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Introduction: MALT1 (mucosa-associated lymphoid tissue lymphoma translocation protein 1) is a key mediator of the NF-κB signaling pathway, the main driver of a subset of B-cell lymphomas and functions by forming a complex with CARMA1 and BCL10 to mediate antigen receptor-induced lymphocyte activation. MALT1 is considered a potential therapeutic target for several subtypes of non-Hodgkin B-cell lymphomas and chronic lymphocytic leukemia (CLL). Previously, we described the discovery of novel and potent MALT1 inhibitors with anti-proliferative effects in non-Hodgkin B-cell lymphoma cells. Here, we highlight the strong anti-tumor activity of our MALT1 inhibitors across multiple tumor models and the combination potential with agents including standard-of-care. Results: Novel small molecule MALT1 inhibitors were identified using Schrodinger's proprietary physics-based free energy perturbation (FEP+) modeling technology. These molecules demonstrate strong MALT1 protein binding affinity, potent inhibition of MALT1 enzymatic activity and anti-proliferative activity in the activated B-cell (ABC) subtype of diffuse large B cell lymphoma (DLBCL) cell lines such as OCI-LY3 and OCI-LY10. In combination with approved agents, these inhibitors demonstrate strong combination potential with Bruton's tyrosine kinase (BTK) inhibitors such as ibrutinib in ABC-DLBCL cell lines. In ABC-DLBCL CDX models, our representative MALT1 inhibitor induces tumor regression as a single agent and complete tumor regression in combination with ibrutinib. Our representative MALT1 inhibitor, when tested in LY2298 PDX models, demonstrates similar results. In addition, our representative MALT1 inhibitor was explored in a CDX model derived from a Mantle cell lymphoma REC-1 cell line, and demonstrates strong anti-tumor activity of ~78% tumor growth inhibition (TGI) as a single agent. Conclusions: Schrodinger's novel, potent MALT1 protease small molecule inhibitors are efficacious in in vitro B-cell lymphoma cell proliferation assays and in in vivo B-cell lymphoma xenograft models. These data suggest that targeting MALT1 may expand therapeutic options for patients with selected B-cell lymphomas, such as ABC-DLBCL, with the possibility of expanding into other B-cell lymphomas such as MCL. Furthermore, these small molecule MALT1 inhibitors demonstrate potential in combination with BTKi to overcome drug-induced resistance in patients with relapsed/refractory B-cell lymphomas. Taken together, the data presented here strongly underscore the therapeutic potential of our MALT1 inhibitor and support further evaluation in clinical trials. Disclosures Weiss: Schrodinger: Current Employment; ARTham Therapeutics: Membership on an entity's Board of Directors or advisory committees.

