New Linear Interaction Method for Binding Affinity Calculations Using a Continuum Solvent Model

Interaction of SARS-CoV-2 with host cells and antibodies: experiment and simulation

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the devastating global COVID-19 pandemic announced by WHO in March 2020. Through unprecedented scientific effort, several vaccines, drugs and antibodies have been developed, saving millions of lives, but the fight against COVID-19 continues as immune escape variants of concern such as Delta and Omicron emerge. To develop more effective treatments and to elucidate the side effects caused by vaccines and therapeutic agents, a deeper understanding of the molecular interactions of SARS-CoV-2 with them and human cells is required. With special interest in computational approaches, we will focus on the structure of SARS-CoV-2 and the interaction of its spike protein with human angiotensin-converting enzyme-2 (ACE2) as a prime entry point of the virus into host cells. In addition, other possible viral receptors will be considered. The fusion of viral and human membranes and the interaction of the spike protein with antibodies and nanobodies will be discussed, as well as the effect of SARS-CoV-2 on protein synthesis in host cells.

Development of 9-(N-arylmethylamino) congeners of noscapine: the microtubule targeting drugs for the management of breast cancer

Noscapine is a natural lead molecule with anticancer activity at a higher concentrations. So, there is an urge for the development of more potent derivatives of noscapine. In this study, we have approached for development of 9-N-arylmethylamino derivatives of noscapine that kills cancer cells without affecting the normal cells. They were designed by substituting N-aryl methyl pharmacophore at the C-9 position and screened out top-ranked three derivatives 13a-c using molecular docking. Further, their theoretical free energy of binding with tubulin was calculated followed by chemical synthesis and experimental validation. In vitro antiproliferative activity of noscapine and its 9-N-arylmethylamino derivatives (13a-c) was carried out using MCF-7 (a triple receptors positive) and MDA-MB-231 (a triple receptor negative) breast cancer cell lines. Further, cytotoxicity to normal cells was examined using human embryonic kidney cells (HEK cells). Inhibition to cell cycle progression and induction of apoptosis was monitored using FACS. The binding of noscapine and 13a-c with tubulin was examined using fluorescence quenching assay. The 9-N-arylmethylamino derivatives of noscapine (13a-c) were found to inhibit the proliferation of cancer cells at a much lower concentration (IC50 values range between 9.1 to 47.3 µM) compared to noscapine (IC50 value is 45.8-59.3 µM). Surprisingly, the proliferation of HEK cells was not inhibited even at a concentration of 100 µM (cytotoxicity is < 5%). These derivatives induced apoptosis by arresting cells at G2/M-phase and also bind to tubulin. The 9-(N-arylmethylamino) noscapinoids have the potential to be a novel therapeutic agent for the treatment of breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03445-3.

Rational design of novel N-alkyl amine analogues of noscapine, their chemical synthesis and cellular activity as potent anticancer agents

The scaffold structure of noscapine (an antitussive plant alkaloid) was modified by inducting N-aryl methyl pharmacophore at C-9 position of the isoquinoline ring to rationally design and screened three novel 9-(N-arylmethylamino) noscapinoids, 15-17 with robust binding affinity with tubulin. The selected 9-(N-arylmethylamino) noscapinoids revealed improved predicted binding energy of -6.694 kcal/mol for 15, -7.118 kcal/mol for 16 and -7.732 kcal/mol for 17, respectively in comparison to the lead molecule (-5.135 kcal/mol). These novel derivatives were chemically synthesized and validated their anticancer activity based on cellular studies using two human breast adenocarcinoma, MCF-7 and MDA-MB-231, as well as with a panel of primary breast tumor cells. These derivatives inhibited cellular proliferation in all the cancer cells that ranged between 3.2 and 32.2 μM, which is 11.9 to 1.8 fold lower than that of noscapine. These novel derivatives effectively arrest the cell cycle in the G2/M phase followed by apoptosis and appearance of apoptotic cells. Thus, we conclude that 9-(N-arylmethyl amino) noscapinoids, 15-17 have a high probability to be a novel therapeutic agent for breast cancers.

