Linear Interaction Energy Method Research Articles

Dynamic Profiling and Binding Affinity Prediction of NBTI Antibacterials against DNA Gyrase Enzyme by Multidimensional Machine Learning and Molecular Dynamics Simulations.

Bacterial type II topoisomerases are well-characterized and clinically important targets for antibacterial chemotherapy. Novel bacterial topoisomerase inhibitors (NBTIs) are a newly disclosed class of antibacterials. Prediction of their binding affinity to these enzymes would be beneficial for de novo design/optimization of new NBTIs. Utilizing in vitro NBTI experimental data, we constructed two comprehensive multidimensional DNA gyrase surrogate models for Staphylococcus aureus (q2 = 0.791) and Escherichia coli (q2 = 0.806). Both models accurately predicted the IC50s of 26 NBTIs from our recent studies. To investigate the NBTI's dynamic profile and binding to both targets, 10 selected NBTIs underwent molecular dynamics (MD) simulations. The analysis of MD production trajectories confirmed key hydrogen-bonding and hydrophobic contacts that NBTIs establish in both enzymes. Moreover, the binding free energies of selected NBTIs were computed by the linear interaction energy (LIE) method employing an in-house derived set of fitting parameters (α = 0.16, β = 0.029, γ = 0.0, and intercept = -1.72), which are successfully applicable to DNA gyrase of Gram-positive/Gram-negative pathogens. Both methods offer accurate predictions of the binding free energies of NBTIs against S. aureus and E. coli DNA gyrase. We are confident that this integrated modeling approach could be valuable in the de novo design and optimization of efficient NBTIs for combating resistant bacterial pathogens.

Molecular simulations of doxorubicin complexed with native and modified cyclodextrins in water

In this study, all-atom molecular dynamics simulations have been performed to investigate from a nanoscopic point of view the interactions in supramolecular complexes formed between doxorubicin and cyclodextrins in an aqueous phase at a temperature of 310 K. All the simulations are on the time scales of microseconds and are carried out on High Performance Computing resources provided by the Greek Supercomputer ARIS. Doxorubicin, which is a well-known anticancer drug belonging to the class of anthracyclines, is simulated with two cyclodextrins of broad utilization in drug delivery, i.e.: γ-cyclodextrin and 2-hydroxypropyl-β-cyclodextrin. In both cases, the complexation process takes place spontaneously during the course of the simulations and a detailed analysis of the formed complexes is conducted in terms of structural, dynamical and thermodynamic properties. In addition, we present a quantitative analysis of how doxorubicin affects, upon complexation, the geometry, the hydrogen bonding network and the hydration of the selected cyclodextrins. Special attention is paid to the thermodynamic description of the simulated systems. More specifically, the standard binding free energy is calculated with an extended version of the linear interaction energy method. The presented in silico approach reveals that doxorubicin forms stable complexes with the chosen cyclodextrin molecules, there is a water solubility enhancement of the drug with respect to its unbound state and also cyclodextrins act protectively to the drug preventing undesired effects, such as the glycosidic degradation. Excellent agreement is found between the presented results and published in silico and experimental findings.

Adsorption of Diclofenac and PFBS on a Hair Keratin Dimer.

Environmental pollution by man-made toxic and persistent organic compounds, found throughout the world in surface and groundwater, has various negative effects on aquatic life systems and even humans. Therefore, it is important to develop and improve water treatment technologies capable of removing such substances from wastewater and purifying drinking water. The two substances investigated are the widely used painkiller diclofenac and a member of the class of "forever chemicals", perfluorobutanesulfonate. Both are known to have serious negative effects on living organisms, especially under long-term exposure, and are detectable in human hair, suggesting adsorption to a part of the hair fiber complex. In this study, a human hair keratin dimer is investigated for its ability to absorb diclofenac and perfluorobutanesulfonate. Initial predictions for binding sites are obtained via molecular docking and subjected to molecular dynamics simulations for more than 1 μs. The binding affinities obtained by the linear interaction energy method are high enough to motivate further research on human hair keratins as a sustainable, low-cost, and easily allocatable filtration material.

