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  1. Article: An Analysis of Proteochemometric and Conformal Prediction Machine Learning Protein-Ligand Binding Affinity Models.

    Parks, Conor / Gaieb, Zied / Amaro, Rommie E

    Frontiers in molecular biosciences

    2020  Volume 7, Page(s) 93

    Abstract: Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we ... ...

    Abstract Protein-ligand binding affinity is a key pharmacodynamic endpoint in drug discovery. Sole reliance on experimental design, make, and test cycles is costly and time consuming, providing an opportunity for computational methods to assist. Herein, we present results comparing random forest and feed-forward neural network proteochemometric models for their ability to predict pIC50 measurements for held out generic Bemis-Murcko scaffolds. In addition, we assess the ability of conformal prediction to provide calibrated prediction intervals in both a retrospective and semi-prospective test using the recently released Grand Challenge 4 data set as an external test set. In total, random forest and deep neural network proteochemometric models show quality retrospective performance but suffer in the semi-prospective setting. However, the conformal predictor prediction intervals prove to be well-calibrated both retrospectively and semi-prospectively showing that they can be used to guide hit discovery and lead optimization campaigns.
    Language English
    Publishing date 2020-06-24
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2814330-9
    ISSN 2296-889X
    ISSN 2296-889X
    DOI 10.3389/fmolb.2020.00093
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  2. Article: Optimal experimental design for efficient toxicity testing in microphysiological systems: A bone marrow application.

    Cairns, Jonathan / Leonard, Emilyanne / Khan, Kainat / Parks, Conor / Maglennon, Gareth / Zhang, Bairu / Lazic, Stanley E / Ewart, Lorna / David, Rhiannon

    Frontiers in pharmacology

    2023  Volume 14, Page(s) 1142581

    Abstract: Introduction: ...

    Abstract Introduction:
    Language English
    Publishing date 2023-03-31
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2587355-6
    ISSN 1663-9812
    ISSN 1663-9812
    DOI 10.3389/fphar.2023.1142581
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Transcriptional signatures associated with persisting CD19 CAR-T cells in children with leukemia.

    Anderson, Nathaniel D / Birch, Jack / Accogli, Theo / Criado, Ignacio / Khabirova, Eleonora / Parks, Conor / Wood, Yvette / Young, Matthew D / Porter, Tarryn / Richardson, Rachel / Albon, Sarah J / Popova, Bilyana / Lopes, Andre / Wynn, Robert / Hough, Rachael / Gohil, Satyen H / Pule, Martin / Amrolia, Persis J / Behjati, Sam /
    Ghorashian, Sara

    Nature medicine

    2023  Volume 29, Issue 7, Page(s) 1700–1709

    Abstract: In the context of relapsed and refractory childhood pre-B cell acute lymphoblastic leukemia (R/R B-ALL), CD19-targeting chimeric antigen receptor (CAR)-T cells often induce durable remissions, which requires the persistence of CAR-T cells. In this study, ...