Introduction: MALT1 (mucosa-associated lymphoid tissue lymphoma translocation protein 1), was identified as a translocation protein fused with cIAP2 in mucosa-associated lymphoid tissue (MALT) B cell lymphomas. MALT1, a key mediator of NF-κB signaling and the main driver of a subset of B-cell lymphomas, functions via formation of a complex with CARMA1 and BCL10 to mediate antigen receptor-induced lymphocyte activation. MALT1 has been considered as a potential therapeutic target for several non-Hodgkin B cell lymphomas as well as chronic lymphocytic leukemia (CLL). Here, we describe the discovery of novel, potent MALT1 inhibitors that result in antiproliferative effects in non-Hodgkin B-cell lymphoma cells. Results: We have identified novel small molecule MALT1 inhibitors using our proprietary physics-based Free Energy Perturbation (FEP+) modeling technology. Our compounds show potent (sub nM) inhibition of MALT1 enzymatic activity, as well as high binding affinity (sub nM) to MALT1 protein measured by Surface Plasmon Resonance (SPR). BCL10 is a binding partner of MALT1 that is cleaved by MALT1 at the C-terminus. Our inhibitors were efficacious in a target engagement assay showing prevention of BCL10 cleavage in Activated B-cell (ABC) subtype of diffuse large B cell lymphoma (DLBCL) cell lines OCI-LY3 and OCI-LY10, which are Bruton tyrosine kinase (BTK) inhibitor ibrutinib-resistant and -responsive respectively. Our compounds are potent inhibitors of IL10 secretion in both OCI-LY3 and OCI-LY10 cells, which is consistent with the inhibition of NF-κB signaling. We also examined the effect of our MALT1 inhibitors on ABC-DLBCL cell proliferation. Our inhibitors demonstrated potent anti-proliferative effects in both OCI-LY3 and OCI-LY10 cell lines, as well as synergistic effects with ibrutinib in a BTKi sensitive ABC-DLBCL cell panel. Examinations of a protease panel and off-target safety screening panel, as well as in vivo high dose tolerability study showed our compound had excellent selectivity and significant safety margin. Plasma IL10 and tumor BCL10 have been identified as robust PD markers in PK/PD studies in both OCI-LY3 and OCI-LY10 tumor bearing mice. Dose-dependent tumor growth inhibition was observed after 3 weeks of treatment in OCI-LY3 xenograft model, with efficacy also observed in combination with venetoclax. Ongoing work: We are continuing to explore the synergistic effects of our compounds with BTK inhibitors in B-cell lymphoma mouse models. Preliminary data showed potent inhibition of IL-2 secretion in Jurkat cells from our compound treatment. Additional studies are ongoing to elucidate the role of MALT1 inhibition in Treg as well as Teffector cells in vitro and in vivo. Refinement of the current inhibitor series, using co-crystal structures, is in progress in preparation for further development of optimized molecules. Conclusion and Future Plans: We have identified novel potent MALT1 protease small molecule inhibitors that are efficacious in the in vitro B-cell lymphoma cell proliferation assays and in the in vivo B-cell lymphoma xenograft model. Our data suggest that targeting MALT1 may expand therapy options for patients with selected B-cell lymphomas, such as ABC-DLBCL. Our work provided insight into the anti-tumor efficacy of our inhibitors in B-cell lymphomas as single agent, and ongoing work will continue to assess the potential combination with BTKi to overcome drug-induced resistance in patients with relapsed/refractory B-cell lymphoma. Disclosures Yin: Schrodinger: Current Employment, Current equity holder in publicly-traded company. Zhe:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Placzek:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Trzoss:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Krilov:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Feng:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Lawrenz:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Pelletier:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Lai:Triplet Therapeutics: Current Employment, Current equity holder in private company. Bell:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Calkins:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Grimes:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Tang:Schrodinger: Current Employment, Current equity holder in publicly-traded company. McRobb:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Gerasyuto:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Feher:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Mondal:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Jensen:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Wright:Schrodinger: Current Employment, Current equity holder in publicly-traded company. Akinsanya:Schrodinger: Current Employment, Current equity holder in publicly-traded company.

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.

© 2015 American Chemical Society. Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

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.

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

Two simple models are developed to accurately account for short-range solvation effects in molecular dynamics simulations of biomolecular systems in water. The methods limit the amount of solvent that must be treated explicitly to two or three solvation shells, thereby eliminating the need for periodic boundary conditions, which significantly reduces the computational complexity. In the dynamic boundary model, a confining potential is imposed on the solvent that responds dynamically to fluctuations in the solvent distribution and conformations of the biomolecular solute. In the exchange boundary solvation model, the molecules at the surface of the explicit solvation shell are allowed to undergo pairwise exchanges with the bulk to maintain a uniform hydration of the solute. The models are used to study the solvation thermodynamics of a 11-residue polyalanine helix in water. A broad range of structural and dynamic properties characterizing both the protein and the surrounding solvent are computed. Comparison with the results obtained from conventional periodic boundary condition simulations shows excellent agreement, and the new solvation models are found to improve computational efficiency by up two orders of magnitude.

Mixtures of organic solvents are often used as membrane mimetics in structure determination of transmembrane proteins by solution NMR; however, the mechanism through which these isotropic solvents mimic the anisotropic environment of cell membranes is not known. Here, we use molecular dynamics simulations to study the solvation thermodynamics of the c-subunit of Escherichia coli F1F0 ATP synthase in membrane mimetic mixtures of methanol, chloroform, and water with varying fractions of components as well as in lipid bilayers. We show that the protein induces a local phase separation of the solvent components into hydrophobic and hydrophilic layers, which provides the anisotropic solvation environment to stabilize the amphiphilic peptide. The extent of this effect varies with solvent composition and is most pronounced in the ternary methanol-chloroform-water mixtures. Analysis of the solvent structure, including the local mole fraction, density profiles, and pair distribution functions, reveals considerable variation among solvent mixtures in the solvation environment surrounding the hydrophobic transmembrane region of the protein. Hydrogen bond analysis indicates that this is primarily driven by the hydrogen-bonding propensity of the essential Asp(61) residue. The impact of the latter on the conformational stability of the solvated protein is discussed. Comparison with the simulations in explicit all-atom models of lipid bilayer indicates a higher flexibility and reduced structural integrity of the membrane mimetic solvated c-subunit. This was particularly true for the deprotonated form of the protein and found to be linked to solvent stabilization of the charged Asp(61).

