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Patent
Quantumbio, Inc. | Date: 2016-02-05

The invention is a diagnostic which overlays quantum mechanical analysis to x-ray crystallography data from one or more proteins to assess and identify the real world conformation, protonation and solvent effects of one or more moieties in said protein. This overlay occurs by scoring and identifying the protomer/tautomer states of the moieties using quantum mechanical analysis. The diagnostic results of the present invention accurately identify protein-ligand binding, rendered as an output to a user of a computer in which the x-ray crystallography data is analysed with semi-empirical Hamiltonian quantum mechanics and


Borbulevych O.Y.,Quantumbio, Inc. | Plumley J.A.,Quantumbio, Inc. | Martin R.I.,Quantumbio, Inc. | Merz K.M.,University of Florida | Westerhoff L.M.,Quantumbio, Inc.
Acta Crystallographica Section D: Biological Crystallography | Year: 2014

Macromolecular crystallographic refinement relies on sometimes dubious stereochemical restraints and rudimentary energy functionals to ensure the correct geometry of the model of the macromolecule and any covalently bound ligand(s). The ligand stereochemical restraint file (CIF) requires a priori understanding of the ligand geometry within the active site, and creation of the CIF is often an error-prone process owing to the great variety of potential ligand chemistry and structure. Stereochemical restraints have been replaced with more robust functionals through the integration of the linear-scaling, semiempirical quantum-mechanics (SE-QM) program DivCon with the PHENIX X-ray refinement engine. The PHENIX/DivCon package has been thoroughly validated on a population of 50 protein-ligand Protein Data Bank (PDB) structures with a range of resolutions and chemistry. The PDB structures used for the validation were originally refined utilizing various refinement packages and were published within the past five years. PHENIX/DivCon does not utilize CIF(s), link restraints and other parameters for refinement and hence it does not make as many a priori assumptions about the model. Across the entire population, the method results in reasonable ligand geometries and low ligand strains, even when the original refinement exhibited difficulties, indicating that PHENIX/DivCon is applicable to both single-structure and high-throughput crystallography. © 2014 International Union of Crystallography.


Pohl G.,City College of New York | Plumley J.A.,City College of New York | Plumley J.A.,Quantumbio, Inc. | Dannenberg J.J.,City College of New York
Journal of Chemical Physics | Year: 2013

We present density functional theory calculations designed to evaluate the importance of π-stacking interactions to the stability of in-register Phe residues within parallel β-sheets, such as amyloids. We have used a model of a parallel H-bonded tetramer of acetylPheNH2 as a model and both functionals that were specifically designed to incorporate dispersion effects (DFs), as well as, several traditional functionals which have not been so designed. None of the functionals finds a global minimum for the π-stacked conformation, although two of the DFs find this to be a local minimum. The stacked phenyls taken from the optimized geometries calculated for each functional have been evaluated using MP2 and CCSD(T) calculations for comparison. The results suggest that π-stacking does not make an important contribution to the stability of this system and (by implication) to amyloid formation. © 2013 AIP Publishing LLC.


Zhang X.,Quantumbio, Inc. | Gibbs A.C.,Johnson and Johnson Pharmaceutical Research and Development L.L.C | Reynolds C.H.,Johnson and Johnson Pharmaceutical Research and Development L.L.C | Peters M.B.,Quantumbio, Inc. | And 2 more authors.
Journal of Chemical Information and Modeling | Year: 2010

