of accidents inflicted by viper snakes. Broad-spectrum metalloprotease inhibitors bearing a hydroxamate zinc-binding group are potential candidates to improve snake bites therapy and could also be used to study toxin-ligand interactions. Therefore, in this work, we used both docking calculations and molecular dynamics simulations to assess the interactions between six hydroxamate inhibitors and two P-I SVMPs selected as models: Atroxlysin-I (hemorrhagic) from Bothrops atrox, and Leucurolysin-a (nonhemorrhagic) from Bothrops leucurus. We also employed a large variety of end-point free energy methods in combination with entropic terms to produce scoring functions of the relative affinities of the inhibitors for the toxins. Then we identified the scoring functions that best correlated with experimental data obtained from kinetic activity assays. In addition, to the characterization of these six molecules as inhibitors of the toxins, this study sheds light on the main enzyme-inhibitor interactions, explaining the broad-spectrum behavior of the inhibitors, and identifies the energetic and entropic terms that improve the performance of the scoring functions.

CREST-A program for the exploration of low-energy molecular chemical space

Conformer-rotamer sampling tool (CREST) is an open-source program for the efficient and automated exploration of molecular chemical space. Originally developed in Pracht et al. [Phys. Chem. Chem. Phys. 22, 7169 (2020)] as an automated driver for calculations at the extended tight-binding level (xTB), it offers a variety of molecular- and metadynamics simulations, geometry optimization, and molecular structure analysis capabilities. Implemented algorithms include automated procedures for conformational sampling, explicit solvation studies, the calculation of absolute molecular entropy, and the identification of molecular protonation and deprotonation sites. Calculations are set up to run concurrently, providing efficient single-node parallelization. CREST is designed to require minimal user input and comes with an implementation of the GFNn-xTB Hamiltonians and the GFN-FF force-field. Furthermore, interfaces to any quantum chemistry and force-field software can easily be created. In this article, we present recent developments in the CREST code and show a selection of applications for the most important features of the program. An important novelty is the refactored calculation backend, which provides significant speed-up for sampling of small or medium-sized drug molecules and allows for more sophisticated setups, for example, quantum mechanics/molecular mechanics and minimum energy crossing point calculations.

Ab Initio Thermochemistry of Highly Flexible Molecules for Thermal Decomposition Analysis

Pyrolysis is a promising technology for chemical recycling of waste plastics, since it enables the generation of high-value chemicals with low capital and operating cost. The calculation of thermodynamic equilibrium composition using the Gibbs free energy minimization approach can determine pyrolysis operating conditions that produce desired products. However, the availability of thermochemical data can limit the application of equilibrium calculations. While density functional theory (DFT) calculations have been commonly used to produce accurate thermochemical data (e.g., enthalpies of formation) of small molecules, the accuracy and computational cost of these calculations are both challenging to handle for large, flexible molecules, exhibiting multiple conformations at elevated (i.e., pyrolysis) temperatures. In this work, we develop a computational framework to calculate accurate, temperature-dependent thermochemistry of large and flexible molecules by combining force field based conformational search, DFT calculations, thermochemical corrections, and Boltzmann statistics. Our framework produces accurately calculated thermochemistry that is used to predict equilibrium thermal decomposition profiles of octadecane, a model compound of polyethylene. Our thermochemistry results are compared against literature data demonstrating a great agreement, and the predicted decomposition profiles rationalize a series of pyrolysis experimental observations. Our work systematically addresses entropic contributions of large molecules and suggests paths for accurate and yet computationally feasible calculations of Gibbs free energies. The first-principles-based thermodynamic equilibrium analysis proposed in this work can be a significant step toward predicting temperature-dependent product distributions from plastic pyrolysis and guide experimentation on chemical plastic recycling.