    Abstract In the context of relapsed and refractory childhood pre-B cell acute lymphoblastic leukemia (R/R B-ALL), CD19-targeting chimeric antigen receptor (CAR)-T cells often induce durable remissions, which requires the persistence of CAR-T cells. In this study, we systematically analyzed CD19 CAR-T cells of 10 children with R/R B-ALL enrolled in the CARPALL trial via high-throughput single-cell gene expression and T cell receptor sequencing of infusion products and serial blood and bone marrow samples up to 5 years after infusion. We show that long-lived CAR-T cells developed a CD4/CD8 double-negative phenotype with an exhausted-like memory state and distinct transcriptional signature. This persistence signature was dominant among circulating CAR-T cells in all children with a long-lived treatment response for which sequencing data were sufficient (4/4, 100%). The signature was also present across T cell subsets and clonotypes, indicating that persisting CAR-T cells converge transcriptionally. This persistence signature was also detected in two adult patients with chronic lymphocytic leukemia with decade-long remissions who received a different CD19 CAR-T cell product. Examination of single T cell transcriptomes from a wide range of healthy and diseased tissues across children and adults indicated that the persistence signature may be specific to long-lived CAR-T cells. These findings raise the possibility that a universal transcriptional signature of clinically effective, persistent CD19 CAR-T cells exists.
    MeSH term(s) Humans ; Antigens, CD19/genetics ; Immunotherapy, Adoptive ; Leukemia, Lymphocytic, Chronic, B-Cell ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/genetics ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/therapy ; Receptors, Antigen, T-Cell ; Remission Induction ; T-Lymphocytes
    Chemical Substances Antigens, CD19 ; Receptors, Antigen, T-Cell ; CD19 molecule, human
    Language English
    Publishing date 2023-07-06
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1220066-9
    ISSN 1546-170X ; 1078-8956
    ISSN (online) 1546-170X
    ISSN 1078-8956
    DOI 10.1038/s41591-023-02415-3
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4212: Show issues Location:
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  4. Article ; Online: Solubility curves and nucleation rates from molecular dynamics for polymorph prediction - moving beyond lattice energy minimization.

    Parks, Conor / Koswara, Andy / DeVilbiss, Frank / Tung, Hsien-Hsin / Nere, Nandkishor K / Bordawekar, Shailendra / Nagy, Zoltan K / Ramkrishna, Doraiswami

    Physical chemistry chemical physics : PCCP

    2017  Volume 19, Issue 7, Page(s) 5285–5295

    Abstract: Current polymorph prediction methods, known as lattice energy minimization, seek to determine the crystal lattice with the lowest potential energy, rendering it unable to predict solvent dependent metastable form crystallization. Facilitated by ... ...

    Abstract Current polymorph prediction methods, known as lattice energy minimization, seek to determine the crystal lattice with the lowest potential energy, rendering it unable to predict solvent dependent metastable form crystallization. Facilitated by embarrassingly parallel, multiple replica, large-scale molecular dynamics simulations, we report on a new method concerned with predicting crystal structures using the kinetics and solubility of the low energy polymorphs predicted by lattice energy minimization. The proposed molecular dynamics simulation methodology provides several new predictions to the field of crystallization. (1) The methodology is shown to correctly predict the kinetic preference for β-glycine nucleation in water relative to α- and γ-glycine. (2) Analysis of nanocrystal melting temperatures show γ- nanocrystals have melting temperatures up to 20 K lower than either α- or β-glycine. This provides a striking explanation of how an energetically unstable classical nucleation theory (CNT) transition state complex leads to kinetic inaccessibility of γ-glycine in water, despite being the thermodynamically preferred polymorph predicted by lattice energy minimization. (3) The methodology also predicts polymorph-specific solubility curves, where the α-glycine solubility curve is reproduced to within 19% error, over a 45 K temperature range, using nothing but atomistic-level information provided from nucleation simulations. (4) Finally, the methodology produces the correct solubility ranking of β- > α-glycine. In this work, we demonstrate how the methodology supplements lattice energy minimization with molecular dynamics nucleation simulations to give the correct polymorph prediction, at different length scales, when lattice energy minimization alone would incorrectly predict the formation of γ-glycine in water from the ranking of lattice energies. Thus, lattice energy minimization optimization algorithms are supplemented with the necessary solvent/solute dependent solubility and nucleation kinetics of polymorphs to predict which structure will come out of solution, and not merely which structure has the most stable lattice energy.
    Language English
    Publishing date 2017-02-15
    Publishing country England
    Document type Journal Article
    ZDB-ID 1476244-4
    ISSN 1463-9084 ; 1463-9076
    ISSN (online) 1463-9084
    ISSN 1463-9076
    DOI 10.1039/c6cp07181c
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  5. Article ; Online: Nanocrystal Dissolution Kinetics and Solubility Increase Prediction from Molecular Dynamics: The Case of α-, β-, and γ-Glycine.

