Improving the LIE Method for Binding Free Energy Calculations of Protein–Ligand Complexes

Identification of a potent and selective LAPTc inhibitor by RapidFire-Mass Spectrometry, with antichagasic activity

BACKGROUND

Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and leads to ~10,000 deaths each year. Nifurtimox and benznidazole are the only two drugs available but have significant adverse effects and limited efficacy. New chemotherapeutic agents are urgently required. Here we identified inhibitors of the acidic M17 leucyl-aminopeptidase from T. cruzi (LAPTc) that show promise as novel starting points for Chagas disease drug discovery.

METHODOLOGY/PRINCIPAL FINDINGS

A RapidFire-MS screen with a protease-focused compound library identified novel LAPTc inhibitors. Twenty-eight hits were progressed to the dose-response studies, from which 12 molecules inhibited LAPTc with IC50 < 34 μM. Of these, compound 4 was the most potent hit and mode of inhibition studies indicate that compound 4 is a competitive LAPTc inhibitor, with Ki 0.27 μM. Compound 4 is selective with respect to human LAP3, showing a selectivity index of >500. Compound 4 exhibited sub-micromolar activity against intracellular T. cruzi amastigotes, and while the selectivity-window against the host cells was narrow, no toxicity was observed for un-infected HepG2 cells. In silico modelling of the LAPTc-compound 4 interaction is consistent with the competitive mode of inhibition. Molecular dynamics simulations reproduce the experimental binding strength (-8.95 kcal/mol), and indicate a binding mode based mainly on hydrophobic interactions with active site residues without metal cation coordination.

CONCLUSIONS/SIGNIFICANCE

Our data indicates that these new LAPTc inhibitors should be considered for further development as antiparasitic agents for the treatment of Chagas disease.

Development of Masitinib Derivatives with Enhanced Mpro Ligand Efficiency and Reduced Cytotoxicity

Recently, a high-throughput screen of 1900 clinically used drugs identified masitinib, an orally bioavailable tyrosine kinase inhibitor, as a potential treatment for COVID-19. Masitinib acts as a broad-spectrum inhibitor for human coronaviruses, including SARS-CoV-2 and several of its variants. In this work, we rely on atomistic molecular dynamics simulations with advanced sampling methods to develop a deeper understanding of masitinib's mechanism of Mpro inhibition. To improve the inhibitory efficiency and to increase the ligand selectivity for the viral target, we determined the minimal portion of the molecule (fragment) that is responsible for most of the interactions that arise within the masitinib-Mpro complex. We found that masitinib forms highly stable and specific H-bond interactions with Mpro through its pyridine and aminothiazole rings. Importantly, the interaction with His163 is a key anchoring point of the inhibitor, and its perturbation leads to ligand unbinding within nanoseconds. Based on these observations, a small library of rationally designed masitinib derivatives (M1-M5) was proposed. Our results show increased inhibitory efficiency and highly reduced cytotoxicity for the M3 and M4 derivatives compared to masitinib.

Xanthatin and 8-epi-xanthatin as new potential colchicine binding site inhibitors: a computational study

CONTEXT

Phytocompounds xanthatin and 8-epi-xanthatin, obtained from Xanthium chinese Mill, showed antitumoral activity in vitro related to the microtubules destabilizing properties of these phytocompounds. Five binding sites for microtubule destabilizing agents have been characterized on tubulin by high-resolution X-ray crystallography: vinca domain, colchicine, pironetin, maytansine site, and more recently, the seventh site. This work aims to develop a comprehensive computational strategy to understand and eventually predict the interaction between xanthatin and 8-epi-xanthatin with the destabilizing-antimitotic binding domain of the tubulin heterodimer. In addition, we propose a putative binding site for these phytocompounds into the microtubule destabilizing binding sites on the tubulin heterodimer. Xanthanolides showed higher stability in the colchicine and pironetin binding sites, whit a greater affinity for the former. In addition, we found that xanthanolides and non-classical colchicine binding site inhibitors share a high structural similarity.

METHODS

The 3D structures for xanthatin and 8-epi-xanthatin were obtained using DFT with the hybrid functional B3LYP and the base 6-31G (d,p), implemented in Gaussian 09. The 3D coordinates for tubulin proteins were downloaded from PDB. The complexes tubulin-xanthanolides were predicted using a Monte-Carlo iterated search combined with the BFGS gradient-based optimizer implemented in the AutoDock Vina. The xanthanolides-tubulin complexes were energy minimized by molecular dynamics simulations at vacuum, and their stabilities were evaluated by solvated molecular dynamics simulations during 100 ns. All molecular dynamics simulations were performed using the conjugate gradient method implemented in NAMD2 and CHARMM36 forcefield.