Quantum mechanical semiempirical comparative binding energy analysis calculations have been carried out for a series of protein kinase B (PKB) inhibitors derived from fragment- and structure-based drug design. These protein-ligand complexes were selected because they represent a consistent set of experimental data that includes both crystal structures and affinities. Seven scoring functions were evaluated based on both the PM3 and the AM1 Hamiltonians. The optimal models obtained by partial least-squares analysis of the aligned poses are predictive as measured by a number of standard statistical criteria and by validation with an external data set. An algorithm has been developed that provides residue-based contributions to the overall binding affinity. These residue-based binding contributions can be plotted in heat maps so as to highlight the most important residues for ligand binding. In the case of these PKB inhibitors, the maps show that Met166, Thr97, Gly43, Glu114, Ala116, and Val50, among other residues, play an important role in determining binding affinity. The interaction energy map makes it easy to identify the residues that have the largest absolute effect on ligand binding. The structure-activity relationship (SAR) map highlights residues that are most critical to discriminating between more and less potent ligands. Taken together the interaction energy and the SAR maps provide useful insights into drug design that would be difficult to garner in any other way. © 2010 American Chemical Society.


Diller D.J.,Pfizer | Humblet C.,Pfizer | Zhang X.,Quantumbio, Inc. | Westerhoff L.M.,Quantumbio, Inc.
Proteins: Structure, Function and Bioinformatics | Year: 2010

Alanine scanning is a powerful experimental tool for understanding the key interactions in protein-protein interfaces. Linear scaling semiempirical quantum mechanical calculations are now sufficiently fast and robust to allow meaningful calculations on large systems such as proteins, RNA and DNA. In particular, they have proven useful in understanding protein-ligand interactions. Here we ask the question: can these linear scaling quantum mechanical methods developed for protein-ligand scoring be useful for computational alanine scanning? To answer this question, we assembled 15 protein-protein complexes with available crystal structures and sufficient alanine scanning data. In all, the data set contains ΔΔGs for 400 single point alanine mutations of these 15 complexes. We show that with only one adjusted parameter the quantum mechanics-based methods outperform both buried accessible surface area and a potential of mean force and compare favorably to a variety of published empirical methods. Finally, we closely examined the outliers in the data set and discuss some of the challenges that arise from this examination. © 2010 Wiley-Liss, Inc.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 147.91K | Year: 2011

DESCRIPTION (provided by applicant): Improving human health by enabling the development of drugs faster and cheaper is an important part of the NIH mission. This is partially achieved by introducing and constantly improving enabling technologies. One suchtechnology is structure based drug design. Determining the structure of a small molecule (drug candidate or lead compound) to a biological receptor (protein implicated in disease) is a necessary step in this methodology. The dominant experimental approachused to achieve this goal is X- ray crystallography, while nuclear magnetic resonance (NMR) plays a lesser role in spite of large investments both in academia and industry. NMR is hampered by the size of protein that can be studied and the need to go through a lengthy structure determination process. However, with the advent of fragment based drug design, NMR is playing a much larger role and it could play an even greater role if it was possible to reduce the time effort necessary to solve the structure ofa protein-ligand complex. Moreover, in cases where it is not possible to obtain a crystal NMR can play a significant role. Through the use of solid-state NMR studies membrane proteins or proteins with solubility problems can be studied or in cases where only homology models of a protein are available NMR could play a role through the validation of active site structure hypotheses generated in homology modeling studies. The aim of the proposed research is to extend and commercialize QuantumBio's successful linear-scaling semiempirical quantum mechanical NMR approach (NMRScore) to chemical shift perturbation (CSP) analysis through the addition of target-observed CSP and ab initio NMR methods. In Phase I of this proposal the limits of applicability will be explored. In the Phase II proposal extension of the methodology via reparameterization of 1H, 13C 17O and 15N NMR will be carried out and a new classical NMR predictor will be developed. Furthermore, the streamlining of the workflow will be researched and implemented. Finally, this proposal is aiming to fully productize and commercialize this breakthrough technology. It is anticipated that by making this application commercially available the use of NMR in structure-based design efforts will be enhanced and theNMR tool and service market size can be further expanded. Significantly, the tool-box of structure based drug design will gain an important new method which will enable drug development for targets inaccessible to today's mainstream drug discovery paradigm. Thus, in the near future important underserved diseases can be targeted more efficiently. PUBLIC HEALTH RELEVANCE: The successful completion of the Fast-Track SBIR grant will have a major impact on improving human health. It will improve the quality of protein structures, facilitate the understanding of biomolecular dynamics and will provide higher quality structural insights into protein/ligand (drug) interactions which will enhance our ability to rationally design novel therapeutics for human diseases.