    Parks, Conor / Koswara, Andy / Tung, Hsien-Hsin / Nere, Nandkishor K / Bordawekar, Shailendra / Nagy, Zoltan K / Ramkrishna, Doraiswami

    Molecular pharmaceutics

    2017  Volume 14, Issue 4, Page(s) 1023–1032

    Abstract: Nanocrystals are receiving increased attention for pharmaceutical applications due to their enhanced solubility relative to their micron-sized counterpart and, in turn, potentially increased bioavailability. In this work, a computational method is ... ...

    Abstract Nanocrystals are receiving increased attention for pharmaceutical applications due to their enhanced solubility relative to their micron-sized counterpart and, in turn, potentially increased bioavailability. In this work, a computational method is proposed to predict the following: (1) polymorph specific dissolution kinetics and (2) the multiplicative increase in the polymorph specific nanocrystal solubility relative to the bulk solubility. The method uses a combination of molecular dynamics and a parametric particle size dependent mass transfer model. The method is demonstrated using a case study of α-, β-, and γ-glycine. It is shown that only the α-glycine form is predicted to have an increasing dissolution rate with decreasing particle size over the range of particle sizes simulated. On the contrary, γ-glycine shows a monotonically increasing dissolution rate with increasing particle size and dissolves at a rate 1.5 to 2 times larger than α- or β-glycine. The accelerated dissolution rate of γ-glycine relative to the other two polymorphs correlates directly with the interfacial energy ranking of γ > β > α obtained from the dissolution simulations, where γ- is predicted to have an interfacial energy roughly four times larger than either α- or β-glycine. From the interfacial energies, α- and β-glycine nanoparticles were predicted to experience modest solubility increases of up to 1.4 and 1.8 times the bulk solubility, where as γ-glycine showed upward of an 8 times amplification in the solubility. These MD simulations represent a first attempt at a computational (pre)screening method for the rational design of experiments for future engineering of nanocrystal API formulations.
    MeSH term(s) Biological Availability ; Chemistry, Pharmaceutical/methods ; Glycine/chemistry ; Kinetics ; Molecular Dynamics Simulation ; Nanoparticles/chemistry ; Particle Size ; Solubility
    Chemical Substances Glycine (TE7660XO1C)
    Language English
    Publishing date 2017-03-08
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't
    ZDB-ID 2138405-8
    ISSN 1543-8392 ; 1543-8384
    ISSN (online) 1543-8392
    ISSN 1543-8384
    DOI 10.1021/acs.molpharmaceut.6b00882
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6250: Show issues Location:
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  6. Article ; Online: D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings.

    Gaieb, Zied / Parks, Conor D / Chiu, Michael / Yang, Huanwang / Shao, Chenghua / Walters, W Patrick / Lambert, Millard H / Nevins, Neysa / Bembenek, Scott D / Ameriks, Michael K / Mirzadegan, Tara / Burley, Stephen K / Amaro, Rommie E / Gilson, Michael K

    Journal of computer-aided molecular design

    2019  Volume 33, Issue 1, Page(s) 1–18

    Abstract: The Drug Design Data Resource aims to test and advance the state of the art in protein-ligand modeling by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017-2018, GC3 ... ...