Prediction of molecular interactions and physicochemical properties relevant for vasopressin V2 receptor antagonism

We have developed two ligand- and receptor-based computational approaches to study the physicochemical properties relevant to the biological activity of vasopressin V2 receptor (V2R) antagonist and eventually to predict the expected binding mode to V2R. The obtained quantitative structure activity relationship (QSAR) model showed a correlation of the antagonist activity with the hydration energy (E_H2O), the polarizability (P), and the calculated partial charge on atom N7 (q6) of the common substructure. The first two descriptors showed a positive contribution to antagonist activity, while the third one had a negative contribution. V2R was modeled and further relaxed on a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocoline (POPC) membrane by molecular dynamics simulations. The receptor antagonist complexes were guessed by molecular docking, and the stability of the most relevant structures was also evaluated by molecular dynamics simulations. As a result, amino acid residues Q96, W99, F105, K116, F178, A194, F307, and M311 were identified with the probably most relevant antagonist-receptor interactions on the studied complexes. The proposed QSAR model could explain the molecular properties relevant to the antagonist activity. The contributions to the antagonist-receptor interaction appeared also in agreement with the binding mode of the complexes obtained by molecular docking and molecular dynamics. These models will be used in further studies to look for new V2R potential antagonist molecules.

New biochemical insights into the dynamics of water molecules at the GMP or IMP binding site of human GMPR enzyme: A molecular dynamics study

Human GMP reductase (hGMPR) enzyme is involved in a cellular metabolic pathway, converting GMP into IMP, and also it is an important target for anti-leukemic agents. Present computational investigations explain dynamical behavior of water molecules during the conformational transition process from GMP to IMP using molecular dynamics simulations. Residues at substrate-binding site of cancerous protein (PDB Id. 2C6Q) are mostly more dynamic in nature than the normal protein (PDB Id. 2BLE). Nineteen conserved water molecules are identified at the GMP/IMP binding site and are classified as (i) conserved stable dynamic and (ii) infrequent dynamic. Water molecules W11, W14, and W16 are classified as conserved stable dynamic due to their immobile character, whereas remaining water molecules (W1, W2, W3, W4, W5, W7, W8, W9, W10, W12, W13, W15, W17, W18, and W19) are infrequent with dynamic nature. Entrance or displacement of these infrequent water molecules at GMP/IMP sites may occur due to forward and backward movement of reference residues involving ligands. Four water molecules of hGMPR-I and nine water molecules of hGMPR-II are observed in repetitive transitions from GMP to IMP pathway, which indicates discrimination between two isoforms of hGMPRs. Water molecules in cancerous protein are more dynamic and unstable compared to normal protein. These water molecules execute rare dynamical events at GMP binding site and could assist in detailed understanding of conformational transitions that influence the hGMPR's biological functionality. The present study should be of interest to the experimental community engaged in leukemia research and drug discovery for CML cancer.

Unbinding ligands from SARS-CoV-2 Mpro via umbrella sampling simulations

The umbrella sampling (US) simulation is demonstrated to be an efficient approach for determining the unbinding pathway and binding affinity to the SARS-CoV-2 Mpro of small molecule inhibitors. The accuracy of US is in the same range as the linear interaction energy (LIE) and fast pulling of ligand (FPL) methods. In detail, the correlation coefficient between US and experiments does not differ from FPL and is slightly smaller than LIE. The root mean square error of US simulations is smaller than that of LIE. Moreover, US is better than FPL and poorer than LIE in classifying SARS-CoV-2 Mpro inhibitors owing to the reciever operating characteristic-area under the curve analysis. Furthermore, the US simulations also provide detailed insights on unbinding pathways of ligands from the binding cleft of SARS-CoV-2 Mpro. The residues Cys44, Thr45, Ser46, Leu141, Asn142, Gly143, Glu166, Leu167, Pro168, Ala191, Gln192 and Ala193 probably play an important role in the ligand dissociation. Therefore, substitutions at these points may change the mechanism of binding of inhibitors to SARS-CoV-2 Mpro.

