Parametrization of a specific free energy function for automated docking against RNA targets using neural networks

Chemometrics and Intelligent Laboratory Systems 82 (2006) 269 – 275 www.elsevier.com/locate/chemolab

Parametrization of a specific free energy function for automated docking against RNA targets using neural networks Florent Barbault a, Liangren Zhang b, Lihe Zhang b, Bo Tao Fan a,* a

ITODYS CNRS UMR 7086, Universite´ Paris 7-Denis Diderot, France b School of Pharmaceutical Sciences, Peking University, China

Received 10 December 2004; received in revised form 6 May 2005; accepted 26 May 2005 Available online 26 January 2006

Abstract A set of 8 RNA – drug complexes was extracted from the NDB database and used to determine new parameters of the empirical free energy model implemented in Autodock software. 248 docking experiments were performed with different values for the contributions of van der Waals, electrostatic, hydrogen bonding, torsion and desolvation, respectively. These parameters were correlated with both RMSD and DG bind for all docking computations using a layered neural network with back-propagation algorithm (BP-NN). The model obtained from the correlation has allowed us to adjust the parameters. The most important differences between new and the default values were observed for the electrostatic, the torsion angle loss of entropy and desolvation, while the others’ terms are comparable with default data. This new set of parameters could be used specifically for virtual screening against RNA targets. D 2005 Elsevier B.V. All rights reserved. Keywords: RNA; Aminoglycoside; Empirical free energy function; Neural network; Automated docking; Autodock software; RNA complex

1. Introduction Among the functional components of cells, polypeptides, enzymes, transporters, receptors and ion channels account for the large majority of targets for therapeutic intervention [1]. Although ribonucleic acids (RNA) has only recently been viewed as a target for small-molecule drug discovery, the advantages of targeting RNA over traditional protein targets are quickly being shown. Contrary to popular belief the structure of RNA is not only a helix. In fact, secondary structures of RNA are largely more complex than DNA with stems, bulges, hairpins, different types of junctions [2]. Therefore, except for the purely coding regions of mRNA, functional RNA molecules and regulatory mRNA domains share in common with proteins a defined three dimensional structures which is required for both molecular recognition and functionality [2]. In contrast to proteins, which are end-products, RNA molecules participate as intermediate carriers of genetic information (messenger RNA), as well as functional inter* Corresponding author. Tel.: +33 14427 4412. E-mail address: [email protected] (B.T. Fan). 0169-7439/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.chemolab.2005.05.014

mediates of the expression cascade which amplifies single genes into many copies of the encoded proteins. The lower copy number of target RNA molecules per cell compared to proteins and the absence of cellular repair mechanisms make RNA-directed therapeutics particularly powerful. The potential for the slower development of drug resistance against small molecules [3] is certainly one of the most interesting characteristic of using RNA as a drug target. In fact, RNA functional domains are more highly conserved and perhaps more accessible than the shapes of enzymatic active sites. Thus, it is expected that pathogens will find it difficult to mutate their RNA and develop resistance. Type 1 human immunodeficiency virus (HIV-1), which has rapidly developed resistance to enzyme inhibitors, illustrates this point. RNA-based antiviral targets offer a large potential solution to the problem. Thus, the transactivating region (TAR) RNA, responsible for gene regulation in HIV-1, was identified as a possible RNA-based target [4,5]. There have been some attempts to discover drugs that interact with RNA [6,7]. These have focused primarily on antibacterial agents because the target of some clinically important antibacterial drugs, originally discovered by soil sample screening, was found to be bacterial RNA. In general, these aminoglycosides are valuable [1], but they have

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ing assays are set up at enormous expense to monitor experimentally the binding of compounds. However, several recent improvements in computational docking methodologies permit effective screening of compounds in silico. Automated docking of ligands into receptors is an effective method for screening a library of potential inhibitors and for predicting the conformation of a ligand bound to a receptor [15,16]. The docking procedure involves minimizing a potential energy function defined as the sum of inter- and intra-molecular potentials. The form of this function varies, but it is usually a sum of different additive pair-wise terms including Lennard –Jones attraction/repulsion, hydrogen bonding, electrostatics, and solvent interaction energies. Automated docking is a global optimization problem, and combinations of simulated annealing, local minimization, and genetic algorithms are used to obtain optimal structures [15]. Empirical free energy models, also known as linear response methods, are useful in understanding the interactions of smallmolecule inhibitors with a wide variety of targets. The underlying principle behind these methods is the use of a training set of target– ligand complexes of known structure and affinity to fit a model that predicts the binding energies and optimal structures of a different series of complexes of unknown structure and affinity. These models are based on existing molecular potential energy function such as those in CHARMM [17] or AMBER [18] in which each term in the function is multiplied by an empirically determined coefficient so that the overall calculated interaction energy is the free

