A Celebration of Women in Computational Chemistry - ACS Publications

Editorial pubs.acs.org/jcim

Cite This: J. Chem. Inf. Model. 2019, 59, 1683−1692

A Celebration of Women in Computational Chemistry

Downloaded by 109.236.53.182 at 21:48:20:518 on May 28, 2019 from https://pubs.acs.org/doi/10.1021/acs.jcim.9b00368.

I

cases, also be present under normal isobaric-isothermic conditions in solution. Unexpectedly short noncovalent distances have attracted the interest of Qi and Kulik who present an analysis for noncovalent distances of 38 000 protein structures, where 13 500 of them were human proteins. These two approaches yielded a number of potential protein targets including two human proteins that interact with resveratrol.30 Natural products have been consistent sources of leads for drug discovery. Lee et al. report a virtual screen utilizing docking of Malaysian Natural Compound Database (NADI) onto ensembles of 22 conformations of Mycobacterium 1685

DOI: 10.1021/acs.jcim.9b00368 J. Chem. Inf. Model. 2019, 59, 1683−1692

Journal of Chemical Information and Modeling

Editorial

tuberculosis’ isocitrate lyase (ICL) obtained from MD simulation and crystal structures. ICL is a persistent factor for the survival of dormant stage M. tuberculosis, thus a potential drug target for tuberculosis treatment. Twenty-two hit compounds complying with Lipinski’s Rule of Five for drug-likeliness were traced to the sourced plants and showed inhibition activity.31 Other natural products, such as Cassane diterpenoids (CAs), constituents of many medical plants of the genus Caesalpinia, exhibit diverse bioactivities, including antiinflammatory and immunomodulatory activity. A work by Wang et al. involves the selection of 102 CA compounds in their quest to explore the possible molecular mechanism of this class of natural products on anti-inflammatory and immunomodulatory activity through a series of in silico methods: chemical-similarity-based target prediction, molecular docking, and MD. As a consequence, four signaling pathways (TCR, TLR, and VEGF signaling pathways and the osteoclast differentiation pathway), by which CAs exert their effect on inflammation and immunomodulation, are identified, and their potential binding mechanisms are thoroughly investigated. Their findings might be useful in explaining the activity of CAs, which in turn could be a valuable reference for drug design research on CAs.32 In another study, Almaghadi and coworkers report molecular docking, MD simulations, and molecular mechanics/Poisson−Boltzmann Surface Area (MM-PBSA) calculations to investigate the binding affinity of isolated (new and known) compounds of Peperomia blanda (Jacq.) Kunth onto methylthioadenosine phosphorylase enzyme. The pharmacotherapeutic potential of the compounds was evaluated using the Prediction of Activity Spectra for Substances (PASS) software.33 The advent of big data has provided unprecedented opportunities for the application of machine learning algorithms by computational chemists. Grisoni et al. develop novel in silico models using machine learning based on a consensus of a multivariate Bernoulli Naive Bayes, a Random Forest, and N-Nearest Neighbor classification models. These models were applied in the identification of organic Androgen Receptor modulators as part of the Collaborative Modeling Project of Androgen Receptor Activity (CoMPARA), coordinated by the National Center of Computational Toxicology (U.S. Environmental Protection Agency). The collaborative project involved 35 international research groups to prioritize the experimental tests of approximatively 40 000 compounds, based on the predictions provided by each participant.34 Integration of ligand bioactivity data for hepatic organic anion transporting polypeptides (OATPs) from five open data sources (ChEMBL, the UCSF−FDA TransPortal database, DrugBank, Metrabase, and IUPHAR) in a semiautomatic KNIME workflow as well as binary classification modeling were performed by Türková et al. to provide insights on hepatic OATP−ligand interactions and selectivity. These insights are important especially to understand the roles of OATPs in different conditions of liver toxicity and cancer.35 A random forest application coupled with support vector machines is applied by Yan et al. to the classification study of 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, into 12 classification models. The 2925 COX-2 inhibitors were further clustered into eight subsets, and the structural features of each subset were investigated leading to the identification of substructures important for activity including halogen, carboxyl, sulfonamide, and methanesulfonyl groups, as well as aromatic nitrogen atoms. The models

