Journal of Chemical Information and Modeling Drug Developemnt Review

Application Notes

NetInfer: A Spider web Server for Prediction of Targets and Therapeutic and Adverse Effects via Network-Based Inference Methods

• Zengrui Wu
,
• Yayuan Peng
,
• Zhuohang Yu
,
• Weihua Li
,
• Guixia Liu
, and
• Yun Tang *

Journal of Chemical Information and Modeling 2020 , 60 , eight , 3687-3691 (Application Annotation)

Publication Date (Web) : July xx, 2020

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Abstract

In this study, we developed a web server named NetInfer for prediction of targets and therapeutic and agin effects via network-based inference methods. Compared with our previously adult standalone version of NetInfer, this web server provides a user-friendly interface. With the web server, users tin easily predict potential target proteins, microRNAs, Anatomical Therapeutic Chemical (ATC) classification codes, or adverse drug events for small molecules of their interests in a few steps. Most of the prediction models were constructed on the basis of our previous studies, where those models have been evaluated systematically and demonstrated high functioning. The high-quality models can generate accurate predictions. As a case study, we predicted ATC codes and target proteins for several drugs. The predicted therapeutic effects of these drugs on cardiovascular diseases and their potential molecular mechanisms were validated by the literature. This successful instance study demonstrated that our web server would exist a powerful tool in drug repositioning and systems pharmacology. The spider web server of NetInfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.

vsFilt: A Tool to Meliorate Virtual Screening by Structural Filtration of Docking Poses

• Irina 5. Gushchina
,
• Aleksandra 1000. Polenova
,
• Dmitry A. Suplatov
,
• Vytas Thousand. Švedas
, and
• Dmitry K. Nilov *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 3692-3696 (Application Note)

Publication Engagement (Web) : August eleven, 2020

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Abstract

The ability of ligands to form crucial interactions with a poly peptide target, feature for the substrate and/or inhibitors, could be considered a structural criterion for identifying potent binders among docked compounds. Structural filtration of predicted poses improves the operation of virtual screening and helps in recovering specifically bound ligands. Here, we present vsFilt—a highly automated and easy-to-utilize Web server for postdocking structural filtration. The new tool can detect various types of interactions that are known to be involved in the molecular recognition, including hydrogen and halogen bonds, ionic interactions, hydrophobic contacts, π-stacking, and cation-π interactions. A case study for poly(ADP-ribose) polymerase ane ligands illustrates the utility of the software. The Web server is freely available at https://biokinet.belozersky.msu.ru/vsfilt.

PyPLIF HIPPOS: A Molecular Interaction Fingerprinting Tool for Docking Results of AutoDock Vina and PLANTS

• Enade P. Istyastono *
,
• Muhammad Radifar
,
• Nunung Yuniarti
,
• Vivitri D. Prasasty
, and
• Sudi Mungkasi

Journal of Chemical Data and Modeling 2020 , 60 , 8 , 3697-3702 (Application Annotation)

Publication Date (Web) : July xx, 2020

• Abstract
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Abstruse

Nosotros describe here our tool named PyPLIF HIPPOS, which was newly developed to clarify the docking results of AutoDock Vina and PLANTS. Its predecessor, PyPLIF (https://github.com/radifar/pyplif), is a molecular interaction fingerprinting tool for the docking results of PLANTS, exclusively. Unlike its predecessor, PyPLIF HIPPOS speeds upwards the computational times past separating the reference generation and docking analysis. PyPLIF HIPPOS also offers more options compared to PyPLIF. PyPLIF HIPPOS for Linux is stored as the Supporting Data in this awarding note and can be accessed in GitHub (https://github.com/radifar/PyPLIF-HIPPOS). Additionally, nosotros nowadays here the application of the tool in a retrospective structure-based virtual screening campaign targeting neuraminidase.

Reviews

The Use of Methods of Estimator-Aided Drug Discovery in the Development of Topoisomerase 2 Inhibitors: Applications and Hereafter Directions

• Mariia Radaeva
,
• Xuesen Dong *
, and
• Artem Cherkasov *

Periodical of Chemic Information and Modeling 2020 , 60 , eight , 3703-3721 (Review)

Publication Date (Web) : July 20, 2020

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ABSTRACT

Topoisomerase II (TopoII) is an enzyme essential for cellular metabolism and replication as it regulates DNA topology. Since inhibition of TopoII induces cell death, information technology is a well-established drug target in cancer therapy; several broadly used anticancer drugs including etoposide and doxorubicin are TopoII inhibitors. However, these therapeutics tend to cause astringent side effects and suffer from relatively low ligand affinity, leaving TopoII targeting with small molecules an active area of research. In contempo years computer-aided drug discovery (CADD) approaches have been actively used to expand knowledge on the role of TopoII in cancer and to develop novel strategies for its inhibition. Herein, nosotros overview studies that employed structure-based approaches such equally docking and molecular dynamic simulations, too every bit ligand-based approaches, such as QSAR (quantitative structure–activeness human relationship) modeling amid others, to gain agreement in TopoII targeting with existing drugs and to search for novel drug candidates.

Machine Learning and Deep Learning

Data Fix Augmentation Allows Deep Learning-Based Virtual Screening to Meliorate Generalize to Unseen Target Classes and Highlight Important Bounden Interactions

• Jack Scantlebury *
,
• Nathan Brown *
,
• Frank Von Delft *
, and
• Charlotte Thousand. Deane *

Journal of Chemical Information and Modeling 2020 , 60 , eight , 3722-3730 (Machine Learning and Deep Learning)

Publication Date (Web) : July 23, 2020

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ABSTRACT

Current deep learning methods for construction-based virtual screening accept the structures of both the protein and the ligand as input but brand fiddling or no use of the poly peptide structure when predicting ligand binding. Here, we prove how a relatively simple method of information ready augmentation forces such deep learning methods to take into account information from the poly peptide. Models trained in this way are more than generalizable (make meliorate predictions on protein/ligand complexes from a different distribution to the preparation data). They too assign more meaningful importance to the protein and ligand atoms involved in binding. Overall, our results show that data prepare augmentation tin can help deep learning-based virtual screening to learn physical interactions rather than information set biases.

Learning Coarse-Grained Potentials for Binary Fluids

• Peiyuan Gao *
,
• Xiu Yang
, and
• Alexandre M. Tartakovsky *

Journal of Chemical Information and Modeling 2020 , 60 , viii , 3731-3745 (Machine Learning and Deep Learning)

Publication Engagement (Web) : July 15, 2020

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ABSTRACT

For a multiple-fluid system, CG models capable of accurately predicting the interfacial properties as a role of curvature are all the same lacking. In this piece of work, we suggest a new probabilistic machine learning (ML) model for learning CG potentials for binary fluids. The water–hexane mixture is selected as a typical immiscible binary liquid–liquid organization. We develop a new CG force field (FF) using the Shinoda-DeVane-Klein (SDK) FF framework and compute parameters in this CG FF using the proposed probabilistic ML method. It is shown that a standard response-surface approach does not provide a unique set of parameters, as it results in a loss function with multiple shallow minima. To address this challenge, we develop a probabilistic ML approach where we compute the probability density function (PDF) of parameters that minimize the loss part. The PDF has a well-defined meridian corresponding to a unique set of parameters in the CG FF that reproduces the desired backdrop of a liquid–liquid interface. We compare the functioning of the new CG FF with several existing FFs for the h2o–hexane mixture, including two atomistic and iii CG FFs with respect to modeling the interface structure and thermodynamic properties. Information technology is demonstrated that the new FF significantly improves the CG model prediction of both the interfacial tension and structure for the h2o–hexane mixture.