Tailored Parameterization of the LIE Method for Calculating the Binding Free Energy of Vps34-Inhibitor Complexes

Vps34 is the only isoform of the PI3K family in fungi, making this protein an attractive target to develop new treatments against pathogenic fungi. The high structural similarity between the active sites of the human and fungal Vps34 makes repurposing of human Vps34 inhibitors an appealing strategy. Nonetheless, while some of the cross-reactive inhibitors might have the potential to treat fungal infections, a safer approach to prevent undesired side effects would be to identify molecules that specifically inhibit the fungal Vps34. This study presents the parameterization of four LIE models for estimating the binding free energy of Vps34-inhibitor complexes. Two models are parameterized using a multiparametric linear regression leaving one or more free parameters, while the other two are based on the LIE-D model. All of the models show good predictive capacity (R ² > 0.7, r > 0.85) and a low mean absolute error (MAE < 0.71 kcal/mol). The current study highlights the advantages of LIE-D-derived models when predicting the weight of the different contributions to the binding free energy. It is expected that this study will provide researchers with a valuable tool to identify new Vps34 inhibitors for relevant applications such as cancer treatment and the development of new antimicrobial agents.

Fighting Against Bacterial Lipopolysaccharide-Caused Infections through Molecular Dynamics Simulations: A Review

Lipopolysaccharide (LPS) is the primary component of the outer leaflet of Gram-negative bacterial outer membranes. LPS elicits an overwhelming immune response during infection, which can lead to life-threatening sepsis or septic shock for which no suitable treatment is available so far. As a result of the worldwide expanding multidrug-resistant bacteria, the occurrence and frequency of sepsis are expected to increase; thus, there is an urge to develop novel strategies for treating bacterial infections. In this regard, gaining an in-depth understanding about the ability of LPS to both stimulate the host immune system and interact with several molecules is crucial for fighting against LPS-caused infections and allowing for the rational design of novel antisepsis drugs, vaccines and LPS sequestration and detection methods. Molecular dynamics (MD) simulations, which are understood as being a computational microscope, have proven to be of significant value to understand LPS-related phenomena, driving and optimizing experimental research studies. In this work, a comprehensive review on the methods that can be combined with MD simulations, recently applied in LPS research, is provided. We focus especially on both enhanced sampling methods, which enable the exploration of more complex systems and access to larger time scales, and free energy calculation approaches. Thereby, apart from outlining several strategies for surmounting LPS-caused infections, this work reports the current state-of-the-art of the methods applied with MD simulations for moving a step forward in the development of such strategies.

Investigation of structural analogs of hydroxychloroquine for SARS-CoV-2 main protease (Mpro): A computational drug discovery study

The main protease (Mpro) is the key enzyme of nCOVID-19 and plays a decisive role that makes it an attractive drug target. Multiple analysis of crystal structures reveals the presence of W1, W2, and W3 water locations in the active site pocket of Mpro; W1 and W2 are unstable and are weakly bonded with protein in comparison to W3 of Mpro-native. So, we adopt the water displacement method to occupy W1 or W2 sites by triggering HCQ or its analogs to inactivate the enzyme. Virtual screening is employed to find out best analogs of HCQ, molecular docking is used for water displacement from catalytic region of Mpro, and finally, MD simulations are conducted for validation of these findings. The docking study reveals that W1 and W2 are occupied by respective atoms of ZINC28706440 whereas W2 by HCQ and indacaterol. Finally, MD results demonstrate (i) HCQ occupies W1 and W2 positions, but its analogs (indacaterol and ZINC28706440) are inadequate to retain either W1 or W2 (ii) His41 and Asp187 are stabilized by W3 in Mpro-native and His41, Cys145 and HCQ by W7 in ZINC28706440, and W4, W5, and W6 make water mediated bridge between indacaterol with His41. The structural, dynamical, and thermodynamic (WFP and J value) profiling parameters suggest that W3, W4, and W7 are prominent in their corresponding positions in comparison with W5 and W6. The final results conclude that ZINC28706440 may act as a best analog of HCQ with acceptable physico-chemical and toxicological scores and may further be synthesized for experimental validation.