undesirable features: they interact with other sites on nucleic acids in human cell, leading to the development of bacterial resistance. Consequently, it is desirable to discover new classes of compounds for drug development against RNA targets. Moreover, in human, where RNA is expressed at even higher levels, drug candidates must be specific for a single RNA site. In recent years, several drugs designed based on three dimensional protein structures were approved for clinical use, yet there has been little reported research on drug discovery based on three dimensional structures of RNA [6,8,9]. Undoubtedly, this neglect is due to the relative paucity in 3D structures of RNA. However, recently more structures are well elucidated thanks to progress in the large-scale synthesis and purification of RNA, and some of them represent potential targets for the design of new drugs. By consequence, we can now design new agents to bind to RNA targets based on the well known three dimensional structures of the receptors. Our goal is to develop new compounds that interact with high affinity and specificity with a single RNA site. The great majority of drugs binding to RNA contains positively charged groups which can neutralize the negatively charged backbone phosphates of the RNA, such as benzimidazoles [10], cyclophanes [11], diphenylfurans [12], spermidine– acridine conjugates [13], and the aminoglycosides [14]. Since aminoglycosides represent the largest family of compounds that interacts with RNA, we decided to use them as a scaffold for subsequent chemical modification that would enhance their pharmaceutical properties. Currently, high-throughput screen-

NH3+

NH3+ O

O

A

HO HO

HO NH3+

O

NH3+

OH

HO

OH

OH

O

OH

O

NH3+

O HO HO

O

NH3+

O

HO

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HO

H2 N

H2 N

OHO

NH3+

B

NH3+

OH

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H3N+ NH3+

H2 N NH3+

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OH OH

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S O

OH

O

N

H3N+

O

OH O

NH3+

HO O

NH3+

D OH

E

H2 N

N

HO

O NH2 + OH

Fig. 1. Chemical formulas of the studied molecules: aminoglycosides tobramycin (A), neomycin-B (B), paromomycin (C) and gentamicin-C1A (E), and the acetylpromazine drug (D).

F. Barbault et al. / Chemometrics and Intelligent Laboratory Systems 82 (2006) 269 – 275

2. Computational Process Empirical free energy functions attempt to correct the ranking disparities of classical molecular mechanics potential energy functions by adding terms that takes into account of desolvation and entropy of the ligand, target, and target– ligand complex, so that the calculated energy is in fact a free energy of binding. The free energy model described here is based on the methods developed by Morris et al. [15,16] for Autodock. This software uses a rapid, grid-based energy evaluation method to calculate the intermolecular interaction energy, therefore the intermolecular terms in the free energy model must be compatible with this methodology. The general equation describing the Autodock free energy model is: ! X Aij Bij DG ¼ DGvdW  6 þ DGHbond rij12 rij i;j ! X qi qj X Cij Dij    E ðt Þ 12  10 þ DGelec rij rij i;j i;j e rij rij X  r2 =2r2 þ DGtor Ntor þ DGsol Si Vj þ Sj Vi ðe ij Þ :

-10

Autodock calculated values

energy of formation of the complex. Given the many approximation made in such models, better agreement with experimental data is usually observed when they are fit to a training set with target– ligand complexes similar to the test set. Furthermore, fitting of multiple models to a single training set to find the model that fits best also improves agreement between experiment and prediction [15]. We report here an empirical free energy function specifically used for modelling aminoglycoside –RNA interactions with the docking program Autodock [15]. Eight structures of aminoglycoside – RNA with known experimental free energy of binding are available in the nucleic acids database [19]. Then, 248 docking calculations were done with different sets of parameters. We correlate these parameters with both root mean square deviation (RMSD) and free energy of binding (DG bind) for all docking results using a BP-NN. This free energy model is applicable for predicting the binding energy and the optimal structure of any RNA –aminoglycoside complex, and it has been implemented for use with the docking program Autodock.