Evaluation of Microscopic Damage of PEEK Polymers under Cyclic Loadings Using Molecular Dynamics Simulations

Full-atomic molecular dynamics simulations were conducted to investigate the time evolution of microscopic damage in polyetheretherketone (PEEK) polymers under cyclic loading conditions. Three characteristics were used to quantify microscopic damage: entropy, distribution of the end-to-end distance of polymers, and the volume fraction of voids. Our results show that the degree of disentanglement of polymers and the volume fraction of voids increase with cyclic loading, which may lead to entropy generation. Uniaxial tensile strength simulations of the polymer system before and after cyclic loading were performed. The tensile strength after cyclic loading was lower than that before loading. Furthermore, two systems with the same entropy and different loading histories showed almost the same strength. These results imply that entropy generation is expressed as the total microscopic damage and can potentially be employed for effective evaluation of the degradation of material characteristics.

ULYSSES: An Efficient and Easy to Use Semiempirical Library for C+

We introduce ULYSSES, a user-friendly and robust C++ library for semiempirical quantum chemical calculations. In its current version, ULYSSES is equipped with a large set of different semiempirical models, most of which are based on the Neglect of Diatomic Differential Overlap (NDDO) approximation. Empirical corrections for dispersion and hydrogen bonding are available for most methods, so that higher quality is achieved in the calculation of energies of nonbonded complexes. The library is furthermore equipped with geometry optimization, as well as modules for calculating molecular properties of general interest. Ideal gas thermodynamics is available and allows single structure as well as conformer (multistructure) averaged properties to be calculated. We offer the possibility to use several vibrational partition functions according to the nature of interactions being studied: for covalent systems, the traditional harmonic oscillator approximation is available; for nonbonded complexes, we systematically extended the partition function proposed by Grimme for all thermodynamic functions. The library is also capable of running Born-Oppenheimer molecular dynamics.

Entropies Derived from the Packing Geometries within a Single Protein Structure

A fast, simple, yet robust method to calculate protein entropy from a single protein structure is presented here. The focus is on the atomic packing details, which are calculated by combining Voronoi diagrams and Delaunay tessellations. Even though the method is simple, the entropies computed exhibit an extremely high correlation with the entropies previously derived by other methods based on quasi-harmonic motions, quantum mechanics, and molecular dynamics simulations. These packing-based entropies account directly for the local freedom and provide entropy for any individual protein structure that could be used to compute free energies directly during simulations for the generation of more reliable trajectories and also for better evaluations of modeled protein structures. Physico-chemical properties of amino acids are compared with these packing entropies to uncover the relationships with the entropies of different residue types. A public packing entropy web server is provided at packing-entropy.bb.iastate.edu, and the application programing interface is available within the PACKMAN (https://github.com/Pranavkhade/PACKMAN) package.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

Spontaneity of nuclear fusion: a qualitative analysis via classical thermodynamics

Background: So far the feasibility of nuclear reactions has been studied only through the evaluation of the reaction rate, which gives us information about the kinetics, while the thermodynamic analysis has been limited to evaluations of the change in enthalpy without any consideration of the change in entropy. Methods: This work examines the thermodynamics of nuclear fusion reactions through a simplified approach. The analysis introduces the thermodynamic study of fission and fusion reactions through their comparison with a chemical process. Results: The main result is that fission reactions are always spontaneous (ΔG < 0) since a lot of energy is released in the form of heat and the system moves spontaneously towards a more disordered state. In contrast, fusion reactions are spontaneous only when the enthalpic contribution of the change in Gibbs energy overcomes the entropic contribution. This condition is verified when the temperature of the process is below a characteristic value T*, calculated as the ratio between the energy corresponding to the mass defect and the change of entropy of the fusion reaction. Conclusions: Due to the unavailability of data related to entropy changes in fusion reactions, only a qualitative thermodynamic analysis has been carried out. Through such analysis, the influence of the operating conditions over the spontaneity of fusion processes has been discussed. The final considerations emphasize the role of the thermodynamics analysis that should be implemented in the current studies that, so far, have been mainly based on the assessment of the reaction rate and exothermicity of fusion reactions.

