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Myotonic dystrophy type I (DM1) is the most common form of adult muscular dystrophy and is a severe condition with no treatment currently available. Recently, small-molecule ligands have been developed as targeted covalent inhibitors that have some selectivity for and covalently inhibit cyclin-dependent kinase 12 (CDK12). CDK12 is involved in the transcription of elongated RNA sections that results in the DM1 condition. The covalent bond is achieved after nucleophilic addition to a Michael acceptor warhead. Previous studies of the conformational preferences of thio-Michael additions have focused on characterizing the reaction profile based on the distance between the sulfur and β-carbon atoms. Rotamerism, however, has not been investigated extensively. Here, we use high-level quantum chemistry calculations, up to coupled cluster with single, double, and perturbative triple excitations [CCSD(T)], to characterize the nucleophilic addition of an archetypal nucleophile, methanethiolate, to various nitrogen-containing Michael acceptors which are representative of the small-molecule covalent inhibitors. By investigating the structural, energetic, and electronic properties of the resulting enolates, as well as their reaction profiles, we show that synclinal additions are generally energetically favored over other additions due to the greater magnitude of attractive noncovalent interactions permitted by the conformation. The calculated transition states associated with the addition process indicate that synclinal addition proceeds via lower energetic barriers than antiperiplanar addition and is the preferred reaction pathway.

Density functional theory (B3LYP-D3(BJ) and ωB97XD) calculations have been used to assess the stereochemical outcomes of the proposed transannular [4 + 2] cycloaddition pathway for the biosynthesis of mandapamate and isomandapamate from macrocyclic intermediates. Calculations reveal that the topological shift between macrocyclic conformers is vital in controlling the stereoselectivity of the downstream steps toward the isomeric mandapamates. A stepwise 4 + 2 type process is energetically favored over a concerted [4 + 2] pathway at room temperature, and is consistent with the stereochemistries found in the natural products.

Introduction: The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships., Area Covered: In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research., Expert Opinion: The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

We advance the quality of first-principles calculations of protein electronic circular dichroism (CD) through an amelioration of a key deficiency of a previous procedure that involved diabatization of electronic states on the amide chromophore (to obtain interamide couplings) in a β-strand conformation of a diamide. This yields substantially improved calculated far-ultraviolet (far-UV) electronic circular dichroism (CD) spectra for β-sheet conformations. The interamide couplings from the diabatization procedure for 13 secondary structural elements (13 diamide structures) are applied to compute the CD spectra for seven example proteins: myoglobin (α helix), jacalin (β strand), concanavalin A (β type I), elastase (β type II), papain (α + β), 3 10 -helix bundle (3 10 -helix) and snow flea antifreeze protein (polyproline). In all cases, except concanavalin A and papain, the CD spectra computed using the interamide couplings from the diabatization procedure yield improved agreement with experiment with respect to previous first-principles calculations.

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., (© 2024. The Author(s).)

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 R 2 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., (© 2024. The Author(s).)

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 IC 50 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., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

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 nm 2 (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 nm 2 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.

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 (Mg 2+ and Ca 2+ ) 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.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

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.

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(sp 3 )-C(sp 3 ) 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(sp 3 )-C(sp 3 ) 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., (© 2022 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.)

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 Kreĭn-SVM represents generalisation of the standard SVM that admits a much larger class of similarity functions. In this study, we propose and develop Kreĭn-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., Competing Interests: A. B. is an employee and shareholder in GSK., (This journal is © The Royal Society of Chemistry.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

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.

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.

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.

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules.

