Machine learning insights into predicting biogas separation in metal-organic frameworks.

Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R2 values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets.

Solvent flashcards: a visualisation tool for sustainable chemistry.

Selecting greener solvents during experiment design is imperative for greener chemistry. While many solvent selection guides are currently used in the pharmaceutical industry, these are often paper-based guides which can make it difficult to identify and compare specific solvents. This work presents a stand-alone version of the solvent flashcards that were developed as part of the AI4Green electronic laboratory notebook. The functionality is an intuitive and interactive interface for the visualisation of data from CHEM21, a pharmaceutical solvent selection guide that categorises solvents according to "greenness". This open-source software is written in Python, JavaScript, HTML and CSS and allows users to directly contrast and compare specific solvents by generating colour-coded flashcards. It can be installed locally using pip, or alternatively the source code is available on GitHub: https://github.com/AI4Green/solvent_flashcards . The documentation can also be found on GitHub or on the corresponding Python Package Index webpage: https://pypi.org/project/solvent-guide/ . SCIENTIFIC CONTRIBUTION: This simple and easy-to-use digital tool provides a visualisation of solvent greenness data through a novel intuitive interface and encourages green chemistry. It offers numerous advantages over traditional solvent selection guides, allowing users to directly customise the solvent list and generate side-by-side comparisons of only the most important solvents. The release as a standalone package will maximise the benefit of this software.

Discovery of novel SOS1 inhibitors using machine learning.

Overactivation of the rat sarcoma virus (RAS) signaling is responsible for 30% of all human malignancies. Son of sevenless 1 (SOS1), a crucial node in the RAS signaling pathway, could modulate RAS activation, offering a promising therapeutic strategy for RAS-driven cancers. Applying machine learning (ML)-based virtual screening (VS) on small-molecule databases, we selected a random forest (RF) regressor for its robustness and performance. Screening was performed with the L-series and EGFR-related datasets, and was extended to the Chinese National Compound Library (CNCL) with more than 1.4 million compounds. In addition to a series of documented SOS1-related molecules, we uncovered nine compounds that have an unexplored chemical framework and displayed inhibitory activity, with the most potent achieving more than 50% inhibition rate in the KRAS G12C/SOS1 PPI assay and an IC50 value in the proximity of 20 µg mL-1. Compared with the manner that known inhibitory agents bind to the target, hit compounds represented by CL01545365 occupy a unique pocket in molecular docking. An in silico drug-likeness assessment suggested that the compound has moderately favorable drug-like properties and pharmacokinetic characteristics. Altogether, our findings strongly support that, characterized by the distinctive binding modes, the recognition of novel skeletons from the carboxylic acid series could be candidates for developing promising SOS1 inhibitors.

Optimizing Excipient Properties to Prevent Aggregation in Biopharmaceutical Formulations.

Excipients are included within protein biotherapeutic solution formulations to improve colloidal and conformational stability but are generally not designed for the specific purpose of preventing aggregation and improving cryoprotection in solution. In this work, we have explored the relationship between the structure and antiaggregation activity of excipients by utilizing coarse-grained molecular dynamics modeling of protein-excipient interaction. We have studied human serum albumin as a model protein, and we report the interaction of 41 excipients (polysorbates, fatty alcohol ethoxylates, fatty acid ethoxylates, phospholipids, glucosides, amino acids, and others) in terms of the reduction of solvent accessible surface area of aggregation-prone regions, proposed as a mechanism of aggregation prevention. Polyoxyethylene sorbitan had the greatest degree of interaction with aggregation-prone regions, decreasing the solvent accessible surface area of APRs by 20.7 nm2 (40.1%). Physicochemical descriptors generated by Mordred are employed to probe the structure-property relationship using partial least-squares regression. A leave-one-out cross-validated model had a root-mean-square error of prediction of 4.1 nm2 and a mean relative error of prediction of 0.077. Generally, longer molecules with a large number of alcohol-terminated PEG units tended to interact more, with qualitatively different protein interactions, wrapping around the protein. Shorter or less ethoxylated compounds tend to form hemimicellar clusters at the protein surface. We propose that an improved design would feature many short chains of 5 to 10 PEG units in many distinct branches and at least some hydrophobic content in the form of medium-length or greater aliphatic chains (i.e., six or more carbon atoms). The combination of molecular dynamics simulation and quantitative modeling is an important first step in an all-purpose protein-independent model for the computer-aided design of stabilizing excipients.