developed on the biggest COX-2 inhibitor data set so far could serve as useful tools for compound screening prior to lab tests.36 Pei et al. develop a random forest method to identify native protein folds from decoys based on pairwise atom distances. They test their method against 12 different data sets, including scrambled atom types as a null model, and compare their method to KECSA2, GOAP, DFIRE, dDFIRE, and RWplus potentials. Their method has the ability to not only distinguish the native from among a set of decoys but also to directly compare two structures and predict whether one is the native structure. Their work also suggests that random forest models can be used to tune the height of peaks in probability functions (or the depth of potential functions) only with the information on peak positions.37 The ability of machine learning approaches to augment more traditional methods (such as DFT) and hence speed up materials discovery has shown great promise. Amos and Kobayashi study the selection of features to predict band gaps in various materials. The effects of structural featurizers in the prediction of band gaps were through machine learning by application to a silver nanoparticle data set and 2254 potential light-harvesting materials with known band gaps. Their model could predict the band gaps of the 2254 light-harvesting materials in the data set with an accuracy reflected in a rootmean-square error of 0.232 eV and mean absolute error of 0.142 eV. Furthermore, the good performance of the model was transferable to the prediction of a set of 72 experimental band gaps that were independent of the training set, giving a root-mean-square error of 0.91 eV and mean absolute error of 0.76 eV.38 As accurate noncovalent interaction computation is quite demanding using first-principles methods, Li et al. propose a general procedure for machine learning ensemble establishment based on noncovalent interactions databases. The models are based on low basis set level density functional theory (DFT) calculations for the benchmark databases. All noncovalent interactions computed by DFT calculations can be improved to high-level accuracy by established ensemble learning models. The authors suggest that the procedure may also be applicable for other chemical databases, which will undoubtedly be useful for experimentalists.39 Jansen et al. describe a KNIME workflow, Biased Complement Diversity (BCD), useful in the design of compound collections for screening from large compound archives. The workflow included several stages, each based on wellestablished procedures (e.g., filtering, clustering, descriptor calculations, diversity selection) with an emphasis on balancing diversity and quality attributes. They demonstrate the application of BCD through four very different examples (e.g., a malaria case study that was based on publicly available data) representing a broad range of assays, quality metrics, screening strategies, and screening set designs. These examples show that BCD is capable of enhancing the target relevance of the screening set while maintaining diversity, and that different strategies can be used to complement parallel screening with other compound sets.40 Other articles using machine learning include the development of DeepIce, a novel deep neural network by Molteni et al, which demonstrates a high degree of accuracy in classifying water molecules as hexagonal ice or liquid water using as input simply the Cartesian coordinates of the nearest neighbors41 as well as that by Oja et al, which focuses on the application of 1686



Editorial

a large variety of toxic exogenous compounds including antibiotics. Weng and Wang construct a Markov state model from swarms of all-atom MD simulation trajectories, which delineates the energetics of the conformational changes of TolC. Further PMF calculation on TolC conformational changes and the ion permeation showed that TolC opens entrance asymmetrically through disruption of interprotomer interactions. Due to the role of TolC in the global problem of multidrug resistance, this work certainly provides a valuable insight into the functioning state of the tripartite efflux pumps.48 Majumdar and co-workers present results from MD simulations and MM-GBSA calculations of the crystal structure of a ligand E7010 bound at the alpha, beta dimer interface of tubulin. They observe reorientation of the ligand in the binding pocket during the simulation and the formation of a water cluster interaction network between the ligand and the protein. They propose the states observed in the simulation to be degenerate energy states for the ligand, the degeneracy of which is facilitated by the water clusters in the binding pocket. The role of such “bridging” water clusters to enhance the protein−ligand interaction will be insightful for designing the next generation prospective compounds in the field of cancer therapeutics.49 A series of MD simulations coupled with free energy calculations to analyze B to A transitions in DNA, which is known to be sensitive to the macroscopic properties of the solution, such as salt and ethanol concentrations were carried out by Zhang and co-workers. With the addition of ethanol, the most stable structure of DNA changes from the B- to Aform, in agreement with previous experimental observation. Their analysis provides a free-energy view of DNA microenvironment and the role of cation motion in the conformational transition. DNA polymerase I from Thermus aquaticus (Taq DNA polymerase) is useful for polymerase chain reactions, however, its activity at low temperatures can cause amplification of unintended products.50 Modeste et al. present a series of MD simulations of DNA polymerase to determine the effects of a specific mutation, I707L, on the dynamics of DNA polymerase at two temperatures. The results demonstrate that the mutation results in a larger sensitivity to temperature changes than the wild type, which is in line with experimental results.51 By employing MD simulations, Amaro and co-workers provide insight into the basis of selectivity for ssDNA over RNA in APOBEC3B. APOBEC3B is recently discovered to be a key molecular driver promoting mutational processes in multiple human cancers. It preferentially binds ssDNA over RNA and catalyzes the conversion of cytosine to uracil. Analysis of these simulations reveal dynamics of A3Boligonucleotide interactions, including the experimentally inaccessible loop 1-oligonucleotide, as well as reveals the rearrangement of the binding site, suggesting a potential intermediate in the binding pathway.52 Other MD works that appear in the special issue include that on NPC1 protein that is involved in Niemann Pick type C disease,53 on meso-Diaminopimelate dehydrogenase (mesoDAPDH), an enzyme for one-step synthesis of D-amino acids from 2-keto acids54 and that involving Epothilone A-tubulin interactions.55 Using a combination of molecular modeling, docking, and MD, Elghobashi-Meinhardt analyzes cholesterol binding sites and investigates the structural flexibility of specific domains of NPC1 at pH 5 and pH 7. Gao et al. measure