Rational design, chemical synthesis and cellular evaluation of novel 1,3-diynyl derivatives of noscapine as potent tubulin binding anticancer agents

We present a new class of derivatives of noscapine, 1,3-diynyl-noscapinoids of an antitussive plant alkaloid, noscapine based on our in silico efforts that binds tubulin and displays anticancer activity against a panel of breast cancer cells. Structure-activity analyses pointed the C-9 position of the isoquinoline ring which was modified by coupling of 1,3-diynyl structural motifs to rationally design and screened a series of novel 1,3-diynyl-noscapinoids (20-22) with robust binding affinity with tubulin. The selected 1,3-diynyl-noscapinoids, 20-22 revealed improved predicted binding energy of -6.568 kcal/mol for 20, -7.367 kcal/mol for 21 and -7.922 kcal/mol for 22, respectively in comparison to the lead molecule (-5.246 kcal/mol). These novel derivatives were chemically synthesized and validated their anticancer activity based on cellular studies using two human breast adenocarcinoma, MCF-7 and MDAMB-231, as well as with a panel of primary breast cancer cells isolated from patients. Interestingly, all these derivatives inhibited cellular proliferation in all the cancer cells that ranged between 6.2 to 38.9 μM, which is 6.7 to 1.5 fold lower than that of noscapine. Unlike previously reported derivatives of noscapine that arrests cells in the S-phase, these novel derivatives effectively inhibit proliferation of cancer cells, arrests cell cycle in the G2/M-phase followed by apoptosis and appearance of apoptotic cells. Thus, we conclude that 1,3-diynyl-noscapinoids have great potential to be a novel therapeutic agent for breast cancers.

9-Arylimino noscapinoids as potent tubulin binding anticancer agent: chemical synthesis and cellular evaluation against breast tumour cells

A library of 9-arylimino derivatives of noscapine was developed by coupling of Schiff base containing imine groups. Virtual screening using molecular docking with tubulin revealed three molecules, 12-14 that bind with high affinity. An improved predicted free energy of binding (FEB) of -5.390, -6.506 and -6.679 kcal/mol for the molecules 12-14 was found compared to noscapine (-5.135 kcal/mol). Furthermore, molecular dynamics simulation in combination with Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) revealed robust binding free energy of -166.03, -169.75 and -170.63 kcal/mol for the molecules 12, 13 and 14, respectively. These derivatives were strategically synthesized and experimentally validated for their anticancer activity. Tubulin binding assay revealed substantial binding of molecules 12-14 with purified tubulin. Further, their anticancer activity was demonstrated using two cancer cell lines (MCF-7 and MDAMB-231) and a panel of primary breast tumour cells. All these derivatives inhibited cellular proliferation in all the cancer cells that ranged between 30.1 and 5.8 µM, which is 1.7 to 7.52 fold lower than that of noscapine. Further, these novel derivatives arrest cell cycle in the G2/M-phase followed by induction of apoptosis. Thus, 9-arylimino noscapinoids 12-14 have a great potential to be a novel therapeutic agent for breast cancers.

Computational Graphics Software for Interactive Docking and Visualization of Ligand-Protein Complementarity

The Dockeye software is designed to complement automated docking protocols by allowing the user's chemical know-how and experience of what makes for good protein-ligand binding, knowledge that is not easily encoded into automated algorithms, to guide the docking. It allows the interactive manipulation of the ligand placement against a protein target. Real-time intuitively comprehensible feedback about the location, spatial density, and the extent of both favorable and unfavorable atomic interactions between ligand and protein is provided through a carefully designed graphical object. It is also a tool for the graphical analysis of the interactions of known protein-ligand complexes. Comparative docking of 58 protein-ligand complexes with Dockeye and Autodock Vina shows how this software can be used synergistically with automated docking programs to significantly improve the task of discovery of ligand placement.

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 fast and high-quality charge model for the next generation general AMBER force field

The General AMBER Force Field (GAFF) has been broadly used by researchers all over the world to perform in silico simulations and modelings on diverse scientific topics, especially in the field of computer-aided drug design whose primary task is to accurately predict the affinity and selectivity of receptor-ligand binding. The atomic partial charges in GAFF and the second generation of GAFF (GAFF2) were originally developed with the quantum mechanics derived restrained electrostatic potential charge, but in practice, users usually adopt an efficient charge method, Austin Model 1-bond charge corrections (AM1-BCC), based on which, without expensive ab initio calculations, the atomic charges could be efficiently and conveniently obtained with the ANTECHAMBER module implemented in the AMBER software package. In this work, we developed a new set of BCC parameters specifically for GAFF2 using 442 neutral organic solutes covering diverse functional groups in aqueous solution. Compared to the original BCC parameter set, the new parameter set significantly reduced the mean unsigned error (MUE) of hydration free energies from 1.03 kcal/mol to 0.37 kcal/mol. More excitingly, this new AM1-BCC model also showed excellent performance in the solvation free energy (SFE) calculation on diverse solutes in various organic solvents across a range of different dielectric constants. In this large-scale test with totally 895 neutral organic solvent-solute systems, the new parameter set led to accurate SFE predictions with the MUE and the root-mean-square-error of 0.51 kcal/mol and 0.65 kcal/mol, respectively. This newly developed charge model, ABCG2, paved a promising path for the next generation GAFF development.