In SilicoIdentification of Triclosan Derivatives as Potential Inhibitors of MutantMycobacterium tuberculosisInhA

Enoyl acyl carrier protein reductase (InhA) is a crucial enzyme for the biosynthesis of mycolic acids which are major compartments of the Mycobacterium tuberculosis (Mtb) cell wall. Direct inhibition of InhA without activation by drug-NADH adduct has clinical utility to overcome drug resistance. We aimed at the in silico identification of triclosan derivatives with the potential inhibitory effect of S94A-InhA as a clinically important mutant form. Caver Web 1.0 server was used to analyze the ligand transport through access tunnels. Two macrocyclic triclosan derivatives ( 4 and 6) could be identified with more energy-favorable transfer routes toward the enzyme active site. Molecular dynamics (MD) simulations (50 ns) of the best-scored compounds revealed the stability of enzyme structure upon binding to 4 and 6. Compound 4 could better retain enzyme stability upon target binding. Results of intermolecular H-bond analysis indicated that both complexes were mediated through hydrophobic contacts. Declined solvent accessible surface area (SASA) for the apo and bound enzyme states indicated non-destabilizing behavior and no structural relaxation. Electrostatic and van der Waals interactions between triclosan derivatives and their surroundings were used to acquire binding free energies through the linear interaction energy (LIE) method based on MD simulations (Average [Formula: see text], [Formula: see text] kcal/mol and [Formula: see text] kcal/mol). Both of the triclosan derivatives showed relatively stable energy variations and their steady accommodation inside enzyme active site could be confirmed during 50 ns. These results may be implicated in further structure-guided approaches against drug-resistant Mtb.

In silico target specific design of potential quinazoline-based anti-NSCLC agents

Non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancers. In spite of great advances, treatment of the disease is a medical challenge. Epidermal-growth factor receptor (EGFR) has been taken as a promising cell surface target to develop anti-NSCLC therapies. The main bottleneck to attain clinical efficacy with current EGFR tyrosine kinase inhibitors (EGFR-TKIs) is the rapid spread of oncogenic mutations. Numerous efforts have been made for the synthesis of diverse EGFR-TKIs against resistance-conferring mutations. One of the best strategies to design potent agents would be to explore existing anti-NSCLC drugs at the nonclinical development stage and prioritize privileged structural patterns. Within current study, conformational stability of clinically frequent EGFR mutants (G719S, T790M, L858R and a double mutant form L858R/T790M) were validated via DynaMut and missense3D computational servers. Subsequently, structure activity relationship (SAR) and scaffold similarity inquiry were used to rationally propose a few erlotinib analogues. Intended molecules were subjected to molecular docking and top-scored binders were further analyzed through 50-ns all atom molecular dynamics (MD) simulations to infer the dynamic behavior. The aim was to offer potential binders to overwhelm clinically frequent EGFR-TK mutants. The linear interaction energy (LIE) method was applied to compute the binding free energies between EGFR and intended ligands. For this purpose, MD-based conformational sampling of ligand-enzyme complexes and ligand-water associations were used to acquire thermodynamic energy averages. Though mechanistic details are to be explored, results of the current study identify synthetically accessible quinazoline small molecules with potential affinity toward frequent EGFR-TK mutants. Communicated by Ramaswamy H. Sarma

Computational Analysis of Triazole-Based Kojic Acid Analogs as Tyrosinase Inhibitors by Molecular Dynamics and Free Energy Calculations

Molecular docking, molecular dynamics (MD) simulations and the linear interaction energy (LIE) method were used here to predict binding modes and free energy for a set of 1,2,3-triazole-based KA analogs as potent inhibitors of Tyrosinase (TYR), a key metalloenzyme of the melanogenesis process. Initially, molecular docking calculations satisfactorily predicted the binding mode of evaluated KA analogs, where the KA part overlays the crystal conformation of the KA inhibitor into the catalytic site of TYR. The MD simulations were followed by the LIE method, which reproduced the experimental binding free energies for KA analogs with an r2 equal to 0.97, suggesting the robustness of our theoretical model. Moreover, the van der Waals contributions performed by some residues such as Phe197, Pro201, Arg209, Met215 and Val218 are responsible for the binding recognition of 1,2,3-triazole-based KA analogs in TYR catalytic site. Finally, our calculations provide suitable validation of the combination of molecular docking, MD, and LIE approaches as a powerful tool in the structure-based drug design of new and potent TYR inhibitors.