Theoretical analysis of divalent cation effects on aptamer recognition of neurotransmitter targets.

Aptamer-based sensing of small molecules such as dopamine and serotonin in the brain, requires characterization of the specific aptamer sequences in solutions mimicking the in vivo environment with physiological ionic concentrations. In particular, divalent cations (Mg2+ and Ca2+) present in brain fluid, have been shown to affect the conformational dynamics of aptamers upon target recognition. Thus, for biosensors that transduce aptamer structure switching as the signal response, it is critical to interrogate the influence of divalent cations on each unique aptamer sequence. Herein, we demonstrate the potential of molecular dynamics (MD) simulations to predict the behaviour of dopamine and serotonin aptamers on sensor surfaces. The simulations enable molecular-level visualization of aptamer conformational changes that, in some cases, are significantly influenced by divalent cations. The correlations of theoretical simulations with experimental findings validate the potential for MD simulations to predict aptamer-specific behaviors on biosensors.

Newcastle 1000 (NEW1000) Study: an Australian population-based prospective pregnancy cohort study design and protocol.

INTRODUCTION: Multiple cohort studies have been established to investigate the impact of early life factors on development and health outcomes. In Australia the majority of these studies were established more than 20 years ago and, although longitudinal in nature, are inherently susceptible to socioeconomic, environmental and cultural influences which change over time. Additionally, rapid leaps in technology have increased our understanding of the complex role of gene-environment interactions in life course health, highlighting the need for new cohort studies with repeated biological sampling and in-depth phenotype data across the first 1000 days of life from conception. METHODS AND ANALYSIS: The Newcastle 1000 (NEW1000) Study, based in the regional city of Newcastle, New South Wales, was developed after an extensive consultation process involving 3 years of discussion with key stakeholders and healthcare consumer organisations and seven healthcare consumer workshops. This prospective population-based pregnancy cohort study will recruit 500 families per year for 5 years, providing detailed, longitudinal, multisystem phenotyping, repeated ultrasound measures and serial sample collection to investigate healthcare consumer identified health outcomes of priority. Stage 1 will involve recruitment of pregnant participants and their partners at 14 weeks gestation, with dense phenotype data and biological samples collected at 14, 20, 28 and 36 weeks gestation and serial ultrasound measures at 20, 28, 36 and 40 weeks, with postpartum follow-up at 6 weeks and 6 months. Biological samples will be used for biomarker discovery and sequencing of the genome, transcriptome, epigenome, microbiome and metabolome. ETHICS AND DISSEMINATION: Ethics approval was obtained from Hunter New England Local Health District Ethics Committee (2020/ETH02881). Outcomes will be published in peer-reviewed journals, disseminated to participants through the NEW1000 website, presented at scientific conferences, and written reports to local, state and national government bodies and key stakeholders in the healthcare system to inform policy and evidence-based practice.

AI4Green: An Open-Source ELN for Green and Sustainable Chemistry.

An Electronic Laboratory Notebook (ELN) combining features, including data archival, collaboration tools, and green and sustainability metrics for organic chemistry, is presented. AI4Green is a web-based application, available as open-source code and free to use. It offers the core functionality of an ELN, namely, the ability to store reactions securely and share them among different members of a research team. As users plan their reactions and record them in the ELN, green and sustainable chemistry is encouraged by automatically calculating green metrics and color-coding hazards, solvents, and reaction conditions. The interface links a database constructed from data extracted from PubChem, enabling the automatic collation of information for reactions. The application's design facilitates the development of auxiliary sustainability applications, such as our Solvent Guide. As more reaction data are captured, subsequent work will include providing "intelligent" sustainability suggestions to the user.

Predicting bioactivity of antibiotic metabolites by molecular docking and dynamics.

