Target flexibility in molecular recognition

Biochimica et Biophysica Acta 1754 (2005) 221 – 224 http://www.elsevier.com/locate/bba

Review

Target flexibility in molecular recognition J. Andrew McCammon * Howard Hughes Medical Institute, La Jolla, CA 92093-0365, USA NSF Center for Theoretical Biological Physics, La Jolla, CA 92093-0365, USA Department of Chemistry and Biochemistry, University of California at San Diego, La Jolla, CA 92093-0365, USA Department of Pharmacology, University of California at San Diego, La Jolla, CA 92093-0365, USA Received 2 June 2005; received in revised form 9 July 2005; accepted 10 July 2005 Available online 12 September 2005

Abstract Induced-fit effects are well known in the binding of small molecules to proteins and other macromolecular targets. Among other targets, protein kinases are particularly flexible proteins, so that such effects should be considered in attempts at structure-based inhibitor design for kinase targets. This paper outlines some recent progress in methods for including target flexibility in computational studies of molecular recognition. A focus is the ‘‘relaxed complex method,’’ in which ligands are docked to an ensemble of conformations of the target, and the best complexes are re-scored to provide predictions of optimal binding geometries. Early applications of this method have suggested a new approach to the development of inhibitors of HIV-1 Integrase. D 2005 Elsevier B.V. All rights reserved. Keywords: Structure-based drug discovery; Computer-aided drug design; Induced fit; Molecular dynamic; Computer simulation; Free energy

Computer-aided drug discovery has become increasingly successful in the past 20 years, due to the increasing availability of experimental structures of molecular targets, the inexorable increases in the performance of computer hardware, and the creation of new theory, algorithms and software. The first clinically useful drugs to emerge from molecular dynamics simulations (used both in the refinement of crystallographic structures and in the computational docking of model compounds to target structures) were the HIV protease inhibitors [1]. Many useful computational methods have been introduced for structure-based drug discovery [2]. Here, we focus on the ‘‘Relaxed Complex Method’’, which has been developed in our group for the docking of potential inhibitors to intrinsically flexible targets. 1. The Relaxed Complex Method The Relaxed Complex Method is a computational approach to discover ligands that may bind even when substantial ‘‘induced fit’’ effects occur in their target molecules [3 –5]. The Relaxed Complex method was inspired by two experimental * Howard Hughes Medical Institute, La Jolla, CA 92093-0365, USA. E-mail address: [email protected]. 1570-9639/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.bbapap.2005.07.041

methods for rapid discovery of ligands that bind strongly to a receptor, namely the ‘‘SAR by NMR’’ method [6] and the ‘‘tether method’’ [7]. These methods recognize that ligands may bind to conformations that occur only rarely in the dynamics of the receptor, and that strong binding often reflects multivalent attachment of the ligand to the receptor. The new computational approach includes single ligand and double ligand variants. The basic element of the new method is the automated flexible docking of small libraries of compounds to a diverse selection of target conformations. The first phase of the approach involves generating the target conformations. This might make use of a long molecular dynamics simulation of the unliganded target molecule, an ‘‘accelerated’’ molecular dynamics simulation that samples conformational space more effectively [8], or some other way of generating target conformations. The second phase involves the rapid docking of mini-libraries of candidate inhibitors to the selected set of conformational snapshots of the target. In this phase, a relatively simple scoring algorithm is used to allow fast docking. The third phase attempts to improve the scoring of the best complexes found in the docking calculations by use of a slower but more accurate algorithm for estimating the standard free energies of binding.