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.

Auto In Silico Ligand Directing Evolution to Facilitate the Rapid and Efficient Discovery of Drug Lead

Motivated by the growing demand for reducing the chemical optimization burden of H2L, we developed auto in silico ligand directing evolution (AILDE, http://chemyang.ccnu.edu.cn/ccb/server/AILDE), an efficient and general approach for the rapid identification of drug leads in accessible chemical space. This computational strategy relies on minor chemical modifications on the scaffold of a hit compound, and it is primarily intended for identifying new lead compounds with minimal losses or, in some cases, even increases in ligand efficiency. We also described how AILDE greatly reduces the chemical optimization burden in the design of mesenchymal-epithelial transition factor (c-Met) kinase inhibitors. We only synthesized eight compounds and found highly efficient compound 5g, which showed an ∼1,000-fold improvement in in vitro activity compared with the hit compound. 5g also displayed excellent in vivo antitumor efficacy as a drug lead. We believe that AILDE may be applied to a large number of studies for rapid design and identification of drug leads.

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AILDE was developed for the rapid identification of drug leads

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A potent drug lead targeted to c-Met was found by synthesizing only eight compounds

Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF

Accurate prediction of the absolute or relative protein-ligand binding affinity is one of the major tasks in computer-aided drug design projects, especially in the stage of lead optimization. In principle, the alchemical free energy (AFE) methods such as thermodynamic integration (TI) or free-energy perturbation (FEP) can fulfill this task, but in practice, a lot of hurdles prevent them from being routinely applied in daily drug design projects, such as the demanding computing resources, slow computing processes, unavailable or inaccurate force field parameters, and difficult and unfriendly setting up and post-analysis procedures. In this study, we have exploited practical protocols of applying the CPU (central processing unit)-TI and newly developed GPU (graphic processing unit)-TI modules and other tools in the AMBER software package, combined with ff14SB/GAFF1.8 force fields, to conduct efficient and accurate AFE calculations on protein-ligand binding free energies. We have tested 134 protein-ligand complexes in total for four target proteins (BACE, CDK2, MCL1, and PTP1B) and obtained overall comparable performance with the commercial Schrodinger FEP+ program (WangJ. Am. Chem. Soc.2015, 137, 2695-2703). The achieved accuracy fits within the requirements for computations to generate effective guidance for experimental work in drug lead optimization, and the needed wall time is short enough for practical application. Our verified protocol provides a practical solution for routine AFE calculations in real drug design projects.

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.

A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain

PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or “PB/LIE” free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α₂ helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo. The overall performance of the model should allow its use in the design of new PDZ ligands in the future.

Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II

Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrödinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q(2)=0.78-0.86 when cross-validation procedures were carried out; and R(2)=0.81-0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8-1.1kcalmol(-1). Also, 76-80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments.

A comparative study of binding affinities for 6,7-dimethoxy-4-pyrrolidylquinazolines as phosphodiesterase 10A inhibitors using the linear interaction energy method

The linear interaction energy (LIE) method was used to estimate the free energies of binding for a set of 27 pyrrolidylquinazoline derivatives as phosphodiesterase 10A inhibitors. Twenty-six X-ray crystal structures of phosphodiesterase 10A and two sampling methods, minimization and Hybrid Monte Carlo, were used to assess the affinity models based on the linear interaction energies. The best model was obtained based on the parameters α=0.16 and β=0.04, which represent non-polar and polar interactions, respectively, with a root mean square error (RMSE) of 0.42kcal/mol (R(2)=0.71) and 0.52kcal/mol (R(2)=0.86) for the training and test sets, respectively. In addition, the applicability domain of the model was investigated. After validation of the models, the best model was subsequently used in a virtual screening process, which resulted in a set of optimized compounds. The models developed in this study could be useful as filter for virtual screening and lead optimization processes for phosphodiesterase 10A drug developments.