The fluctuating elastic boundary (FEB) model for molecular dynamics has recently been developed and validated through simulations of liquid argon. In the FEB model, a flexible boundary which consists of particles connected by springs is used to confine the solvated system, thereby eliminating the need for periodic boundary conditions. In this study, we extend this model to the simulation of bulk water and solvated alanine dipeptide. Both the confining potential and boundary particle interaction functions are modified to preserve the structural integrity of the boundary and prevent the leakage of the solute-solvent system through the boundary. A broad spectrum of structural and dynamic properties of liquid water are computed and compared with those obtained from conventional periodic boundary condition simulations. The applicability of the model to biomolecular simulations is investigated through the analysis of conformational population distribution of solvated alanine dipeptide. In most cases we find remarkable agreement between the two simulation approaches.

A new approach is developed to study the dynamics of the localized process in solutions and other condensed phase systems. The approach employs a fluctuating elastic boundary (FEB) model which encloses the simulated system in an elastic bag that mimics the effects of the bulk solvent. This alleviates the need for periodic boundary conditions and allows for a reduction in the number of solvent molecules that need to be included in the simulation. The boundary bag is modeled as a mesh of quasi-particles connected by elastic bonds. The FEB model allows for volume and density fluctuations characteristic of the bulk system, and the shape of the boundary fluctuates during the course of the simulation to adapt to the configuration fluctuations of the explicit solute-solvent system inside. The method is applied to the simulation of a Lennard-Jones model of liquid argon. Various structural and dynamical quantities are computed and compared with those obtained from conventional periodic boundary simulations. The agreement between the two is excellent in most cases, thus validating the viability of the FEB method.

We apply a computational scheme, in which geometrical (through-space) and topological (through-bond) distances are combined to construct geometry-dependent structural D/D matrices, in order to develop a matrix invariant characterization of Top7, a de novo sequence design by Kuhlman et al. (B. Kuhlman, G. Dantas, G. C. Ireton. G. Varani, B. L. Stoddard and D. Baker, Science, 2003, 302, 1364), which was shown to exhibit a novel α/β globular protein fold. A sequence of invariants KΦ, known as the “folding profile” of a protein, are extracted from the D/D matrix and its related higher order representations, and a similarity measure is introduced allowing invariant-based quantification of similarity between protein forms. Quantitative comparison of the Top7 crystal structure with the computationally designed model is achieved for the full protein backbone and a specific substructural segment: Lys46–Tyr76. The approach appears particularly attractive as it allows both global comparisons, as well as characterization of local structural features of proteins.

A method for calculating the quantum canonical rate constant of chemical reactions in a many body system by means of a short-time flux autocorrelation function combined with a maximum entropy numerical analytic continuation scheme is presented. The rate constant is expressed as the time integral of the real-time flux autocorrelation function. The real-time flux autocorrelation function is evaluated for short times fully quantum mechanically by path integral Monte Carlo simulations. The maximum entropy approach is then used to extract the rate from the short real-time flux autocorrelation data. We present two numerical tests, one for proton transfer in harmonic dissipative environments in the deep tunneling regime and the other for the two-level model of primary charge separation in the photosynthetic reaction center. The results obtained using the flux autocorrelation data up to the time of no more than βℏ are in excellent agreement with the exact quantum calculation over a wide range of parameters includ...

The structure and thermodynamics of a dipolar hard-sphere fluid at a discretely polarized hard wall are investigated using Monte Carlo computer simulation. In contrast to a continuously polarized wall, the discrete case exhibits significantly enhanced adsorption of the fluid at the surface relative to that observed for an unpolarized wall. Significant orientational ordering of the first liquid layer is observed plus correlation with the wall polarization that extends over several layers. The relative potential and free energy are calculated as a function of the polarization angle using thermodynamic integration.

We present the reactive flux analytic continuation (RFAC) method, based on the quantum reactive flux formalism combined with a numerical analytic continuation approach to calculate quantum canonical rates in condensed phase systems. We express the imaginary time reactive-flux correlation function in terms of a frequency dependent rate constant, and use path integral formalism to derive a working expression suitable for Monte Carlo simulation techniques. The imaginary time data obtained by simulation is analytically continued to the real time using the maximum entropy method to obtain the reaction rate. Motivated by the success of the method to predict the rates for a simple one dimensional parabolic barrier model, we assess its accuracy for a condensed phase reaction modeled by a double-well coupled to a harmonic bath. We note that the method is applicable to a more general Hamiltonian as long as the reaction coordinate can be identified. The reaction rates computed in this fashion are in very good agreement with analytic and numerically exact results. We demonstrate the applicability of the method for a wide range of model parameters and temperatures.