General Protocol for the Accurate Prediction of Molecular ^thirteenC/¹H NMR Chemical Shifts via Motorcar Learning Augmented DFT

• Peng Gao
,
• Jun Zhang *
,
• Qian Peng
,
• Jie Zhang
, and
• Vassiliki-Alexandra Glezakou

Journal of Chemical Data and Modeling 2020 , 60 , eight , 3746-3754 (Machine Learning and Deep Learning)

Publication Appointment (Spider web) : June thirty, 2020

• Abstract
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Abstruse

An authentic prediction of NMR chemical shifts at affordable computational toll is very important for different types of structural assignments in experimental studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) are 2 of the well-nigh popular computational methods for NMR adding, yet they often fail to resolve ambiguities in structural assignments. Here, nosotros nowadays a new method that uses car learning (ML) techniques (DFT + ML) that significantly increases the accuracy of 13C/1H NMR chemical shift prediction for a multifariousness of organic molecules. The input of the generalizable DFT + ML model contains two critical parts: ane is a vector providing insights into chemical environments, which tin can be evaluated without knowing the verbal geometry of the molecule; the other ane is the DFT-calculated isotropic shielding abiding. The DFT + ML model was trained with a data set containing 476 13C and 270 1H experimental chemical shifts. For the DFT methods used here, the root hateful square deviations (RMSDs) for the errors between predicted and experimental 13C/1H chemic shifts can exist every bit small as 2.10/0.18 ppm, which is much lower than those from simple DFT (five.54/0.25 ppm), or DFT + linear regression (LR) (4.77/0.23 ppm) approaches. Information technology also has a smaller maximum absolute error than two previously proposed NMR-predicting ML models. The robustness of the DFT + ML model is tested on two classes of organic molecules (TIC10 and hyacinthacines), where the correct isomers were unambiguously assigned to the experimental ones. Overall, the DFT + ML model shows promise for structural assignments in a diverseness of systems, including stereoisomers, that are frequently challenging to determine experimentally.

autoBioSeqpy: A Deep Learning Tool for the Classification of Biological Sequences

• Runyu Jing
,
• Yizhou Li
,
• Li Xue
,
• Fengjuan Liu
,
• Menglong Li *
, and
• Jiesi Luo *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 3755-3764 (Machine Learning and Deep Learning)

Publication Appointment (Web) : July 27, 2020

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Abstract

Deep learning has proven to be a powerful method with applications in diverse fields including image, language, and biomedical data. Thanks to the libraries and toolkits such as TensorFlow, PyTorch, and Keras, researchers can employ dissimilar deep learning architectures and data sets for rapid modeling. However, the available implementations of neural networks using these toolkits are normally designed for a specific enquiry and are difficult to transfer to other work. Hither, nosotros present autoBioSeqpy, a tool that uses deep learning for biological sequence classification. The advantage of this tool is its simplicity. Users only need to fix the input data set and so use a control line interface. And then, autoBioSeqpy automatically executes a series of customizable steps including text reading, parameter initialization, sequence encoding, model loading, grooming, and evaluation. In improver, the tool provides various set-to-employ and adapt model templates to meliorate the usability of these networks. Nosotros introduce the awarding of autoBioSeqpy on three biological sequence problems: the prediction of type III secreted proteins, poly peptide subcellular localization, and CRISPR/Cas9 sgRNA action. autoBioSeqpy is freely available with examples at https://github.com/jingry/autoBioSeqpy.

Predictive Modeling of NMR Chemical Shifts without Using Diminutive-Level Annotations

• Seokho Kang
,
• Youngchun Kwon
,
• Dongseon Lee
, and
• Youn-Suk Choi *

Journal of Chemical Data and Modeling 2020 , sixty , 8 , 3765-3769 (Auto Learning and Deep Learning)

Publication Date (Web) : July 21, 2020

• Abstract
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Abstruse

Recently, machine learning has been successfully applied to the prediction of nuclear magnetic resonance (NMR) chemic shifts. To build a prediction model, the existing methods require a grooming data set that comprises molecules whose NMR-agile atoms are annotated with their chemical shifts. However, the laborious task of atomic-level annotation must exist manually conducted by chemists. Thus, information technology becomes hard to perform large-scale annotation. To accost this outcome, we propose a weakly supervised learning method to enable the predictive modeling of NMR chemic shifts without requiring explicit atomic-level annotations in the training data fix. For the preparation data set, the proposed method only requires the notation of chemic shifts at the molecular level. As a prediction model, nosotros build a bulletin passing neural network (MPNN) that predicts the chemical shifts of individual NMR-active atoms in a molecule. Using a loss function that is invariant to the permutation of atoms in a molecule, the model is trained in a weakly supervised manner to minimize the molecular-level difference between a set of predicted chemical shifts and the corresponding fix of actual chemical shifts beyond the training data set. Accordingly, during the training, the chemical shifts predicted by the model are approximately aligned with the bodily chemic shifts in a data-driven fashion. The proposed method performs comparably to the existing fully supervised methods in terms of predicting the chemical shifts of 1H and 13C NMR spectra for modest molecules.

Uncertainty Quantification Using Neural Networks for Molecular Property Prediction

• Lior Hirschfeld
,
• Kyle Swanson
,
• Kevin Yang
,
• Regina Barzilay *
, and
• Connor Due west. Coley *

Periodical of Chemical Data and Modeling 2020 , 60 , 8 , 3770-3780 (Machine Learning and Deep Learning)

Publication Date (Web) : July 23, 2020

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Abstruse

Uncertainty quantification (UQ) is an important component of molecular property prediction, especially for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and resources. The need for UQ is especially astute for neural models, which are becoming increasingly standard nevertheless are challenging to translate. While several approaches to UQ have been proposed in the literature, at that place is no clear consensus on the comparative operation of these models. In this newspaper, we study this question in the context of regression tasks. Nosotros systematically evaluate several methods on five regression data sets using multiple complementary performance metrics. Our experiments show that none of the methods we tested is unequivocally superior to all others, and none produces a particularly reliable ranking of errors across multiple information sets. While we believe that these results show that existing UQ methods are not sufficient for all common use cases and farther inquiry is needed, we conclude with a applied recommendation equally to which existing techniques seem to perform well relative to others.