Free energy of binding

-15

-20

-25

-30

-35 -35

-30

-25 -20 ANN predicted values

-15

-10

Fig. 2. Graphical representation of the Autodock calculated free energy of binding and the predicted values by BP-NN.

bonding for donor/acceptor pairs, and the third one is the distance-dependent dielectric electrostatics, these terms represent the intermolecular interaction energy. Parameters A through D are parameters based on the AMBER [18] force field. In the hydrogen bonding term E(t) is a directional attenuation coefficient such that an optimal hydrogen bond geometry results in a maximal hydrogen bonding contribution. The hydrogen bonding energy in solution E Hbond, is added to the second term. This constant represents the loss of free energy due to hydrogen bonding of the ligand to water molecules in solution. The dielectric constant is distantly dependent for evaluation of the electrostatic potential energy. The five empirically determined parameters are f vdW (van der Waals interaction), f Hbond (12-10 hydrogen bonding), f elec (electrostatic interaction), DG tor (torsional entropy loss), and f sol (change in solvent-exposed surface area of target and

RMSD

6

5

The first term of the equation stands for Lennard – Jones 12-6 attraction/repulsion. The second term is the hydrogen Table 1 Database of RNA – ligand complexes with their respective experimental free energy of binding DG binding (kcal mol 1)

PDB code Ligand

Target

1tob 2tob 1ei2 1nem 1qd3 1byj 1pbr 1lvj

RNA aptamer I 12.48 RNA aptamer II 12.24 Tau exon RNA 8.00 RNA aptamer 9.60 HIV-1 Tar RNA 8.00 Ribosome A-site RNA 10.98 RNA-16S ribosome 9.19 HIV-1 Tar RNA 9.56

Autodock calculated values

i;j

Tobramycin Tobramycin Neomycin Neomycin Neomycin Gentamicin C1A Paromomycin Acetylpromazine

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4

3

2

1

0 0

1

2

3

4

5

6

ANN predicted values

Fig. 3. Graphical representation of the Autodock calculated RMSD and the predicted values by BP-NN.

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Table 2 Summary of the parameters for the free energy of binding function and the variation between the default set of parameters and the new set

Autodock RNA – Autodock Variation

vdW

Elec

Hbond

Torsion

Desolvation

0.1485 0.155 0.0065

0.1146 0.101 0.0136

0.0656 0.056 0.0096

0.3113 0.361 0.0497

0.1711 0.153  0.0181

ligand). 30 different protein –ligand complex was used to develop the original Autodock parameters [16]. The 3D structures of 8 different aminoglycosides– RNA complex were extracted from the Nucleic Acid Database [19]. Two of them represent solution structures of the tobramycin (Fig. 1A) antibiotic bound to the stem-loop RNA aptamer I [20,21] (pdb code 1tob) and RNA aptamer II [22] (pdb code 2tob). The experimental free energy of binding was determined experimentally [23] by relative fluorescent and are reported in Table 1. Three structures represent the binding of neomycin-B antibiotics (Fig. 1B) to the Tau exon RNA [24] (pdb code 1ei2), to the RNA aptamer [25,26] (pdb code 1nem) and to the HIV-1 Tar RNA [27,28] (pdb code 1qd3). We also added the structure of the ribosomal A-site complex with the gentamicin C1A antibiotic [29] (Fig. 1E and pdb code 1byj), and the structure of paromomycin Fig. 1C) bound to the RNA-16S ribosome [30,31] (pdb code 1pbr). Finally, we added the structure of a HIV-1 Tar RNA complex with Acetylpromazine (Fig. 1D pdb code 1lvj) [32]. This last compound is aromatic and we thought it was important to add this type of ligands in our training set in order to be as exhaustive as we can. All the experimental free energies are given in Table 1. Some amino groups in the aminoglycosides molecules are positively charged at physiological pH (pH = 7.4), which is crucial for the RNA interaction. In all cases all ligands were extracted from the experimental 3D structures. The desolvation parameters for Autodock were derived from similar atom types in amino acids [33]. All docking results were done with the Autodock software. In Autodock 3.0, the search methods include evolutionary methods, for example, Lamarckian Genetic Algorithm (LGA), and Monte Carlo simulated annealing (SA). LGA is a dramatic improvement on the Genetic Algorithm, and it appears as much more efficient and robust