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The scheme described above represents the single ligand method. The double ligand variant recognizes that two ligands with relatively low binding affinities to the target can be linked to form a high-affinity ligand. Because the binding of the first ligand could introduce unfavorable interactions for the binding of the second ligand, the combination of the best-ranked ligands for respective binding sites does not necessarily produce the best composite compound. Continuing from the previous single-ligand studies, the first ligand may therefore be treated as part of the target, and the docking simulations of the second ligand may be repeated in a limited search space, based on the allowable lengths of linkers. Again, the binding of the second ligand is subsequently re-scored by other more accurate approaches. 2. Simple docking and rescoring to ensembles of protein conformations The first applications of the Relaxed Complex methods focused on an experimentally well-characterized system, FKBP [3,4]. A long molecular dynamics calculation was used to sample the FKBP conformations, and the AutoDock software [9] was used for the initial docking. The re-scoring was done using the MM/PBSA routines from the AMBER software [10] and APBS evaluation of the electrostatic energies [11]. The first paper [3] considered the binding of compounds 2 and 9 from the ‘‘SAR by NMR’’ paper by Shuker et al. [6] to snapshots obtained from a 2-ns molecular dynamics calculation. It was shown that the binding of the ligands is quite sensitive to conformational fluctuations of the target protein FKBP-12, even though the latter is a relatively rigid protein. In particular, with the AutoDock 3.0.5 scoring function, the binding energies of compound 2 covered a range of 3 to 4 kcal/ mol; this corresponds to a 100- to 1000-fold difference in binding affinities of the same ligand for slightly different conformations of the target protein (Fig. 1). In the second paper [4], re-scoring was done using the MM/ PBSA approach [10]. The solutions of the Poisson – Boltzmann equation were obtained using the APBS software [11]. As in

the first paper, significant ranges of binding energies were found for the ligands (dimethylsulfoxide, 4-hydroxy-2-butanone, and tetrahydrothiophene-1-oxide, in this case). These variations result in part from steric effects, since the difference between the largest and smallest solvent accessible molecular surface of the FKBP-12 binding site is found to be about 187 A2. For these ligands, use of the MM/PBSA re-scoring allowed the correct prediction of the binding modes, in comparison to the crystallographic structures, even though these ligands had weak affinities for the target. The MM/PBSA re-scoring has proven successful in ranking a number of ligands that bind to the FK506 binding protein FKBP-12 [4]. With the advent of a new docking algorithm (the Lamarckian genetic algorithm) and a very successful empirical free energy function, AutoDock [9] is able to perform very efficient docking of large flexible ligands and so has been used in our Relaxed Complex scheme. The so-called Lamarckian genetic algorithm is the hybrid of the original Genetic Algorithm [12] with the adaptive local search method. The local searcher modifies the phenotype, which is allowed to update the genotype. The so-called genome in the genetic algorithm consists of floating point ‘‘genes’’, each of which encodes one state variable describing the molecular position, orientation and conformation. The ligand begins randomly outside the protein, and explores translations, orientations, and conformations until an ideal site is found. In order to maintain the consistency of the free energy function parameters (see below), the restrained electrostatic potential (RESP) method [13] has been used to derive the partial charges of the ligands. 3. Rescoring with more accurate free energy calculations The third phase of the Relaxed Complex method improves the scoring of the best complexes found in the docking calculations. This is done by using a type of simulation inspired by the MM/PBSA (Molecular Mechanics/Poisson –Boltzmann, Surface Area) Method to calculate more accurate free energies of binding for a number of best-ranked complexes [10]. In the MM/PBSA method, protein –ligand complexes that have been

Fig. 1. Compares experimental findings and our relaxed complex docking results. On the left of the figure is the complex of FKBP-12 with compounds 2 and 9 as judged by Shuker’s SAR by NMR chemical shifts (from Fig. 3 of [6]). On the right is the complex generated by our Relaxed Complex computational docking scheme. As can be seen, the computed quaternary structure correlates well with the observed complex structure.

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subject to molecular dynamics simulations with an explicit solvent are post-processed with a continuum solvent model to estimate the free energy of binding of the ligand to the protein. Typically, the ligand and protein are separated and kept in fixed conformations corresponding to that of the complex. The solvation energies are then calculated using the PBSA method; the Poisson– Boltzmann equation provides an estimate of the electrostatic contributions to solvation, and the Surface Area method is used to provide a simple estimate of the nonpolar contributions to solvation. The advantage of replacing the explicit solvent by the continuum model is that it avoids the extensive sampling of configurations needed to achieve converged estimates of the solvation free energy in the explicit case. Molecular Mechanics (MM) is used to account for the direct interactions between ligand and protein in the complex. We have recently improved on the MM/PBSA approach in several ways [14]. Key to this has been the estimation of the values of the configuration integrals and corresponding standard free energies, based on statistical mechanical theory [15]. The PB (Poisson –Boltzmann) calculations have been performed using our new APBS software [11]. 4. Double-ligand Relaxed Complex method Two ligands with low binding affinities (e.g., dissociation constants in the millimolar range) to a target protein can be linked to form a high-affinity ligand. Therefore, it may be possible to design a potent drug by combining two or more ligands with relatively weak affinities. However, the binding of