Antibiotics enter the environment through waste streams, where they can exert selective pressure for antimicrobial resistance in bacteria. However, many antibiotics are excreted as partly metabolized forms, or can be subject to partial breakdown in wastewater treatment, soil, or through natural processes in the environment. If a metabolite is bioactive, even at sub-lethal levels, and also stable in the environment, then it could provide selection pressure for resistance. (5S)-penicilloic acid of piperacillin has previously been found complexed to the binding pocket of penicillin binding protein 3 (PBP3) of Pseudomonas aeruginosa. Here, we predicted the affinities of all potentially relevant antibiotic metabolites of ten different penicillins to that target protein, using molecular docking and molecular dynamics simulations. Docking predicts that, in addition to penicilloic acid, pseudopenicillin derivatives of these penicillins, as well as 6-aminopenicillanic acid (6APA), could also bind to this target. MD simulations further confirmed that (5R)-pseudopenicillin and 6APA bind the target protein, in addition to (5S)-penicilloic acid. Thus, it is possible that these metabolites are bioactive, and, if stable in the environment, could be contaminants selective for antibiotic resistance. This could have considerable significance for environmental surveillance for antibiotics as a means to reduce antimicrobial resistance, because targeted mass spectrometry could be required for relevant metabolites as well as the native antibiotics.

Krein support vector machine classification of antimicrobial peptides.

Antimicrobial peptides (AMPs) represent a potential solution to the growing problem of antimicrobial resistance, yet their identification through wet-lab experiments is a costly and time-consuming process. Accurate computational predictions would allow rapid in silico screening of candidate AMPs, thereby accelerating the discovery process. Kernel methods are a class of machine learning algorithms that utilise a kernel function to transform input data into a new representation. When appropriately normalised, the kernel function can be regarded as a notion of similarity between instances. However, many expressive notions of similarity are not valid kernel functions, meaning they cannot be used with standard kernel methods such as the support-vector machine (SVM). The Krein-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Krein-SVM models for AMP classification and prediction by employing the Levenshtein distance and local alignment score as sequence similarity functions. Utilising two datasets from the literature, each containing more than 3000 peptides, we train models to predict general antimicrobial activity. Our best models achieve an AUC of 0.967 and 0.863 on the test sets of each respective dataset, outperforming the in-house and literature baselines in both cases. We also curate a dataset of experimentally validated peptides, measured against Staphylococcus aureus and Pseudomonas aeruginosa, in order to evaluate the applicability of our methodology in predicting microbe-specific activity. In this case, our best models achieve an AUC of 0.982 and 0.891, respectively. Models to predict both general and microbe-specific activities are made available as web applications.

Prenatal Stress Induces Translational Disruption Associated with Myelination Deficits.

Disruptions to neurodevelopment are known to be linked to behavioral disorders in childhood and into adulthood. The fetal brain is extremely vulnerable to stimuli that alter inhibitory GABAergic pathways and critical myelination processes, programing long-term neurobehavioral disruption. The maturation of the GABAergic system into the major inhibitory pathway in the brain and the development of oligodendrocytes into mature cells capable of producing myelin are integral components of optimal neurodevelopment. The current study aimed to elucidate prenatal stress-induced mechanisms that disrupt these processes and to delineate the role of placental pathways in these adverse outcomes. Pregnant guinea pig dams were exposed to prenatal stress with strobe light exposure for 2 h/day on gestational age (GA) 35, 40, 45, 50, 55, 60, and 65, and groups of fetuses and placentae were collected after the stress exposure on GA40, GA50, GA60, and GA69 (term). Fetal plasma, placental, and brain tissue were collected for allopregnanolone and cortisol quantification with ELISA. Relative mRNA expression of genes of specific pathways of interest was examined with real-time PCR in placental and hippocampal tissue, and myelin basic protein (MBP) was quantified immunohistochemically in the hippocampus and surrounding regions for assessment of mature myelin. Prenatal stress in mid-late gestation resulted in disruptions to the translational machinery responsible for the production of myelin and decreased myelin coverage in the hippocampus and surrounding regions. The male placenta showed an initial protective increase in allopregnanolone concentrations in response to maternal psychosocial stress. The male and female placentae had a sex-dependent increase in neurosteroidogenic enzymes at term following prenatal stress. Independent from exposure to prenatal stress, at gestational day 60 - a critical period for myelin development, the placentae of female fetuses had increased capability of preventing cortisol transfer to the fetus through expression of 11-beta-hydroxysteroid dehydrogenase types 1 and 2. The deficits early in the process of maturation of myelination indicate that the reduced myelination observed at childhood equivalence in previous studies begins in fetal life. This negative programing persists into childhood, potentially due to dysregulation of MBP translation processes. Expression patterns of neurosteroidogenic enzymes in the placenta at term following stress may identify at-risk fetuses that have been exposed to a stressful in utero environment.