QSAR methods to predict drug permeability at different pH values.42 A detailed analysis of classification models and related decision trees suggests that they are suitable for predicting classes of permeability for passively transported drug substances, including specifically within the Biopharmaceutical Classification (BCS) framework. The extensive amount of details provided in at each step of the modeling process should be sufficient for model reproduction and further use.42 Pecoraro and co-workers present an overview of computational approaches (such as QSPR and MD simulations) to estimate skin permeability. They mainly, but not exclusively, present methods calculating skin permenation from a water vehicle without enhancers in the formulation or in the skin membrane. This review is timely as it not only highlights the main advances and the principle approaches in computational methods used to predict skin permeability property but also addresses the main issues and challenges. All these are required to improve the development of transdermal pharmaceutical formulations, as well as to understand the dermal toxicity prediction of compound prediction.43 MD simulation is evidently one of the most popular computational chemistry tools. It has been used to provide insights into protein structure, function and dynamics, as well the mutational effects on protein function. Sabsay et al. construct MEK1 structures in the active form using homology modeling. By employing docking and MD simulations with the homology-modeled structures, they address the mechanistic and energetic features relevant to the biological function of MEK1. MEK1 is a protein kinase in the MAPK cellular signaling pathway, notable for its dual specificity and its potential as a drug target for a variety of cancer therapies. Thus, the results may be useful in providing insights for the pursuit of structure-guided mutagenesis and drug design.44 MD simulations have been applied to study transport of various molecules facilitated with membrane proteins, which are necessary for maintaining homeostasis in living cells. In humans, dysfunction of these proteins leads to many diseases. This motivates Pieńko and Trylska to provide a broad review of MD simulation methods and their variations (Brownian dynamics, steered MD, accelerated MD) and associated methods (e.g., umbrella sampling, ABS, metadynamics) and their successful applications to explore transport events.45 Wu and co-workers report MD simulations and umbrella sampling of acetate translocation through the acetate channel SuccinateAcetate Permease (SatP). Acetate is a central metabolite that plays a key role in almost all organisms, and acetate channels are often essential for their survival. Free energy calculations are carried out to get an estimate of the barrier height for acetate transport. Their study deepens the understanding of the molecular mechanism of acetate transport through the channel SatP and is expected to facilitate the drug discovery on this target.46 The human multidrug transporter P-glycoprotein (P-gp) transports over 200 chemically diverse substrates, influencing their bioavailability and tissue distribution. Using MD and potential of mean force calculations, Subramanian et al. study substrate binding and permeation through P-gp, with the objective to characterize the competitive and noncompetitive effects present in this system for five compounds which would bind to P-gp transporter. Based on the results, a general scheme that accounts for the observed noncompetitive and competitive substrate interactions with P-gp is proposed.47 TolC, is an outer membrane channel protein, which pumps out 1687



Editorial

kinetics for a wild type enzyme and seven mutants.54 MD reveals that these point mutations reduced the productive conformations by the newly formed or eliminated interactions between the residues and ligands.54 Dolenc and co-workers analyze the structure and dynamics of an important chemotherapeutic drug analogue, Epothilone A (EpoA). Exhaustive MD-based analysis of the existing crystallographic and NMR data in water and DMSO highlights and removes certain discrepancies between the known data and provide a rich model of the conformational behavior of EpoA bound to tubulin.55 Together with intrinsically disordered regions (IDRs), intrinsically disordered proteins (IDPs) comprise approximately 25% of the human proteome. The inherent disorder of IDRs and IDPs is required for functions such as cellular regulation and signaling, and they are also associated with human diseases, such as diabetes, neurodegenerative diseases, and cancer. The challenge in the study of IDPs and IDRs is how to build structural and dynamical models that allow researchers to interrogate their nature. Ha-Duong and coworkers review the state of the art of MD simulations of IDP conformational ensembles, with a special focus on studies that back-calculated and directly compared theoretical and experimental NMR or SAXS observables, such as chemical shifts, 3J-couplings (3Jc), residual dipolar couplings, or SAXS intensities. Besides the IDP propensity to form local secondary structures, their dynamic extension or compactness also appears important for their activity.56 An example of intrinsically disordered proteins is Amyloid-β (Aβ). Its interaction with transition metal ions, can affect its mechanism and kinetics of aggregation. Coskuner-Weber and co-worker57 review the current understanding of transition metal interactions with amyloid-β obtained from computational chemistry studies (DFT calculations, ab initio MD, QM/ MM, and MD simulations) with particular emphasis on the current view of the coordination chemistry between transition metal ions and amyloid-β. This information represents an important foundation for future metal ion chelator and drug design studies aiming to combat Alzheimer’s disease. In order to explore the mechanism of long-distance conformational coupling in SecA, the DEAD-box motor of the Sec protein secretion in bacteria, Karathanou and Bondar implement algorithms that provide simplified graph representations of the protein’s dynamic hydrogen-bond networks. The water network is identified through an extensive search of all atom unbiased MD trajectories, of the wild type and selected mutations, thus emphasizing the specific role of the water as a “mechanical “ element.58 In another article, Palermo presents state-of-the-art MD and QM/MM studies to predict the active structure of CRISPRCas9 for both HNH and RuvC cleavage sites. The CRISPRCas9 system is emerging as the forefront technology for editing and manipulating nucleic acids. They observe a canonical twometal aided architecture in the RuvC active site, which is poised to operate the catalysis, in analogy with other DNA/RNA processing enzymes. The conformational dynamics of the RuvC domain also reveals that an arginine f inger stably contacts the scissile phosphate, as in other metal-aided phosphatases with the function of stabilizing the formation of a catalytically active complex. These findings could advance the fundamental understanding of the CRISPR-Cas9 mechanistic action toward improved genome editing.59

