Potential energy functions

The emergence of machine learning force fields in drug design

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

Angiotensin-(1-5) is a Potent Endogenous Angiotensin AT 2 -Receptor Agonist

Background

The renin-angiotensin system involves many more enzymes, receptors and biologically active peptides than originally thought. With this study, we investigated whether angiotensin-(1-5) [Ang-(1-5)], a 5-amino acid fragment of angiotensin II, has biological activity, and through which receptor it elicits effects.

Methods

The effect of Ang-(1-5) (1µM) on nitric oxide release was measured by DAF-FM staining in human aortic endothelial cells (HAEC), or Chinese Hamster Ovary (CHO) cells stably transfected with the angiotensin AT 2 -receptor (AT 2 R) or the receptor Mas. A potential vasodilatory effect of Ang-(1-5) was tested in mouse mesenteric and human renal arteries by wire myography; the effect on blood pressure was evaluated in normotensive C57BL/6 mice by Millar catheter. These experiments were performed in the presence or absence of a range of antagonists or inhibitors or in AT 2 R-knockout mice. Binding of Ang-(1-5) to the AT 2 R was confirmed and the preferred conformations determined by in silico docking simulations. The signaling network of Ang-(1-5) was mapped by quantitative phosphoproteomics.

Results

Key findings included: (1) Ang-(1-5) induced activation of eNOS by changes in phosphorylation at Ser1177 eNOS and Tyr657 eNOS and thereby (2) increased NO release from HAEC and AT 2 R-transfected CHO cells, but not from Mas-transfected or non-transfected CHO cells. (3) Ang-(1-5) induced relaxation of preconstricted mouse mesenteric and human renal arteries and (4) lowered blood pressure in normotensive mice - effects which were respectively absent in arteries from AT 2 R-KO or in PD123319-treated mice and which were more potent than effects of the established AT 2 R-agonist C21. (5) According to in silico modelling, Ang-(1-5) binds to the AT 2 R in two preferred conformations, one differing substantially from where the first five amino acids within angiotensin II bind to the AT 2 R. (6) Ang-(1-5) modifies signaling pathways in a protective RAS-typical way and with relevance for endothelial cell physiology and disease.

Conclusions

Ang-(1-5) is a potent, endogenous AT 2 R-agonist.

Influence of the Lennard-Jones Combination Rules on the Simulated Properties of Organic Liquids at Optimal Force-Field Parametrization

We recently introduced the CombiFF scheme [Oliveira et al., J. Chem. Theory Comput. 2020, 16, 7525], an approach for the automated refinement of force-field parameters against experimental condensed-phase data for large compound families. Using this scheme, once the time-consuming task of target-data selection and curation has been performed, the force-field optimization itself is both straightforward and fast. As a result, CombiFF provides an ideal framework for evaluating the influence of functional-form decisions on the accuracy of a force field at an optimal level of parametrization. We already used this approach to assess the effect of using an all-atom representation compared to united-atom representations in the force field [Oliveira et al., J. Chem. Theory Comput. 2022, 18, 6757]. Here, CombiFF is applied to assess the effect of three Lennard-Jones combination rules, geometric-mean (GM), Lorentz-Berthelot (LB), or Waldman-Hagler (WH), on the simulated properties of organic liquids. The comparison is performed in terms of the experimental liquid density ρ_liq, vaporization enthalpy ΔH_vap, surface-tension coefficient γ, static relative dielectric permittivity ϵ, and self-diffusion coefficient D. The calibrations of the three force-field variants are carried out independently against 2044 experimental values for ρ_liq, and ΔH_vap concerning 1516 compounds. The resulting root-mean-square deviations from experiment are 30.0, 26.9, and 36.7 kg m^-3 for ρ_liq and 2.8, 2.8, and 2.9 kJ mol^-1 for ΔH_vap, when applying the GM, LB, and WH combination rules, respectively. In terms of these (and the other) properties, the three combination rules perform comparatively well, with the GM and LB results being more similar to each other and slightly more accurate compared to experiment. In contrast, the use of distinct combination rules for the parameter calibration and property calculation leads to much larger errors.

ANTIAGE-DB: A Database and Server for the Prediction of Anti-Aging Compounds Targeting Elastase, Hyaluronidase, and Tyrosinase

Natural products bear a multivariate biochemical profile with antioxidant, anti-inflammatory, antibacterial, and antitumoral properties. Along with their natural sources, they have been widely used both as anti-aging and anti-melanogenic agents due to their effective contribution in the elimination of reactive oxygen species (ROS) caused by oxidative stress. Their anti-aging activity is mainly related to their capacity of inhibiting enzymes like Human Neutrophil Elastase (HNE), Hyaluronidase (Hyal) and Tyrosinase (Tyr). Herein, we accumulated literature information (covering the period 1965–2020) on the inhibitory activity of natural products and their natural sources towards these enzymes. To navigate this information, we developed a database and server termed ANTIAGE-DB that allows the prediction of the anti-aging potential of target compounds. The server operates in two axes. First a comparison of compounds by shape similarity can be performed against our curated database of natural products whose inhibitory potential has been established in the literature. In addition, inverse virtual screening can be performed for a chosen molecule against the three targeted enzymes. The server is open access, and a detailed report with the prediction results is emailed to the user. ANTIAGE-DB could enable researchers to explore the chemical space of natural based products, but is not limited to, as anti-aging compounds and can predict their anti-aging potential. ANTIAGE-DB is accessed online.