Chemical Data

Tautomer Standardization in Chemical Databases: Deriving Concern Rules from Breakthrough Chemistry

• Christopher G. Baker *
,
• Nathan J. Kidley
,
• Konstantinos Papachristos
,
• Matthew Hotson
,
• Rob Carson
,
• David Gravestock
,
• Martin Pouliot
,
• Jim Harrison
, and
• Alan Dowling

Journal of Chemic Information and Modeling 2020 , 60 , 8 , 3781-3791 (Chemical Data)

Publication Appointment (Web) : July nine, 2020

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ABSTRACT

Databases of small, potentially bioactive molecules are ubiquitous across the industry and academia. Designed such that each unique compound should appear simply once, the multiplicity of ways in which many compounds tin can be represented means that these databases require methods for standardizing the representation of chemistry. This is usually achieved through the use of "Chemistry Concern Rules", sets of predefined rules that depict the "business firm style" of the database in question. At Syngenta, the historical arroyo to the pattern of chemistry concern rules has been to focus on consistency of representation, with chemical relevance given secondary consideration. In this work, nosotros overturn that convention. Through the use of quantum chemical science calculations, we define a fix of chemical science business organisation rules for tautomer standardization that reproduces gas-stage energetic preferences. We keep to show that, compared to our celebrated arroyo, this method yields tautomers that are in amend agreement with those observed experimentally in condensed phases and that are better suited for employ in predictive models.

Toward a Computational Ecotoxicity Assay

• Natasha Kamerlin
,
• Mickaël G. Delcey
,
• Sergio Manzetti
, and
• David van der Spoel *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 3792-3803 (Chemical Information)

Publication Date (Web) : July 10, 2020

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ABSTRACT

Thousands of anthropogenic chemicals are released into the environs each twelvemonth, posing potential hazards to human being and ecology health. Toxic chemicals may cause a multifariousness of agin health furnishings, triggering immediate symptoms or delayed effects over longer periods of time. It is thus crucial to develop methods that tin apace screen and predict the toxicity of chemicals to limit the potential harmful impacts of chemical pollutants. Computational methods are being increasingly used in toxicity predictions. Here, the method of molecular docking is assessed for screening potential toxicity of a diverseness of xenobiotic compounds, including pesticides, pharmaceuticals, pollutants, and toxins derived from the chemical industry. The method predicts the binding energy of pollutants to a set of carefully selected receptors under the assumption that toxicity in many cases is related to interference with biochemical pathways. The strength of the applied method lies in its rapid generation of interaction maps between potential toxins and the targeted enzymes, which could quickly yield molecular-level information and insight into potential perturbation pathways, aiding in the prioritization of chemicals for further tests. Two scoring functions are compared: Autodock Vina and the machine-learning scoring function RF-Score-VS. The results are promising, although hampered by the accuracy of the scoring functions. The strengths and weaknesses of the docking protocol are discussed, every bit well as time to come directions for improving the accuracy for the purpose of toxicity predictions.

Can One Hear the Shape of a Molecule (from its Coulomb Matrix Eigenvalues)?

• Joshua Schrier *

Journal of Chemical Data and Modeling 2020 , 60 , 8 , 3804-3811 (Chemical Information)

Publication Engagement (Spider web) : July 15, 2020

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ABSTRACT

Coulomb matrix eigenvalues (CMEs) are global 3D representations of molecular structure, which have been previously used to predict atomization energies, prioritize geometry searches, and interpret rotational spectra. The backdrop of the CME representation and its relationship to molecular structure are established using the Gershgorin circle theorem. Numerical bounds are studied using a data set of 309 000 conformational samples of all constitutional isomers of acyclic alkanes, CnH2n+two, from methane (n = one) to undecane (n = 11), to institute the extent to which the CME preserves chemical intuitions well-nigh isomer and conformer similarity and its ability to distinguish constitutional isomers. Neither supervised nor unsupervised machine-learning algorithms can perfectly distinguish constitutional isomers as the molecular size increases, just the misclassification rate can be kept beneath i%.

Computational Chemistry

Deciphering the Inhibition Mechanism of under Trial Hsp90 Inhibitors and Their Analogues: A Comparative Molecular Dynamics Simulation

• Asma Nazar
,
• Ghulam Abbas
, and
• Syed Sikander Azam *

Journal of Chemical Data and Modeling 2020 , lx , 8 , 3812-3830 (Computational Chemistry)

Publication Engagement (Web) : July thirteen, 2020

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Abstract

Heat shock protein ninety (Hsp90) performs functions in cellular activities together with other signaling pathways. Hsp90 is evolutionarily conserved and universally articulated as a human cancer-causing agent involved in lung cancer and breast cancer followed by colon and rectum cancers. It has emerged as an effective drug candidate, and inhibition may affect several signaling pathways associated with cancer spread. Therefore, in-silico approaches, molecular docking, molecular dynamics simulation, and bounden free energy calculations were practical to create insights into the inhibition mechanism confronting Hsp90 to identify new cancer therapeutic drugs. Top-docked Hsp90-inhibitor complexes with their analogues were selected as the best complexes based on the Aureate fitness score and orientation. The significant interaction of Hsp90 inhibitors and their analogues were observed to exist bound with active site residues likewise as residing within the same cavity region. System stability factors RMSD, RMSF, beta-factor, and radius of gyration were analyzed for pinnacle-docked complexes and ensure strong binding interaction betwixt inhibitors and the Hsp90 cavity. Cavity bound inhibitors were found to retain consequent hydrogen bonding during the simulation. The radial distribution function (RDF) illustrated that interacting agile site residues drive the binding and stability of the inhibitors. Similarly, the centric frequency distribution, which is an indigenously developed analytical tool, produced noteworthy knowledge of the hydrogen-bonding pattern. Results yielded new insights into the blueprint of cancer therapeutic drugs against Hsp90. This finding suggests that under trial Hsp90 inhibitors MPC-3100 could be a potential starting bespeak into the development of potential anticancer agents with the possibility of future directions for the improvement of early existing Hsp90 inhibitors CNF-2024 and SNX-5422 as an anticancer agent.

Belittling Model of Electron Density and Its Automobile Learning Inference

• Bruno Cuevas-Zuviría *
 and
• Luis F. Pacios *

Journal of Chemical Data and Modeling 2020 , sixty , 8 , 3831-3842 (Computational Chemistry)

Publication Date (Web) : August 4, 2020

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Abstract

We present an belittling model representation of the electron density ρ(r) in molecules in the form of expansions of a few functions (exponentials and a Gaussian) per atom. Based on a former analytical model of ρ(r) in atoms, nosotros devised its molecular implementation by introducing the anisotropy inherent in the electron distribution of atoms in molecules by means of proper anisotropic functions. The resulting model named A2MD (anisotropic analytical model of density) takes an analytical form highly suitable for obtaining the electron density in large biomolecules as its computational toll scales linearly with the number of atoms. To obtain the parameters of the model, we beginning devised a plumbing fixtures procedure to reference electron densities obtained in ab initio correlated quantum calculations. Second, in guild to skip costly ab initio calculations, we also adult a machine learning (ML)-based predictor that used neural networks trained on broad molecular datasets to determine the parameters of the model. The resulting ML methodology that we named A2MDnet (A2MD network-trained) was able to provide reliable electron densities as a basis to predict molecular features without requiring quantum calculations. The results presented together with the low computational scaling associated to the A2MD representation of ρ(r) suggest potential applications to obtain reliable electron densities and ρ(r)-based molecular properties in biomacromolecules.