Grant
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase II | Award Amount: 1.13M | Year: 2015

DESCRIPTION provided by applicant Our long term goal is to provide a solution to the protein ligand binding affinity and pose prediction problems The protein ligand docking and scoring problem is one of the central problems in computational biology because of its importance in understanding intermolecular interactions and because of its practical payoff The transformative impact molecular docking and scoring can have in the design of next generation medicines cannot be overstated If we could routinely and accurately design molecules using these approaches it would revolutionize drug discovery by winnowing out compounds with no activity while focusing more effort and scrutiny on highly active compounds In this proposal we describe a novel method we call Movable Type MT which addresses the protein ligand binding and scoring problem using fundamental statistical mechanics combined with a novel way to generate the ensemble of a ligand in a protein binding pocket Via a rapid assembly of the necessary partition functions we directly obtain binding free energies and the low free energy poses Conceptually the MT method is analogous to block and type set printing which allows us to efficiently evaluate partition functions describing regions or systems of interest In this approach we construct two databases that describe the probability of certain pairwise interactions as a function of r obtained from a knowledge base Protein Databank PDB or the Cambridge Structural Database CSD and the energetics of the pairwise interactions as a function of r obtained from empirical potentials which can be either derived from the probabilities or can utilize extant pairwise potentials like AMBER Overall the MT method is a general one and can use a broad range of two body potential functions and can be extended to higher order interactions if so desired In the present project we will extend and further validat the MT method and develop commercial quality software to deliver this methodology to users via the web and GUI This will involve collaboration between the academic laboratory and the industrial laboratory development of a new implementation of the method in order to commercially deploy the technology construction of a graphical user interface based on MOE along with a web based interface and finally use of this software in real life structure based drug discovery problems on site with our pharmaceutical collaborators see Letters of Support PUBLIC HEALTH RELEVANCE The successful completion of the SBIR grant will have a major impact on improving human health It will improve the quality of protein structures facilitate our understanding of bio molecular structure and function and will provide higher quality structural insights into protein ligand drug interactions which will enhance our ability o rationally design novel therapeutics for human diseases


Grant
Agency: Department of Health and Human Services | Branch: National Institutes of Health | Program: SBIR | Phase: Phase I | Award Amount: 141.09K | Year: 2015

DESCRIPTION provided by applicant Success in structure based drug design SBDD and fragment based drug design are ultimately largely dependent upon the quality of the three dimensional D structure of protein ligand and protein protein complexes that is the core structural technique Both nuclear magnetic resonance NMR and X ray crystallography are used to determine experimental models pertaining to these structures Through a previous project QuantumBio Inc improved the quality of the X ray refinement through integration of the Companyandapos s quantum mechanics QM based DivCon Discovery Suite with the PHENIX crystallographic package This natural synergy brings the power and accuracy of quantum mechanics to the field of X ray refinement as it plays to the core strengths of QM methods e g no atom types support for more andquot exoticandquot chemical systems metals and so on This early success has led to an expanded commercial offering that was released in February The present proposal is focused on a further improvement the accuracy of the X ray refinement protocol by incorporating improved explicit solvent structure determination It is quite clear an there is a growing body of evidence that indicates that the influences of solvent have significant impact on the ligand and receptor structure as well as on the energetics of the binding Very often exploration of an active site such as in lead discovery and optimization is a question of whether or not additional andquot unseenandquot waters are mediating the interactions between ligand protein cofactor and so on An intrinsic problem in macromolecular X ray crystallography is that only a partial number of solvent molecules can be unambiguously revealed due to the resolution limitations Unlike approaches such as WaterMap conventional D RISM and SZMAP which are used to predict waters regardless of their agreement with experiment the key innovation of this method is through the use of an advanced explicit water determination algorithm to filter crystallographic data and generate the complete experimental solvent structure within the macromolecular complex In preliminary studies performed with our partners the evidence that this approach is applicable to the problem at hand is quite compelling Specifically the results for the A lysozyme crystal structure EPE have shown that the application of the new solvation methodology leads to twice as many waters or a improvement in the hydration shell for the low resolution lysozyme structure accompanied by the improvement of the overall crystallographic statistics The method also successfully found key crystallographic andquot bridgingandquot waters along with active site pocket stabilization waters when executed on the protein ligand complex represented in PDBid ERQ Together these preliminary results are quite encouraging and completion of this SBIR will allow us to completely generalize and validate the method and prepare it for commercial deployment PUBLIC HEALTH RELEVANCE The successful completion of the SBIR grant will have a major impact on improving human health It will improve the quality of protein structures facilitate the understanding of biomolecular dynamics and will provide higher quality structural insights into protein ligand drug interactions which will enhance our ability to rationally desig novel therapeutics for human diseases The research will yield software package and refinement service available to the pharmaceutical industry for the determination of improved X ray models