Comparison of the United- and All-Atom Representations of (Halo)alkanes Based on Two Condensed-Phase Force Fields Optimized against the Same Experimental Data Set

The level of accuracy that can be achieved by a force field is influenced by choices made in the interaction-function representation and in the relevant simulation parameters. These choices, referred to here as functional-form variants (FFVs), include for example the model resolution, the charge-derivation procedure, the van der Waals combination rules, the cutoff distance, and the treatment of the long-range interactions. Ideally, assessing the effect of a given FFV on the intrinsic accuracy of the force-field representation requires that only the specific FFV is changed and that this change is performed at an optimal level of parametrization, a requirement that may prove extremely challenging to achieve in practice. Here, we present a first attempt at such a comparison for one specific FFV, namely the choice of a united-atom (UA) versus an all-atom (AA) resolution in a force field for saturated acyclic (halo)alkanes. Two force-field versions (UA vs AA) are optimized in an automated way using the CombiFF approach against 961 experimental values for the pure-liquid densities ρ_liq and vaporization enthalpies ΔH_vap of 591 compounds. For the AA force field, the torsional and third-neighbor Lennard-Jones parameters are also refined based on quantum-mechanical rotational-energy profiles. The comparison between the UA and AA resolutions is also extended to properties that have not been included as parameterization targets, namely the surface-tension coefficient γ, the isothermal compressibility κ_T, the isobaric thermal-expansion coefficient α_P, the isobaric heat capacity c_P, the static relative dielectric permittivity ϵ, the self-diffusion coefficient D, the shear viscosity η, the hydration free energy ΔG_wat, and the free energy of solvation ΔG_che in cyclohexane. For the target properties ρ_liq and ΔH_vap, the UA and AA resolutions reach very similar levels of accuracy after optimization. For the nine other properties, the AA representation leads to more accurate results in terms of η; comparably accurate results in terms of γ, κ_T, α_P, ϵ, D, and ΔG_che; and less accurate results in terms of c_P and ΔG_wat. This work also represents a first step toward the calibration of a GROMOS-compatible force field at the AA resolution.

Identification of Kukoamine A, Zeaxanthin, and Clexane as New Furin Inhibitors

The endogenous protease furin is a key protein in many different diseases, such as cancer and infections. For this reason, a wide range of studies has focused on targeting furin from a therapeutic point of view. Our main objective consisted of identifying new compounds that could enlarge the furin inhibitor arsenal; secondarily, we assayed their adjuvant effect in combination with a known furin inhibitor, CMK, which avoids the SARS-CoV-2 S protein cleavage by means of that inhibition. Virtual screening was carried out to identify potential furin inhibitors. The inhibition of physiological and purified recombinant furin by screening selected compounds, Clexane, and these drugs in combination with CMK was assayed in fluorogenic tests by using a specific furin substrate. The effects of the selected inhibitors from virtual screening on cell viability (293T HEK cell line) were assayed by means of flow cytometry. Through virtual screening, Zeaxanthin and Kukoamine A were selected as the main potential furin inhibitors. In fluorogenic assays, these two compounds and Clexane inhibited both physiological and recombinant furin in a dose-dependent way. In addition, these compounds increased physiological furin inhibition by CMK, showing an adjuvant effect. In conclusion, we identified Kukoamine A, Zeaxanthin, and Clexane as new furin inhibitors. In addition, these drugs were able to increase furin inhibition by CMK, so they could also increase its efficiency when avoiding S protein proteolysis, which is essential for SARS-CoV-2 cell infection.

The automated optimisation of a coarse-grained force field using free energy data

Atomistic models provide a detailed representation of molecular systems, but are sometimes inadequate for simulations of large systems over long timescales. Coarse-grained models enable accelerated simulations by reducing the number of degrees of freedom, at the cost of reduced accuracy. New optimisation processes to parameterise these models could improve their quality and range of applicability. We present an automated approach for the optimisation of coarse-grained force fields, by reproducing free energy data derived from atomistic molecular simulations. To illustrate the approach, we implemented hydration free energy gradients as a new target for force field optimisation in ForceBalance and applied it successfully to optimise the un-charged side-chains and the protein backbone in the SIRAH protein coarse-grain force field. The optimised parameters closely reproduced hydration free energies of atomistic models and gave improved agreement with experiment.

Systematic optimization of a fragment-based force field against experimental pure-liquid properties considering large compound families: application to oxygen and nitrogen compounds

The CombiFF approach is a workflow for the automated refinement of force-field parameters against experimental condensed-phase data, considering entire classes of organic molecules constructed using a fragment library via combinatorial isomer enumeration. One peculiarity of this approach is that it relies on an electronegativity-equalization scheme to account for induction effects within molecules, with values of the atomic hardness and electronegativity as electrostatic parameters, rather than the partial charges themselves. In a previous article [M. P. Oliveira, M. Andrey, S. R. Rieder, L. Kern, D. F. Hahn, S. Riniker, B. A. C. Horta and P. H. Hünenberger, J. Chem. Theory. Comput. 2020, 16, 7525], CombiFF was introduced and applied to calibrate a GROMOS-compatible united-atom force field for the saturated acyclic (halo-)alkane family. Here, this scheme is employed for the construction of a corresponding force field for saturated acyclic compounds encompassing eight common chemical functional groups involving oxygen and/or nitrogen atoms, namely: ether, aldehyde, ketone, ester, alcohol, carboxylic acid, amine, and amide. Monofunctional as well as homo-polyfunctional compounds are considered. A total of 1712 experimental liquid densities ρliq and vaporization enthalpies ΔHvap concerning 1175 molecules are used for the calibration (339 molecules) and validation (836 molecules) of the 102 non-bonded interaction parameters of the force field. Using initial parameter values based on the GROMOS 2016H66 parameter set, convergence is reached after five iterations. Given access to one processor per simulated system, this operation only requires a few days of wall-clock computing time. After optimization, the root-mean-square deviations from experiment are 29.9 (22.4) kg m-3 for ρliq and 4.1 (5.5) kJ mol-1 for ΔHvap for the calibration (validation) set. Thus, a very good level of agreement with experiment is achieved in terms of these two properties, although the errors are inhomogeneously distributed across the different chemical functional groups.