    Abstract The Drug Design Data Resource aims to test and advance the state of the art in protein-ligand modeling by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017-2018, GC3 centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, and included both pose-prediction and affinity-ranking components. GC3 was structured much like the prior challenges GC2015 and GC2. First, Stage 1 tested pose prediction and affinity ranking methods; then all available crystal structures were released, and Stage 2 tested only affinity rankings, now in the context of the available structures. Unique to GC3 was the addition of a Stage 1b self-docking subchallenge, in which the protein coordinates from all of the cocrystal structures used in the cross-docking challenge were released, and participants were asked to predict the pose of CatS ligands using these newly released structures. We provide an overview of the outcomes and discuss insights into trends and best-practices.
    MeSH term(s) Binding Sites ; Cathepsins/chemistry ; Computer-Aided Design ; Crystallography, X-Ray ; Databases, Protein ; Drug Design ; Ligands ; Molecular Docking Simulation/methods ; Protein Binding ; Protein Conformation ; Protein Kinase Inhibitors/chemistry ; Protein Kinases/chemistry ; Thermodynamics
    Chemical Substances Ligands ; Protein Kinase Inhibitors ; Protein Kinases (EC 2.7.-) ; Cathepsins (EC 3.4.-) ; cathepsin S (EC 3.4.22.27)
    Language English
    Publishing date 2019-01-10
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 808166-9
    ISSN 1573-4951 ; 0920-654X
    ISSN (online) 1573-4951
    ISSN 0920-654X
    DOI 10.1007/s10822-018-0180-4
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  7. Book ; Online: Mining for Potent Inhibitors through Artificial Intelligence and Physics

    Li, Jie / Zhang, Oufan / Wang, Yingze / Sun, Kunyang / Guan, Xingyi / Bagni, Dorian / Haghighatlari, Mojtaba / Kearns, Fiona L. / Parks, Conor / Amaro, Rommie E. / Head-Gordon, Teresa

    A Unified Methodology for Ligand Based and Structure Based Drug Design

    2021  

    Abstract: The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for ... ...

    Abstract The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with real-time 3D molecular docking using AutoDock Vina, thereby simultaneously creating chemical novelty while constraining molecules for shape and molecular compatibility with target active sites. Moreover, through the use of various types of reward functions, we can generate new molecules that are chemically similar to a target ligand, which can be grown from known protein bound fragments, as well as to create molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflow that filters out Pan-assay interference compounds, Lipinski rule violations, and poor synthetic accessibility, with options for cross-validation against other docking scoring functions and automation of a molecular dynamics simulation to measure pose stability. Because our approach only relies on the structure of the target protein, iMiner can be easily adapted for future development of other inhibitors or small molecule therapeutics of any target protein.
    Keywords Quantitative Biology - Biomolecules ; Physics - Biological Physics ; Physics - Chemical Physics ; Physics - Data Analysis ; Statistics and Probability
    Subject code 006
    Publishing date 2021-10-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: D3R grand challenge 4: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies.

    Parks, Conor D / Gaieb, Zied / Chiu, Michael / Yang, Huanwang / Shao, Chenghua / Walters, W Patrick / Jansen, Johanna M / McGaughey, Georgia / Lewis, Richard A / Bembenek, Scott D / Ameriks, Michael K / Mirzadegan, Tara / Burley, Stephen K / Amaro, Rommie E / Gilson, Michael K

    Journal of computer-aided molecular design

    2020  Volume 34, Issue 2, Page(s) 99–119

    Abstract: The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins ...

    Abstract The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.
    MeSH term(s) Amyloid Precursor Protein Secretases/antagonists & inhibitors ; Amyloid Precursor Protein Secretases/metabolism ; Aspartic Acid Endopeptidases/antagonists & inhibitors ; Aspartic Acid Endopeptidases/metabolism ; Drug Design ; Enzyme Inhibitors/chemistry ; Enzyme Inhibitors/pharmacology ; Humans ; Ligands ; Machine Learning ; Molecular Docking Simulation ; Small Molecule Libraries/chemistry ; Small Molecule Libraries/pharmacology ; Thermodynamics
    Chemical Substances Enzyme Inhibitors ; Ligands ; Small Molecule Libraries ; Amyloid Precursor Protein Secretases (EC 3.4.-) ; Aspartic Acid Endopeptidases (EC 3.4.23.-) ; BACE1 protein, human (EC 3.4.23.46)
    Language English
    Publishing date 2020-01-23
    Publishing country Netherlands
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 808166-9
    ISSN 1573-4951 ; 0920-654X
    ISSN (online) 1573-4951
    ISSN 0920-654X
    DOI 10.1007/s10822-020-00289-y
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