Computing Cellulase Kinetics with a Two-Domain Linear Interaction Energy Approach

While heterogeneous enzyme reactions play an essential role in both nature and green industries, computational predictions of their catalytic properties remain scarce. Recent experimental work demonstrated the applicability of the Sabatier principle for heterogeneous biocatalysis. This provides a simple relationship between binding strength and the catalytic rate and potentially opens a new way for inexpensive computational determination of kinetic parameters. However, broader implementation of this approach will require fast and reliable prediction of binding free energies of complex two-phase systems, and computational procedures for this are still elusive. Here, we propose a new framework for the assessment of the binding strengths of multidomain proteins, in general, and interfacial enzymes, in particular, based on an extended linear interaction energy (LIE) method. This two-domain LIE (2D-LIE) approach was successfully applied to predict binding and activation free energies of a diverse set of cellulases and resulted in robust models with high accuracy. Overall, our method provides a fast computational screening tool for cellulases that have not been experimentally characterized, and we posit that it may also be applicable to other heterogeneously acting biocatalysts.

Estimating the ligand-binding affinity via λ-dependent umbrella sampling simulations

The umbrella sampling (US) approach has been demonstrated to be a very efficient method for estimating the ligand-binding affinity. However, most of the calculated values overestimate experimental ones that are probably caused by the inaccurate representation of the interaction between the ligand and the surrounding molecules. The issue can be resolved via the implementation aspects of λ-alteration simulation into the US approach, which we call the λ-dependent umbrella sampling (λUS) scheme. In particular, the electrostatic and van der Waals interactions were simultaneously changed by using the coupling parameter λ during λUS simulations. The mean value of obtained results, ∆ G US λ = 0.20 = - 11.59 ± 1.51 kcal mol^-1 , is in good fitting to the mean value of respective experiments, ∆G_EXP =  - 11.26 ± 0.89 kcal mol^-1 . Moreover, the correlation between the proposed approach and experiment is quite good with a value of R US λ = 0.20 = 0.82 ± 0.10 . The λUS scheme significantly enhances the calculated accuracy since the RMSE of the proposed scheme is smaller than traditional US simulations, RMSE US λ = 0.20 = 2.99 ± 0.82 kcal mol^-1 versus RMSE US λ = 0.00 = 5.48 ± 0.81 kcal mol^-1 . Furthermore, the precision is increased since the computed error via λUS approach, δ US λ = 0.20 = 1.51 kcal mol^-1 , was smaller than those of the US simulation, δ US λ = 0.00 = 1.78 kcal mol^-1 . Overall, the proposed approach perhaps provides an efficient way to accurately and precisely estimate the ligand-binding free energy.

A computational strategy to understand structure-activity relationship of 1,3-disubstituted imidazole [1,5-α] pyrazine derivatives described as ATP competitive inhibitors of the IGF-1 receptor related to Ewing sarcoma

We followed a comprehensive computational strategy to understand and eventually predict the structure-activity relationship of thirty-three 1,3-disubstituted imidazole [1,5-α] pyrazine derivatives described as ATP competitive inhibitors of the IGF-1 receptor related to Ewing sarcoma. The quantitative structure-activity relationship model showed that the inhibitory potency is correlated with the molar volume, a steric descriptor and the net charge calculated value on atom C1 (q1) and N4 (q4) of the pharmacophore, all of them appearing to give a positive contribution to the inhibitory activity. According to experimental and calculated values, the most potent compound would be 3-[4-(azetidin-2-ylmethyl) cyclohexyl]-1-[3-(benzyloxy) phenyl] imidazo [1,5-α]pyrazin-8-amine (compound 23). Docking was used to guess important residues involved in the ATP-competitive inhibitory activity. It was validated by 200 ns of molecular dynamics (MD) simulation using improved linear interaction energy (LIE) method. MD of previously preferred structures by docking shows that the most potent ligand could establish hydrogen bonds with the ATP-binding site of the receptor, and the Ser979 and Ser1059 residues contribute favourably to the binding stability of compound 23. MD simulation also gave arguments about the chemical structure of the compound 23 being able to fit in the ATP-binding pocket, expecting to remain stable into it during the entire simulation and allowing us to hint the significant contribution expected to be given by electrostatic and hydrophobic interactions to the ligand-receptor complex stability. This computational combined strategy here described could represent a useful and effective prime approach to guide the identification of tyrosine kinase inhibitors as new lead compounds.

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.

How Good is Jarzynski's Equality for Computer-Aided Drug Design?