Types of potentials in a mitotic spindle

The dynamics of a mitotic spindle is very important to understand if the functioning of mitosis has to be understood and defined very accurately. There are a number of forces involved in such a process. Despite of the fact that there have been numerous studies done on the functioning of a mitotic spindle, there is still not a very precise understanding of this system and how it behaves. This study aims at understanding and expressing all the possible potentials which might be responsible in a mitotic spindle and its mechanism.

Structural insights on ligand recognition at the human leukotriene B4 receptor 1

The leukotriene B4 receptor 1 (BLT1) regulates the recruitment and chemotaxis of different cell types and plays a role in the pathophysiology of infectious, allergic, metabolic, and tumorigenic human diseases. Here we present a crystal structure of human BLT1 (hBLT1) in complex with a selective antagonist MK-D-046, developed for the treatment of type 2 diabetes and other inflammatory conditions. Comprehensive analysis of the structure and structure-activity relationship data, reinforced by site-directed mutagenesis and docking studies, reveals molecular determinants of ligand binding and selectivity toward different BLT receptor subtypes and across species. The structure helps to identify a putative membrane-buried ligand access channel as well as potential receptor binding modes of endogenous agonists. These structural insights of hBLT1 enrich our understanding of its ligand recognition and open up future avenues in structure-based drug design.

Negative Image-Based Screening: Rigid Docking Using Cavity Information

Rational drug discovery relies heavily on molecular docking-based virtual screening, which samples flexibly the ligand binding poses against the target protein's structure. The upside of flexible docking is that the geometries of the generated docking poses are adjusted to match the residue alignment inside the target protein's ligand-binding pocket. The downside is that the flexible docking requires plenty of computing resources and, regardless, acquiring a decent level of enrichment typically demands further rescoring or post-processing. Negative image-based screening is a rigid docking technique that is ultrafast and computationally light but also effective as proven by vast benchmarking and screening experiments. In the NIB screening, the target protein cavity's shape/electrostatics is aligned and compared against ab initio-generated ligand 3D conformers. In this chapter, the NIB methodology is explained at the practical level and both its weaknesses and strengths are discussed candidly.

Reaction-field electrostatics in molecular dynamics simulations: development of a conservative scheme compatible with an atomic cutoff

Shifting and switching schemes are developed to enable strict energy conservation in molecular dynamics simulations relying on reaction-field electrostatic (as well as Lennard-Jones) interactions with an atom-based cutoff truncation.

Atom Type Independent Modeling of the Conformational Energy of Benzylic, Allylic, and Other Bonds Adjacent to Conjugated Systems

Applications of computational methods to predict binding affinities for protein/drug complexes are routinely used in structure-based drug discovery. Applications of these methods often rely on empirical force fields (FFs) and their associated parameter sets and atom types. However, it is widely accepted that FFs cannot accurately cover the entire chemical space of drug-like molecules, due to the restrictive cost of parametrization and the poor transferability of existing parameters. To address these limitations, initiatives have been carried out to develop more transferable methods, in order to allow for more rigorous descriptions of any drug-like molecule. We have previously reported H-TEQ, a method which does not rely on atom types and incorporates well established chemical principles to assign parameters to organic molecules. The previous implementation of H-TEQ (a torsional barrier prediction method) only covered saturated and lone pair containing molecules; here, we report our efforts to incorporate conjugated systems into our model. The next step was the evaluation of the introduction of unsaturations. The developed model (H-TEQ3.0) has been validated on a wide variety of molecules containing heteroaromatic groups, alkyls, and fused ring systems. Our method performs on par with one of the most commonly used FFs (GAFF2), without relying on atom types or any prior parametrization.

Getting Docking into Shape Using Negative Image-Based Rescoring

The failure of default scoring functions to ensure virtual screening enrichment is a persistent problem for the molecular docking algorithms used in structure-based drug discovery. To remedy this problem, elaborate rescoring and postprocessing schemes have been developed with a varying degree of success, specificity, and cost. The negative image-based rescoring (R-NiB) has been shown to improve the flexible docking performance markedly with a variety of drug targets. The yield improvement is achieved by comparing the alternative docking poses against the negative image of the target protein's ligand-binding cavity. In other words, the shape and electrostatics of the binding pocket is directly used in the similarity comparison to rank the explicit docking poses. Here, the PANTHER/ShaEP-based R-NiB methodology is tested with six popular docking softwares, including GLIDE, PLANTS, GOLD, DOCK, AUTODOCK, and AUTODOCK VINA, using five validated benchmark sets. Overall, the results indicate that R-NiB outperforms the default docking scoring consistently and inexpensively, demonstrating that the methodology is ready for wide-scale virtual screening usage.