To uncover the effects of carbon nanoparticles (NPs) including graphene and carbon nanotubes on the aggregation of prion proteins, whose misfolding and aggregation lead to prion diseases, a ThT fluorescence assay and an MD simulation were performed by Liu et al. The ThT fluorescence assay reveals that both graphene and carbon nanotubes can inhibit the fibril formation of prion proteins, especially graphene. Further MD simulation of the PrP127-147 tetramer with or without carbon NPs suggests that the interactions between prion proteins and carbon NPs reduce the aggregation tendency of PrP127-147 by decreasing the interpeptide interactions and thus inhibiting β-sheet formation. The obtained results can increase our understanding on the interaction between nanoparticles and amyloid-related proteins.60 Investigation and study of protein hydration dynamics have become a topic of huge interest in recent years. Dahanayake and co-workers carry out MD simulation with the aim to understand the correlation between hydration dynamics and protein solvent shell structure using MD simulations of Candida antarctica lipase B. A linear correlation is established between the diffusion constant and the integral of the first solvent shell of the protein−water radial distribution function and shown to be generalized among proteins.61 MD simulations have also been applied to study allosteric signaling of various enzymes and receptors. Lipoxygenases (LOXs) are enzymes crucial for catalyzing lipid oxidation, thus regulating a broad range of cellular activities. MikulskaRuminska et al. present a systematic method of approach for characterizing the sequence, structure, dynamics, and allosteric signaling properties of LOXs using a combination of structurebased models and methods and bioinformatics tools applied to a data set of 88 structures. The analysis elucidates the signature dynamics of the lipoxygenase family and its differentiation among members, as well as key sites that enable its adaptation to specific substrate binding and allosteric activity.62 Khrenova et al. present the results of molecular modeling of conformational changes in the Y231C and F295S mutants of human aspartoacylase (hAsp), with which they propose a mechanism of allosteric regulation of enzyme activity of these protein variants. The hAsp enzyme hydrolyzes one of the most abundant amino acid derivatives in the brain, N-acetylaspartate. Application of the dynamical network analysis and the Markov state model provide a detailed description of dynamically induced structural changes of the protein where the decreased availability of the active site for substrate molecules in the mutated enzymes explains their diminishing activity observed in clinical experiments.63 The metabotropic glutamate 5 (mGlu5) receptor is a class C G protein-coupled receptor (GPCR) that is implicated in several CNS disorders making it a popular drug discovery target. Llinas del Torrent et al. analyze experimental data and perform docking and MD for three sets of positive allosteric modulators (PAM) and negative allosteric modulators (NAM) of mGlu5 ligands. The results consistently show the role of specific interactions formed between ligand substituents and amino acid side chains that block or promote local movements associated with receptor activation. The work provides an explanation for how such small structural changes lead to remarkable differences in functional activity. While this work can greatly help drug discovery programs avoid these switches, it also provides valuable insight into the mechanisms of class C GPCR allosteric activation.64 Zhang et al. present combined 1688