Accurate determination of the binding affinity of the ligand to the receptor remains a difficult problem in computer-aided drug design. Here, we study and compare the efficiency of Jarzynski’s equality (JE) combined with steered molecular dynamics and the linear interaction energy (LIE) method by assessing the binding affinity of 23 small compounds to six receptors, including β-lactamase, thrombin, factor Xa, HIV-1 protease (HIV), myeloid cell leukemia-1, and cyclin-dependent kinase 2 proteins. It was shown that Jarzynski’s nonequilibrium binding free energy ΔG_neq^Jar correlates with the available experimental data with the correlation levels R = 0.89, 0.86, 0.83, 0.80, 0.83, and 0.81 for six data sets, while for the binding free energy ΔG_LIE obtained by the LIE method, we have R = 0.73, 0.80, 0.42, 0.23, 0.85, and 0.01. Therefore, JE is recommended to be used for ranking binding affinities as it provides accurate and robust results. In contrast, LIE is not as reliable as JE, and it should be used with caution, especially when it comes to new systems.

Understanding the disrupting mechanism of the Tau aggregation motif "306 VQIVYK311 " by phenylthiazolyl-hydrazides inhibitors

Alzheimer's disease is a progressive neurodegenerative disorder characterized by the abnormal processing of the Tau and the amyloid precursor proteins. The unusual aggregation of Tau is based on the formation of intermolecular β-sheets through two motifs: ²⁷⁵ VQIINK²⁸⁰ and ³⁰⁶ VQIVYK³¹¹ . Phenylthiazolyl-hydrazides (PTHs) are capable of inhibiting/disassembling Tau aggregates. However, the disaggregation mechanism of Tau oligomers by PTHs is still unknown. In this work, we studied the disruption of the oligomeric form of the Tau motif ³⁰⁶ VQIVYK³¹¹ by PTHs through molecular docking, molecular dynamics, and free energy calculations. We predicted hydrophobic interactions as the major driving forces for the stabilization of Tau oligomer, with V306 and I308 being the major contributors. Nonpolar component of the binding free energy is essential to stabilize Tau-PTH complexes. PTHs disrupted mainly the van der Waals interactions between the monomers, leading to oligomer destabilization. Destabilization of full Tau filament by PTHs and emodin was not observed in the sampled 20 ns; however, in all cases, the nonpolar component of the binding free energy is essential for the formation of Tau filament-PTH and Tau filament-emodin. These results provide useful clues for the design of more effective Tau-aggregation inhibitors.

Identification of a Novel Multifunctional Ligand for Simultaneous Inhibition of Amyloid-Beta (Aβ42) and Chelation of Zinc Metal Ion

Zinc binding to β-amyloid structure could promote amyloid-β aggregation, as well as reactive oxygen species (ROS) production, as suggested in many experimental and theoretical studies. Therefore, the introduction of multifunctional drugs capable of chelating zinc metal ion and inhibiting Aβ aggregation is a promising strategy in the development of AD treatment. The present study has evaluated the efficacy of a new bifunctional peptide drug using molecular docking and molecular dynamics (MD) simulations. This drug comprises two different domains, an inhibitor domain, obtained from the C-terminal hydrophobic region of Aβ, and a Zn²⁺ chelating domain, derived from rapeseed meal, merge with a linker. The multifunctionality of the ligand was evaluated using a comprehensive set of MD simulations spanning up to 3.2 μs including Aβ relaxation, ligand-Zn²⁺ bilateral interaction, and, more importantly, ligand-Zn²⁺-Aβ₄₂ trilateral interactions. Analysis of the results strongly indicated that the bifunctional ligand can chelate zinc metal ion and avoid Aβ aggregation simultaneously. The present study illustrated that the proposed ligand has considerable hydrophobic interactions and hydrogen bonding with monomeric Aβ in the presence of zinc metal ion. Therefore, in light of these considerable interactions and contacts, the α-helical structure of Aβ has been enhanced, while the β-sheet formation is prevented and the α-helix native structure is protected. Furthermore, the analysis of interactions between Aβ and ligand-zinc complex revealed that the zinc metal ion is coordinated to Met13, the ending residue of the ligand and merely one residue in Aβ. The results have proven the previous experimental and theoretical findings in the literature about Aβ interactions with zinc metal ion and also Aβ interactions with the first domain of the proposed ligand. Moreover, the current research has evaluated the chelation using MD simulation and linear interaction energy (LIE) methods, and the result has been satisfactorily verified with previous experimental and theoretical (DFT) studies.