Computational Insights into β-Carboline Inhibition of Monoamine Oxidase A.

Monoamine oxidases (MAOs) are an important group of enzymes involved in the degradation of neurotransmitters and their imbalanced mode of action may lead to the development of various neuropsychiatric or neurodegenerative disorders. In this work, we report the results of an in-depth computational study in which we performed a static and a dynamic analysis of a series of substituted β-carboline natural products, found mainly in roasted coffee and tobacco smoke, that bind to the active site of the MAO-A isoform. By applying molecular docking in conjunction with structure-based pharmacophores and molecular dynamics simulations coupled with dynamic pharmacophores, we extensively investigated the geometric aspects of MAO-A binding. To gain insight into the energetics of binding, we used the linear interaction energy (LIE) method and determined the key anchors that allow productive β-carboline binding to MAO-A. The results presented herein could be applied in the rational structure-based design and optimization of β-carbolines towards preclinical candidates that would target the MAO-A enzyme and would be applicable especially in the treatment of mental disorders such as depression.

Structural bioinformatics analysis of SARS-CoV-2 variants reveals higher hACE2 receptor binding affinity for Omicron B.1.1.529 spike RBD compared to wild type reference

To date, more than 263 million people have been infected with SARS-CoV-2 during the COVID-19 pandemic. In many countries, the global spread occurred in multiple pandemic waves characterized by the emergence of new SARS-CoV-2 variants. Here we report a sequence and structural-bioinformatics analysis to estimate the effects of amino acid substitutions on the affinity of the SARS-CoV-2 spike receptor binding domain (RBD) to the human receptor hACE2. This is done through qualitative electrostatics and hydrophobicity analysis as well as molecular dynamics simulations used to develop a high-precision empirical scoring function (ESF) closely related to the linear interaction energy method and calibrated on a large set of experimental binding energies. For the latest variant of concern (VOC), B.1.1.529 Omicron, our Halo difference point cloud studies reveal the largest impact on the RBD binding interface compared to all other VOC. Moreover, according to our ESF model, Omicron achieves a much higher ACE2 binding affinity than the wild type and, in particular, the highest among all VOCs except Alpha and thus requires special attention and monitoring.

A computationally affordable approach for accurate prediction of the binding affinity of JAK2 inhibitors.

Janus kinase 2 (JAK2) inhibitors are potential anticancer drugs in the treatment of lymphoma, leukemia, thrombocytosis and particularly myeloproliferative diseases.However, the resemblance among JAK family members has challenged the identification of highly selective inhibitors for JAK2 to reduce undesired side effects. As a result, a robust search for promising JAK2 inhibitors using a computational approach that can effectively nominate new potential candidates to be further analyzed through laborious experimental operations has become necessary. In this study, the binding affinities of JAK2 inhibitors were rapidly and precisely estimated using the fast pulling of ligand (FPL) simulations combined with a modified linear interaction energy (LIE) method. The approach correlates with the experimental binding affinities of JAK2 inhibitors with a correlation coefficient of R = 0.82 and a root-mean-square error of 0.67kcal•mol-1. The data reveal that the FPL/LIE method is highly approximate in anticipating the relative binding free energies of known JAK2 inhibitors with an affordable consumption of computational resources, and thus, it is very promising to be applied in in silico screening for new potential JAK2 inhibitors from a large number of molecules available.

Structural Basis of Inhibition of Human Insulin-Regulated Aminopeptidase (IRAP) by Benzopyran-Based Inhibitors.

Inhibition of the insulin-regulated aminopeptidase (IRAP) improves memory and cognition in animal models. The enzyme has recently been crystallized and several series of inhibitors reported. We herein focused on one series of benzopyran-based inhibitors of IRAP known as the HFI series, with unresolved binding mode to IRAP, and developed a robust computational model to explain the structure-activity relationship (SAR) and potentially guide their further optimization. The binding model here proposed places the benzopyran ring in the catalytic binding site, coordinating the Zn2+ ion through the oxygen in position 3, in contrast to previous hypothesis. The whole series of HFI compounds was then systematically simulated, starting from this binding mode, using molecular dynamics and binding affinity estimated with the linear interaction energy (LIE) method. The agreement with experimental affinities supports the binding mode proposed, which was further challenged by rigorous free energy perturbation (FEP) calculations. Here, we found excellent correlation between experimental and calculated binding affinity differences, both between selected compound pairs and also for recently reported experimental data concerning the site directed mutagenesis of residue Phe544. The computationally derived structure-activity relationship of the HFI series and the understanding of the involvement of Phe544 in the binding of this scaffold provide valuable information for further lead optimization of novel IRAP inhibitors.