Efficient Quantum Chemical Calculation of Structure Ensembles and Free Energies for Nonrigid Molecules

The application of quantum chemical, automatic multilevel modeling workflows for the determination of thermodynamic (e.g., conformation equilibria, partition coefficients, pK_a values) and spectroscopic properties of relatively large, nonrigid molecules in solution is described. Key points are the computation of rather complete structure (conformer) ensembles with extremely fast but still reasonable GFN2-xTB or GFN-FF semiempirical methods in the CREST searching approach and subsequent refinement at a recently developed, accurate r²SCAN-3c DFT composite level. Solvation effects are included in all steps by accurate continuum solvation models (ALPB, (D)COSMO-RS). Consistent inclusion of thermostatistical contributions in the framework of the modified rigid-rotor-harmonic-oscillator approximation (mRRHO) based on xTB/FF computed PES is also recommended.

Multibasin Quasi-Harmonic Approach for the Calculation of the Configurational Entropy of Small Molecules in Solution

Entropy is a key thermodynamic property governing most biomolecular processes, including binding. Nonetheless, quantification of the configurational entropy of a single molecule in solution remains a grand challenge. Here, we present an original approach for the calculation of absolute molecular entropies based on the analysis of converged molecular dynamics (MD) simulations. Our method, named quasi-harmonic multibasin (QHMB), relies on a multibasin decomposition of the simulated trajectory by root-mean-square deviation clustering and subsequent quasi-harmonic analysis (QHA) of extracted sub-trajectories. Last, the entropy of the landscape is evaluated using the Gibbs formula. Because of the nature of QHA, this method is directly applicable to explicit-solvent simulations to access configurational entropies in solution. When compared with calorimetric data from NIST, QHMB is shown to predict absolute entropies in the gas phase for 23 small molecules with a root-mean-squared error of 0.36 kcal/mol from the experiments. In addition, the introduction of a QHMB correction in MM/GBSA calculations to account for the ligand configurational entropy loss on binding is shown to improve the correlation between calculated and experimental binding affinities with R² increasing from 0.67 to 0.78. Because this entropy correction penalizes large and flexible ligands more strongly, it might be useful to reduce the false-positive rate in virtual screening. The availability of an automatic procedure to compute QHMB entropies makes it a new available tool in the field of drug discovery.

Understanding the Conformational Properties of Fluorinated Polypeptides: Molecular Modelling of Unguisin A

In this work, we investigate the conformational properties of unguisin A, a natural macrocyclic heptapeptide that incorporates a γ-aminobutyric acid (Gaba), and four of its difluorinated stereoisomers at the Gaba residue. According to nuclear magnetic resonance (NMR) experiments, their secondary structure depends dramatically on the stereochemistry of the fluorinated carbon atoms. However, many molecular details of the structure and flexibility of these systems remain unknown, so that a rationale of the conformational changes induced by the fluorine atoms in the macrocycle is still missing. To fill this gap, we apply enhanced molecular dynamics (MD) techniques to explore the peptide conformational space in dimethyl sulfoxide solution followed by 4-8 μs of conventional MD simulations that provide extensive equilibrium sampling. The simulations, which compare reasonably well with the NMR-based observations, show that the secondary structure of the macrocycle is altered substantially upon fluorination, except for the (S,S) diastereomer. It also turns out that the conformations of the fluorinated peptides are visited during the enhanced MD simulation of natural unguisin A, suggesting thus that conformations accessible to the unsubstituted macrocyclic peptide may be selected by fluorination. Therefore, computational characterization of the macrocyclic peptides could be helpful in the rational design of stereoselective fluorinated peptides with fine-tuned conformation and activity.

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.

Entropy of Proteins Using Multiscale Cell Correlation

A new multiscale method is presented to calculate the entropy of proteins from molecular dynamics simulations. Termed Multiscale Cell Correlation (MCC), the method decomposes the protein into sets of rigid-body units based on their covalent-bond connectivity at three levels of hierarchy: molecule, residue, and united atom. It evaluates the vibrational and topographical entropy from forces, torques, and dihedrals at each level, taking into account correlations between sets of constituent units that together make up a larger unit at the coarser length scale. MCC gives entropies in close agreement with normal-mode analysis and smaller than those using quasiharmonic analysis as well as providing much faster convergence. Moreover, MCC provides an insightful decomposition of entropy at each length scale and for each type of amino acid according to their solvent exposure and whether they are terminal residues. While the residue entropy depends weakly on solvent exposure, there is greater variation in entropy components for larger, more polar amino acids, which have increased conformational entropy but reduced vibrational entropy with greater solvent exposure.