Exploring Amyloid-β Dimer Structure Using Molecular Dynamics Simulations

A major hallmark of Alzheimer's disease (AD) is the aggregation of amyloid-β peptides in the brains of people afflicted by the disease. The exact pathway to this catastrophic event is unknown. In this work, a total of 9.5 μs molecular dynamics simulations have been performed to investigate the structure and dynamics of the smallest form of toxic Aβ oligomers, i.e., the Aβ dimers. This study suggests that specific hydrophobic regions are vital in the aggregation process. Different possible structures for Aβ dimers are reported along with their relative binding affinity. These data may be used to design better Aβ-aggregation inhibitors. The diversity of the dimer structures suggests several aggregation pathways.

Discovery of potent and selective inhibitors of the Escherichia coli M1-aminopeptidase via multicomponent solid-phase synthesis of tetrazole-peptidomimetics

The Escherichia coli neutral M1-aminopeptidase (ePepN) is a novel target identified for the development of antimicrobials. Here we describe a solid-phase multicomponent approach which enabled the discovery of potent ePepN inhibitors. The on-resin protocol, developed in the frame of the Distributed Drug Discovery (D3) program, comprises the implementation of parallel Ugi-azide four-component reactions with resin-bound amino acids, thus leading to the rapid preparation of a focused library of tetrazole-peptidomimetics (TPMs) suitable for biological screening. By dose-response studies, three compounds were identified as potent and selective ePepN inhibitors, as little inhibitory effect was exhibited for the porcine ortholog aminopeptidase. The study allowed for the identification of the key structural features required for a high ePepN inhibitory activity. The most potent and selective inhibitor (TPM 11) showed a non-competitive inhibition profile of ePepN. We predicted that both diastereomers of compound TPM 11 bind to a site distinct from that occupied by the substrate. Theoretical models suggested that TPM 11 has an alternative inhibition mechanism that doesn't involve Zn coordination. On the other hand, the activity landscape analysis provided a rationale for our findings. Of note, compound TMP 2 showed in vitro antibacterial activity against Escherichia coli. Furthermore, none of the three identified inhibitors is a potent haemolytic agent, and only two compounds showed moderate cytotoxic activity toward the murine myeloma P3X63Ag cells. These results point to promising compounds for the future development of rationally designed TPMs as antibacterial agents.

d-Amino Acid Pseudopeptides as Potential Amyloid-Beta Aggregation Inhibitors

A causative factor for neurotoxicity associated with Alzheimer's disease is the aggregation of the amyloid-β (Aβ) peptide into soluble oligomers. Two all d-amino acid pseudo-peptides, SGB1 and SGD1, were designed to stop the aggregation. Molecular dynamics (MD) simulations have been carried out to study the interaction of the pseudo-peptides with both Aβ13⁻23 (the core recognition site of Aβ) and full-length Aβ1⁻42. Umbrella sampling MD calculations have been used to estimate the free energy of binding, ∆G, of these peptides to Aβ13⁻23. The highest ∆Gbinding is found for SGB1. Each of the pseudo-peptides was also docked to Aβ1⁻42 and subjected up to seven microseconds of all atom molecular dynamics simulations. The resulting structures lend insight into how the dynamics of Aβ1⁻42 are altered by complexation with the pseudo-peptides and confirmed that SGB1 may be a better candidate for developing into a drug to prevent Alzheimer's disease.

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol-1), with good cross-validation statistics.

Protein surface roughness accounts for binding free energy of Plasmepsin II-ligand complexes

The calculation of absolute binding affinities for protein-inhibitor complexes remains as one of the main challenges in computational structure-based ligand design. The present work explored the calculations of surface fractal dimension (as a measure of surface roughness) and the relationship with experimental binding free energies of Plasmepsin II complexes. Plasmepsin II is an attractive target for novel therapeutic compounds to treat malaria. However, the structural flexibility of this enzyme is a drawback when searching for specific inhibitors. Concerning that, we performed separate explicitly solvated molecular dynamics simulations using the available high-resolution crystal structures of different Plasmepsin II complexes. Molecular dynamics simulations allowed a better approximation to systems dynamics and, therefore, a more reliable estimation of surface roughness. This constitutes a novel approximation in order to obtain more realistic values of fractal dimension, because previous works considered only x-ray structures. Binding site fractal dimension was calculated considering the ensemble of structures generated at different simulation times. A linear relationship between binding site fractal dimension and experimental binding free energies of the complexes was observed within 20 ns. Previous studies of the subject did not uncover this relationship. Regression model, coined FD model, was built to estimate binding free energies from binding site fractal dimension values. Leave-one-out cross-validation showed that our model reproduced accurately the absolute binding free energies for our training set (R²  = 0.76; <|error|> =0.55 kcal/mol; SD_error  = 0.19 kcal/mol). The fact that such a simple model may be applied raises some questions that are addressed in the article.

Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration

We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.

T. Vo A, Tristan A, Carreño L, Quijada M, Nguyen VT, Perry G, Larionov OV, Lleonart R, Fernández PL. Identification of Inhibitors of CD36-Amyloid Beta Binding as Potential Agents for Alzheimer's Disease

Neuroinflammation is one of the hallmarks of Alzheimer's disease pathology. Amyloid β has a central role in microglia activation and the subsequent secretion of inflammatory mediators that are associated with neuronal toxicity. The recognition of amyloid β by microglia depends on the expression of several receptors implicated in the clearance of amyloid and in cell activation. CD36 receptor expressed on microglia interacts with fibrils of amyloid inducing the release of proinflammatory cytokines and amyloid internalization. The interruption of the interaction CD36-amyloid β compromises the activation of microglia cells. We have developed and validated a new colorimetric assay to identify potential inhibitors of the binding of amyloid β to CD36. We have found seven molecules, structural analogues of the Trichodermamide family of natural products that interfere with the interaction CD36-amyloid β. By combining molecular docking and dynamics simulations, we suggested the second fatty acids binding site within the large luminal hydrophobic tunnel, present in the extracellular domain of CD36, as the binding pocket of these compounds. Free energy calculations predicted the nonpolar component as the driving force for the binding of these inhibitors. These molecules also inhibited the production of TNF-α, IL-6, and IL-1β by peritoneal macrophages stimulated with fibrils of amyloid β. This work serves as a platform for the identification of new potential anti-inflammatory agents for the treatment of Alzheimer's disease.

Improved QM/MM Linear-Interaction Energy Model for Substrate Recognition in Zinc-Containing Metalloenzymes

One of the essential challenges in the description of receptor-drug interactions in the presence of various polyvalent cations (such as zinc, magnesium, or iron) is the accurate assessment of the electronic effects due to cofactor binding. The effects can range from partial electronic polarization of the proximal atoms in a receptor and bound substrate to long-range effects related to partial charge transfer and electronic delocalization effects between the cofactor and the drug. Here, we examine the role of the explicit account for electronic effects for a panel of small-molecule inhibitors binding to the zinc-aminopeptidase PfA-M1, an essential target for antimalarial drug development. Our study on PfA-M1:inhibitor interactions at the QM level reveals that the partial charge and proton transfer due to bound zinc ion are important mechanisms in the inhibitors' recognition and catalysis. The combination of classical MD simulations with a posteriori QM/MM corrections with novel DFTB parameters for the zinc cation and the linear-interaction energy (LIE) approach offers by far the most accurate estimates for the PfA-M1:inhibitor binding affinities, opening the door for future inhibitor design.

Structural basis of H2A.Z recognition by SRCAP chromatin-remodeling subunit YL1

Histone variant H2A.Z, a universal mark of dynamic nucleosomes flanking gene promoters and enhancers, is incorporated into chromatin by SRCAP (SWR1), an ATP-dependent, multicomponent chromatin-remodeling complex. The YL1 (Swc2) subunit of SRCAP (SWR1) plays an essential role in H2A.Z recognition, but how it achieves this has been unclear. Here, we report the crystal structure of the H2A.Z-binding domain of Drosophila melanogaster YL1 (dYL1-Z) in complex with an H2A.Z-H2B dimer at 1.9-Å resolution. The dYL1-Z domain adopts a new whip-like structure that wraps over H2A.Z-H2B, and preferential recognition is largely conferred by three residues in loop 2, the hyperacidic patch and the extended αC helix of H2A.Z. Importantly, this domain is essential for deposition of budding yeast H2A.Z in vivo and SRCAP (SWR1)-catalyzed histone H2A.Z replacement in vitro. Our studies distinguish YL1-Z from known H2A.Z chaperones and suggest a hierarchical mechanism based on increasing binding affinity facilitating H2A.Z transfer from SRCAP (SWR1) to the nucleosome.