Regulation of inflammatory genes in decidual cells: Involvement of the bromodomain and extra-terminal family proteins.

The decidua undergoes proinflammatory activation in late pregnancy, promoting labor. Bromodomain and Extra-Terminal (BET) family proteins interact with acetylated histones and may control gene expression in inflammation. Here, we assessed whether BETs are involved in inflammatory gene regulation in human decidual cells. We have treated primary cultures of decidual stromal cells (DSCs) from term pregnancies with endotoxin (LPS) and measured the expression of a panel of pro-and anti-inflammatory genes. BET involvement was assessed using the selective BET inhibitors (+)-JQ1 and I-BET-762 or the negative control compound (-)-JQ1. Histone 3 and -4 acetylation and BETs binding at the target gene promoters were determined to assess whether these processes are involved in the actions of LPS, BETs, and BET inhibitors. LPS increased the expression of the proinflammatory (PTGS2, IL6, CXCL8/IL8, TNF) and the anti-inflammatory (IL10, IDO1) genes of the panel. The constitutively expressed inflammatory genes (PTGS1, PTGES) were unaffected. The BET inhibitors, but not the control compound, reduced the basal and LPS-induced expression of PTGS1, PTGS2, IL6, CXCL8/IL8, IL10, and IDO1. TNF expression was not changed by BET inhibition. The dominant BETs were Bromodomain-containing protein -2 (BRD2) and -4L (BRD4L) in DSCs. LPS increased histone 4 acetylation at the CXCL8/IL8 and TNF promoters and histone 3 and -4 acetylation at the IDO1 promoter, while (+)-JQ1 abrogated histone acetylation at several promoters. Overall, histone acetylation and promoter binding of BETs showed no consistent relationship with gene expression across the gene panel and the treatments. BET proteins, predominantly BRD2 and BRD4L, control critical pro- and anti-inflammatory genes in DSCs. TNF induction exemplifies a BET-independent pathway. Changing histone acetylation at the promoters is not a general obligatory requirement for inflammatory gene expression in response to LPS. BETs likely act at chromatin loci separate from the examined promoters. BET inhibitors may block decidual activation at labor.

Cysteine-Selective Modification of Peptides and Proteins via Desulfurative C-C Bond Formation.

The site-selective modification of peptides and proteins facilitates the preparation of targeted therapeutic agents and tools to interrogate biochemical pathways. Among the numerous bioconjugation techniques developed to install groups of interest, those that generate C(sp3 )-C(sp3 ) bonds are significantly underrepresented despite affording proteolytically stable, biogenic linkages. Herein, a visible-light-mediated reaction is described that enables the site-selective modification of peptides and proteins via desulfurative C(sp3 )-C(sp3 ) bond formation. The reaction is rapid and high yielding in peptide systems, with comparable translation to proteins. Using this chemistry, a range of moieties is installed into model systems and an effective PTM-mimic is successfully integrated into a recombinantly expressed histone.

Machine learning for yield prediction for chemical reactions using in situ sensors.

Machine learning models were developed to predict product formation from time-series reaction data for ten Buchwald-Hartwig coupling reactions. The data was provided by DeepMatter and was collected in their DigitalGlassware cloud platform. The reaction probe has 12 sensors to measure properties of interest, including temperature, pressure, and colour. Colour was a good predictor of product formation for this reaction and machine learning models were able to learn which of the properties were important. Predictions for the current product formation (in terms of % yield) had a mean absolute error of 1.2%. For predicting 30, 60 and 120 min ahead the error rose to 3.4, 4.1 and 4.6%, respectively. The work here presents an example into the insight that can be obtained from applying machine learning methods to sensor data in synthetic chemistry.