Rational design of biaryl pharmacophore inserted noscapine derivatives as potent tubulin binding anticancer agents

We have strategically designed a series of noscapine derivatives by inserting biaryl pharmacophore (a major structural constituent of many of the microtubule-targeting natural anticancer compounds) onto the scaffold structure of noscapine. Molecular interaction of these derivatives with α,β-tubulin heterodimer was investigated by molecular docking, molecular dynamics simulation, and binding free energy calculation. The predictive binding affinity indicates that the newly designed noscapinoids bind to tubulin with a greater affinity. The predictive binding free energy (ΔG(bind, pred)) of these derivatives (ranging from -5.568 to -5.970 kcal/mol) based on linear interaction energy (LIE) method with a surface generalized Born (SGB) continuum solvation model showed improved binding affinity with tubulin compared to the lead compound, natural α-noscapine (-5.505 kcal/mol). Guided by the computational findings, these new biaryl type α-noscapine congeners were synthesized from 9-bromo-α-noscapine using optimized Suzuki reaction conditions for further experimental evaluation. The derivatives showed improved inhibition of the proliferation of human breast cancer cells (MCF-7), human cervical cancer cells (HeLa) and human lung adenocarcinoma cells (A549), compared to natural noscapine. The cell cycle analysis in MCF-7 further revealed that these compounds alter the cell cycle profile and cause mitotic arrest at G2/M phase more strongly than noscapine. Tubulin binding assay revealed higher binding affinity to tubulin, as suggested by dissociation constant (Kd) of 126 ± 5.0 µM for 5a, 107 ± 5.0 µM for 5c, 70 ± 4.0 µM for 5d, and 68 ± 6.0 µM for 5e compared to noscapine (Kd of 152 ± 1.0 µM). In fact, the experimentally determined value of ΔG(bind, expt) (calculated from the Kd value) are consistent with the predicted value of ΔG(bind, pred) calculated based on LIE-SGB. Based on these results, one of the derivative 5e of this series was used for further toxicological evaluation. Treatment of mice with a daily dose of 300 mg/kg and a single dose of 600 mg/kg indicates that the compound does not induce detectable pathological abnormalities in normal tissues. Also there were no significant differences in hematological parameters between the treated and untreated groups. Hence, the newly designed noscapinoid, 5e is an orally bioavailable, safe and effective anticancer agent with a potential for the treatment of cancer and might be a candidate for clinical evaluation.

A conformational factorisation approach for estimating the binding free energies of macromolecules

We present a conformational factorization approach. The theory is based on a superposition partition function, decomposed as a sum over contributions from local minima. The factorisation greatly reduces the number of minima that need to be considered, by employing the same local configurations for groups that are sufficiently distant from the binding site. The theory formalises the conditions required to analyse how our definition of the binding site region affects the free energy difference between the apo and holo states. We employ basin-hopping parallel tempering to sample minima that contribute significantly to the partition function, and calculate the binding free energies within the harmonic normal mode approximation. A further significant gain in efficiency is achieved using a recently developed local rigid body framework in both the sampling and the normal mode analysis, which reduces the number of degrees of freedom. We benchmark this approach for human aldose reductase (PDB code 2INE). When varying the size of the rigid region, the free energy difference converges for factorisation of groups at a distance of 14 Å from the binding site, which corresponds to 80% of the protein being locally rigidified.

Virtual screening strategies: recent advances in the identification and design of anti-cancer agents

Virtual screening (VS) is a well-established technique, which is now routinely employed in computer aided drug designing process. VS can be broadly classified into two categories, i.e., ligand-based and structure-based approach. In recent years, VS has emerged as a time saving and cost effective technique, capable of screening millions of compounds in a user friendly manner. In the area of cancer drug design, VS methods have been widely used and helped in identifying novel molecules as potential anti-cancer agents. Both ligand-based VS (LBVS) structure-based VS (SBVS) methods have been highly useful in the identification of a number of potential anti-cancer agents exhibiting activities in nanomolar range. In tune with the rapid progress in the enhancement of computational power, VS has witnessed significant change in terms of speed and hit rate and in future it is expected that VS will be a preferential alternative to high throughput screening (HTS). This review, discusses recent trends and contribution of VS in the area of anti-cancer drug discovery.