Solvation Thermodynamics in Different Solvents: Water–Chloroform Partition Coefficients from Filigree Inhomogeneous Solvation Theory

• Johannes Kraml
,
• Florian Hofer
,
• Anna Due south. Kamenik
,
• Franz Waibl
,
• Ursula Kahler
,
• Michael Schauperl
, and
• Klaus R. Liedl *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 3843-3853 (Computational Chemistry)

Publication Date (Web) : July 8, 2020

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Abstract

Reliable information on partition coefficients plays a key role in drug development, equally solubility decisively affects bioavailability. In a physicochemical context, the partitioning coefficient of a solute between two different solvents can be described as a function of solvation gratis energies. Hence, substantial scientific efforts have been made toward accurate predictions of solvation free energies in various solvents. The filigree inhomogeneous solvation theory (GIST) facilitates the adding of solvation free energies. In this report, we introduce an extended version of the GIST algorithm, which enables the adding for chloroform in addition to water. Furthermore, GIST allows localization of enthalpic and entropic contributions. We examination our approach by calculating sectionalization coefficients betwixt h2o and chloroform for a gear up of eight small molecules. We report a Pearson correlation coefficient of 0.96 between experimentally adamant and calculated partition coefficients. The capability to reliably predict partition coefficients between water and chloroform and the possibility to localize their contributions permit the optimization of a compound'south partition coefficient. Therefore, we presume that this methodology will be of groovy benefit for the efficient evolution of pharmaceuticals.

Multiscale Modeling of Two-Photon Probes for Parkinson's Diagnostics Based on Monoamine Oxidase B Biomarker

• North. Arul Murugan *
 and
• Robert Zaleśny *

Periodical of Chemical Information and Modeling 2020 , sixty , 8 , 3854-3863 (Computational Chemistry)

Publication Date (Web) : July 27, 2020

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ABSTRACT

Monoamine oxidase B (MAO-B) is a potential biomarker for Parkinson'southward disease (PD), a neurodegenerative affliction associated with the loss of motor activities in man subjects. The disease state is associated with dopamine deprival, and so the inhibitors of MAO-B can serve equally therapeutic drugs for PD. Since the expression level of MAO-B directly correlates to the affliction progress, the distribution and population of this enzyme can exist employed to monitor illness development. Ane of the approaches bachelor for estimating the population is two-photon imaging. The ligands used for two-photon imaging should have high binding analogousness and binding specificity toward MAO-B forth with pregnant two-photon absorption cross sections when they are bound to the target. In this article, we study using a multiscale modeling approach, the binding affinity and spectroscopic properties (one- and 2-photon absorption) of 3 (Flu1, Flu2, Flu3) of the currently available probes for monitoring the MAO-B level. We report that the bounden affinity of the probes can be explained using the molecular size and bounden cavity volume. The experimentally determined 1-photon assimilation spectrum is well reproduced by the employed QM/MM approaches, and the most accurate spectral shifts, on passing from one probe to another, are obtained at the coupled-cluster (CC2) level of theory. An important conclusion from this study is too the demonstration that intrinsic molecular two-photon absorption strengths (δ2PA) increase in the social club δ2PA (Flu1) > δ2PA (Flu2) > δ2PA (Flu3). This is in contrast with experimental data, which predict like values of two-photon absorption cantankerous sections for Flu1 and Flu3. We demontrate, based on the results of electronic-structure calculations for Flu1 that this discrepancy cannot exist explained by an explicit account for neighboring residues (which could lead to charge transfer betwixt a probe and neighboring aromatic amino acids thus boosting δ2PA). In summary, we show that the employed multiscale approach not only can optimize two-photon absorption properties and verify bounden affinity, but it can also help in detailed analyses of experimental data.

Extending the Martini Coarse-Grained Forcefulness Field to N-Glycans

• Aishwary T. Shivgan
,
• Jan K. Marzinek
,
• Roland G. Huber
,
• Alexander Krah
,
• Richard H. Henchman
,
• Paul Matsudaira
,
• Chandra S. Verma *
, and
• Peter J. Bond *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 3864-3883 (Computational Chemical science)

Publication Engagement (Web) : July half dozen, 2020

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Abstract

Glycans play a vital role in a large number of cellular processes. Their circuitous and flexible nature hampers structure–function studies using experimental techniques. Molecular dynamics (MD) simulations can assist in understanding dynamic aspects of glycans if the force field parameters used tin can reproduce key experimentally observed properties. Hither, we present optimized coarse-grained (CG) Martini force field parameters for Northward-glycans, calibrated confronting experimentally derived binding affinities for lectins. The CG bonded parameters were obtained from atomistic (ATM) simulations for different glycan topologies including high mannose and complex glycans with various branching patterns. In the CG model, additional rubberband networks are shown to improve maintenance of the overall conformational distribution. Solvation free energies and octanol–h2o partition coefficients were likewise calculated for diverse N-glycan disaccharide combinations. When using standard Martini nonbonded parameters, we observed that glycans spontaneously aggregated in the solution and required down-scaling of their interactions for reproduction of ATM model radial distribution functions. Nosotros also optimized the nonbonded interactions for glycans interacting with seven lectin candidates and show that a relatively modest scaling down of the glycan-poly peptide interactions can reproduce free energies obtained from experimental studies. These parameters should be of utilize in studying the role of glycans in various glycoproteins and carbohydrate binding proteins as well as their complexes, while benefiting from the efficiency of CG sampling.

Molassembler: Molecular Graph Construction, Modification, and Conformer Generation for Inorganic and Organic Molecules

• January-Grimo Sobez
 and
• Markus Reiher *

Journal of Chemical Information and Modeling 2020 , lx , viii , 3884-3900 (Computational Chemistry)

Publication Engagement (Web) : July 1, 2020

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Abstruse

We present the graph-based molecule software Molassembler for building organic and inorganic molecules. Molassembler provides algorithms for the structure of molecules built from any set up of elements from the periodic table. In particular, polynuclear transition-metal complexes and clusters can be considered. Structural information is encoded as a graph. Stereocenter configurations are interpretable from Cartesian coordinates into an abstract index of permutation for an extensible set of polyhedral shapes. Substituents are distinguished through a ranking algorithm. Graph and stereocenter representations are freely modifiable, and the chiral state is propagated where possible through incurred ranking changes. Conformers are generated with full stereoisomer control past 4 spatial dimension Distance Geometry with a refinement fault function including dihedral terms. Molecules are comparable past an extended graph isomorphism, and their representation is canonicalizeable. Molassembler is written in C++ and provides Python bindings.

How Exercise Modest Molecule Aggregates Inhibit Enzyme Activity? A Molecular Dynamics Written report

• Mohammad A. Ghattas *
,
• Sara Al Rawashdeh
,
• Noor Atatreh
, and
• Richard A. Bryce *

Periodical of Chemical Information and Modeling 2020 , threescore , 8 , 3901-3909 (Computational Chemistry)

Publication Engagement (Spider web) : July 6, 2020

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Abstract

Small molecule compounds which form colloidal aggregates in solution are problematic in early on drug discovery; adsorption of the target protein by these aggregates tin atomic number 82 to false positives in inhibition assays. In this work, we probe the molecular basis of this inhibitory mechanism using molecular dynamics simulations. Specifically, nosotros examine in aqueous solution the adsorption of the enzymes β-lactamase and PTP1B onto aggregates of the drug miconazole. In accordance with experiment, molecular dynamics simulations notice germination of miconazole aggregates as well as subsequent association of these aggregates with β-lactamase and PTP1B. When complexed with aggregate, the proteins practice not showroom significant alteration in protein tertiary structure or dynamics on the microsecond time scale of the simulations, merely they practice indicate persistent occlusion of the protein active site past miconazole molecules. MD simulations further suggest this occlusion can occur via surficial interactions of protein with miconazole but also potentially by envelopment of the protein by miconazole. The heterogeneous polarity of the miconazole aggregate surface seems to underpin its action equally an invasive and nonspecific inhibitory agent. A deeper understanding of these protein/amass systems has implications not but for drug design but also for their exploitation as tools in drug delivery and analytical biochemistry.

