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The parametrization and validation of the OPLS3 force field for small molecules and proteins are reported. Enhancements with respect to the previous version (OPLS2.1) include the addition of off-atom charge sites to represent halogen bonding and aryl nitrogen lone pairs as well as a complete refit of peptide dihedral parameters to better model the native structure of proteins. To adequately cover medicinal chemical space, OPLS3 employs over an order of magnitude more reference data and associated parameter types relative to other commonly used small molecule force fields (e.g., MMFF and OPLS_2005). As a consequence, OPLS3 achieves a high level of accuracy across performance benchmarks that assess small molecule conformational propensities and solvation. The newly fitted peptide dihedrals lead to significant improvements in the representation of secondary structure elements in simulated peptides and native structure stability over a number of proteins. Together, the improvements made to both the small molecule and protein force field lead to a high level of accuracy in predicting protein-ligand binding measured over a wide range of targets and ligands (less than 1 kcal/mol RMS error) representing a 30% improvement over earlier variants of the OPLS force field.

Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

Accurate and efficient affinity calculations are critical to enhancing the contribution of in silico modeling during the lead optimization phase of a drug discovery campaign. Here, we present a large-scale study of the efficacy of data fusion strategies to leverage results from end-point MM/GBSA calculations in multiple receptors to identify potent inhibitors among an ensemble of congeneric ligands. The retrospective analysis of 13 congeneric ligand series curated from publicly available data across seven biological targets demonstrates that in 90% of the individual receptor structures MM/GBSA scores successfully identify subsets of inhibitors that are more potent than a random selection, and data fusion strategies that combine MM/GBSA scores from each of the receptors significantly increase the robustness of the predictions. Among nine different data fusion metrics based on consensus scores or receptor rankings, the SumZScore (i.e., converting MM/GBSA scores into standardized Z-Scores within a receptor and computing the sum of the Z-Scores for a given ligand across the ensemble of receptors) is found to be a robust and physically meaningful metric for combining results across multiple receptors. Perhaps most surprisingly, even with relatively low to modest overall correlations between SumZScore and experimental binding affinities, SumZScore tends to reliably prioritize subsets of inhibitors that are at least as potent as those that are prioritized from a "best" single receptor identified from known compounds within the congeneric series.

G protein-coupled receptors (GPCRs) represent a large family of signaling proteins that includes many therapeutic targets. GPCR ligands include odorants, tastants, and neurotransmitters and vary in size and properties. Dramatic chemical diversity may occur even among ligands of the same receptor. Our goal is to unravel the structural and chemical features that determine GPCRs' promiscuity toward their ligands. We perform statistical analysis using more than 30 descriptors related to the sequence, physicochemical, structural, and energetic properties of the GPCR binding sites-we find that the chemical variability of antagonists significantly correlates with the binding site hydrophobicity and anticorrelates with the number of hydrogen bond donors in the binding site. The number of disulfide bridges in the extracellular region of a receptor anticorrelates with the range of molecular weights of its antagonists, highlighting the role of the entrance pathway in determining the size selectivity for GPCR antagonists. The predictive capability of the model is successfully validated using a separate set of GPCRs, using either X-ray structures or homology models.

Author(s): Fechner, Uli; de Graaf, Chris; Torda, Andrew E; Gussregen, Stefan; Evers, Andreas; Matter, Hans; Hessler, Gerhard; Richmond, Nicola J; Schmidtke, Peter; Segler, Marwin HS; Waller, Mark P; Pleik, Stefanie; Shea, Joan-Emma; Levine, Zachary; Mullen, Ryan; van den Broek, Karina; Epple, Matthias; Kuhn, Hubert; Truszkowski, Andreas; Zielesny, Achim; Fraaije, Johannes Hans; Gracia, Ruben Serral; Kast, Stefan M; Bulusu, Krishna C; Bender, Andreas; Yosipof, Abraham; Nahum, Oren; Senderowitz, Hanoch; Krotzky, Timo; Schulz, Robert; Wolber, Gerhard; Bietz, Stefan; Rarey, Matthias; Zimmermann, Markus O; Lange, Andreas; Ruff, Manuel; Heidrich, Johannes; Onlia, Ionut; Exner, Thomas E; Boeckler, Frank M; Bermudez, Marcel; Firaha, Dzmitry S; Holloczki, Oldamur; Kirchner, Barbara; Tautermann, Christofer S; Volkamer, Andrea; Eid, Sameh; Turk, Samo; Rippmann, Friedrich; Fulle, Simone; Saleh, Noureldin; Saladino, Giorgio; Gervasio, Francesco L; Haensele, Elke; Banting, Lee; Whitley, David C; Oliveira Santos, Jana Sopkova-de; Bureau, Ronan; Clark, Timothy; Sandmann, Achim; Lanig, Harald; Kibies, Patrick; Heil, Jochen; Hoffgaard, Franziska; Frach, Roland; Engel, Julian; Smith, Steven; Basu, Debjit; Rauh, Daniel; Kohlbacher, Oliver; Boeckler, Frank M; Essex, Jonathan W; Bodnarchuk, Michael S; Ross, Gregory A; Finkelmann, Arndt R; Goller, Andreas H; Schneider, Gisbert; Husch, Tamara; Schutter, Christoph; Balducci, Andrea; Korth, Martin; Ntie-Kang, Fidele; Gunther, Stefan; Sippl, Wolfgang; Mbaze, Luc Meva'a

We present a new computational strategy for the design and evaluation of novel enzymatic pathways for the biosynthesis of fuels and chemicals. The approach combines the use of the Biochemical Network Integrated Computational Explorer (BNICE) framework and a structure-based screening method for rapid generation and evaluation of novel enzymatic reactions and pathways. The strategy is applied to a case study of 1-butanol production from pyruvate, which yielded nine novel biosynthetic pathways. Using screening criteria based on pathway length, thermodynamic feasibility, and metabolic flux analysis, all nine novel pathways were deemed to be attractive candidates. To further assess their feasibility of implementation, we introduced a new screening criterion based on structural complementarity using molecular docking methods. We show that this approach correctly reproduces the native binding poses for a wide range of enzymes in key classes related to 1-butanol production and provides qualitative agreement with experimental measures of catalytic activity for different substrates. In addition, we show that the structure-based methods can be used to select specific proteins that may be promising candidates to catalyze novel reactions.

Detailed understanding of protein-ligand interactions is crucial to the design of more effective drugs. This is particularly true when targets are protein interfaces which have flexible, shallow binding sites that exhibit substantial structural rearrangement upon ligand binding. In this study, we use molecular dynamics simulations and free energy calculations to explore the role of ligand-induced conformational changes in modulating the activity of three generations of Bcl-X(L) inhibitors. We show that the improvement in the binding affinity of each successive ligand design is directly related to a unique and measurable reduction in local flexibility of specific regions of the binding groove, accompanied by the corresponding changes in the secondary structure of the protein. Dynamic analysis of ligand-protein interactions reveals that the latter evolve with each new design consistent with the observed increase in protein stability, and correlate well with the measured binding affinities. Moreover, our free energy calculations predict binding affinities which are in qualitative agreement with experiment, and indicate that hydrogen bonding to Asn100 could play a prominent role in stabilizing the bound conformations of latter generation ligands, which has not been recognized previously. Overall our results suggest that molecular dynamics simulations provide important information on the dynamics of ligand-protein interactions that can be useful in guiding the design of small-molecule inhibitors of protein interfaces.