The bromodomain-containing protein 4 (BRD4), a member of the bromodomain and extra-terminal domain (BET) family, plays a key role in several diseases, especially cancers. With increased interest in BRD4 as a therapeutic target, many X-ray crystal structures of the protein in complex with small molecule inhibitors are publicly available over the recent decade. In this study, we use this structural information to investigate the conformations of the first bromodomain (BD1) of BRD4. Structural alignment of 297 BRD4-BD1 complexes shows a high level of similarity between the structures of BRD4-BD1, regardless of the bound ligand. We employ WONKA, a tool for detailed analyses of protein binding sites, to compare the active site of over 100 of these crystal structures. The positions of key binding site residues show a high level of conformational similarity, with the exception of Trp81. A focused analysis on the highly conserved water network in the binding site of BRD4-BD1 is performed to identify the positions of these water molecules across the crystal structures. The importance of the water network is illustrated using molecular docking and absolute free energy perturbation simulations. 82% of the ligand poses were better predicted when including water molecules as part of the receptor. Our analysis provides guidance for the design of new BRD4-BD1 inhibitors and the selection of the best structure of BRD4-BD1 to use in structure-based drug design, an important approach for faster and more cost-efficient lead discovery.

For benzene, toluene, aniline, fluorobenzene, and phenol, even sophisticated treatments of electron correlation, such as MRCI and XMS-CASPT2 calculations, show oscillator strengths typically lower than experiment. Inclusion of a simple pseudo-diabatization approach to perturb the S 1 state with approximate vibronic coupling to the S 2 state for each molecule results in more accurate oscillator strengths. Their absolute values agree better with experiment for all molecules except aniline. When the coupling between the S 1 and S 2 states is strong at the S 0 geometry, the simple diabatization scheme performs less well with respect to the oscillator strengths relative to the adiabatic values. However, we expect the scheme to be useful in many cases where the coupling is weak to moderate (where the maximum component of the coupling has a magnitude less than 1.5 au). Such calculations give an insight into the effects of vibronic coupling of excited states on UV/vis spectra.

A fully quantitative theory of the relationship between protein conformation and optical spectroscopy would facilitate deeper insights into biophysical and simulation studies of protein dynamics and folding. In contrast to intense bands in the far-ultraviolet, near-UV bands are much weaker and have been challenging to compute theoretically. We report some advances in the accuracy of calculations in the near-UV, which were realised through the consideration of the vibrational structure of the electronic transitions of aromatic side chains.

One promising strategy to combat antimicrobial resistance is to use bacteriophages that attach to the sex pili produced by transmissible antimicrobial resistance (AMR) plasmids, infect AMR bacteria and select for loss of the AMR plasmids, prolonging the life of existing antimicrobials. The maturation protein of the bacteriophage MS2 attaches to the pili produced by Incompatibility group F plasmid-containing bacteria. This interaction initiates delivery of the viral genetic material into the bacteria. Using protein-protein docking we constructed a model of the F pilus comprising a trimer of subunits binding to the maturation protein. Interactions between the maturation protein and the F pilus were investigated using molecular dynamics simulations. In silico alanine scanning and in silico single-point mutations were explored, with the longer term aim of increasing the affinity of the maturation protein to other Incompatibility group pili, without reducing the strength of binding to F pilin. We report our computational findings on which residues are required for the maturation protein and F pilin to interact, those which had no effect on the interaction and the mutations which led to a stronger interaction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The urgent need for new treatments for the chronic lung disease idiopathic pulmonary fibrosis (IPF) motivates research into antagonists of the RGD binding integrin αvβ6, a protein linked to the initiation and progression of the disease. Molecular dynamics (MD) simulations of αvβ6 in complex with its natural ligand, pro-TGF-β1, show the persistence over time of a bidentate Arg-Asp ligand-receptor interaction and a metal chelate interaction between an aspartate on the ligand and an Mg 2+ ion in the active site. This is typical of RGD binding ligands. Additional binding site interactions, which are not observed in the static crystal structure, are also identified. We investigate an RGD mimetic, which serves as a framework for a series of potential αvβ6 antagonists. The scaffold includes a derivative of the widely utilized 1,8-naphthyridine moiety, for which we present force field parameters, to enable MD and relative free energy perturbation (FEP) simulations. The MD simulations highlight the importance of hydrogen bonding and cation-π interactions. The FEP calculations predict relative binding affinities, within 1.5 kcal mol -1 , on average, of experiments.