A Practical Perspective: The Effect of Ligand Conformers on the Negative Image-Based Screening

Negative image-based (NIB) screening is a rigid molecular docking methodology that can also be employed in docking rescoring. During the NIB screening, a negative image is generated based on the target protein’s ligand-binding cavity by inverting its shape and electrostatics. The resulting NIB model is a drug-like entity or pseudo-ligand that is compared directly against ligand 3D conformers, as is done with a template compound in the ligand-based screening. This cavity-based rigid docking has been demonstrated to work with genuine drug targets in both benchmark testing and drug candidate/lead discovery. Firstly, the study explores in-depth the applicability of different ligand 3D conformer generation software for acquiring the best NIB screening results using cyclooxygenase-2 (COX-2) as the example system. Secondly, the entire NIB workflow from the protein structure preparation, model build-up, and ligand conformer generation to the similarity comparison is performed for COX-2. Accordingly, hands-on instructions are provided on how to employ the NIB methodology from start to finish, both with the rigid docking and docking rescoring using noncommercial software. The practical aspects of the NIB methodology, especially the effect of ligand conformers, are discussed thoroughly, thus, making the methodology accessible for new users.

Alumina: discriminative analysis using 3D correlation of solid-state NMR parameters

Synthetic transition aluminas (χ, κ, θ, γ, δ, η, ρ) exhibit unique adsorptive and catalytic properties leading to numerous practical applications. Generated by thermal transformation of aluminium (oxy)hydroxides, structural differences between them arise from the variability of aluminium coordination numbers and degree of dehydroxylation. Unequivocal identification of these phases using X-ray diffraction has proven to be very difficult. Quadrupolar interactions of 27Al nuclei, highly sensitive to each site symmetry, render advanced 27Al solid-state NMR a unique spectroscopic tool to fingerprint and identify the different phases. In this paper, 27Al NMR spectroscopic data on alumina reported in literature are collected in a comprehensive library. Based on this dataset, a new 3D correlative method of NMR parameters is presented, enabling fingerprinting and identification of such phases. Providing a gold standard from crystalline samples, this approach demonstrates that any sort of crystalline, ill crystallized or amorphous, mixed periodic or aperiodically ordered transition alumina can now be assessed beyond the current limitations of characterisation. Adopting the presented approach as a standard characterisation of alumina samples will readily reveal NMR parameter-structure-property relations suitable to develop new or improved applications of alumina. Methodological guidance is provided to assist consistent implementation of this characterisation throughout the fields involved.

Discovery of Retinoic Acid-Related Orphan Receptor γt Inverse Agonists via Docking and Negative Image-Based Screening

Retinoic acid-related orphan receptor γt (RORγt) has a vital role in the differentiation of T-helper 17 (TH17) cells. Potent and specific RORγt inverse agonists are sought for treating TH17-related diseases such as psoriasis, rheumatoid arthritis, and type 1 diabetes. Here, the aim was to discover novel RORγt ligands using both standard molecular docking and negative image-based screening. Interestingly, both of these in silico techniques put forward mostly the same compounds for experimental testing. In total, 11 of the 34 molecules purchased for testing were verified as RORγt inverse agonists, thus making the effective hit rate 32%. The pIC₅₀ values for the compounds varied from 4.9 (11 μM) to 6.2 (590 nM). Importantly, the fact that the verified hits represent four different cores highlights the structural diversity of the RORγt inverse agonism and the ability of the applied screening methodologies to facilitate much-desired scaffold hopping for drug design.

Validating Molecular Dynamics Simulations against Experimental Observables in Light of Underlying Conformational Ensembles

Far from the static, idealized conformations deposited into structural databases, proteins are highly dynamic molecules that undergo conformational changes on temporal and spatial scales that may span several orders of magnitude. These conformational changes, often intimately connected to the functional roles that proteins play, may be obscured by traditional biophysical techniques. Over the past 40 years, molecular dynamics (MD) simulations have complemented these techniques by providing the "hidden" atomistic details that underlie protein dynamics. However, there are limitations of the degree to which molecular simulations accurately and quantitatively describe protein motions. Here we show that although four molecular dynamics simulation packages (AMBER, GROMACS, NAMD, and ilmm) reproduced a variety of experimental observables for two different proteins (engrailed homeodomain and RNase H) equally well overall at room temperature, there were subtle differences in the underlying conformational distributions and the extent of conformational sampling obtained. This leads to ambiguity about which results are correct, as experiment cannot always provide the necessary detailed information to distinguish between the underlying conformational ensembles. However, the results with different packages diverged more when considering larger amplitude motion, for example, the thermal unfolding process and conformational states sampled, with some packages failing to allow the protein to unfold at high temperature or providing results at odds with experiment. While most differences between MD simulations performed with different packages are attributed to the force fields themselves, there are many other factors that influence the outcome, including the water model, algorithms that constrain motion, how atomic interactions are handled, and the simulation ensemble employed. Here four different MD packages were tested each using best practices as established by the developers, utilizing three different protein force fields and three different water models. Differences between the simulated protein behavior using two different packages but the same force field, as well as two different packages with different force fields but the same water models and approaches to restraining motion, show how other factors can influence the behavior, and it is incorrect to place all the blame for deviations and errors on force fields or to expect improvements in force fields alone to solve such problems.

The non-biphenyl-tetrazole angiotensin AT1 receptor antagonist eprosartan is a unique and robust inverse agonist of the active state of the AT1 receptor

BACKGROUND AND PURPOSE

Conditions such as hypertension and renal allograft rejection are accompanied by chronic, agonist-independent, signalling by angiotensin II AT1 receptors. The current treatment paradigm for these diseases entails the preferred use of inverse agonist AT1 receptor blockers (ARBs). However, variability in the inverse agonist activities of common biphenyl-tetrazole ARBs for the active state of AT1 receptors often leads to treatment failure. Therefore, characterization of robust inverse agonist ARBs for the active state of AT1 receptors is necessary.