Fast Rescoring Protocols to Better the Functioning of Structure-Based Virtual Screening Performed on Protein–Protein Interfaces

• Natesh Singh
,
• Ludovic Chaput
, and
• Bruno O. Villoutreix *

Journal of Chemical Information and Modeling 2020 , lx , viii , 3910-3934 (Computational Chemical science)

Publication Date (Web) : Baronial 3, 2020

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Abstruse

Protein–poly peptide interactions (PPIs) are attractive targets for drug design because of their essential function in numerous cellular processes and disease pathways. However, in full general, PPIs brandish exposed binding pockets at the interface, and every bit such, take been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an effort to recover PPI inhibitors from a fix of active and inactive molecules for xi targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the hereafter. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) packet Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors: (i) shape similarity and (2) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, iv standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and Ten-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results signal that the topological scoring algorithms could exist valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the all-time scoring function on this data fix, outperforming all rescoring techniques in terms of VS metrics. The described methodology could aid in the rational blueprint of small-molecule PPI inhibitors and has straight applications in many therapeutic areas, including cancer, CNS, and infectious diseases such every bit COVID-19.

Computational Biochemistry

Coarse-Grained Parameters for Divalent Cations within the SIRAH Forcefulness Field

• Florencia Klein
,
• Daniela Cáceres
,
• Mónica A. Carrasco
,
• Juan Carlos Tapia
,
• Julio Caballero
,
• Jans Alzate-Morales
, and
• Sergio Pantano *

Journal of Chemical Information and Modeling 2020 , sixty , 8 , 3935-3943 (Computational Biochemistry)

Publication Date (Web) : July xx, 2020

• Abstract
• Full text
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Abstract

Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose pregnant challenges in achieving an accurate structural and dynamical description of many biological assemblies, peculiarly to coarse-grained (CG) models. Although the reduced computational toll of CG methods often makes them the technique of selection for the report of large biomolecular systems, the parameterization of metal ions is notwithstanding very crude or not available for the vast majority of CG forcefulness fields. Hither, we show that incorporating statistical information retrieved from the Protein Information Banking company (PDB) to set specific Lennard-Jones interactions tin produce structurally authentic CG molecular dynamics simulations using the SIRAH force field. We provide a set of interaction parameters for calcium, magnesium, and zinc ions, which cover more than 80% of the metallic-bound structures reported in the PDB. Simulations performed on several proteins and DNA systems prove that information technology is possible to forbid the use of topological constraints by modifying specific Lennard-Jones interactions.

Versatile Dimerisation Process of Translocator Protein (TSPO) Revealed by an Extensive Sampling Based on a Coarse-Grained Dynamics Study

• Rajas Rao
,
• Julien Diharce
,
• Bérénice Dugué
,
• Mariano A. Ostuni
,
• Frédéric Cadet
, and
• Catherine Etchebest *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 3944-3957 (Computational Biochemistry)

Publication Date (Web) : July 22, 2020

• Abstract
• Full text
• PDF

ABSTRACT

Translocator protein (TSPO), a mitochondrial membrane protein, has been extensively studied, and its role is still debated and continues to exist enigmatic. From a structural perspective, despite availability of atomic structures from different species, the possible oligomeric land and its 3D construction remains elusive. In the present study, nosotros attempted to written report dynamics of TSPO from the perspective of oligomerization. In this aim, nosotros examined if and how TSPO monomers could gather to form a dimer. Accordingly, we performed several fibroid-grained molecular dynamics simulations considering two dissimilar initial configurations, one with a pair of TSPO monomers distantly placed in a model of a bilayer composed of DMPC/cholesterol mixture and the other with preformed dimer models with different starting interactions. We identify stable TSPO dimers with various interfaces, some of which were consistent with earlier experimental observations on putative TSPO oligomer interfaces. For nearly of the stable ones, interactions betwixt effluvious residues were significantly overrepresented in diverse oligomeric organizations. Interestingly, we identified dissimilar communication pathways that involve dimer interfaces. Additionally, we observed that cholesterol molecules in shut interaction with the TSPO dimer were able to translocate through the bilayer. This phenomenon might exist related to the putative mechanism of cholesterol transport and could exist increased and favored by the dimer germination. Overall, our observations shed new light on TSPO oligomerization and bring new perspectives on its dynamics, as well its interactions with protein and ligand partners.

Identification of a New Allosteric Bounden Site for Cocaine in Dopamine Transporter

• Liang Xu *
 and
• Liao Y. Chen

Journal of Chemical Information and Modeling 2020 , 60 , viii , 3958-3968 (Computational Biochemistry)

Publication Date (Web) : July ten, 2020

• Abstract
• Full text
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ABSTRACT

Dopamine (DA) transporter (DAT) is a major target for psychostimulant drugs of abuse such every bit cocaine that competitively binds to DAT, inhibits DA reuptake, and consequently increases synaptic DA levels. In addition to the central binding site within DAT, the available experimental prove suggests the existence of alternative binding sites on DAT, but detection and label of these sites are challenging by experiments alone. Here, we integrate multiple computational approaches to probe the potential binding sites on the wild-type Drosophila melanogaster DAT and place a new allosteric site that displays loftier analogousness for cocaine. This site is located on the surface of DAT, and binding of cocaine is primarily dominated by interactions with hydrophobic residues surrounding the site. We show that cocaine binding to this new site allosterically reduces the bounden of DA/cocaine to the central binding pocket, and simultaneous binding of two cocaine molecules to a single DAT seems infeasible. Furthermore, we detect that binding of cocaine to this site stabilizes the conformation of DAT merely alters the conformational population and thereby reduces the accessibility by DA, providing molecular insights into the inhibitory mechanism of cocaine. In improver, our results point that the conformations induced by cocaine binding to this site may be relevant to the oligomerization of DAT, highlighting a potential role of this new site in modulating the function of DAT.