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the first ligand could introduce unfavorable interactions for the binding of the second ligand; thus, the combination of the bestranked ligands for respective binding sites does not necessarily produce the best composite compound. Here, computational approaches can help elucidate the complex binding relationships with atomic detail. Continuing from the previous singleligand studies, the first ligand can be treated as part of the receptor, and the docking simulations of the second ligand can be repeated using a limited search space, based on the allowable lengths of linkers. Again, the binding of the second ligand would be subsequently re-scored by MM/PBSA and other approaches. Fig. 2 depicts preliminary work published by Lin et al. [4]. 5. Perspective and application to drug discovery The Relaxed Complex Method has been introduced to help account for the effects of target flexibility in computational studies of molecular recognition and binding. Because it involves the docking of full molecules, it is complementary to other methods such as the Dynamic Pharmacophore Method, which involves the docking of functional group probes to an ensemble of target conformations [16,17]. The latter method seems particularly well suited for somewhat earlier, higherthroughput stages of a drug discovery program, because it yields a pharmacophore that represents a consensus among a number of somewhat different target conformations. Methods that use soft harmonic modes to sample receptor conformations have also proven to be very fast and effective [18]. The

Fig. 2. This highlights the progression from the single ligand Relaxed Complex method to the double ligand method. Compound 2 was docked to every 10th snapshot from the MD run of FKBP-12 (the single ligand method). The best-ranked complex of compound 2 with FKBP-12 is shown on the left. Compound 2 was then treated as part of the receptor, and compound 9 was then docked to the sub-region of FKBP-12 that was within a possible linker distance from compound 2.

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Relaxed Complex Method is more likely to be useful in the later stages of a drug discovery program, since it is generally more computationally demanding. But, despite its recent origin, the Relaxed Complex Method has already proven valuable in suggesting a new approach to the development of inhibitors of HIV-1 Integrase [5]. Acknowledgments The author thanks his former postdoctoral fellow Jung-Hsin Lin (now Assistant Professor, National Taiwan University) and graduate students Alex Perryman and Julie Schames Pressman for their very important contributions to the work that is reviewed here. This work has been supported in part by the Howard Hughes Medical Institute, the National Institutes of Health, the National Science Foundation, the NSF Center for Theoretical Biological Physics, the W. M. Keck Foundation, the National Biomedical Computing Resource, the San Diego Supercomputer Center, and Accelrys Inc. References [1] C.N. Hodge, T.P. Straatsma, J.A. McCammon, A. Wlodawer, Rational design of HIV protease inhibitors, in: W. Chiu, R.M. Burnett, R. Garcea (Eds.), Structural Biology of Viruses, Oxford Univ. Press, 1997, pp. 451 – 473. [2] C.F. Wong, J.A. McCammon, Protein flexibility and computer-aided drug design, Annu. Rev. Pharmacol. Toxicol. 43 (2003) 31 – 45. [3] J.H. Lin, A. Perryman, J. Schames, J.A. McCammon, Computational drug design accommodating receptor flexibility—the relaxed complex scheme, J. Am. Chem. Soc. 124 (2002) 5632 – 5633. [4] J.H. Lin, A. Perryman, J. Schames, J.A. McCammon, The relaxed complex method: accommodating receptor flexibility for drug design with an improved scoring scheme, Biopolymers 68 (2003) 47 – 62. [5] J. Schames, R.H. Henchman, J.S. Siegel, C.A. Sotriffer, H. Ni, J.A. McCammon, Discovery of a novel binding trench in HIV integrase, J. Med. Chem. 47 (2004) 1879 – 1881.

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