Editorial

time scales inaccessible to all-atom models. This special issue captures the work by Neelamraju et al., who developed the software package Go-Kit. Written in python2.7, this software aims to increase accessibility of coarse-grained Go-like models and facilitates the setup of MD simulations and discrete path sampling. The usefulness of Go-Kit, embedded in open source software packages, GROMACS and PATHSAMPLE, is illustrated in a case study for the ribosomal protein S6.70 Hu et al. develop a CG model for polyimide (PI) at 800 K and 1 atm by applying iterative Boltzmann inversion (IBI) and the density correction method to derive the bonded and nonbonded interaction potentials. They demonstrate that the CG force field built at a single thermodynamic state model does well as far as the temperature transferability (temperature range of 300−800 K at P = 1 atm), and a good pressure transferability (pressure range from 0.1 to 30 MPa) by comparing different thermodynamic and mechanical properties with the atomistic simulation results. The corresponding values of the elastic modulus of the CG model at different temperatures roughly match with those of the atomistic model.71 Understanding the behavior of copolymers in a selected solvent system is of particular interest to tune the intricate balance of microphase separation/mixing, which is the key mechanism behind the structure formation in thermoplastic polyurethanes (TPUs). In their article, Avaz Seven et al. present a set of dissipative particle dynamics simulations performed on TPUs mixing different building blocks and solvents. Interaction parameters are derived in a bottom-up fashion from atomistic MD and findings are supported by thermodynamic arguments. The approach developed here is useful for designing novel TPUs with well-defined conformational characteristics, controlled morphologies, and advanced functional properties.72 While almost all of the accepted papers are on scientific applications of or methods related to computational chemistry, this issue also includes a perspective that provides a glimpse of five computational chemists, led by Georgia McGaughey at Vertex, who ventured into research and development work in the pharmaceutical industry. The authors share their experiences, inspiration, achievements, and contributions in taking compounds to the clinical stage and market. This perspective underscores that highlighting female role models can immensely impact women in academic, industrial, or government workplaces.73 In our previous editorial, we introduced this Special Issue by highlighting Marie Curie as an impactful role model in advancing women in science.1 Curie’s illustrious career being the first female professor in the Sorbonne’s history did not limit her to also successfully raise two daughters, both of whom also had distinguished careers. Her daughter, Irene, in particular, following her mother’s footsteps working in radioactivity, also won a Noble Prize in Chemistry in 1935 together with her husband, Frédéric Joliot-Curie, for their discovery of artificial radioactivity. Interestingly, the timing of the publication of this special issue falls in the month of May 2019, coinciding with many Mother’s Day celebrations worldwide. Thus, we end our comments on this special issue by highlighting a mother and daughter’s joint article, authored by computational chemists Saraswathi Vishveshwara and Smitha Vishveshwara, who, together with Vasundhara Gadiyaram, present a graph theoretical approach to analyze protein 3D structure properties;74 this newly developed

accelerated MD (aMD) and conventional MD simulations coupled with PMF calculations, correlation analysis, principal component analysis (PCA), and protein structure network (PSN) studies on GPCR oligomers in order to address several effects associated with the dimerization and the mutations of I52V and V150A on the CCR5 homodimer. The results reveal that the dimer with interface involved in TM1, TM2, TM3, and TM4 is stable for the CCR5 homodimer. The dimerization induces an asymmetric impact on the overall structure and the ligand-binding pocket. The PSN result further reveals the allosteric pathway of the ligand-binding pocket between the two protomers. The results from PMF, PCA, and the correlation analysis clearly indicate that the two mutations induce strong anticorrelation motions in the interface, finally leading to its separation. The observations from the work could advance our understanding of the structure of the G proteincoupled receptor dimers and implications for their function.65 A method to calculate the binding free energies of cosolvent molecules to proteins from simulations using the MixMD approach is presented by Ghanakota et al. The free energy and relative entropy ranking of the top-four MixMD binding sites were computed and analyzed across allosteric protein targets: Abl Kinase, Androgen Receptor, Pdk1 Kinase, Farnesyl Pyrophosphate Synthase, Chk1 Kinase, Glucokinase, and Protein Tyrosine Phosphatase 1B. The authors conclude that estimating the entropy of the probe molecules in the binding hotspots yields information about the conformational flexibility that may be useful when designing drug-like molecules to complement a binding site.66 The advent of nanotechnology has seen a growing interest in the nature of fluid flow and transport under nanoconfinement. Roy et al. present MD simulations to characterize water dynamics in a carbon nanotube (CNT) in the presence of the organic cosolvent HFIP. Water molecules within the nanochannels show clear signatures of dynamical slowdown relative to bulk water even for pure systems. These results lend insights into devising ways of modulating solvent properties within nanochannels with cosolvent impurities.67 Understanding the underlying mechanisms on sensitivitydecrease of the cocrystal, 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12hexaazaisowurtzitane (CL-20)/2,4,6-trinitrotoluene (TNT), CL-20/TNT is essential for wide applications of the promising high-energetic CL-20. In this special issue, Ren et al. present ReaxFF MD simulations of CL-20/TNT cocrystals for temperatures from 1000 to 3000 K. Reaction dynamics are analyzed using the VARxMD approach and intermediate structures are re-evaluated via DFT calculations. The authors make a direct comparison between CL-20/TNT cocrystal consumption and pure CL-20 consumption. They come to the conclusion that TNT hinders the formation of key intermediates in the CL-20 consumption, which is therefore slowed down.68 Kjølbye et al. use MD simulations to study the interactions between Humicola insolens (HiC), a cutinase that has potential applications in the detergent industry, and sodium dodecyl sulfate (SDS), a common component of detergents. The main emphasis of the work is to define the basis of interaction between the protein and SDS in order to understand how SDS may reduce its activity. The results suggest a mechanism of cutinase inhibition by SDS, which involves the nucleation of aggregates of SDS molecules on hydrophobic patches on the cutinase surface.69 Coarse grained (CG) models have also gained popularity as they allow to simulate large-scale biomolecular processes on 1689