Implicit solvent methods for free energy estimation

Solvation is a fundamental contribution in many biological processes and especially in molecular binding. Its estimation can be performed by means of several computational approaches. The aim of this review is to give an overview of existing theories and methods to estimate solvent effects giving a specific focus on the category of implicit solvent models and their use in Molecular Dynamics. In many of these models, the solvent is considered as a continuum homogenous medium, while the solute can be represented at the atomic detail and at different levels of theory. Despite their degree of approximation, implicit methods are still widely employed due to their trade-off between accuracy and efficiency. Their derivation is rooted in the statistical mechanics and integral equations disciplines, some of the related details being provided here. Finally, methods that combine implicit solvent models and molecular dynamics simulation, are briefly described.

Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging

Accurate calculations of free energies involved in small-molecule binding to a receptor are challenging. Interactions between ligand, receptor, and solvent molecules have to be described precisely, and a large number of conformational microstates has to be sampled, particularly for ligand binding to a flexible protein. Linear interaction energy models are computationally efficient methods that have found considerable success in the prediction of binding free energies. Here, we parametrize a linear interaction model for implicit solvation with coefficients adapted by ligand and binding site relative polarities in order to predict ligand binding free energies. Results obtained for a diverse series of ligands suggest that the model has good predictive power and transferability. We also apply implicit ligand theory and propose approximations to average contributions of multiple ligand-receptor poses built from a protein conformational ensemble and find that exponential averages require proper energy discrimination between plausible binding poses and false-positives (i.e., decoys). The linear interaction model and the averaging procedures presented can be applied independently of each other and of the method used to obtain the receptor structural representation.

Molecular recognition of metabotropic glutamate receptor type 1 (mGluR1): synergistic understanding with free energy perturbation and linear response modeling

Metabotropic glutamate receptors (mGluRs) constitute an important family of the G-protein coupled receptors. Due to their widespread distribution in the central nervous system (CNS), these receptors are attractive candidates for understanding the molecular basis of various cognitive processes as well as for designing inhibitors for relevant psychiatric and neurological disorders. Despite many studies on drugs targeting the mGluR receptors to date, the molecular level details on the ligand binding dynamics still remain unclear. In this study, we performed in silico experiments for mGluR1 with 29 different ligands including known synthetic agonists and antagonists as well as natural amino acids. The ligand-receptor binding affinities were estimated by the use of atomistic simulations combined with the mathematically rigorous, Free Energy Perturbation (FEP) method, which successfully recognized the native agonist l-glutamate among the highly favorable binders, and also accurately distinguished antagonists from agonists. Comparative contact analysis also revealed the binding mode differences between natural and non-natural amino acid-based ligands. Several factors potentially affecting the ligand binding affinity and specificity were identified including net charges, dipole moments, and the presence of aromatic rings. On the basis of these findings, linear response models (LRMs) were built for different sets of ligands that showed high correlations (R(2) > 0.95) to the corresponding FEP binding affinities. These results identify some key factors that determine ligand-mGluR1 binding and could be used for future inhibitor designs and support a role for in silico modeling for understanding receptor ligand interactions.

HADDOCK(2P2I): a biophysical model for predicting the binding affinity of protein-protein interaction inhibitors

The HADDOCK score, a scoring function for both protein-protein and protein-nucleic acid modeling, has been successful in selecting near-native docking poses in a variety of cases, including those of the CAPRI blind prediction experiment. However, it has yet to be optimized for small molecules, and in particular inhibitors of protein-protein interactions, that constitute an "unmined gold reserve" for drug design ventures. We describe here HADDOCK(2P2I), a biophysical model capable of predicting the binding affinity of protein-protein complex inhibitors close to experimental error (~2-fold larger). The algorithm was trained and 4-fold cross-validated against experimental data for 27 inhibitors targeting 7 protein-protein complexes of various functions and tested on an independent set of 24 different inhibitors for which K(d)/IC50 data are available. In addition, two popular ligand topology generation and parametrization methods (ACPYPE and PRODRG) were assessed. The resulting HADDOCK(2P2I) model, derived from the original HADDOCK score, provides insights into inhibition determinants: while the role of electrostatics and desolvation energies is case-dependent, the interface area plays a more critical role compared to protein-protein interactions.