EXPERIMENTAL APPROACH

To identify the robust inverse agonist for active state of AT1 receptors and its molecular mechanism, we performed site-directed mutagenesis, competition binding assay, inositol phosphate production assay and molecular modelling for both ground-state wild-type AT1 receptors and active-state N111G mutant AT1 receptors.

KEY RESULTS

Although candesartan and telmisartan exhibited weaker inverse agonist activity for N111G- compared with WT-AT1 receptors, only eprosartan exhibited robust inverse agonist activity for both N111G- and WT- AT1 receptors. Specific ligand-receptor contacts for candesartan and telmisartan are altered in the active-state N111G- AT1 receptors compared with the ground-state WT-AT1 receptors, suggesting an explanation of their attenuated inverse agonist activity for the active state of AT1 receptors. In contrast, interactions between eprosartan and N111G-AT1 receptors were not significantly altered, and the inverse agonist activity of eprosartan was robust.

CONCLUSIONS AND IMPLICATIONS

Eprosartan may be a better therapeutic option than other ARBs. Comparative studies investigating eprosartan and other ARBs for the treatment of diseases caused by chronic, agonist-independent, AT1 receptor activation are warranted.

Structural basis for selectivity and diversity in angiotensin II receptors

Angiotensin II receptors, AT₁R and AT₂R, serve as key components of the renin-angiotensin-aldosterone system. While AT₁R plays a central role in the regulation of blood pressure, the function of AT₂R is enigmatic with a variety of reported effects. To elucidate the mechanisms for the functional diversity and ligand selectivity between these receptors, we report crystal structures of the human AT₂R bound to an AT₂R-selective and an AT₁R/AT₂R-dual ligand, respectively, capturing the receptor in an active-like conformation. Unexpectedly, helix VIII was found in a non-canonical position, stabilizing the active-like state, but at the same time preventing the recruitment of G proteins/β-arrestins, in agreement with the lack of signaling responses in standard cellular assays. Structure-activity relationship, docking and mutagenesis studies revealed the interactions critical for ligand binding and selectivity. Our results thus provide insights into the structural basis for distinct functions of the angiotensin receptors, and may guide the design of novel selective ligands.

Ultrafast protein structure-based virtual screening with Panther

Molecular docking is by far the most common method used in protein structure-based virtual screening. This paper presents Panther, a novel ultrafast multipurpose docking tool. In Panther, a simple shape-electrostatic model of the ligand-binding area of the protein is created by utilizing the protein crystal structure. The features of the possible ligands are then compared to the model by using a similarity search algorithm. On average, one ligand can be processed in a few minutes by using classical docking methods, whereas using Panther processing takes <1 s. The presented Panther protocol can be used in several applications, such as speeding up the early phases of drug discovery projects, reducing the number of failures in the clinical phase of the drug development process, and estimating the environmental toxicity of chemicals. Panther-code is available in our web pages (http://www.jyu.fi/panther) free of charge after registration.

The Dependence of Amyloid-β Dynamics on Protein Force Fields and Water Models

We studied the dynamics of Aβ40 , involved in Alzheimer's disease, by using 21 methods combined from Amber03, Amber99sb-ILDN, Charmm27, Charmm22*, OPLS-2001, OPLS-2006, OPLS-2008, Gromos96-43a1, Gromos96-53a6, Gromos96-54a7, and the water models SPC, TIP3P, TIP4P. Major differences in the structural ensembles were systematized: Amber03, Charmm27, and Gromos96-54a7 stabilize the helices; Gromos96-43a1 and Gromos53a6 favor the β-strands (with Charmm22* and Amber99sb-ILDN in between), and OPLS produces unstructured ensembles. The accuracy of the NMR chemical shifts was in the order: Charmm22*>Amber99sb-ILDN>OPLS-2008≈Gromos96-43a1>Gromos96-54a7≈OPLS-2001>OPLS-2006>Gromos96-53a6>Charmm27>Amber03. The computed (3) JHNHα -coupling constants were sensitive to experiment type and Karplus parameterization. Overall, the ensembles of Charmm22* and Amber99sb-ILDN provided the best agreement with experimental NMR and circular dichroism data, providing a model for the real Aβ monomer ensemble. Also, the polar water model TIP3P significantly favored helix and compact conformations.

Structure-Function Basis of Attenuated Inverse Agonism of Angiotensin II Type 1 Receptor Blockers for Active-State Angiotensin II Type 1 Receptor

Ligand-independent signaling by the angiotensin II type 1 receptor (AT1R) can be activated in clinical settings by mechanical stretch and autoantibodies as well as receptor mutations. Transition of the AT1R to the activated state is known to lower inverse agonistic efficacy of clinically used AT1R blockers (ARBs). The structure-function basis for reduced efficacy of inverse agonists is a fundamental aspect that has been understudied not only in relation to the AT1R but also regarding other homologous receptors. Here, we demonstrate that the active-state transition in the AT1R indeed attenuates an inverse agonistic effect of four biphenyl-tetrazole ARBs through changes in specific ligand-receptor interactions. In the ground state, tight interactions of four ARBs with a set of residues (Ser109(TM3), Phe182(ECL2), Gln257(TM6), Tyr292(TM7), and Asn295(TM7)) results in potent inverse agonism. In the activated state, the ARB-AT1R interactions shift to a different set of residues (Val108(TM3), Ser109(TM3), Ala163(TM4), Phe182(ECL2), Lys199(TM5), Tyr292(TM7), and Asn295(TM7)), resulting in attenuated inverse agonism. Interestingly, V108I, A163T, N295A, and F182A mutations in the activated state of the AT1R shift the functional response to the ARB binding toward agonism, but in the ground state the same mutations cause inverse agonism. Our data show that the second extracellular loop is an important regulator of the functional states of the AT1R. Our findings suggest that the quest for discovering novel ARBs, and improving current ARBs, fundamentally depends on the knowledge of the unique sets of residues that mediate inverse agonistic potency in the two states of the AT1R.