Efficient In Silico Saturation Mutagenesis of a Member of the Caspase Protease Family.

Rational-design methods have proven to be a valuable toolkit in the field of protein design. Numerical approaches such as free-energy calculations or QM/MM methods are fit to widen the understanding of a protein-sequence space but require large amounts of computational time and power. Here, we apply an efficient method for free-energy calculations that combines the one-step perturbation (OSP) with the third-power-fitting (TPF) approach. It is fit to calculate full free energies of binding from three different end states only. The nonpolar contribution to the free energies are calculated for a set of chosen amino acids from a single simulation of a judiciously chosen reference state. The electrostatic contributions, on the other hand, are predicted from simulations of the neutral and charged end states of the individual amino acids. We used this method to perform in silico saturation mutagenesis of two sites in human Caspase-2. We calculated relative binding free energies toward two different substrates that differ in their P1′ site and in their affinity toward the unmutated protease. Although being approximate, our approach showed very good agreement upon validation against experimental data. 76% of the predicted relative free energies of amino acid mutations was found to be true positives or true negatives. We observed that this method is fit to discriminate amino acid mutations because the rate of false negatives is very low (<1.5%). The approach works better for a substrate with medium/low affinity with a Matthews correlation coefficient (MCC) of 0.63, whereas for a substrate with very low affinity, the MCC was 0.38. In all cases, the combined TPF + OSP approach outperformed the linear interaction energy method.

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.

How Well Does the Extended Linear Interaction Energy Method Perform in Accurate Binding Free Energy Calculations?

With continually increased computer power, molecular mechanics force field-based approaches, such as the endpoint methods of molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) and molecular mechanics generalized Born surface area (MM-GBSA), have been routinely applied in both drug lead identification and optimization. However, the MM-PB/GBSA method is not as accurate as the pathway-based alchemical free energy methods, such as thermodynamic integration (TI) or free energy perturbation (FEP). Although the pathway-based methods are more rigorous in theory, they suffer from slow convergence and computational cost. Moreover, choosing adequate perturbation routes is also crucial for the pathway-based methods. Recently, we proposed a new method, coined extended linear interaction energy (ELIE) method, to overcome some disadvantages of the MM-PB/GBSA method to improve the accuracy of binding free energy calculation. In this work, we have systematically assessed this approach using in total 229 protein-ligand complexes for eight protein targets. Our results showed that ELIE performed much better than the molecular docking and MM-PBSA method in terms of root-mean-square error (RMSE), correlation coefficient (R), predictive index (PI), and Kendall's τ. The mean values of PI, R, and τ are 0.62, 0.58, and 0.44 for ELIE calculations. We also explored the impact of the length of simulation, ranging from 1 to 100 ns, on the performance of binding free energy calculation. In general, extending simulation length up to 25 ns could significantly improve the performance of ELIE, while longer molecular dynamics (MD) simulation does not always perform better than short MD simulation. Considering both the computational efficiency and achieved accuracy, ELIE is adequate in filling the gap between the efficient docking methods and computationally demanding alchemical free energy methods. Therefore, ELIE provides a practical solution for the routine ranking of compounds in lead optimization.

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 200ns 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.

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 ΔGneqJar 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 ΔGLIE 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.

Identification of a C2-symmetric diol based human immunodeficiency virus protease inhibitor targeting Zika virus NS2B-NS3 protease