Accessing the applicability of polarized protein-specific charge in linear interaction energy analysis

The reliability of the linear interaction energy (LIE) depends on the atomic charge model used to delineate the Coulomb interaction between the ligand and its environment. In this work, the polarized protein-specific charge (PPC) implementing a recently proposed fitting scheme has been examined in the LIE calculations of the binding affinities for avidin and β-secretase binding complexes. This charge fitting scheme, termed delta restrained electrostatic potential, bypasses the prevalent numerical difficulty of rank deficiency in electrostatic-potential-based charge fitting methods via a dual-step fitting strategy. A remarkable consistency between the predicted binding affinities and the experimental measurement has been observed. This work serves as a direct evidence of PPC's applicability in rational drug design.

Rational design, synthesis, and biological evaluation of third generation α-noscapine analogues as potent tubulin binding anti-cancer agents

Systematic screening based on structural similarity of drugs such as colchicine and podophyllotoxin led to identification of noscapine, a microtubule-targeted agent that attenuates the dynamic instability of microtubules without affecting the total polymer mass of microtubules. We report a new generation of noscapine derivatives as potential tubulin binding anti-cancer agents. Molecular modeling experiments of these derivatives 5a, 6a-j yielded better docking score (-7.252 to -5.402 kCal/mol) than the parent compound, noscapine (-5.505 kCal/mol) and its existing derivatives (-5.563 to -6.412 kCal/mol). Free energy (ΔG_bind) calculations based on the linear interaction energy (LIE) empirical equation utilizing Surface Generalized Born (SGB) continuum solvent model predicted the tubulin-binding affinities for the derivatives 5a, 6a-j (ranging from -4.923 to -6.189 kCal/mol). Compound 6f showed highest binding affinity to tubulin (-6.189 kCal/mol). The experimental evaluation of these compounds corroborated with theoretical studies. N-(3-brormobenzyl) noscapine (6f) binds tubulin with highest binding affinity (K_D, 38 ± 4.0 µM), which is ~ 4.0 times higher than that of the parent compound, noscapine (K_D, 144 ± 1.0 µM) and is also more potent than that of the first generation clinical candidate EM011, 9-bromonoscapine (K_D, 54 ± 9.1 µM). All these compounds exhibited substantial cytotoxicity toward cancer cells, with IC₅₀ values ranging from 6.7 µM to 72.9 µM; compound 6f showed prominent anti-cancer efficacy with IC₅₀ values ranging from 6.7 µM to 26.9 µM in cancer cells of different tissues of origin. These compounds perturbed DNA synthesis, delayed the cell cycle progression at G2/M phase, and induced apoptotic cell death in cancer cells. Collectively, the study reported here identified potent, third generation noscapinoids as new anti-cancer agents.

Parametrisation of the free energy of ATP binding to wild-type and mutant Kir6.2 potassium channels

ATP-sensitive K(+) (K(ATP)) channels, comprised of pore-forming Kir6.x and regulatory SURx subunits, play important roles in many cellular functions; because of their sensitivity to inhibition by intracellular ATP, K(ATP) channels provide a link between cell metabolism and membrane electrical activity. We constructed structural homology models of Kir6.2 and a series of Kir6.2 channels carrying mutations within the putative ATP-binding site. Computational docking was carried out to determine the conformation of ATP in its binding site. The Linear Interaction Energy (LIE) method was used to estimate the free-energy of ATP binding to wild-type and mutant Kir6.2 channels. Comparisons of the theoretical binding free energies for ATP with those determined from mutational experiments enabled the identification of the most probable conformation of ATP bound to the Kir6.2 channel. A set of LIE parameters was defined that may enable prediction of the effects of additional Kir6.2 mutations within the ATP binding site on the affinity for ATP.

Virtual Screening for RNA-Interacting Small Molecules

Computational virtual screening is useful and powerful strategy for rapid discovery of small biologically active molecules in the early stage of drug discovery. The discovery of a broad range of important biological processes involved by RNA has increased the importance of RNA as a new drug target. To apply structure-based virtual screening methods to the discovery of RNA-binding ligands, many RNA 3D structure prediction programs and optimized docking algorithms have been developed. In this chapter, a number of successful cases of virtual screening targeting RNA will be introduced.