N-Hydroxyethyl acrylamide as a functional eROP initiator for the preparation of nanoparticles under "greener" reaction conditions.

N-Hydroxyethyl acrylamide was used as a functional initiator for the enzymatic ring-opening polymerisation of ε-caprolactone and δ-valerolactone. N-Hydroxyethyl acrylamide was found not to undergo self-reaction in the presence of Lipase B from Candida antarctica under the reaction conditions employed. By contrast, this is a major problem for 2-hydroxyethyl methacrylate and 2-hydroxyethyl acrylate which both show significant transesterification issues leading to unwanted branching and cross-linking. Surprisingly, N-hydroxyethyl acrylamide did not react fully during enzymatic ring-opening polymerisation. Computational docking studies helped us understand that the initiated polymer chains have a higher affinity for the enzyme active site than the initiator alone, leading to polymer propagation proceeding at a faster rate than polymer initiation leading to incomplete initiator consumption. Hydroxyl end group fidelity was confirmed by organocatalytic chain extension with lactide. N-Hydroxyethyl acrylamide initiated polycaprolactones were free-radical copolymerised with PEGMA to produce a small set of amphiphilic copolymers. The amphiphilic polymers were shown to self-assemble into nanoparticles, and to display low cytotoxicity in 2D in vitro experiments. To increase the green credentials of the synthetic strategies, all reactions were carried out in 2-methyl tetrahydrofuran, a solvent derived from renewable resources and an alternative for the more traditionally used fossil-based solvents tetrahydrofuran, dichloromethane, and toluene.

Ganaxolone versus Phenobarbital for Neonatal Seizure Management.

OBJECTIVE: Seizures are more common in the neonatal period than at any other stage of life. Phenobarbital is the first-line treatment for neonatal seizures and is at best effective in approximately 50% of babies, but may contribute to neuronal injury. Here, we assessed the efficacy of phenobarbital versus the synthetic neurosteroid, ganaxolone, to moderate seizure activity and neuropathology in neonatal lambs exposed to perinatal asphyxia. METHODS: Asphyxia was induced via umbilical cord occlusion in term lambs at birth. Lambs were treated with ganaxolone (5mg/kg/bolus then 5mg/kg/day for 2 days) or phenobarbital (20mg/kg/bolus then 5mg/kg/day for 2 days) at 6 hours. Abnormal brain activity was classified as stereotypic evolving (SE) seizures, epileptiform discharges (EDs), and epileptiform transients (ETs) using continuous amplitude-integrated electroencephalographic recordings. At 48 hours, lambs were euthanized for brain pathology. RESULTS: Asphyxia caused abnormal brain activity, including SE seizures that peaked at 18 to 20 hours, EDs, and ETs, and induced neuronal degeneration and neuroinflammation. Ganaxolone treatment was associated with an 86.4% reduction in the number of seizures compared to the asphyxia group. The total seizure duration in the asphyxia+ganaxolone group was less than the untreated asphyxia group. There was no difference in the number of SE seizures between the asphyxia and asphyxia+phenobarbital groups or duration of SE seizures. Ganaxolone treatment, but not phenobarbital, reduced neuronal degeneration within hippocampal CA1 and CA3 regions, and cortical neurons, and ganaxolone reduced neuroinflammation within the thalamus. INTERPRETATION: Ganaxolone provided better seizure control than phenobarbital in this perinatal asphyxia model and was neuroprotective for the newborn brain, affording a new therapeutic opportunity for treatment of neonatal seizures. ANN NEUROL 2022;92:1066-1079.

Examining Neurosteroid-Analogue Therapy in the Preterm Neonate For Promoting Hippocampal Neurodevelopment.