Understanding the Bounden Specificity of 1000-Protein Coupled Receptors toward One thousand-Proteins and Arrestins: Application to the Dopamine Receptor Family unit

• A. J. Preto
,
• Carlos A. Five. Barreto
,
• Salete J. Baptista
,
• José Guilherme de Almeida
,
• Agostinho Lemos
,
• André Melo
,
• M. Nátalia D. South. Cordeiro
,
• Zeynep Kurkcuoglu
,
• Rita Melo
, and
• Irina Southward. Moreira *

Journal of Chemic Information and Modeling 2020 , sixty , 8 , 3969-3984 (Computational Biochemistry)

Publication Engagement (Web) : July 21, 2020

• Abstract
• Full text
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ABSTRACT

Thousand-Protein coupled receptors (GPCRs) are involved in a myriad of pathways key for human physiology through the formation of complexes with intracellular partners such equally Chiliad-proteins and arrestins (Arrs). All the same, the structural and dynamical determinants of these complexes are all the same largely unknown. Herein, we developed a computational big-information pipeline that enables the structural characterization of GPCR complexes with no available construction. This pipeline was used to study a well-known group of catecholamine receptors, the human dopamine receptor (DXR) family and its complexes, producing novel insights into the physiological properties of these important drug targets. A detailed description of the poly peptide interfaces of all members of the DXR family (D1R, D2R, D3R, D4R, and D5R) and the corresponding protein interfaces of their binding partners (Arrs: Arr2 and Arr3; G-proteins: Gi1, Gi2, Gi3, Go, Gob, Gq, Gslo, Gssh, Gt2, and Gz) was generated. To produce reliable structures of the DXR family in complex with either G-proteins or Arrs, we performed homology modeling using equally templates the structures of the β2-adrenergic receptor (β2AR) leap to Gs, the rhodopsin bound to Gi, and the recently acquired neurotensin receptor-ane (NTSR1) and muscarinic 2 receptor (M2R) spring to arrestin (Arr). Among others, the piece of work demonstrated that the three partner groups, Arrs and Gs- and Gi-proteins, are all structurally and dynamically distinct. Additionally, it was revealed the involvement of different structural motifs in 1000-protein selective coupling between D1- and D2-like receptors. Having constructed and analyzed l models involving DXR, this work represents an unprecedented large-scale assay of GPCR-intracellular partner interface determinants. All data is available at www.moreiralab.com/resources/dxr.

Conserved Luminal C-Terminal Domain Dynamically Controls Interdomain Advice in Sarcolipin

• Rodrigo Aguayo-Ortiz
,
• Eli Fernández-de Gortari
, and
• Fifty. Michel Espinoza-Fonseca *

Journal of Chemical Data and Modeling 2020 , 60 , 8 , 3985-3991 (Computational Biochemistry)

Publication Date (Web) : July fifteen, 2020

• Abstract
• Full text
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Abstruse

Sarcolipin (SLN) mediates Ca2+ send and metabolism in muscle by regulating the activity of the Ca2+ pump SERCA. SLN has a conserved luminal C-final domain that contributes to its functional divergence amid homologous SERCA regulators, merely the precise mechanistic role of this domain remains poorly understood. Nosotros used all-atom molecular dynamics (MD) simulations of SLN totaling 77.5 μs to show that the N- (NT) and C-terminal (CT) domains function in concert. Analysis of the Physician simulations showed that serial deletions of the SLN C-terminus do not touch the stability of the peptide nor induce dissociation of SLN from the membrane simply promote a gradual decrease in both the tilt angle of the transmembrane helix and the local thickness of the lipid bilayer. Mutual data assay showed that the NT and CT domains communicate with each other in SLN and that interdomain communication is partially or completely abolished upon deletion of the conserved segment Tyr29-Tyr31 every bit well as by serial deletions beyond this domain. Phosphorylation of SLN at residue Thr5 also induces changes in the communication between the CT and NT domains, which thus provides additional evidence for interdomain advice within SLN. We found that interdomain communication is contained of the force field used and lipid composition, which thus demonstrates that communication betwixt the NT and CT domains is an intrinsic functional feature of SLN. We propose the novel hypothesis that the conserved C-terminus is an essential element required for dynamic control of SLN regulatory function.

Technology a Histone Reader Protein past Combining Directed Development, Sequencing, and Neural Network Based Ordinal Regression

• Jonathan Parkinson
,
• Ryan Hard
,
• Richard I. Ainsworth
,
• Nan Li
, and
• Wei Wang *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 3992-4004 (Computational Biochemistry)

Publication Appointment (Web) : August 5, 2020

• Abstract
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Abstruse

Directed development is a powerful approach for engineering proteins with enhanced affinity or specificity for a ligand of involvement but typically requires many rounds of screening/library mutagenesis to obtain mutants with desired properties. Furthermore, mutant libraries generally but embrace a minor fraction of the available sequence space. Hither, for the first time, nosotros use ordinal regression to model protein sequence data generated through successive rounds of sorting and amplification of a protein–ligand system. We bear witness that the ordinal regression model trained on but two sorts successfully predicts chromodomain CBX1 mutants that would have stronger binding affinity with the H3K9me3 peptide. Furthermore, we can extract the predictive features using contextual regression, a method to interpret nonlinear models, which successfully guides identification of strong binders not fifty-fifty present in the original library. We accept demonstrated the ability of this approach past experimentally confirming that we were able to attain the same comeback in binding analogousness previously achieved through a more laborious directed evolution process. This report presents an arroyo that reduces the number of rounds of selection required to isolate strong binders and facilitates the identification of strong binders non present in the original library.

Pattern and Dynamics of FLT3 Duplications

• Guido Todde
 and
• Ran Friedman *

Journal of Chemic Data and Modeling 2020 , 60 , eight , 4005-4020 (Computational Biochemistry)

Publication Date (Spider web) : July 17, 2020

• Abstract
• Total text
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Abstract

FMS-similar tyrosine kinase 3 (FLT3) is mutated in ∼30% of patients that suffer from acute myeloid leukemia (AML). In about 25% of all AML patients, in-frame insertions are observed in the sequence. Virtually of those insertions are internal tandem duplications (ITDs) of a sequence from the protein. The characteristics of such mutations in terms of length, sequence, and location were hitherto studied in different populations, just non in a comprehensive mutation database. Here, in-frame insertions into the FLT3 gene were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. These were analyzed with respect to the length, location, and sequence of the mutations. Furthermore, characteristic strings (sequences) of different lengths were identified. Mutations were shown to occur most often in the juxtamembrane attachment (JM-Z) domain of FLT3, followed by the hinge domain and first tyrosine kinase domain (TKD1), upstream of the phosphate-binding loop (P-loop). Interestingly, at that place are specific hot spot residues where insertions are more likely to occur. The insertions vary in length between ane and 67 amino acids, with the largest insertions spanning the phosphate binding loop. Insertions that occur downstream of the P-loop are shorter. Our analysis farther shows that acidic and aromatic residues are enriched in the insertions. Finally, molecular dynamics simulations were run for FLT3 with ITD insertions in the hinge and tyrosine kinase domains. On the basis of the findings, a mechanism is proposed for activation past ITDs, according to which there is no directly coupling between the length of the insertion and the action of the mutated protein. The event of insertions on the sensitivity of FLT3 to kinase inhibitors is discussed based on our findings.