Role of computational methods in pharmaceutical sciences

Over the past two decades computational methods have eased up the financial and experimental burden of early drug discovery process. The in silico methods have provided support in terms of databases, data mining of large genomes, network analysis, systems biology on the bioinformatics front and structure-activity relationship, similarity analysis, docking, and pharmacophore methods for lead design and optimization. This review highlights some of the applications of bioinformatics and chemoinformatics methods that have enriched the field of drug discovery. In addition, the review also provided insights into the use of free energy perturbation methods for efficiently computing binding energy. These in silico methods are complementary and can be easily integrated into the traditional in vitro and in vivo methods to test pharmacological hypothesis.

Conformational Transitions and Convergence of Absolute Binding Free Energy Calculations

The Binding Energy Distribution Analysis Method (BEDAM) is employed to compute the standard binding free energies of a series of ligands to a FK506 binding protein (FKBP12) with implicit solvation. Binding free energy estimates are in reasonably good agreement with experimental affinities. The conformations of the complexes identified by the simulations are in good agreement with crystallographic data, which was not used to restrain ligand orientations. The BEDAM method is based on λ -hopping Hamiltonian parallel Replica Exchange (HREM) molecular dynamics conformational sampling, the OPLS-AA/AGBNP2 effective potential, and multi-state free energy estimators (MBAR). Achieving converged and accurate results depends on all of these elements of the calculation. Convergence of the binding free energy is tied to the level of convergence of binding energy distributions at critical intermediate states where bound and unbound states are at equilibrium, and where the rate of binding/unbinding conformational transitions is maximal. This finding mirrors similar observations in the context of order/disorder transitions as for example in protein folding. Insights concerning the physical mechanism of ligand binding and unbinding are obtained. Convergence for the largest FK506 ligand is achieved only after imposing strict conformational restraints, which however require accurate prior structural knowledge of the structure of the complex. The analytical AGBNP2 model is found to underestimate the magnitude of the hydrophobic driving force towards binding in these systems characterized by loosely packed protein-ligand binding interfaces. Rescoring of the binding energies using a numerical surface area model corrects this deficiency. This study illustrates the complex interplay between energy models, exploration of conformational space, and free energy estimators needed to obtain robust estimates from binding free energy calculations.

The linear interaction energy method for the prediction of protein stability changes upon mutation

The coupling of protein energetics and sequence changes is a critical aspect of computational protein design, as well as for the understanding of protein evolution, human disease, and drug resistance. To study the molecular basis for this coupling, computational tools must be sufficiently accurate and computationally inexpensive enough to handle large amounts of sequence data. We have developed a computational approach based on the linear interaction energy (LIE) approximation to predict the changes in the free-energy of the native state induced by a single mutation. This approach was applied to a set of 822 mutations in 10 proteins which resulted in an average unsigned error of 0.82 kcal/mol and a correlation coefficient of 0.72 between the calculated and experimental ΔΔG values. The method is able to accurately identify destabilizing hot spot mutations; however, it has difficulty in distinguishing between stabilizing and destabilizing mutations because of the distribution of stability changes for the set of mutations used to parameterize the model. In addition, the model also performs quite well in initial tests on a small set of double mutations. On the basis of these promising results, we can begin to examine the relationship between protein stability and fitness, correlated mutations, and drug resistance.

Calculation of configurational entropy with a Boltzmann-quasiharmonic model: the origin of high-affinity protein-ligand binding

Accurate assessment of configurational entropy remains a large challenge in biology. While many methods exist to calculate configurational entropy, there is a balance between accuracy and computational demands. Here we calculate ligand and protein conformational entropies using the Boltzmann-quasiharmonic (BQH) method, which treats the first-order entropy term by the Boltzmann expression for entropy while determining correlations using the quasiharmonic model. This method is tested by comparison with the exact Clausius expression for entropy on a range of test molecules ranging from small ligands to a protein. Using the BQH method, we then analyze the rotational and translational (R/T) entropy change upon ligand binding for five protein complexes to explore the origins of extremely tight affinity. The results suggest that in these systems such affinity is achieved by a combination of simultaneously maintaining good protein-ligand contacts while allowing significant residual R/T motion of the ligand through suitable protein motions.