Reversible addition-fragmentation chain transfer (RAFT) dispersion polymerisation of methyl methacrylate (MMA) is performed in supercritical carbon dioxide (scCO 2 ) with 2-(dodecylthiocarbonothioylthio)-2-methylpropionic acid (DDMAT) present as chain transfer agent (CTA) and surprisingly shows good control over PMMA molecular weight. Kinetic studies of the polymerisation in scCO 2 also confirm these data. By contrast, only poor control of MMA polymerisation is obtained in toluene solution, as would be expected for this CTA which is better suited for acrylates. In this regard, we select a range of CTAs and use them to determine the parameters that must be considered for good control in dispersion polymerisation in scCO 2 . A thorough investigation of the nucleation stage during the dispersion polymerisation reveals an unexpected " in situ two-stage" mechanism that strongly determines how the CTA works. Finally, using a novel computational solvation model, we identify a correlation between polymerisation control and degree of solubility of the CTAs. All of this ultimately gives rise to a simple, elegant and counterintuitive guideline to select the best CTA for RAFT dispersion polymerisation in scCO 2 ., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Infrared (IR) absorption provides important chemical fingerprints of biomolecules. Protein secondary structure determination from IR spectra is tedious since its theoretical interpretation requires repeated expensive quantum-mechanical calculations in a fluctuating environment. Herein we present a novel machine learning protocol that uses a few key structural descriptors to rapidly predict amide I IR spectra of various proteins and agrees well with experiment. Its transferability enabled us to distinguish protein secondary structures, probe atomic structure variations with temperature, and monitor protein folding. This approach offers a cost-effective tool to model the relationship between protein spectra and their biological/chemical properties.

Treatment for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes Covid-19, may well be predicated on knowledge of the structures of protein of this virus. However, often these cannot be determined easily or quickly. Herein, we provide calculated circular dichroism (CD) spectra in the far- and near-UV, and infra-red (IR) spectra in the amide I region for experimental structures and computational models of SARS-CoV-2 proteins. The near-UV CD spectra offer greatest sensitivity in assessing the accuracy of models., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Elsevier B.V. All rights reserved.)

Vibrational spectroscopy is an essential tool in chemical analyses, biological assays, and studies of functional materials. Over the past decade, various coherent nonlinear vibrational spectroscopic techniques have been developed and enabled researchers to study time-correlations of the fluctuating frequencies that are directly related to solute-solvent dynamics, dynamical changes in molecular conformations and local electrostatic environments, chemical and biochemical reactions, protein structural dynamics and functions, characteristic processes of functional materials, and so on. In order to gain incisive and quantitative information on the local electrostatic environment, molecular conformation, protein structure and interprotein contacts, ligand binding kinetics, and electric and optical properties of functional materials, a variety of vibrational probes have been developed and site-specifically incorporated into molecular, biological, and material systems for time-resolved vibrational spectroscopic investigation. However, still, an all-encompassing theory that describes the vibrational solvatochromism, electrochromism, and dynamic fluctuation of vibrational frequencies has not been completely established mainly due to the intrinsic complexity of intermolecular interactions in condensed phases. In particular, the amount of data obtained from the linear and nonlinear vibrational spectroscopic experiments has been rapidly increasing, but the lack of a quantitative method to interpret these measurements has been one major obstacle in broadening the applications of these methods. Among various theoretical models, one of the most successful approaches is a semiempirical model generally referred to as the vibrational spectroscopic map that is based on a rigorous theory of intermolecular interactions. Recently, genetic algorithm, neural network, and machine learning approaches have been applied to the development of vibrational solvatochromism theory. In this review, we provide comprehensive descriptions of the theoretical foundation and various examples showing its extraordinary successes in the interpretations of experimental observations. In addition, a brief introduction to a newly created repository Web site (http://frequencymap.org) for vibrational spectroscopic maps is presented. We anticipate that a combination of the vibrational frequency map approach and state-of-the-art multidimensional vibrational spectroscopy will be one of the most fruitful ways to study the structure and dynamics of chemical, biological, and functional molecular systems in the future.