Efficient Conformational Sampling of Collective Motions of Proteins with Principal Component Analysis-Based Parallel Cascade Choice Molecular Dynamics

• Takunori Yasuda
,
• Yasuteru Shigeta
, and
• Ryuhei Harada *

Periodical of Chemical Information and Modeling 2020 , 60 , eight , 4021-4029 (Computational Biochemistry)

Publication Engagement (Web) : Baronial 13, 2020

• Abstract
• Full text
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Abstruse

Molecular dynamics (Doc) simulation has get a powerful tool because it provides a time series of poly peptide dynamics at high temporal–spatial resolution. However, the accessible timescales of MD simulation are shorter than those of the biologically rare events. By and large, long-time MD simulations over microseconds are required to detect the rare events. Therefore, it is desirable to develop rare-event-sampling methods. For a rare-result-sampling method, we have developed parallel cascade pick MD (PaCS-MD). PaCS-Md generates transition pathways from a given source construction to a target structure by repeating brusque-fourth dimension MD simulations. The fundamental indicate in PaCS-Doc is how to select reasonable candidates (protein configurations) with high potentials to brand transitions toward the target structure. In the present study, based on principal component analysis (PCA), we advise PCA-based PaCS-Physician to notice rare events of collective motions of a given poly peptide. Here, the PCA-based PaCS-MD is composed of the post-obit two steps. At kickoff, equally a preliminary run, PCA is performed using an MD trajectory from the target structure to ascertain a primary coordinate (PC) subspace for describing the collective motions of interest. PCA provides principal modes as eigenvectors to projection a poly peptide configuration onto the PC subspace. So, as a product run, all the snapshots of brusk-time MD simulations are ranked by inner products (IPs), where an IP is defined between a snapshot and the target structure. Then, snapshots with college values of the IP are selected equally reasonable candidates, and short-time Doc simulations are independently restarted from them. By referring to the values of the IP, the PCA-based PaCS-Dr. repeats the short-fourth dimension Md simulations from the reasonable candidates that are highly correlated with the target construction. Every bit a demonstration, nosotros applied the PCA-based PaCS-Medico to adenylate kinase and detected its large-amplitude (open–airtight) transition with a nanosecond-guild computational cost.

Sectionalization of Benzoic Acid into 1,ii-Dimyristoyl-sn-glycero-3-phosphocholine and Blood–Brain Barrier Mimetic Bilayers

• Christopher M. Siwy
,
• Bryan M. Delfing
,
• Amy M. Smith
, and
• Dmitri K. Klimov *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 4030-4046 (Computational Biochemistry)

Publication Appointment (Spider web) : July 16, 2020

• Abstruse
• Full text
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Abstruse

Using an all-atom explicit water model and replica exchange umbrella sampling simulations, we investigated the molecular mechanisms of benzoic acid partitioning into 2 model lipid bilayers. The first was formed of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids, whereas the second was composed of an equimolar mixture of DMPC, i,2-dimyristoyl-sn-glycero-three-phosphoethanolamine, palmitoylsphingomyelin, and cholesterol to constitute a blood–brain barrier (BBB) mimetic bilayer. Comparative assay of benzoic acid segmentation into the two bilayers has revealed qualitative similarities. Partitioning into the DMPC and BBB bilayers is thermodynamically favorable although insertion into the former lowers the free energy of benzoic acrid by approximately an additional 1 kcal mol–1. The segmentation energetics for the two bilayers is also largely like based on the balance of benzoic acid interactions with apolar fatty acid tails, polar lipid headgroups, and water. In both bilayers, benzoic acid retains a considerable number of remainder water molecules until reaching the bilayer midplane where it experiences virtually consummate dehydration. Upon insertion into the bilayers, benzoic acid undergoes several rotations primarily determined by the interactions with the lipid headgroups. Nonetheless, in addition to the depth of the gratuitous energy minimum, the BBB bilayer differs from the DMPC analogue by a much deeper location of the free energy minimum and the appearance of a loftier free energy barrier and positioning of benzoic acid well-nigh the midplane. Furthermore, DMPC and BBB bilayers exhibit different structural responses to benzoic acid insertion. Taken together, the BBB mimetic bilayer is preferable for an accurate description of benzoic acid sectionalization.

Pharmaceutical Modeling

Integrated Binary QSAR-Driven Virtual Screening and In Vitro Studies for Finding Novel hMAO-B-Selective Inhibitors

• Yusuf Serhat Is
,
• Busecan Aksoydan
,
• Murat Senturk
,
• Mine Yurtsever *
, and
• Serdar Durdagi *

Journal of Chemic Data and Modeling 2020 , lx , 8 , 4047-4055 (Pharmaceutical Modeling)

Publication Date (Web) : July 16, 2020

• Abstruse
• Full text
• PDF

Abstract

The increased activity of monoamine oxidase (MAO) enzymes may atomic number 82 to serious consequences since they reduce the level of neurotransmitters and are associated with astringent neurodegenerative diseases. The inhibition of this enzyme, especially the B isoform, plays a vital role in the treatment of Parkinson'due south disease (PD). This study is aimed to find novel man MAO-B (hMAO-B) selective inhibitors. A total of 256.750 compounds from the Otava small molecules database were virtually screened gradually by employing several screening techniques for this purpose. Initially, a high-throughput virtual screening (HTVS) method was employed, and 10% of the molecules having high docking scores were subjected to binary QSAR models for further screening of their therapeutic activities against PD, Alzheimer'southward disease (AD), and depression as well as for their toxicity and pharmacokinetic properties. Then, enzyme selectivity of the ligands towards the A and B forms that passed through all the filters were studied using the induced-fit docking method and molecular dynamics simulations. At the end of this exhaustive research, nosotros identified two hit molecules ligand iii (Otava ID: 7131545) and ligand 4 (Otava ID: 7566820). Based on the in vitro results, these two compounds (ligands three and iv) together with ligands 1 and ii found in our previous study showed activeness at the nanomolar (nM) level, and the results indicated that these four ligands inhibit hMAO-B better than the FDA-approved drug selegiline.

SCAM Detective: Authentic Predictor of Small, Colloidally Aggregating Molecules

• Vinicius M. Alves
,
• Stephen J. Capuzzi
,
• Rodolpho C. Braga
,
• Daniel Korn
,
• Joshua Due east. Hochuli
,
• Kyle H. Bowler
,
• Adam Yasgar
,
• Ganesha Rai
,
• Anton Simeonov
,
• Eugene N. Muratov
,
• Alexey V. Zakharov *
, and
• Alexander Tropsha *

Journal of Chemical Information and Modeling 2020 , lx , 8 , 4056-4063 (Pharmaceutical Modeling)

Publication Date (Spider web) : July 17, 2020

• Abstract
• Total text
• PDF

ABSTRACT

Minor, colloidally aggregating molecules (SCAMs) are the most mutual source of imitation positives in high-throughput screening (HTS) campaigns. Although SCAMs tin exist experimentally detected and suppressed by the addition of detergent in the assay buffer, detergent sensitivity is not routinely monitored in HTS. Computational methods are thus needed to flag potential SCAMs during HTS triage. In this study, we take adult and rigorously validated quantitative structure-interference relationship (QSIR) models of detergent-sensitive aggregation in several HTS campaigns nether various assay conditions and screening concentrations. In detail, nosotros have modeled detergent-sensitive aggregation in an AmpC β-lactamase analysis, the preferred HTS counter-screen for aggregation, as well as in another assay that measures cruzain inhibition. Our models increment the accuracy of aggregation prediction by ∼53% in the β-lactamase assay and by ∼46% in the cruzain assay compared to previously published methods. We besides talk over the importance of both analysis weather condition and screening concentrations in the development of QSIR models for diverse interference mechanisms besides aggregation. The models developed in this study are publicly available for fast prediction inside the SCAM detective web application (https://scamdetective.mml.unc.edu/).