Background: Preterm birth can lead to brain injury and currently there are no targeted therapies to promote postnatal brain development and protect these vulnerable neonates. We have previously shown that the neurosteroid-analogue ganaxolone promotes white matter development and improves behavioural outcomes in male juvenile guinea pigs born preterm. Adverse side effects in this previous study necessitated this current follow-up dosing study, where a focus was placed upon physical wellbeing during the treatment administration and markers of neurodevelopment at the completion of the treatment period. Methods: Time-mated guinea pigs delivered preterm (d62) by induction of labour or spontaneously at term (d69). Preterm pups were randomized to receive no treatment (Prem-CON) or ganaxolone at one of three doses [0.5 mg/kg ganaxolone (low dose; LOW-GNX), 1.0 mg/kg ganaxolone (mid dose; MID-GNX), or 2.5 mg/kg ganaxolone (high dose; HIGH-GNX) in vehicle (45% ß-cyclodextrin)] daily until term equivalence age. Physical parameters including weight gain, ponderal index, supplemental feeding, and wellbeing (a score based on respiration, activity, and posture) were recorded throughout the preterm period. At term equivalence, brain tissue was collected, and analysis of hippocampal neurodevelopment was undertaken by immunohistochemistry and RT-PCR. Results: Low and mid dose ganaxolone had some impacts on early weight gain, supplemental feeding, and wellbeing, whereas high dose ganaxolone significantly affected all physical parameters for multiple days during the postnatal period when compared to the preterm control neonates. Deficits in the preterm hippocampus were identified using neurodevelopmental markers including mRNA expression of oligodendrocyte lineage cells (CSPG4, MBP), neuronal growth (INA, VEGFA), and the GABAergic/glutamatergic system (SLC32A1, SLC1A2, GRIN1, GRIN2C, DLG4). These deficits were not affected by ganaxolone at the doses used at the equivalent of normal term. Conclusion: This is the first study to investigate the effects of a range of doses of ganaxolone to improve preterm brain development. We found that of the three doses, only the highest dose of ganaxolone (2.5 mg/kg) impaired key indicators of physical health and wellbeing over extended periods of time. Whilst it may be too early to see improvements in markers of neurodevelopment, further long-term study utilising the lower doses are warranted to assess functional outcomes at ages when preterm birth associated behavioural disorders are observed.

Evaluating changes in GABAergic and glutamatergic pathways in early life following prenatal stress and postnatal neurosteroid supplementation.

BACKGROUND: A correct balance of activity of the GABA and glutamate systems is vital for optimal neurodevelopment and general CNS function, and the dysregulation of this balance has been implicated in a number of neurological conditions. Maternal exposure to stressors is known to have long lasting, deleterious impacts on neurobehaviour, and similarly, results in dysregulation of inhibitory and excitatory pathways in the offspring. The current study aimed to examine effects on these pathways in a guinea pig model of prenatal stress and to elucidate whether increased neuroprotective support by postnatal neurosteroid supplementation would ameliorate adverse outcomes. METHODS: Prenatal stress was achieved by exposing pregnant guinea pigs dams to a strobe light for 2hrs/day on gestational age (GA) 50, 55, 60 and 65. Dams were allowed to spontaneously deliver (~GA70) and pups were orally administered either allopregnanolone analogue, ganaxolone (5 mg/kg/day in 45% cyclodextrin), the translocator protein (TSPO) agonist, emapunil (XBD173; 0.3 mg/kg/day in 1% tragacanth gum) or vehicle on postnatal days (PND) 1-7. Hippocampal samples were collected at PND30 to measure relative mRNA expression of components involved in the inhibitory GABAergic pathway and exctitatory glutamatergic pathway by real-time PCR. GABAergic interneurons were quantified by assessing immunohistochemical protein expression of markers parvalbumin, calbindin and calretinin. RESULTS: mRNA expression of GABAergic pathway components at one week of age indicated immature expression profiles of the GABAA receptors as well as decreased GABA synthesis and transport suggesting reduced extrasynaptically-mediated tonic inhibition. Expression profiles of the pathways examined evolved between one week and one month of age but an imbalance in inhibitory/excitatory components persisted. The allopregnanolone analogue ganaxolone offered some protection against excitotoxicity in female hippocampus, however neurosteroid supplementation with ganaxolone or emapunil were unable to fully correct the GABAergic/glutamatergic imbalance observed following prenatal stress. CONCLUSION: Prenatal stress leads to programmed lasting effects on the major inhibitory and excitatory pathways in the guinea pig brain that continue evolving between the equivalent of early and late childhood. Neurosteroid therapies particularly improved outcomes in females. Further studies are required to identify additional therapeutic targets that are able to fully restore imbalances in the excitatory and inhibitory systems, which may act to prevent development of childhood behavioural disorders.