Conformational study of the structure of 18-thiacrown-6

Conformational analysis was performed for 18-thiacrown-6 (18t6) using the CONFLEX method and the MMFF94s force field. Computations were performed for some of the low energy conformations at the HF, B3LYP, CAM-B3LYP, M06, M06L, M062x, M06HF and MP2 levels. The computations were also performed using the DFT-D3 method along with the TPSS and PBE functionals. The study predicted a new C2 conformation as the ground state conformation of 18t6. This new C2 conformation is more stable than the experimentally known solid state conformation by 4.7 kcal/mol at the MP2/6-311G** level. This conformation has all of the SCCS dihedral angles adopt exodentate structure. However, the experimentally known conformation of the solid phase has two of the SCCS dihedral angles violating this exodentate rule. It was concluded that for 18t6 stability a linear dihedral SCCS angle requirement is more important than a gauche CSCC dihedral angle requirement.

Recent Developments and Applications of the CHARMM force fields

Empirical force fields commonly used to describe the condensed phase properties of complex systems such as biological macromolecules are continuously being updated. Improvements in quantum mechanical (QM) methods used to generate target data, availability of new experimental target data, incorporation of new classes of compounds and new theoretical developments (eg. polarizable methods) make force-field development a dynamic domain of research. Accordingly, a number of improvements and extensions of the CHARMM force fields have occurred over the years. The objective of the present review is to provide an up-to-date overview of the CHARMM force fields. A limited presentation on the historical aspects of force fields will be given, including underlying methodologies and principles, along with a brief description of the strategies used for parameter development. This is followed by information on the CHARMM additive and polarizable force fields, including examples of recent applications of those force fields.

Identification and biological evaluation of a series of 1H-benzo[de]isoquinoline-1,3(2H)-diones as hepatitis C virus NS5B polymerase inhibitors

The hepatitis C virus (HCV) NS5B RNA-dependent RNA polymerase (RdRp) plays a central role in virus replication. NS5B has no functional equivalent in mammalian cells and, as a consequence, is an attractive target for inhibition. Herein, we present 1H-benzo[de]isoquinoline-1,3(2H)-diones as a new series of selective inhibitors of HCV NS5B polymerase. The HTS hit 1 shows submicromolar potency in two different HCV replicons (1b and 2b) and displays no activity on other polymerases (HIV-RT, Polio-pol, GBV-b-pol). These inhibitors act during the pre-elongation phase by binding to NS5B non-nucleoside binding site Thumb Site II as demonstrated by crystal structure of compound 1 with the DeltaC55-1b and DeltaC21-2b enzymes and by mutagenesis studies. SAR in this new series reveals inhibitors, such as 20, with low micromolar activity in the HCV replicon and with good activity/toxicity window in cells.

Molecular simulations of carbohydrates and protein-carbohydrate interactions: motivation, issues and prospects

The characterization of the 3D structure of oligosaccharides, their conjugates and analogs is particularly challenging for traditional experimental methods. Molecular simulation methods provide a basis for interpreting sparse experimental data and for independently predicting conformational and dynamic properties of glycans. Here, we summarize and analyze the issues associated with modeling carbohydrates, with a detailed discussion of four of the most recently developed carbohydrate force fields, reviewed in terms of applicability to natural glycans, carbohydrate-protein complexes and the emerging area of glycomimetic drugs. In addition, we discuss prospectives and new applications of carbohydrate modeling in drug discovery.

The SAAP force field: development of the single amino acid potentials for 20 proteinogenic amino acids and Monte Carlo molecular simulation for short peptides

Molecular simulation by using force field parameters has been widely applied in the fields of peptide and protein research for various purposes. We recently proposed a new all-atom protein force field, called the SAAP force field, which utilizes single amino acid potentials (SAAPs) as the fundamental elements. In this article, whole sets of the SAAP force field parameters in vacuo, in ether, and in water have been developed by ab initio calculation for all 20 proteinogenic amino acids and applied to Monte Carlo molecular simulation for two short peptides. The side-chain separation approximation method was employed to obtain the SAAP parameters for the amino acids with a long side chain. Monte Carlo simulation for Met-enkephalin (CHO-Tyr-Gly-Gly-Phe-Met-NH2) by using the SAAP force field revealed that the conformation in vacuo is mainly controlled by strong electrostatic interactions between the amino acid residues, while the SAAPs and the interamino acid Lennard-Jones potentials are predominant in water. In ether, the conformation would be determined by the combination of the three components. On the other hand, the SAAP simulation for chignolin (H-Gly-Tyr-Asp-Pro-Glu-Thr-Gly-Thr-Trp-Gly-OH) reasonably reproduced a native-like beta-hairpin structure in water although the C-terminal and side-chain conformations were different from the native ones. It was suggested that the SAAP force field is a useful tool for analyzing conformations of polypeptides in terms of intrinsic conformational propensities of the single amino acid units.