than simulated annealing. Details on ‘‘Autodock’’ were described elsewhere [15,16]. For the 8 structures we calculated 31 docking processes with different values for van der Waals, hydrogen bonding, electrostatic, torsion and desolvation parameters. These values were established as a logical variation between 0 and the double of the original Autodock values. During docking processes, a maximum of 20 conformers was considered for each compound (default set is 10 conformers). In order to get a model that describes the docking process with RNA we correlated all the 248 docking results (31 different docking processes with 8 structures) with a neural network. The neural network is a very popular method in correlation since 1980s. Particularly, the BP-NN is widely applied, and it was also used in this study. Six terms were correlated, a number from 1 to 8 designing the molecule and the five parameters of the free energy function, to the root mean square deviation from the experimental coordinates of the ligand and the free energy of binding. We think this is essential to correlate both the free energy of binding and the RMSD of ligand because we need to have the correct placement of the ligand with the best ranking result and consequently the free energy of binding. Because only eight structures of RNA/aminoglycosides complex are available we cannot use a test dataset. Therefore, we chose the Leave-OneOut (LOO) cross-validation strategy in order to get a robust model. 3. Results and discussion All the docking results were collected for the 8 structures. In each case we selected the values of free energy of binding and RMSD for the best predicted ligand. In the BP-NN, there are a large number of controlling parameters, namely, the number of hidden layers, the number of hidden neurons, the learning rate, the momentum term, epochs, transfer functions, and initialization methods of weights. The prediction performance is evaluated using the mean square error based on leave-one-out cross-validation of the data set. This procedure is a robust way to get good model of correlation. The architecture of neural network was determined as 6-10-2. The neural network presents a final RMS error of 0.234 and a

Table 3 Docking calculation results for the 8 RNA structures with the Autodock default set of parameters, the Artificial Neural Network prediction and the RNA – Autodock new set of parameters Name

1tob 2tob 1ei2 1nem 1qd3 1byj 1pbr 1lvj

Exper

Autodock

ANN

DG

DG

DDG

RMSD

DG

DDG

RMSD

DG

DDG

RMSD

12.48 12.24 8.00 9.60 8.00 10.98 9.19 9.56

10.20 11.70 8.67 17.11 10.90 11.18 10.00 12.99

2.28 0.54 0.67 7.51 2.90 0.20 0.81 3.43

2.26 1.50 2.33 1.03 2.16 4.99 2.34 3.55

12.00 12.00 8.43 17.09 9.60 10.43 9.43 11.24

0.48 0.24 0.43 7.49 1.60 0.55 0.24 1.68

2.03 1.18 1.48 0.89 1.38 1.98 1.35 3.05

12.20 11.60 9.04 17.04 10.00 10.30 9.94 13.30

 0.28  0.65 1.04 7.44 2.00  0.68 0.75 3.74

1.40 1.26 1.22 0.85 1.21 2.28 1.10 2.05

˚. The free energy of binding are in kcal mol 1 and the RMSD are in A

RNA – Autodock

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final max error of 0.683 showing that the model is good. We represent the calculated RMSD and free energy of binding determined by Autodock and the predicted values by BP-NN in Figs. 2 and 3, the correlation coefficient values (r 2 = 0.977 and 0.997, respectively) indicate that the neural network model has good performance both for the RMSD and free energy of binding. We used the neural network model to elaborate a new set of Autodock parameters more suitable for RNA. Firstly, we generated 80,000 random values of parameters and then we calculated them with the neural network model. The best values of parameters were chosen with a good agreement with the free energy of binding and with a weak RMSD. Then, we attempted to improve these parameters manually. The new set

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of parameters are listed in Table 2. The van der Waals parameter is a little bit increased from 0.1485 to 0.155, showing that the van der Waals interaction is more important in the aminoglycosides/RNA interaction than for others protein/ligand complexes. By contrast, the electrostatic term is lightly decreased from 0.1146 to 0.101. This can be explained by the fact that RNA and aminoglycosides are charged molecules, and this ionic interaction is largely present in the binding process which is less frequent in protein/ligand complexes. The hydrogen bond parameter is also slightly decreased from 0.0656 to 0.056, certainly for the same reason as for the electrostatic term since hydrogen bonding interaction is largely influenced by electrostatic. The torsion parameter is increased more distinctly from 0.3113 to 0.361,