Editorial

Responsiveness and Biocompatibility. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00985. (8) Adilakshmi, A.; et al. Theoretical Investigation of Steric Effect Influence on Reactivity of Substituted Butadienes with Bromocyclobutenone. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00177. (9) Baranowska-Łaczkowska, A.; Łaczkowski, K. Z.; Fernández, B. The Role of Substituents in Optical Rotation of Oxiranes, Oxetanes, and Oxathietanes. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00970. (10) Nedyalkova, M.; et al. Calculating the Partition Coefficients of Organic Solvents in Octanol/Water and Octanol/Air. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00212. (11) Trujillo, C.; et al. Planarity or Nonplanarity: Modulating Guanidine Derivatives as α2-Adrenoceptors Ligands. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00140. (12) Bose, A.; Makri, N. Quasiclassical Correlation Functions from the Wigner Density Using the Stability Matrix. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00081. (13) Stepanović, S.; et al. The Irony of Manganocene: An Interplay between the Jahn−Teller Effect and Close-Lying Electronic and Spin States. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00870. (14) Kearns, F. L.; et al. Modeling Boronic Acid Based Fluorescent Saccharide Sensors: Computational Investigation of d-Fructose Binding to Dimethylaminomethylphenylboronic Acid. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00987. (15) Mohajeri, A.; Omidvar, A.; Setoodeh, H. Fine Structural Tuning of Thieno[3,2-b] Pyrrole Donor for Designing BananaShaped Semiconductors Relevant to Organic Field Effect Transistors. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00738. (16) Sánchez-Sanz, G.; Trujillo, C. Cyclohexane-Based Scaffold Molecules Acting as Anion Transport, Anionophores, via Noncovalent Interactions. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.9b00154. (17) Parida, R.; et al. A New Class of Superhalogen Based Anion Receptor in Li-Ion Battery Electrolytes. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00035. (18) Sikorska, C. Magnesium-Based Oxyfluoride Superatoms: Design, Structure, and Electronic Properties. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00083. (19) Liu, H.; et al. Theoretical Design of D−π−A−A Sensitizers with Narrow Band Gap and Broad Spectral Response Based on Boron Dipyrromethene for Dye-Sensitized Solar Cells. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00187. (20) Ranaghan, K. E.; et al. Projector-Based Embedding Eliminates Density Functional Dependence for QM/MM Calculations of Reactions in Enzymes and Solution. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00940. (21) Böselt, L.; et al. Determination of Absolute Stereochemistry of Flexible Molecules Using a Vibrational Circular Dichroism Spectra Alignment Algorithm. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00789. (22) Hunter, M. A.; et al. Doping Effects on the Performance of Paired Metal Catalysts for the Hydrogen Evolution Reaction. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00179. (23) Zhang, X.; et al. Free Energies of Catalytic Species Adsorbed to Pt(111) Surfaces under Liquid Solvent Calculated Using Classical and Quantum Approaches. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.9b00089. (24) Levré, D.; et al. ZINClick v.18: Expanding Chemical Space of 1,2,3-Triazoles. J. Chem. Inf. Model. 2018, DOI: 10.1021/acs.jcim.8b00615. (25) Damm-Ganamet, K. L.; et al. Accelerating Lead Identification by High Throughput Virtual Screening: Prospective Case Studies from the Pharmaceutical Industry. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00941. (26) Worrell, B. L.; et al. In Silico Characterization of Structural Distinctions between Isoforms of Human and Mouse Sphingosine Kinases for Accelerating Drug Discovery. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00931.

method undoubtedly has the potential to solve various problems in this field. The tremendous outpouring of support from scientists all over the world in response to this special issue demonstrates that publication platforms such as this are very much appreciated for female scientists in this field. It is clear that female scientists have contributed significantly to progress in the area of Computational Chemistry, though of course we note that only a handful could be captured in this special issue. We thank all the authors and all involved for their contributions that have made this special issue a reality and hope that this issue contributes to the visibility and progress of women in Computational Chemistry.

Habibah A. Wahab*,†,‡ Rommie E. Amaro*,§ Zoe Cournia*,¶

■

† School of Pharmaceutical Sciences and ‡Centre for Research on Women and Gender (KANITA), Universiti Sains Malaysia, 11800 Minden, Pulau Pinang Malaysia § Department of Chemistry and Biochemistry, University of California, San Diego, 3234 Urey Hall, #0340, 9500 Gilman Drive, La Jolla, California 92093-0340, United States ¶ Biomedical Research Foundation, Academy of Athens, 11527 Athens, Greece

AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (H.A.W.). *E-mail: [email protected] (R.E.A.). *E-mail: [email protected] (Z.C.). ORCID

Habibah A. Wahab: 0000-0002-8353-8679 Rommie E. Amaro: 0000-0002-9275-9553 Zoe Cournia: 0000-0001-9287-364X Notes

Views expressed in this editorial are those of the authors and not necessarily the views of the ACS.