2-(2-Thienyl)-5,6-dihydroxy-4-carboxypyrimidines as inhibitors of the hepatitis C virus NS5B polymerase: discovery, SAR, modeling, and mutagenesis

Infections caused by hepatitis C virus (HCV) are a significant world health problem for which novel therapies are in urgent demand. The polymerase of HCV is responsible for the replication of viral RNA. We recently disclosed dihydroxypyrimidine carboxylates 2 as novel, reversible inhibitors of the HCV NS5B polymerase. This series was further developed into 5,6-dihydroxy-2-(2-thienyl)pyrimidine-4-carboxylic acids such as 34 (EC50 9.3 microM), which now show activity in the cell-based HCV replication assay. The structure-activity relationship of these inhibitors is discussed in the context of their physicochemical properties and of the polymerase crystal structure. We also report the results of mutagenesis experiments which support the proposed binding model, which involves pyrophosphate-like chelation of the active site Mg ions.

A new generation of statistical potentials for proteins

We propose a novel and flexible derivation scheme of statistical, database-derived, potentials, which allows one to take simultaneously into account specific correlations between several sequence and structure descriptors. This scheme leads to the decomposition of the total folding free energy of a protein into a sum of lower order terms, thereby giving the possibility to analyze independently each contribution and clarify its significance and importance, to avoid overcounting certain contributions, and to deal more efficiently with the limited size of the database. In addition, this derivation scheme appears as quite general, for many previously developed potentials can be expressed as particular cases of our formalism. We use this formalism as a framework to generate different residue-based energy functions, whose performances are assessed on the basis of their ability to discriminate genuine proteins from decoy models. The optimal potential is generated as a combination of several coupling terms, measuring correlations between residue types, backbone torsion angles, solvent accessibilities, relative positions along the sequence, and interresidue distances. This potential outperforms all tested residue-based potentials, and even several atom-based potentials. Its incorporation in algorithms aiming at predicting protein structure and stability should therefore substantially improve their performances.

An information theoretic approach to macromolecular modeling: II. Force fields

In this article, we explore the information content of molecular force-field calculations. We make use of exhaustive lattice models of molecular conformations and reduced alphabet sequences to determine the relative resolving power of pairwise interaction-based force fields. We find that sequence-specific interactions that operate over longer distances offer greater amounts of information than nearest-neighbor or non-sequence-specific interactions. In a companion article in this issue, we explored the information content of sequence alignment procedures and the calculation of gap penalties. Both articles have implications for protein and nucleic-acid computations.

Database-derived potentials dependent on protein size for in silico folding and design

Knowledge-based potentials are widely used in simulations of protein folding, structure prediction, and protein design. Their advantages include limited computational requirements and the ability to deal with low-resolution protein models compatible with long-scale simulations. Their drawbacks comprehend their dependence on specific features of the dataset from which they are derived, such as the size of the proteins it contains, and their physical meaning is still a subject of debate. We address these issues by probing the theoretical validity of these potentials as mean-force potentials that take the solvent implicitly into account and involve entropic contributions due to atomic degrees of freedom and solvation. The dependence on the size of the system is checked on distance-dependent amino acid pair potentials, derived from six protein structure sets containing proteins of increasing length N. For large inter-residue distances, they are found to display the theoretically predicted 1/N behavior weighted by a factor depending on the boundaries and the compressibility of the system. For short distances, different trends are observed according to the nature of the residue pairs and their ability to form, for example, electrostatic, cation-pi or pi-pi interactions, or hydrophobic packing. The results of this analysis are used to devise a novel protein size-dependent distance potential, which displays an improved performance in discriminating native sequence-structure matches among decoy models.

The SAAP force field. A simple approach to a new all-atom protein force field by using single amino acid potential (SAAP) functions in various solvents

A simple strategy to compose a new all-atom protein force field (named as the SAAP force field), which utilizes the single amino acid potential (SAAP) functions obtained in various solvents by ab initio molecular orbital calculation applying the isodensity polarizable continuum model (IPCM), is presented. We considered that the total energy function of a protein force field (E(TOTAL)) is divided into three components; a single amino acid potential term (E(SAAP)), an interamino acid nonbonded interaction term (E(INTER)), and a miscellaneous term (E(OTHERS)), which is ignored (or considered to be constant) at the current version of the force field. The E(INTER) term consists of electrostatic interactions (E(ES')) and van der Waals interactions (E(LJ')). Despite simplicity, the SAAP force field implicitly involves the correlation among individual terms of the Lifson's potential function within a single amino acid unit and can treat solvent effects unambiguously by choosing the SAAP function in an appropriate solvent and the dielectric constant (D) of medium. Application of the SAAP force field to the Monte Carlo simulation of For-Ala(2)-NH(2) in vacuo reasonably reproduced the results of the extensive conformational search by ab initio molecular orbital calculation. In addition, the preliminary Monte Carlo simulations for For-Gly(10)-NH(2) and For-Ala(10)-NH(2) showed reversible transitions from the extended to the pseudosecondary structures in water (D = 78.39) as well as in ether (D = 4.335). The result suggested that the new approach is efficient for fast modeling of protein structures in various environments. Decomposition analysis of the total energy function (E(TOTAL)) by using the SAAP force field suggested that conformational propensities of single amino acids (i.e., the E(SAAP) term) may play definitive roles on the topologies of protein secondary structures.