We investigate the accuracy of the recently proposed centroid molecular dynamics (CMD) method [J. Cao and G. A. Voth, J. Chem. Phys. 100, 5106 (1994)] in the presence of highly anharmonic steep short range repulsive potentials. Such potentials are often present in condensed phases and govern collisions between solvent particles. We compare the results of CMD simulations with exact quantum results for several model one- and two-dimensional nondissipative systems and a one-dimensional system under isobaric conditions. We show that, for nondissipative systems, CMD is accurate only for very short times, and is unable to reproduce the effects of quantum coherences, which play an important role in these few-dimensional systems. CMD gives much better results under isobaric conditions. The correlation functions and the general lineshape of the absorption cross-section in the dipole limit are well reproduced. This is primarily due to dephasing of quantum coherences through inhomogeneous broadening.

We propose a method which uses centroid molecular dynamics (CMD) [J. Cao and G. A. Voth, J. Chem. Phys. 100, 5106 (1994)] real-time data in conjunction with the imaginary-time data generated using path integral Monte Carlo simulations in a numerical analytic continuation scheme based on the maximum entropy approach. We show that significant improvement is achieved by including short-time CMD data with the imaginary-time data. In particular, for a particle bilinearly coupled to a harmonic bath, these methods lead to significant improvements over previous calculations and even allow accurate determination of transport coefficients such as the diffusion coefficient and mobility for this system. In addition we show how maximum entropy method can be used to extract accurate dynamic information from short-time CMD data, and that this approach is superior to the direct Fourier transform of long-time data for systems characterized by broad, featureless spectral distributions.

We consider the characterization of 3-D sequences of proteins. Using as the input the geometry of the sequences from matrices (dij)k, in which dij is the Euclidean distances between points along the path of the protein (K = 1, 2, 3,…) we derive the average values of suitably normalized matrix elements, referred to as protein profile. So derived invariants were used for characterization. The degree of similarity between differently folded models of proteins is examined using the derived protein profiles.

We consider the problem of quantitative characterization of the molecular surface. We start with a set of matrices, the elements of which give interatomic separation and higher powers of the separations. Averaged row sums of individual matrices suitably normalized give molecular profiles. The problem that we consider is how to generalize this approach to 2-dimensional and 3-dimensional objects. By using a large number of random points distributed over the molecular surface or molecular volume, respectively, we arrive at matrices from which one can extract invariants that offer a good characterization of the molecular surface and the molecular volume. It is suggested that the ratio V/S, where V and S are components of the volume and surface profile for a molecule, respectively, represents a novel shape index. © 1997 John Wiley & Sons, Inc. Int J Quant Chem 65: 1065–1076, 1997

The stabilization of secondary structure is believed to play an important role in the peptide-protein binding interaction. In this study, the α-helical conformation and structural stability of single and double stapled all-hydrocarbon cross-linked p53 peptides when bound and unbound to MDM2 are investigated. We determined the effects of the peptide sequence, the stereochemistry of the cross-linker, the conformation of the double bond in the alkene bridge, and the length of the bridge, to the relative stability of the α-helix structure. The binding affinity calculations by WaterMap provided over one hundred hydration sites in the MDM2 binding pocket where water density is greater than twice that of the bulk, and the relative value of free energy released by displacing these hydration sites. In agreement with the experimental data, potentials of mean force obtained by weighted histogram analysis methods indicated the order of peptides from lowest to highest binding affinity. Our study provides a comprehensive rationalization of the relationship between peptide stapling strategy, the secondary structural stability, and the binding affinity of p53/MDM2 complex. We hope our efforts can help to further the development of a new generation p53/MDM2 inhibitors that can reactivate the function of p53 as tumor suppressor gene.

Using the information on the interatomic separations in a molecule, one can construct structural invariants that are the components of a molecular profile. The entries in the profile are derived by averaging different powers of the interatomic separations, suitably normalized so that the increasing powers do not dominate the sequence. Although only a few hundreds of structures have been so analyzed, no two different chemical structures were found to be characterized by the same sequences. A critical test for the conjecture that molecular profiles are unique is to consider structurally closely related systems that are very similar and have several similar properties. In this contribution we investigated the cuboctahedron and the accompanying polyhedron obtained by rotating half of the cuboctahedron against the other half, resulting in the so-called twist cutoctahedron. We show that even this pair of closely related structures has different profiles. We have also examined the generalized molecular profiles obtained by inserting n additional points along each edge of the polyhedra. The convergence of the profiles as n increases is discussed. It appears thus that these generalized molecular profiles, called line profiles or bond profiles, are likely to lead to a unique characterization of structures in which not only the geometry of atoms is recorded but also the geometry of the connectivity of the structure. © 1996 John Wiley & Sons, Inc.