Molecular modeling and rational design of hydrocarbon-stapled/halogenated helical peptides targeting CETP self-binding site: Therapeutic implication for atherosclerosis

The human plasma cholesteryl ester transfer protein (CETP) collects triglycerides from very-/low-density lipoproteins (V/LDL) and exchanges them for cholesteryl esters from high-density lipoproteins (HDL), which has recognized as an important therapeutic target for atherosclerosis. The protein has a C-terminal amphipathic α-helix that serves as self-binding peptide to fulfill biological function by dynamically binding to/unbinding from its cognate site (termed self-binding site) in the same protein. Previously, we successfully derived and halogenated the helical peptide to competitively disrupt the self-binding behavior of CETP C-terminal tail. However, the halogenated peptides have only a limited affinity increase as compared to native helical peptide (∼3-fold), thus exhibiting only a moderate competitive potency. Here, instead of optimizing the direct intermolecular interaction of peptide with CETP self-binding site we attempt to further improve the peptide competitive potency by reducing its conformational flexibility with hydrocarbon-stapling technique. Computational analysis reveals that the helical peptide has large intrinsic disorder in unbound free state, which would incur a considerable entropy penalty upon rebinding to the self-binding site. All-hydrocarbon bridge is designed and optimized on native and halogenated peptides in terms of the helical pattern and binding mode of self-binding peptide. Dynamics simulation and circular dichroism indicate that the stapling can considerably reduce peptide disorder in free state. Energetics calculation and fluorescence assay conform that the binding affinity of stapled/halogenated peptides is improved substantially (by > 5-fold), thus exhibiting an effective competition potency with native peptide for the self-binding site. Structural examination suggests that the binding modes and nonbonded interactions of native and halogenated peptides are not influenced essentially due to the stapling.

Entropy of Simulated Liquids Using Multiscale Cell Correlation

Accurately calculating the entropy of liquids is an important goal, given that many processes take place in the liquid phase. Of almost equal importance is understanding the values obtained. However, there are few methods that can calculate the entropy of such systems, and fewer still to make sense of the values obtained. We present our multiscale cell correlation (MCC) method to calculate the entropy of liquids from molecular dynamics simulations. The method uses forces and torques at the molecule and united-atom levels and probability distributions of molecular coordinations and conformations. The main differences with previous work are the consistent treatment of the mean-field cell approximation to the approriate degrees of freedom, the separation of the force and torque covariance matrices, and the inclusion of conformation correlation for molecules with multiple dihedrals. MCC is applied to a broader set of 56 important industrial liquids modeled using the Generalized AMBER Force Field (GAFF) and Optimized Potentials for Liquid Simulations (OPLS) force fields with 1.14*CM1A charges. Unsigned errors versus experimental entropies are 8.7 J K - 1 mol - 1 for GAFF and 9.8 J K - 1 mol - 1 for OPLS. This is significantly better than the 2-Phase Thermodynamics method for the subset of molecules in common, which is the only other method that has been applied to such systems. MCC makes clear why the entropy has the value it does by providing a decomposition in terms of translational and rotational vibrational entropy and topographical entropy at the molecular and united-atom levels.

Free Energies and Entropies of Binding Sites Identified by MixMD Cosolvent Simulations

In our recent efforts to map protein surfaces using mixed-solvent molecular dynamics (MixMD) (Ghanakota, P.; Carlson, H. A. Moving Beyond Active-Site Detection: MixMD Applied to Allosteric Systems. J. Phys. Chem. B 2016, 120, 8685-8695), we were able to successfully capture active sites and allosteric sites within the top-four most occupied hotspots. In this study, we describe our approach for estimating the thermodynamic profile of the binding sites identified by MixMD. First, we establish a framework for calculating free energies from MixMD simulations, and we compare our approach to alternative methods. Second, we present a means to obtain a relative ranking of the binding sites by their configurational entropy. The theoretical maximum and minimum free energy and entropy values achievable under such a framework along with the limitations of the techniques are discussed. Using this approach, the free energy and relative entropy ranking of the top-four MixMD binding sites were computed and analyzed across our allosteric protein targets: Abl Kinase, Androgen Receptor, Pdk1 Kinase, Farnesyl Pyrophosphate Synthase, Chk1 Kinase, Glucokinase, and Protein Tyrosine Phosphatase 1B.

