Bioactive Conformational Biasing: A New Method for Focusing Conformational Ensembles on Bioactive-Like Conformers

Self Cite

The identification of bound conformations, namely, conformations adopted by ligands when binding their target is critical for target-based and ligand-based drug design. Bound conformations could be obtained computationally from unbound conformational ensembles generated by conformational search tools. However, these tools also generate many nonrelevant conformations thus requiring a focusing mechanism. To identify such a mechanism, this work focuses on a comparison of energies and structural properties of bound and unbound conformations for a set of FDA approved drugs whose complexes are available in the PDB. Unbound conformational ensembles were initially obtained with three force fields. These were merged, clustered, and reminimized using the same force fields and four QM methods. Bound conformations of all ligands were represented by their crystal structures or by approximations to these structures. Energy differences were calculated between global minima of the unbound state or the Boltzmann averaged energies of the unbound ensemble and the approximated bound conformations. Ligand conformations which resemble the X-ray conformation (RMSD < 1.0 Å) were obtained in 91%-97% and 96%-98% of the cases using the ensembles generated by the individual force fields and the reminimized ensembles, respectively, yet only in 52%-56% (original ensembles) and 47%-65% (reminimized ensembles) as global energy minima. The energy window within which the different methods identified the bound conformation (approximated by its closest local energy minimum) was found to be at 4-6 kcal/mol with respect to the global minimum and marginally lower with respect to a Boltzmann averaged energy of the unbound ensemble. Better approximations to the bound conformation obtained with a constrained minimization using the crystallographic B-factors or with a newly developed Knee Point Detection (KPD) method gave lower values (2-5 kcal/mol). Overall, QM methods gave lower energy differences than force field methods. These energy thresholds could be used for focusing conformational ensembles on bound conformations. For example, when using energy cutoffs which corresponded to retaining 50% and 70% of the ensembles, QM methods and CHARMm offer 60-65% and 80-84% probability of obtaining the bound conformation, respectively. In contrast, none of the structural criteria considered in this work was able to differentiate between bound and unbound conformations.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Focusing Conformational Ensembles on Bioactive Conformations: A Molecular Mechanics/Quantum Mechanics Study

Avgy-David

Senderowitz

2015

Self Cite

“…So, the methods for conformational analysis and sampling of small organic molecules remain a very active field of research. 10,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] New developments have taken advantage of increased computational resources as well methodological advances, e.g. around the distance geometry approach.…”

Section: Introductionmentioning

confidence: 99%

“…via pharmacophore-based or scaffold-hopping searches, and can also be used in conjunction with docking. So, the methods for conformational analysis and sampling of small organic molecules remain a very active field of research. ,– New developments have taken advantage of increased computational resources as well methodological advances, e.g. around the distance geometry approach. , Overall, these efforts have led to the availability of high-throughput conformation generators, such as CAESAR, Catalyst, ConfGen, MOE Conformation Import, Omega, and Rubicon .…”

Section: Introductionmentioning

confidence: 99%

Drug-like Bioactive Structures and Conformational Coverage with the LigPrep/ConfGen Suite: Comparison to Programs MOE and Catalyst

Chen

Foloppe

2010

160

Computational conformational sampling underpins many aspects of small molecule modeling and design in pharmaceutical work. This work examined in detail the widely distributed LigPrep/ConfGen software suite and the conformational models it produces for drug-like compounds. We also compare LigPrep/ConfGen to MOE and Catalyst. Tests of the conformational sampling protocols included the reproduction of known bioactive structures of ligands, characterization of the size, coverage and diversity of the output conformational models, and relative computation times. The present tests will help the user to make informed choices among the predefined ConfGen protocols (Very fast, Fast, Intermediate, and Comprehensive), and the adjustable input parameters. The parameters governing the initial compound preparation (LigPrep) and the subsequent conformational sampling were explored. This analysis has led to a new protocol called "ConfGen Optimized", which improves upon the predefined protocols. ConfGen Optimized is computationally tractable and reproduced 80% of the bioactive structures within 1 A, versus 66% for the default ConfGen Fast protocol. We also addressed the issue of the reproduction of compact/folded bioactive structures by ConfGen. It involved the compilation of a new set of 50 folded diverse drug-like bioactive structures. This indicates that heuristics penalizing folded conformers hinder reproduction of some binding modes. Overall, ConfGen offers great flexibility of use and provides a valuable addition to the molecular modeling toolbox.

“…[22][23][24][25][26] A number of lead design strategies spanning frontier areas of drug design like QSAR, 27,28 high-throughput screening, 29 pharmacophore mapping, 30 combinatorial libraries, 31 traditional drug design techniques, 32,33 etc., have been used either individually or as a combination to understand the nuances underlying specificity and selectivity in kinases, which are one of the most sought after class of druggable targets. Different aspects of kinases like size of gatekeeper residue, 34 metabolic properties of the target involved in signal transduction, 35,36 protonation switch in drug binding, 37 network analysis and fingerprint of structure and sequence of kinome, 38 recognition properties affecting selectivity, 39 chemogenomics, 40 and fragment-based drug design [41][42][43][44] have been probed. In spite of the ongoing efforts, kinase inhibitors lack specificity and selectivity.…”

Section: Introductionmentioning

confidence: 99%

Sequence, Structure, and Active Site Analyses of p38 MAP Kinase: Exploiting DFG-out Conformation as a Strategy to Design New Type II Leads

Badrinarayan

Sastry

2010

A new knowledge, structure, and sequence based strategy involving the effective exploitation of the DFG-out conformation is delineated. A comprehensive analysis of the structure, sequence, cocrystals, and active sites of p38 MAP kinase crystal structures present in Protein Data Bank (PDB) and the FDA approved MAP kinase drugs has been done, and the information is used for the design of type II leads. The 98 crystal structures, 138 cocrystals, and 31 FDA drugs comprise of 7 different sequences of 2 organisms viz., Homo sapiens and Mus musculus differing in sequence length, constituting both homo- and heterochains. Multiple sequence alignment with ClustalW showed >95% sequence similarity with highly conserved domains and a high propensity for mutations in the activation loop. The bound ligands were extracted, and their interactions with DFG in and out conformations were studied. These cocrystals and FDA drugs were fragmented on the basis of their binding interactions and their affinity to ATP and allosteric sites. The fragment library thus generated contains 106 fragments with overlapping drug fragments. A blue print constituting three main parts viz., head (ATP region), linker (DFG region), and tail (allosteric region) has thus been formulated and used to design 64 type II p38 MAP kinase inhibitors. The above strategy has been employed to design potent type II p38 MAP kinase inhibitors, which are shown to be very promising.