Zika virus (ZIKV) is an emerging mosquito-borne flavivirus and infection by ZIKV Asian lineage is known to cause fetal brain anomalies and Guillain-Barrés syndrome. The WHO declared ZIKV a global public health emergency in 2016. However, currently neither vaccines nor antiviral prophylaxis/treatments are available. In this study, we report the identification of a C2‐symmetric diol‐based Human immunodeficiency virus type-1 (HIV) protease inhibitor active against ZIKV NS2B-NS3 protease. The compound, referred to as 9b, was identified by in silico screening of a library of 6265 protease inhibitors. Molecular dynamics (MD) simulation studies revealed that compound 9b formed a stable complex with ZIKV protease. Interaction analysis of compound 9b’s binding pose from the cluster analysis of MD simulations trajectories predicted that 9b mostly interacted with ZIKV NS3. Although designed as an aspartyl protease inhibitor, compound 9b was found to inhibit ZIKV serine protease in vitro with IC50 = 143.25 ± 5.45 µM, in line with the in silico results. Additionally, linear interaction energy method (LIE) was used to estimate binding affinities of compounds 9b and 86 (a known panflavivirus peptide hybrid with IC50 = 1.64 ± 0.015 µM against ZIKV protease). The LIE method correctly predicted the binding affinity of compound 86 to be lower than that of 9b, proving to be superior to the molecular docking methods in scoring and ranking compounds. Since most of the reported ZIKV protease inhibitors are positively charged peptide-hybrids, with our without electrophilic warheads, compound 9b represents a less polar and more drug-like non-peptide hit compound useful for further optimization.Communicated by Ramaswamy Sarma

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.

Relative Binding Energies Predict Crystallographic Binding Modes of Ethionamide Booster Lead Compounds.

Transcriptional repressor EthR from Mycobacterium tuberculosis is a valuable target for antibiotic booster drugs. We previously reported a virtual screening campaign to identify EthR inhibitors for development. Two ligand binding orientations were often proposed, though only the top scoring pose was utilized for filtering of the large data set. We obtained biophysically validated hits, some of which yielded complex crystal structures. In some cases, the crystallized binding mode and top scoring mode agree, while for others an alternate ligand binding orientation was found. In this contribution, we combine rigid docking, molecular dynamics simulations, and the linear interaction energy method to calculate binding free energies and derive relative binding energies for a number of EthR inhibitors in both modes. This strategy allowed us to correctly predict the most favorable orientation. Therefore, this widely applicable approach will be suitable to triage multiple binding modes within EthR and other potential drug targets with similar characteristics.

Adequate prediction for inhibitor affinity of Aβ40 protofibril using the linear interaction energy method.

The search for efficient inhibitors targeting Aβ oligomers and fibrils is an important issue in Alzheimer's disease treatment. As a consequence, an accurate and computationally cheap approach to estimate the binding affinity for many ligands interacting with Aβ peptides is very important. Here, the calculated binding free energies of 30 ligands interacting with 12Aβ11–40 peptides using the linear interaction energy (LIE) approach are found to be in good correlation with experimental data (R = 0.79). The binding affinities of these complexes are also calculated by using free energy perturbation (FEP) and molecular mechanic/Poisson–Boltzmann surface area (MM/PBSA) methods. The time-consuming FEP method provides results with similar correlation (R = 0.72), whereas MM/PBSA calculations show very low correlation with experimental data (R = 0.27). In all complexes, van der Waals interactions contribute much more than electrostatic interactions. The LIE model, which is much less time-consuming than both the FEP and MM/PBSA methods, opens the door to accurate and rapid affinity prediction of ligands with Aβ peptides and the design of new ligands.

Binding Free Energies of Conformationally Disordered Peptides Through Extensive Sampling and End-Point Methods.

The ability to obtain binding free energies from molecular simulation techniques provides a valuable support to the interpretation and design of experiments. Among all methods available, the most widely used equilibrium free energy methods range from highly accurate and computationally expensive perturbation theory-based methods, such as free energy perturbation (FEP), or thermodynamic integration (TI), through end-point methods, such as molecular mechanics with generalized Born and surface area solvation (MM/GBSA) or MM/PBSA, when the Poisson-Boltzmann method is used instead of GB, and linear interaction energy (LIE) methods, to scoring functions, which are relatively simple empirical functions widely used as part of molecular docking protocols. Because the use of FEP and TI approaches is restricted to cases where the perturbation leading from an initial to final state is negligible or minimal, their application to cases where large conformational changes are involved between bound and unbound states is rather complex, if not prohibitive in terms of convergence. Here we describe a protocol that involves the use of extensive conformational sampling through molecular dynamics (MD) in combination with end-point methods (MM/GB(PB)SA) with an additional quasi-harmonic entropy component, for the calculation of the relative binding free energies of highly flexible, or intrinsically disordered, peptides to a structured receptor.