Affinity Calculations of Cyclodextrin Host-Guest Complexes: Assessment of Strengths and Weaknesses of End-Point Free Energy Methods

The end-point methods like MM/PBSA or MM/GBSA estimate the free energy of a biomolecule by combining its molecular mechanics energy with solvation free energy and entropy terms. On the one hand, their performance largely depends on the particular system of interest, and despite numerous attempts to improve their reliability that have resulted in many variants, there is still no clear alternative to improve their accuracy. On the other hand, the relatively small cyclodextrin host-guest complexes, for which high-quality binding calorimetric data are usually available, are becoming reference models for testing the accuracy of free energy methods. In this work, we further assess the performance of various MM/PBSA-like approaches as applied to cyclodextrin complexes. To this end, we select a set of complexes between β-cyclodextrin and 57 small organic molecules that has been previously studied with the binding energy distribution analysis method in combination with an implicit solvent model ( Wickstrom, L.; He, P.; Gallicchio, E.; Levy, R. M. J. Chem. Theory Comput. 2013 , 9 , 3136 - 3150 ). For each complex, a conventional 1.0 μs molecular dynamics simulation in explicit solvent is performed. Then we employ semiempirical quantum chemical calculations, several variants of the MM-PB(GB)SA methods, entropy estimations, etc., to assess the reliability of the end-point affinity calculations. The best end-point protocol in this study, which combines DFTB3 energies with entropy corrections, yields estimations of the binding free energies that still have substantial errors (RMSE = 2.2 kcal/mol), but it exhibits a good prediction capacity in terms of ligand ranking ( R² = 0.66) that is close to or even better than that of rigorous free energy methodologies. Our results can be helpful to discriminate between the intrinsic limitations of the end-point methods and other sources of error, such as the underlying energy and continuum solvation methods.

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.

Prediction of Protein Configurational Entropy (Popcoen)

A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/ .

Ligand Strain and Entropic Effects on the Binding of Macrocyclic and Linear Inhibitors: Molecular Modeling of Penicillopepsin Complexes

Using extensive molecular dynamics simulations, we investigate the structure and dynamics of the complexes formed between penicillopepsin and two peptidomimetic inhibitors: a linear compound, isovaleryl(P₄)-valine(P₃)-asparagine(P₂)-leucine(P₁)-phosphonate-phenylalanine(P₁'), and its macrocylic analog that includes a methylene bridge between the Asn(P₂) and Phe(P₁') side chains. The macrocyclic inhibitor, which has a 420-fold stronger affinity than that of the acyclic one, has been considered to lower the entropic penalty for binding. To better understand this binding preference, the solution structure of the inhibitors is studied by molecular dynamics simulations. Subsequently, we assess the influence of the enzyme/inhibitor contacts, the enzyme-induced inhibitor strain, the variation of the ligand configurational entropy and the enzyme reorganization by combining molecular-mechanics Poisson-Boltzmann surface area and normal mode calculations with conformational entropy calculations. We find that there is no relevant entropic stabilization on the binding of the cyclic inhibitor with respect to the acyclic analog because the methylene bridge does not reduce appreciably the conformational flexibility of the free inhibitor. The most important factors explaining the stronger affinity of the macrocyclic inhibitor are the conformational filtering and the lower ligand strain induced by the methylene bridge.