Computational perspectives into plasmepsins structure-function relationship: implications to inhibitors design

The development of efficient and selective antimalariais remains a challenge for the pharmaceutical industry. The aspartic proteases plasmepsins, whose inhibition leads to parasite death, are classified as targets for the design of potent drugs. Combinatorial synthesis is currently being used to generate inhibitor libraries for these enzymes, and together with computational methodologies have been demonstrated capable for the selection of lead compounds. The high structural flexibility of plasmepsins, revealed by their X-ray structures and molecular dynamics simulations, made even more complicated the prediction of putative binding modes, and therefore, the use of common computational tools, like docking and free-energy calculations. In this review, we revised the computational strategies utilized so far, for the structure-function relationship studies concerning the plasmepsin family, with special focus on the recent advances in the improvement of the linear interaction estimation (LIE) method, which is one of the most successful methodologies in the evaluation of plasmepsin-inhibitor binding affinity.

Rational design, synthesis and biological evaluations of amino-noscapine: a high affinity tubulin-binding noscapinoid

Noscapine and its derivatives are important microtubule-interfering agents shown to have potent anti-tumor activity. The binding free energies (ΔG (bind)) of noscapinoids computed using linear interaction energy (LIE) method with a surface generalized Born (SGB) continuum solvation model were in agreement with the experimental ΔG (bind) with average root mean square error of 0.082 kcal/mol. This LIE-SGB model guided us in designing a novel derivative of noscapine, amino-noscapine [(S)-3-((R)-9-amino-4-methoxy-6-methyl-5,6,7,8-tetrahydro [1, 3] dioxolo[4,5-g]isoquinolin-5-yl)-6,7-dimethoxy isobenzo-furan-1(3H)-one] that has higher tubulin binding activity (predicted ΔG (bind) = -6.438 kcal/mol and experimental ΔG (bind) = -6.628 kcal/mol) than noscapine, but does not significantly change the total extent of the tubulin subunit/polymer ratio. The modes of interaction of amino-noscapine with the binding pocket of tubulin involved three hydrogen bonds and are distinct compared to noscapine which involved only one hydrogen bond. Also the patterns of non-bonded interactions are albeit different between both the lignads. The 'blind docking' approach (docking of ligand with different binding sites of a protein and their evaluations) as well as the reasonable accuracy of calculating ΔG (bind) using LIE-SGB model constitutes the first evidence that this class of compounds binds to tubulin at a site overlapping with colchicine-binding site or close to it. Our results revealed that amino-noscapine has better anti-tumor activity than noscapine.

Novel monocyclam derivatives as HIV entry inhibitors: Design, synthesis, anti-HIV evaluation, and their interaction with the CXCR4 co-receptor

The CXCR4 receptor has been shown to interact with the human immunodeficiency virus (HIV) envelope glycoprotein gp120, leading to fusion of viral and cell membranes. Therefore, ligands that can attach to this receptor represent an important class of therapeutic agents against HIV, thus inhibiting the first step in the cycle of viral infection: the virus-cell entry/fusion. Herein we describe the in silico design, synthesis, and biological evaluation of novel monocyclam derivatives as HIV entry inhibitors. In vitro activity testing of these compounds in cell cultures against HIV strains revealed EC(50) values in the low micromolar range without cytotoxicity at the concentrations tested. Docking and molecular dynamics simulations were performed to predict the binding interactions between CXCR4 and the novel monocyclam derivatives. A binding mode of these compounds is proposed which is consistent with the main existing site-directed mutagenesis data on the CXCR4 co-receptor. Moreover, molecular modeling comparisons were performed between these novel monocyclams, previously reported non-cyclam compounds from which the monocyclams are derived, and the well-known AMD3100 bicyclam CXCR4 inhibitors. Our results suggest that these three structurally diverse CXCR4 inhibitors bind to overlapping but not identical amino acid residues in the transmembrane regions of the receptor.

Modeling Protein-Ligand Binding by Mining Minima

We present the first application of the mining minima algorithm to protein-small molecule binding. This end-point approach use an empirical force field and implicit solvent models, treats the protein binding-site as fully flexible and estimates free energies as sums over local energy wells. The calculations are found to yield encouraging agreement with experiment for three sets of HIV-1protease inhibitors and a set of phosphodiesterase 10a inhibitors. The contributions of various aspects of the model to its accuracy are examined, and the Poisson-Boltzmann correction is found to be the most critical. Interestingly, the computed changes in configurational entropy upon binding fall roughly along the same entropy-energy correlation previously observed for smaller host-guest systems. Strengths and weaknesses of the method are discussed, as are the prospects for enhancing accuracy and speed.