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.07M | Year: 2012

DESCRIPTION (provided by applicant): Improving human health by enabling the development of drugs faster and cheaper is an important part of the NIH mission. This is partially achieved by introducing and constantly improving enabling technologies. One suchtechnology is structure based drug design. Determining the structure of a small molecule (drug candidate or lead compound) to a biological receptor (protein implicated in disease) is a necessary step in this methodology. The dominant experimental approachused to achieve this goal is X- ray crystallography, while nuclear magnetic resonance (NMR) plays a lesser role in spite of large investments both in academia and industry. NMR is hampered by the size of protein that can be studied and the need to go through a lengthy structure determination process. However, with the advent of fragment based drug design, NMR is playing a much larger role and it could play an even greater role if it was possible to reduce the time effort necessary to solve the structure ofa protein-ligand complex. Moreover, in cases where it is not possible to obtain a crystal NMR can play a significant role. Through the use of solid-state NMR studies membrane proteins or proteins with solubility problems can be studied or in cases where only homology models of a protein are available NMR could play a role through the validation of active site structure hypotheses generated in homology modeling studies. The aim of the proposed research is to extend and commercialize QuantumBio's successful linear-scaling semiempirical quantum mechanical NMR approach (NMRScore) to chemical shift perturbation (CSP) analysis through the addition of target-observed CSP and ab initio NMR methods. In Phase I of this proposal the limits of applicability will be explored. In the Phase II proposal extension of the methodology via reparameterization of 1H, 13C 17O and 15N NMR will be carried out and a new classical NMR predictor will be developed. Furthermore, the streamlining of the workflow will be researched and implemented. Finally, this proposal is aiming to fully productize and commercialize this breakthrough technology. It is anticipated that by making this application commercially available the use of NMR in structure-based design efforts will be enhanced and theNMR tool and service market size can be further expanded. Significantly, the tool-box of structure based drug design will gain an important new method which will enable drug development for targets inaccessible to today's mainstream drug discovery paradigm. Thus, in the near future important underserved diseases can be targeted more efficiently.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 165.30K | Year: 2014

DESCRIPTION (provided by applicant): Our long-term goal is to provide a solution to the protein-ligand binding affinity and pose prediction problems. The protein-ligand docking and scoring problem is one of the central problems in computational biology because of its importance in understanding intermolecular interactions, and because of its practical payoff. The transformative impact molecular docking and scoring can have in the design of next generation medicines cannot be overstated. If we could routinely and accurately design molecules using these approaches it would revolutionize drug discovery by winnowing out compounds with no activity while focusing more effort and scrutiny on highly active compounds. In this proposal we describe a novel method we call Movable Type (MT), which addresses the protein ligand binding and scoring problem using fundamental statistical mechanics combined with a novel way to generate the ensemble of a ligand in a protein binding pocket. Via a rapid assembly of the necess

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