Alchemical Free Energy Methods Applied to Complexes of the First Bromodomain of BRD4.

Accurate and rapid predictions of the binding affinity of a compound to a target are one of the ultimate goals of computer aided drug design. Alchemical approaches to free energy estimations follow the path from an initial state of the system to the final state through alchemical changes of the energy function during a molecular dynamics simulation. Herein, we explore the accuracy and efficiency of two such techniques: relative free energy perturbation (FEP) and multisite lambda dynamics (MSλD). These are applied to a series of inhibitors for the bromodomain-containing protein 4 (BRD4). We demonstrate a procedure for obtaining accurate relative binding free energies using MSλD when dealing with a change in the net charge of the ligand. This resulted in an impressive comparison with experiment, with an average difference of 0.4 ± 0.4 kcal mol-1. In a benchmarking study for the relative FEP calculations, we found that using 20 lambda windows with 0.5 ns of equilibration and 1 ns of data collection for each window gave the optimal compromise between accuracy and speed. Overall, relative FEP and MSλD predicted binding free energies with comparable accuracy, an average of 0.6 kcal mol-1 for each method. However, MSλD makes predictions for a larger molecular space over a much shorter time scale than relative FEP, with MSλD requiring a factor of 18 times less simulation time for the entire molecule space.

Effect of Oriented Electric Fields on Biologically Relevant Iron-Sulfur Clusters: Tuning Redox Reactivity for Catalysis.

Enzyme-based iron-sulfur clusters, exemplified in families such as hydrogenases, nitrogenases, and radical S-adenosylmethionine enzymes, feature in many essential biological processes. The functionality of biological iron-sulfur clusters extends beyond simple electron transfer, relying primarily on the redox activity of the clusters, with a remarkable diversity for different enzymes. The active-site structure and the electrostatic environment in which the cluster resides direct this redox reactivity. Oriented electric fields in enzymatic active sites can be significantly strong, and understanding the extent of their effect on iron-sulfur cluster reactivity can inform first steps toward rationally engineering their reactivity. An extensive systematic density functional theory-based screening approach using OPBE/TZP has afforded a simple electric field-effect representation. The results demonstrate that the orientation of an external electric field of strength 28.8 MV cm-1 at the center of the cluster can have a significant effect on its relative stability in the order of 35 kJ mol-1. This shows clear implications for the reactivity of iron-sulfur clusters in enzymes. The results also demonstrate that the orientation of the electric field can alter the most stable broken-symmetry state, which further has implications on the directionality of initiated electron-transfer reactions. These insights open the path for manipulating the enzymatic redox reactivity of iron-sulfur cluster-containing enzymes by rationally engineering oriented electric fields within the enzymes.

Kernel Methods for Predicting Yields of Chemical Reactions.

The use of machine learning methods for the prediction of reaction yield is an emerging area. We demonstrate the applicability of support vector regression (SVR) for predicting reaction yields, using combinatorial data. Molecular descriptors used in regression tasks related to chemical reactivity have often been based on time-consuming, computationally demanding quantum chemical calculations, usually density functional theory. Structure-based descriptors (molecular fingerprints and molecular graphs) are quicker and easier to calculate and are applicable to any molecule. In this study, SVR models built on structure-based descriptors were compared to models built on quantum chemical descriptors. The models were evaluated along the dimension of each reaction component in a set of Buchwald-Hartwig amination reactions. The structure-based SVR models outperformed the quantum chemical SVR models, along the dimension of each reaction component. The applicability of the models was assessed with respect to similarity to training. Prospective predictions of unseen Buchwald-Hartwig reactions are presented for synthetic assessment, to validate the generalizability of the models, with particular interest along the aryl halide dimension.