Utilizing a force-matching procedure, we parametrize new force fields systematically for large conjugated systems. We model both conjugated polymers and molecular crystals that contain diketopyrrolopyrrole, thiophene, and thieno[3,2- b ]thiophene units. These systems have recently been found to have low band gaps, which exhibit high efficiency for photovoltaic devices. The equilibrium structures, forces, and energies of the building block chromophores, diketopyrrolopyrrole, thiophene, and thieno[3,2- b ]thiophene computed using our parameters are comparable to those computed using the reference electronic structure method. We assess the suitability of this new force field for electronic property calculations by comparing the electronic excitation properties computed along classical and ab initio molecular dynamics trajectories. For both trajectories, we find similar distributions of TDDFT-calculated excitation energies and oscillator strengths for the building block chromophore diketopyrrolopyrrole-thieno[3,2- b ]thiophene. The structural, dynamic, and electronic properties of the macromolecular assemblies built upon these chromophores are characterized. For both polymers and molecular crystals, pronounced peaks around 0° or 180° are observed for the torsions between chromophores under ambient conditions. The high planarity in these systems can promote local ordering and π-π stacking, thereby potentially facilitating charge transport across these materials. For the model conducting polymers, we found that the fluctuations in the density of states per chain per monomer is negligibly small and does not vary significantly with chains comprising 20-40 monomers. Analysis of the electron-hole distributions and the transition density matrices indicates that the delocalized length is approximately 4-6 monomers, which is in good agreement with other theoretical and experimental studies of different conducting polymers. For the molecular crystals, our investigation of the characteristic time scale of the fluctuation in the excitonic couplings shows that a low-frequency vibration below 100 cm -1 is observed for the nearest neighbors. These observations are in line with previous studies on other molecular crystals, in which low-frequency vibrations are believed to be responsible for the large modulation of the excitonic coupling. Thus, our approach and the new force fields provide a direct route for studying the structure-property relations and the molecular level origins of the high efficiency of these classes of materials.

We present an atomistic force field for the azo-moiety of the photoswitchable FK-11-X peptide. We use the parameters to study the unfolding of the peptide through molecular dynamics simulations. The unfolded ensemble contains many different structures, ranging from a partially unfolded peptide to a fully unfolded structure. The averaged computed far-ultraviolet circular dichroism (CD) spectrum of the set of structures, which was simulated using the newly developed force field, agrees well with experiment. The rate of the simulated unfolding process was estimated to have a time constant of 5.80 ± 0.03 ns from the time evolution of the CD spectrum of the peptide, computed from the backbone conformations sampled over 40 simulated trajectories. Our estimated time constant is faster than, but not inconsistent with, previous experimental estimates from time-resolved infrared and optical rotatory dispersion spectroscopy.

Cyclacene nanobelts have not been synthesized in over 60 years and remain one of the last unsynthesized building blocks of carbon nanotubes. Recent work has predicted that Hückel-cyclacenes containing Dewar benzenoid ring isomers are the most stable isomeric forms for several of the smaller sizes of cyclacene belts. Here, we give a more complete picture of the isomers that are possible within these nanobelt systems by simulating embedded Ladenburg (prismane) benzenoid rings in Hückel-[ n ]cyclacenes ( n = 5-14) and embedded Dewar benzenoid rings in twisted Möbius-[ n ]cyclacenes ( n = 9-14). The Möbius-[9]cyclacene isomer containing one Dewar benzenoid defect and the Hückel-[5]cyclacene isomer containing two maximally spaced Ladenburg benzenoid defects are found to be more stable than their conventional Kekulé benzenoid ring counterparts. The isomers that contain Dewar and Ladenburg benzenoid rings have larger electronic singlet-triplet energy gaps and lower polyradical character when compared with the conventional isomers.