Fig. 4. Results of flexible docking onto rigid RNA receptors for complexes of known structure. The experimental ligand is in white, the ligand with Autodock default set of parameters is in blue and the ligand with the new set of parameters is in red. Pdb code, 1byj(A), 2tob(B), 1tob(C), 1ei2(D), 1nem(E), 1pdb(F), 1lvj(G) and 1qd3(H). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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this term represents the torsional entropy loss by the torsion of ligand. Aminoglycosides are chained ring systems, then the glycosidic angles are crucial for the docking orientation, and it is logical to see this parameter increased. The last parameter represent the change in solvent-exposed surface area of target and ligand, this term is a little bit decreased from 0.1711 to 0.153. This relative low influence of the solvent had been already described by dynamics simulation with explicit solvent [6]. It seems important to note that the variations of parameters always occur for the second number after the point. As the free energy is the sum for all atoms, these variations are relatively important for the large systems like RNA. They are therefore necessary for obtaining a good docking state definition. In order to test these new parameters we determined the ligand/ RNA interactions for the 8 structures with the Autodock software according to the new set of parameters called ‘‘RNA – Autodock parameters’’. For the tobramycin antibiotic bound to the RNA aptamer I (pdb code 1tob) the RNA – Autodock function shows a clear improvement, since the RMSD goes from 2.26 to 1.40 ˚ . The free energy of binding is also improved with a A variation from  2.28 to  0.28 kcal mol 1. For the other interaction of this antibiotic to the aptamer II (pdb code 2tob) the improvement is less significant with a RMSD and ˚ and free energy variations, respectively, from 1.50 to 1.26 A 1 1  0.54 kcal mol to  0.65 kcal mol . Concerning the complexes of neomycin with the Tau exon RNA (pdb code 1ei2) and the HIV-1 Tat responsive element (pdb code 1qd3) the RNA –Autodock function improved largely the RMSD, ˚ and 2.16 to 1.21 A ˚, respectively, from 2.33 to 1.22 A whereas the free energies of binding are still well elucidated. The neomycin interaction with the RNA aptamer (pdb 1nem) is also well structurally determined with the two functions ˚ . However, the free since the RMSD is in the range of 1 A energies of binding are always overestimated with a variation of more than 7 kcal mol 1 with all the functions considered. The gentamicin antibiotic docked to the A-site ribosomal fragment (pdb code 1byj) is significantly improved with the new set of Autodock parameters. Indeed, the RMSD goes ˚ with a free energy of binding who from 4.99 to 2.28 A is still consent determined. For the paromomycin antibiotic complex with the 30S ribosomal RNA fragment (pdb code 1pbr) the RNA – Autodock parameters present also a net ˚ with a good diminution of the RMSD from 2.34 to 1.10 A DG value. Finally, the structure representing the interaction of the acetylpromazine, which is not an aminoglycoside, with the TAR HIV-1 fragment (pdb code 1lvj) presents a real improvement with the RNA – Autodock function since the ˚ . All the results are RMSD changed from 3.55 to 2.05 A given in Table 3 and the docking structures are displayed on Fig. 4. In all the cases the comparison of the predicted values of RMSD and free energies of binding using BP-NN with the values calculated using RNA –Autodock parameters (see Table 1) are always in the same range showing the good correlation performance of the model.

4. Conclusion In summary, we have established a docking protocol to predict the free energy of binding of aminoglycosides to a RNA fragment. In general, the agreement with experimental free energies of binding is within experimental error, suggesting that the model fits the data well. In the same way the structure of the ligand in the docking state is significantly more accurate with our new parameters than with the Autodock default values designed for protein/ligand systems. In this first attempt, the parameters are still limited to aminoglycosides/ RNA complexes. The results obtained show the good performance of this new set of parameters. This work will be extended to other non-aminoglycosides/RNA systems. In fact, the docking calculation for the acetylpromazine drug with HIV1 Tar fragment is enhanced with the RNA –Autodock function. This result shows the possibility to establish a new set of parameters for non-aminoglycosides/RNA interactions. Finally, the new set of parameters determined in this work will be immediately used as an automated docking tool for virtual screening strategy. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]

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