■

REFERENCES

(1) Wahab, H. A.; Amaro, R. E.; Cournia, Z. Women in Computational Chemistry. J. Chem. Inf. Model. 2018, 58 (11), 2175−2177. (2) Lim, V. T.; et al. Assessing the Conformational Equilibrium of Carboxylic Acid via Quantum Mechanical and Molecular Dynamics Studies on Acetic Acid. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00835. (3) Qi, H. W.; Kulik, H.J. Evaluating Unexpectedly Short Noncovalent Distances in X-ray Crystal Structures of Proteins with Electronic Structure Analysis. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00144. (4) Kaynak, B. T.; Doruker, P. Protein−Ligand Complexes as Constrained Dynamical Systems. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00946. (5) Fuller, J.; et al. Charge Density in Enzyme Active Site as a Descriptor of Electrostatic Preorganization. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00958. (6) Chojecki, M.; Rutkowska-Zbik, D.; Korona, T. Dimerization Behavior of Methyl Chlorophyllide a as the Model of Chlorophyll a in the Presence of Water MoleculesTheoretical Study. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00984. (7) Zheng, D.; et al. Hydrogen Bonding Promoted Tautomerism between Azo and Hydrazone Forms in Calcon with Multistimuli 1690



Editorial

(27) Zhang, H.; et al. Ranking Reversible Covalent Drugs: From Free Energy Perturbation to Fragment Docking. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00959. (28) Greenidge, P.; et al. How to Computationally Stack the Deck for Hit-to-Lead Generation: In Silico Molecular Interaction Energy Profiling for De Novo siRNA Guide Strand Surrogate Selection. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00892. (29) Sydow, D.; et al. Advances and Challenges in Computational Target Prediction. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00832. (30) Kores, K.; et al. Discovery of Novel Potential Human Targets of Resveratrol by Inverse Molecular Docking. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00981. (31) Lee, Y.-V.; et al. Applications of Ensemble Docking in Potential Inhibitor Screening for Mycobacterium tuberculosis Isocitrate Lyase Using a Local Plant Database. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00963. (32) Wang, Y.; et al. In Silico Exploration of the Molecular Mechanism of Cassane Diterpenoids on Anti-inflammatory and Immunomodulatory Activity. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00862. (33) Al-Madhagi, W. M.; et al. Bioassay-guided isolation and in silico study of antibacterial compounds from petroleum ether extract of Peperomia blanda (Jacq.) Kunth. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00969. (34) Grisoni, F.; Consonni, V.; Ballabio, D. Machine Learning Consensus To Predict the Binding to the Androgen Receptor within the CoMPARA Project. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00794. (35) Türková, A.; Jain, S.; Zdrazil, B. Integrative Data Mining, Scaffold Analysis, and Sequential Binary Classification Models for Exploring Ligand Profiles of Hepatic Organic Anion Transporting Polypeptides. J. Chem. Inf. Model. 2018, DOI: 10.1021/acs.jcim.8b00466. (36) Qin, Z.; Yan, A.; et al. Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00876. (37) Pei, J.; Zheng, Z.; Merz, K.M. Random Forest Refinement of the KECSA2 Knowledge-Based Scoring Function for Protein Decoy Detection. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00734. (38) Amos, R. D.; Kobayashi, R. Feature Engineering for Materials ChemistryDoes Size Matter? J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00977. (39) Li, W.; et al. Efficient Corrections for DFT Noncovalent Interactions Based on Ensemble Learning Models. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00878. (40) Jansen, J. M.; et al. Biased Complement Diversity Selection for Effective Exploration of Chemical Space in Hit-Finding Campaigns. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00048. (41) Fulford, M.; Salvalaglio, M.; Molteni, C. DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00005. (42) Oja, M.; Sild, S.; Maran, U. Logistic Classification Models for pH−Permeability Profile: Predicting Permeability Classes for the Biopharmaceutical Classification System. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00833. (43) Pecoraro, B.; et al. Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00934. (44) Sabsay, K. R.; et al. Computational Models for Activated Human MEK1: Identification of Key Active Site Residues and Interactions. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00989. (45) Pieńko, T.; Trylska, J. Computational Methods Used to Explore Transport Events in Biological Systems. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00974. (46) Wu, M.; et al. Molecular Mechanism of Acetate Transport through the Acetate Channel SatP. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00975.