DNA Shape versus Sequence Variations in the Protein Binding Process

The binding process of a protein with a DNA involves three stages: approach, encounter, and association. It has been known that the complexation of protein and DNA involves mutual conformational changes, especially for a specific sequence association. However, it is still unclear how the conformation and the information in the DNA sequences affects the binding process. What is the extent to which the DNA structure adopted in the complex is induced by protein binding, or is instead intrinsic to the DNA sequence? In this study, we used the multiscale simulation method to explore the binding process of a protein with DNA in terms of DNA sequence, conformation, and interactions. We found that in the approach stage the protein can bind both the major and minor groove of the DNA, but uses different features to locate the binding site. The intrinsic conformational properties of the DNA play a significant role in this binding stage. By comparing the specific DNA with the nonspecific in unbound, intermediate, and associated states, we found that for a specific DNA sequence, ∼40% of the bending in the association forms is intrinsic and that ∼60% is induced by the protein. The protein does not induce appreciable bending of nonspecific DNA. In addition, we proposed that the DNA shape variations induced by protein binding are required in the early stage of the binding process, so that the protein is able to approach, encounter, and form an intermediate at the correct site on DNA.

Molecular Dynamics Studies of Matrix Metalloproteases

Matrix metalloproteases are multidomain enzymes with a remarkable proteolytic activity located in the extracellular environment. Their catalytic activity and structural properties have been intensively studied during the last few decades using both experimental and theoretical approaches, but many open questions still remain. Extensive molecular dynamics simulations enable the sampling of the configurational space of a molecular system, thus contributing to the characterization of the structure, dynamics, and ligand binding properties of a particular MMP. Based on previous computational experience, we provide in this chapter technical and methodological guidelines that may be useful to and stimulate other researchers to perform molecular dynamics simulations to help address unresolved questions concerning the molecular mode of action of MMPs.

Vibrational entropy of a protein: large differences between distinct conformations

In this article, it is investigated whether vibrational entropy (VE) is an important contribution to the free energy of globular proteins at ambient conditions. VE represents the major configurational-entropy contribution of these proteins. By definition, it is an average of the configurational entropies of the protein within single minima of the energy landscape, weighted by their occupation probabilities. Its large part originates from thermal motion of flexible torsion angles giving rise to the finite peak widths observed in torsion angle distributions. While VE may affect the equilibrium properties of proteins, it is usually neglected in numerical calculations as its consideration is difficult. Moreover, it is sometimes believed that all well-packed conformations of a globular protein have similar VE anyway. Here, we measure explicitly the VE for six different conformations from simulation data of a test protein. Estimates are obtained using the quasi-harmonic approximation for three coordinate sets, Cartesian, bond-angle-torsion (BAT), and a new set termed rotamer-degeneracy lifted BAT coordinates by us. The new set gives improved estimates as it overcomes a known shortcoming of the quasi-harmonic approximation caused by multiply populated rotamer states, and it may serve for VE estimation of macromolecules in a very general context. The obtained VE values depend considerably on the type of coordinates used. However, for all coordinate sets we find large entropy differences between the conformations, of the order of the overall stability of the protein. This result may have important implications on the choice of free energy expressions used in software for protein structure prediction, protein design, and NMR refinement.

Sampling Assessment for Molecular Simulations Using Conformational Entropy Calculations

The extent and significance of conformational sampling is a major factor determining the reliability of long-scale molecular simulations of large and flexible biomolecules. Although several methods have been proposed to quantify the effective sample size of molecular simulations by transforming root mean squared distances between pairs of configurations into statistical/probabilistic quantities, there is still no standard technique for measuring the size of sampling. In this work, we study conformational entropy (Sconform) as a purely informational and probabilistic measure of sampling that does not require the adoption of any clustering protocol or distance metric between configurations. In addition Sconform, which is calculated from the probability mass functions associated with discretized dihedral angles, offers other potential advantages for sampling assessment (e.g., global character, thermodynamic significance, relationship with internal degrees of freedom, etc.). The utility of Sconform in sampling assessment is illustrated by carrying out test calculations on configurations produced by two extended molecular dynamics simulations, namely, a 2.0 μs trajectory of a highly flexible 17-residue peptide and the trajectory data set of the 1.0 ms bovine pancreatic trypsin inhibitor simulation provided by the D. E. Shaw research group.