The mechanism (or mechanisms) of enthalpy− entropy (H/S) compensation in protein−ligand binding remains controversial, and there are still no predictive models (theoretical or experimental) in which hypotheses of ligand binding can be readily tested. Here we describe a particularly well-defined system of protein and ligands - human carbonic anhydrase (HCA) and a series of benzothiazole sulfonamide ligands with different patterns of fluorination - that we use to define enthalpy/entropy (H/S) compensation in this system thermodynamically and structurally. The binding affinities of these ligands (with the exception of one ligand, in which the deviation is understood) to HCA are, despite differences in fluorination pattern, indistinguishable; they nonetheless reflect significant and compensating changes in enthalpy and entropy of binding. Analysis reveals that differences in the structure and thermodynamic properties of the waters surrounding the bound ligands are an important contributor to the observed H/S compensation. These results support the hypothesis that the molecules of water filling the active site of a protein, and surrounding the ligand, are as important as the contact interactions between the protein and the ligand for biomolecular recognition, and in determining the thermodynamics of binding.

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.

PTEN is an important control element of PI3K/AKT signaling involved in controlling the processes of embryonic development, cell migration and apoptosis. While its dysfunction is implicated in a large fraction of cancers, PTEN activity in the same pathway may also contribute to metabolic syndromes such as diabetes. In those cases, selective inhibitors of PTEN may be useful. A new class of chiral PTEN inhibitors based on the 3-deoxy-phosphatidylinositol derivatives was recently identified [Wang et al. (2008) J. Am. Chem. Soc. 130, 7746]. However, lack of detailed understanding of protein-ligand interactions has hampered efforts to develop effective agonists or antagonists of PTEN. Here, we use computational modeling to characterize the interactions of the diverse 3-deoxyphosphatidylinositol inhibitors with the PTEN protein. We show that, while each of the compounds binds with the inositol headgroup inserting into the proposed active site of the PTEN phosphatase domain, hydrogen bonding restrictions lead to distinct binding geometries for ligand pairs of opposite chirality. We furthermore demonstrate that the binding modes differ primarily in the orientation of acyl tails of the ligands and that the activity of the compounds is primarily controlled by the effectiveness of tail-protein contacts. These findings are confirmed by binding affinity calculations which are in good agreement with experiment. Finally, we show that while more potent D-series ligands bind in a manner similar to that of the native substrate, an alternate hydrophobic pocket suitable for binding the opposite chirality L-series inhibitors exists, offering the possibility of designing highly selective PTEN- targeting compounds.

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.

We present a method based on augmenting an exact relation between a frequency-dependent diffusion constant and the imaginary time velocity autocorrelation function, combined with the maximum entropy numerical analytic continuation approach to study transport properties in quantum liquids. The method is applied to the case of liquid para -hydrogen at two thermodynamic state points: a liquid near the triple point and a high-temperature liquid. Good agreement for the self-diffusion constant and for the real-time velocity autocorrelation function is obtained in comparison to experimental measurements and other theoretical predictions. Improvement of the methodology and future applications are discussed.

We revisit the problem of self-diffusion in normal liquid helium above the lambda transition. Several different methods are applied to compute the velocity autocorrelation function. Since it is still impossible to determine the exact result for the velocity autocorrelation function from simulation, we appeal to the computation of short-time moments to determine the accuracy of the different approaches at short times. The main conclusion reached from our study is that both the quantum mode-coupling theory and the numerical analytic continuation approach must be regarded as a viable and competitive methods for the computation of dynamical properties of quantum systems.

Reactivation of the p53 cell apoptosis pathway through inhibition of the p53-hDM2 interaction is known to be a viable approach to suppressing tumor growth in many human cancers and stabilization of the helical structure of p53 analogs via a hydrocarbon cross-link (staple) has been found to lead to increased potency and inhibition of protein-protein binding. However, details of the structure and dynamic stability of the stapled peptides and their relationship to the nature and location of hydrocarbon linker are not well understood. Here, we use extensive molecular dynamics simulations to study a series of stapled α−helical peptides over a range of temperatures in solution. The peptides are found to exhibit substantial variations in predicted helicities that are in good agreement with the experimental values. In addition, we find significant variation in local structural flexibility of the peptides with the position of the linker, which appears to be more closely related to the observed differences in activity than the absolute helical stability. These simulations provide new insights into the design of α−helical stapled peptides and could aid in the development of potent inhibitors of protein interfaces.