(47) Subramanian, N.; et al. Probing the Pharmacological Binding Sites of P-Glycoprotein Using Umbrella Sampling Simulations. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00624. (48) Weng, J.; Wang, W. Structural Features and Energetics of the Periplasmic Entrance Opening of the Outer Membrane Channel TolC Revealed by Molecular Dynamics Simulation and Markov State Model Analysis. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00957. (49) Majumdar, S.; Basu, D.; Dastidar, S. G. Conformational States of E7010 Is Complemented by Microclusters of Water Inside the α,βTubulin Core. J. Chem. Inf. Model. 2018, DOI: 10.1021/acs.jcim.8b00538. (50) Zhang, H.; et al. Changes in Microenvironment Modulate the B- to A-DNA Transition. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00885. (51) Modeste, E.; et al. A Molecular Dynamics Investigation of the Thermostability of Cold-Sensitive I707L KlenTaq1 DNA Polymerase and Its Wild-Type Counterpart. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00022. (52) Wagner, J. R.; Amaro, R. S.; et al. Determinants of Oligonucleotide Selectivity of APOBEC3B. J. Chem. Inf. Model. 2018, DOI: 10.1021/acs.jcim.8b00427. (53) Elghobashi-Meinhardt, N. Computational Tools Unravel Putative Sterol Binding Sites in the Lysosomal NPC1 Protein. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00186. (54) Gao, X.; et al. Insight into the Highly Conserved and Differentiated Cofactor-Binding Sites of meso-Diaminopimelate Dehydrogenase StDAPDH. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00879. (55) Dolenc, J.; et al. Conformational Properties of the Chemotherapeutic Drug Analogue Epothilone A: How to Model a Flexible Protein Ligand Using Scarcely Available Experimental Data. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00171. (56) Chan-Yao-Chong, M.; Durand, D.; Ha-Duong, T. Molecular Dynamics Simulations Combined with Nuclear Magnetic Resonance and/or Small-Angle X-ray Scattering Data for Characterizing Intrinsically Disordered Protein Conformational Ensembles. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00928. (57) Strodel, B.; Coskuner-Weber, O. Transition Metal Ion Interactions with Disordered Amyloid-β Peptides in the Pathogenesis of Alzheimer’s Disease: Insights from Computational Chemistry Studies. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00983. (58) Karathanou, K.; Bondar, A.-N. Using graphs of dynamic hydrogen-bond networks to dissectconformational coupling in a protein motor. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00979. (59) Palermo, G. Structure and Dynamics of the CRISPR−Cas9 Catalytic Complex. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00988. (60) Zhou, S.; Liu, H.; et al. Carbon Nanoparticles Inhibit the Aggregation of Prion Protein as Revealed by Experiments and Atomistic Simulations. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00725. (61) Dahanayake, J. N.; et al. Protein Solvent Shell Structure Provides Rapid Analysis of Hydration Dynamics. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00009. (62) Mikulska-Ruminska, K.; et al. Characterization of Differential Dynamics, Specificity, and Allostery of Lipoxygenase Family Members. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00006. (63) Kots, E. D.; Khrenova, M. G.; Nemukhin, A. V. Allosteric Control of N-Acetyl-Aspartate Hydrolysis by the Y231C and F295S Mutants of Human Aspartoacylase. J. Chem. Inf. Model. 2018, DOI: 10.1021/acs.jcim.8b00666. (64) Llinas del Torrent, C.; et al. Mechanisms Underlying Allosteric Molecular Switches of Metabotropic Glutamate Receptor 5. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00924. (65) Zhang, F.; et al. Molecular Mechanism Regarding Allosteric Modulation of Ligand Binding and the Impact of Mutations on 1691



Editorial

Dimerization for CCR5 Homodimer. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00850. (66) Ghanakota, P.; DasGupta, D.; Carlson, H.A. Free energies and entropies of binding sites identified by MixMD cosolvent simulations. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00925. (67) Roy, P.; et al. Cosolvent Impurities in SWCNT Nanochannel Confinement: Length Dependence of Water Dynamics Investigated with Atomistic Simulations. J. Chem. Inf. Model. 2019, DOI: 10.1021/ acs.jcim.8b00889. (68) Ren, C.; Li, X.; Guo, L. Chemical Insight on Decreased Sensitivity of CL-20/TNT Cocrystal Revealed by ReaxFF MD Simulations. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00952. (69) Kjølbye, L. R.; et al. Molecular Modeling Investigation of the Interaction between Humicola insolens Cutinase and SDS Surfactant Suggests a Mechanism for Enzyme Inactivation. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00857. (70) Neelamraju, S.; Wales, D.J.; Gosavi, S. Go-Kit: A Tool To Enable Energy Landscape Exploration of Proteins. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00007. (71) Hu, C.; Lu, T.; Guo, H. Developing a Transferable CoarseGrained Model for the Prediction of Thermodynamic, Structural, and Mechanical Properties of Polyimides at Different Thermodynamic State Points. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00887. (72) Avaz Seven, S.; et al. Tuning Interaction Parameters of Thermoplastic Polyurethanes in a Binary Solvent To Achieve Precise Control over Microphase Separation. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00781. (73) McGaughey, G.; et al. A Brief Recent History of Women in Computational Chemistry at Vertex Pharmaceuticals. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.8b00891. (74) Gadiyaram, V.; Vishveshwara, S.; Vishveshwara, S. From Quantum Chemistry to Networks in Biology: A Graph Spectral Approach to Protein Structure Analyses. J. Chem. Inf. Model. 2019, DOI: 10.1021/acs.jcim.9b00002.

1692


A Celebration of Women in Computational Chemistry - ACS Publications

Recommend Documents