Enhanced sampling of the molecular potential energy surface using mutually orthogonal latin squares: application to peptide structures

The computational identification of the optimal three-dimensional fold of even a small peptide chain from its sequence, without reference to other known structures, is a complex problem. There have been several attempts at solving this by sampling the potential energy surface of the molecule in a systematic manner. Here we present a new method to carry out the sampling, and to identify low energy conformers of the molecule. The method uses mutually orthogonal Latin squares to select (of the order of) n(2) points from the multidimensional conformation space of size m(n), where n is the number of dimensions (i.e., the number of conformational variables), and m specifies the fineness of the search grid. The sampling is accomplished by first calculating the value of the potential energy function at each one of the selected points. This is followed by analysis of these values of the potential energy to obtain the optimal value for each of the n-variables separately. We show that the set of the n-optimal values obtained in this manner specifies a low energy conformation of the molecule. Repeated application of the method identifies other low energy structures. The computational complexity of this algorithm scales as the fourth power of the size of the molecule. We applied this method to several small peptides, such as the neuropeptide enkephalin, and could identify a set of low energy conformations for each. Many of the structures identified by this method have also been previously identified and characterized by experiment and theory. We also compared the best structures obtained for the tripeptide (Ala)(3) by the present method, with those obtained by an exhaustive grid search, and showed that the algorithm is successful in identifying all the low energy conformers of this molecule.

ICFF: a new method to incorporate implicit flexibility into an internal coordinate force field

We introduce a new method to accurately "project" a Cartesian force field onto an internal coordinate molecular model with fixed-bond geometry. The algorithm automatically generates the Internal Coordinate Force Field (ICFF), which is a close approximation of the "source" Cartesian force field. The ICFF method reduces the number of free variables in a model by at least 10-fold and facilitates the fast convergence of geometry optimizations, an advantage that is critical for many applications such as the docking of flexible ligands or conformational modeling of macromolecules. Although covalent geometry is fixed in an ICFF model, implicit flexibility is incorporated into the force field parameters in the following two ways. First, we formulate an empirical torsion energy term in ICFF as a sixfold Fourier series and develop a procedure to calculate the Fourier coefficients from the conformational energy profiles of the fully flexible Cartesian model. The ICFF torsion parameters thus represent not only torsion component of the source force field, but also bond bending, bond stretching, and "1-4" van der Waals interactions. Second, we use a soft polynomial repulsion function for "1-5" and "1-6" interactions to mimic the flexibility of bonds, connecting these atoms. Also, we suggest a way to use a local part of the Cartesian force field to automatically generate fixed covalent geometries, compatible with the ICFF energy function. Here, we present an implementation of the ICFF algorithm, which employs the MMFF94s Cartesian force field as a "source." Extensive benchmarking of ICFF with a representative set of organic molecules demonstrates that the implicit flexibility model accurately reproduces MMFF94s equilibrium conformational energy differences (RMSD approximately 0.64 kcal) and, most importantly, detailed torsion energy profiles (RMSD approximately 0.37 kcal). This accuracy is characteristic of the method, because all the ICFF parameters (except one scaling factor in the "1-5,1-6" repulsion term) are derived directly from the source Cartesian force field and do not depend on any particular molecular set. In contrast, the rigid geometry model with the MMFF94s energy function yields highly biased estimations in this test with the RMSD exceeding 1.2 kcal for the equilibrium energy comparisons and approximately 3.4 kcal for the torsion energy profiles.

Interatomic potentials and solvation parameters from protein engineering data for buried residues

Van der Waals (vdW) interaction energies between different atom types, energies of hydrogen bonds (H-bonds), and atomic solvation parameters (ASPs) have been derived from the published thermodynamic stabilities of 106 mutants with available crystal structures by use of an originally designed model for the calculation of free-energy differences. The set of mutants included substitutions of uncharged, inflexible, water-inaccessible residues in alpha-helices and beta-sheets of T4, human, and hen lysozymes and HI ribonuclease. The determined energies of vdW interactions and H-bonds were smaller than in molecular mechanics and followed the "like dissolves like" rule, as expected in condensed media but not in vacuum. The depths of modified Lennard-Jones potentials were -0.34, -0.12, and -0.06 kcal/mole for similar atom types (polar-polar, aromatic-aromatic, and aliphatic-aliphatic interactions, respectively) and -0.10, -0.08, -0.06, -0.02, and nearly 0 kcal/mole for different types (sulfur-polar, sulfur-aromatic, sulfur-aliphatic, aliphatic-aromatic, and carbon-polar, respectively), whereas the depths of H-bond potentials were -1.5 to -1.8 kcal/mole. The obtained solvation parameters, that is, transfer energies from water to the protein interior, were 19, 7, -1, -21, and -66 cal/moleA(2) for aliphatic carbon, aromatic carbon, sulfur, nitrogen, and oxygen, respectively, which is close to the cyclohexane scale for aliphatic and aromatic groups but intermediate between octanol and cyclohexane for others. An analysis of additional replacements at the water-protein interface indicates that vdW interactions between protein atoms are reduced when they occur across water.

Improved recognition of native-like protein structures using a family of designed sequences

The goal of the inverse protein folding problem is to identify amino acid sequences that stabilize a given target protein conformation. Methods that attempt to solve this problem have proven useful for protein sequence design. Here we show that the same methods can provide valuable information for protein fold recognition and for ab initio protein structure prediction. We present a measure of the compatibility of a test sequence with a target model structure, based on computational protein design. The model structure is used as input to design a family of low free energy sequences, and these sequences are compared with the test sequence by using a metric in sequence space based on nearest-neighbor connectivity. We find that this measure is able to recognize the native fold of a myoglobin sequence among different globin folds. It is also powerful enough to recognize near-native protein structures among non-native models.