Gross domestic product Release from the Open Conformation of Gα Requires Allosteric Signaling from the Agonist-Bound Human β_two Adrenergic Receptor

• Vikash Kumar
,
• Hannah Hoag
,
• Safaa Sader
,
• Nicolas Scorese
,
• Haiguang Liu *
, and
• Chun Wu *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 4064-4075 (Pharmaceutical Modeling)

Publication Date (Web) : July 28, 2020

• Abstruse
• Total text
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Abstruse

G-poly peptide-coupled receptors (GPCRs) transmit signals into the cell in response to ligand binding at its extracellular domain, which is characterized by the coupling of agonist-induced receptor conformational modify to guanine nucleotide (Gdp) substitution with guanosine triphosphate on a heterotrimeric (αβγ) guanine nucleotide-binding poly peptide (G-protein), leading to the activation of the G-protein. The signal transduction mechanisms take been widely researched in vivo and in silico. However, coordinated communication from stimulating ligands to the leap Gdp withal remains elusive. In the present study, we used microsecond (μS) molecular dynamic (MD) simulations to directly probe the communication from the β2 adrenergic receptor (β2AR) with an agonist or an antagonist or no ligand to Gross domestic product jump to the open up conformation of the Gα protein. Molecular mechanism-general Born expanse calculation results indicated either the agonist or the antagonist destabilized the binding between the receptor and the G-protein only the agonist acquired a higher level of destabilization than the antagonist. This is consistent with the office of agonist in the activation of the G-protein. Interestingly, while GDP remained leap with the Gα-protein for the two inactive systems (antagonist-leap and apo form), GDP dissociated from the open conformation of the Gα protein for the agonist activated arrangement. Data obtained from Medico simulations indicated that the receptor and the Gα subunit play a big part in coordinated communication and nucleotide substitution. Based on residuum interaction network analysis, we observed that engagement of agonist-bound β2AR with an α5 helix of Gα is essential for the Gdp release and the residues in the phosphate-binding loop, α1 helix, and α5 helix play very important roles in the Gross domestic product release. The insights on GPCR–G-protein advice volition facilitate the rational design of agonists and antagonists that target both agile and inactive GPCR bounden pockets, leading to more precise drugs.

Interdimeric Curvature in Tubulin–Tubulin Complexes Delineates the Microtubule-Destabilizing Properties of Plocabulin

• Karen R. Navarrete
 and
• Verónica. A. Jiménez *

Journal of Chemical Information and Modeling 2020 , 60 , 8 , 4076-4084 (Pharmaceutical Modeling)

Publication Appointment (Web) : July 20, 2020

• Abstruse
• Full text
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Abstruse

Plocabulin is a novel microtubule (MT) destabilizer agent with potent antineoplastic activity. This compound binds to the maytansine site at the longitudinal interface between tubulin dimers and exerts a hinge-similar issue that disrupts normal microtubule associates. Plocabulin has emerged as a valuable model for the rational design of novel MT destabilizers because of its unique structural and mechanistic features. To make progress on this matter, detailed molecular-level understanding of the ligand–protein interactions responsible for plocabulin association and the conformation and energetic furnishings arising from plocabulin binding on the longitudinal interaction between tubulin dimers must be provided. In this work, fully atomistic Doctor simulations and MM/GBSA binding gratuitous-energy calculations were used to examine the clan of plocabulin to 1 or two tubulin dimers in longitudinal arrangement. Our results revealed that plocabulin binding is favored past the add-on of a 2d tubulin dimer and that this ligand promotes the associates of curved tetrameric arrangements with strengthened longitudinal interdimeric interactions compared to ligand-gratuitous systems. The applicability of these findings to the rational discovery of novel MT destabilizers was tested using Dr. and MM/GBSA calculations as filtering tools to narrow the results of virtual screening amid an FDA-approved drug database. Our results confirmed that tight-binding ligands do not necessarily exert the expected conformational and energetic furnishings on longitudinal tubulin–tubulin interactions, which is a matter to consider in futurity design strategies.

Bioinformatics

Multiview Joint Learning-Based Method for Identifying Small-Molecule-Associated MiRNAs by Integrating Pharmacological, Genomics, and Network Knowledge

• Cong Shen
,
• Jiawei Luo *
,
• Zihan Lai
, and
• Pingjian Ding

Journal of Chemic Data and Modeling 2020 , lx , 8 , 4085-4097 (Bioinformatics)

Publication Appointment (Web) : July 10, 2020

• Abstruse
• Total text
• PDF

Abstract

The emergence of a big corporeality of pharmacological, genomic, and network knowledge information provides new challenges and opportunities for drug discovery and development. Identification of real small-molecule drug (SM)–miRNA associations is not only important in the evolution of effective drug repositioning just also crucial in providing a better understanding of the mechanisms by which minor-molecule drugs achieve the purpose of treating diseases by regulating miRNA expression. Nonetheless, challenges remain in accurately determining potential associations between small molecules and miRNAs using information from multiomics data. In this study, we adopted a novel framework called SMAJL to improve the prediction of small-scale molecule–miRNA associations with joint learning. Get-go, we use enhancing matrix completions to obtain the network knowledge of small molecule–miRNA associations. Then, we extract the information of small-scale-molecule fingerprints and miRNA sequences into feature vectors to obtain pocket-size-molecule structure and miRNA sequence information. Finally, we incorporate small-molecule structure data, miRNA sequence information, and heterogeneous network cognition into a articulation learning model based on a Restricted Boltzmann Automobile (RBM) to predict clan scores. To validate the effectiveness of our method, the SMAJL model is compared with 4 state-of-the-art methods in terms of five-fold cross-validation. The results demonstrate that the AUC and AUPRC of the SMAJL are obviously superior to those of other comparison methods. The SMAJL model also achieved great results in terms of robustness and instance studies, farther demonstrating its potent predictive ability.

Improving Enzyme Optimum Temperature Prediction with Resampling Strategies and Ensemble Learning

• Japheth E. Gado
,
• Gregg T. Beckham *
, and
• Christina M. Payne *

Journal of Chemical Information and Modeling 2020 , threescore , 8 , 4098-4107 (Bioinformatics)

Publication Engagement (Web) : July eight, 2020

• Abstract
• Total text
• PDF

Abstract

Accurate prediction of the optimal catalytic temperature (Topt) of enzymes is vital in biotechnology, as enzymes with loftier Topt values are desired for enhanced reaction rates. Recently, a automobile learning method (temperature optima for microorganisms and enzymes, TOME) for predicting Topt was developed. TOME was trained on a ordinarily distributed data prepare with a median Topt of 37 °C and less than 5% of Topt values above 85 °C, limiting the method's predictive capabilities for thermostable enzymes. Due to the distribution of the training data, the hateful squared fault on Topt values greater than 85 °C is nearly an order of magnitude higher than the error on values between thirty and 50 °C. In this study, nosotros apply ensemble learning and resampling strategies that tackle the data imbalance to significantly subtract the mistake on high Topt values (>85 °C) past lx% and increase the overall R2 value from 0.527 to 0.632. The revised method, temperature optima for enzymes with resampling (TOMER), and the resampling strategies practical in this work are freely bachelor to other researchers as Python packages on GitHub.

Errata

Mastheads

Issue Editorial Masthead

Journal of Chemical Information and Modeling 2020 , threescore , eight , XXX-XXX (Article)

Publication Date (Web) : August 24, 2020

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Source: https://pubs.acs.org/toc/jcisd8/60/8

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