A late-stage functionalization of the aromatic ring in amino acid derivatives is described. The key step is a copper-catalysed diversification of a boronate ester by amination (Chan-Lam reaction) that can be carried out on a complex β-aryl-β-amino acid scaffold. This not only considerably extends the substrate scope of amination partners, but also delivers an array of potent and selective integrin inhibitors as potential treatment agents of idiopathic pulmonary fibrosis (IPF). This versatile chemical strategy, which is amenable to high-throughput-array protocols, allows the installation of pharmaceutically valuable heteroaromatic fragments at a late stage by direct coupling to NH heterocycles, leading to compounds with drug-like attributes. It thus constitutes a useful addition to the medicinal chemist's repertoire., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The synthesis of cyclacene nanobelts remains an elusive goal dating back over 60 years. These molecules represent the last unsynthesized building block of carbon nanotubes and may be useful both as seed molecules for the preparation of structurally well-defined carbon nanotubes and for understanding the behavior and formation of zigzag nanotubes more broadly. Here we report the discovery that isomers containing two Dewar benzenoid rings are the preferred form for several sizes of cyclacene. The predicted lower polyradical character and higher singlet-triplet stability that these isomers possess compared with their pure benzenoid counterparts suggest that they may be more stable synthetic targets than the structures that have previously been identified. Our findings should facilitate the exploration of new routes to cyclacene synthesis through Dewar benzene chemistry.

Ultraviolet (UV) synchrotron radiation circular dichroism (SRCD) spectroscopy has made an important contribution to the determination and understanding of the structure of bio-molecules. In this paper, we report an innovative approach that we term time-resolved SRCD (tr-SRCD), which overcomes the limitations of current broadband UV SRCD setups. This technique allows accessing ultrafast time scales (down to nanoseconds), previously measurable only by other methods, such as infrared (IR), nuclear magnetic resonance (NMR), fluorescence and absorbance spectroscopies, and small angle X-ray scattering (SAXS). The tr-SRCD setup takes advantage of the natural polarization of the synchrotron radiation emitted by a bending magnet to record broadband UV CD faster than any current SRCD setup, improving the acquisition speed from 10 mHz to 130 Hz and the accessible temporal resolution by several orders of magnitude. We illustrate the new approach by following the isomer concentration changes of an azopeptide after a photoisomerization. This breakthrough in SRCD spectroscopy opens up a wide range of potential applications to the detailed characterization of biological processes, such as protein folding and protein-ligand binding., (© 2019 Author(s).)

Although successful in the structural determination of ordered biomolecules, the spectroscopic investigation of oligopeptides in solution is hindered by their complex and rapidly changing conformational ensemble. The measured circular dichroism (CD) spectrum of an oligopeptide is an ensemble average over all microstates, severely limiting its interpretation, in contrast to ordered biomolecules. Spectral deconvolution methods to estimate the secondary structure contributions in the ensemble are still mostly based on databases of larger ordered proteins. Here, we establish how the interpretation of CD spectra of oligopeptides can be enhanced by the ability to compute the same observable from a set of atomic coordinates. Focusing on two representative oligopeptides featuring a known propensity toward an α-helical and β-hairpin motif, respectively, we compare and cross-validate the structural information coming from deconvolution of the experimental CD spectra, sequence-based de novo structure prediction, and molecular dynamics simulations based on enhanced sampling methods. We find that small conformational variations can give rise to significant changes in the CD signals. While for the simpler conformational landscape of the α-helical peptide de novo structure prediction can already give reasonable agreement with the experiment, an extended ensemble of conformers needs to be considered for the β-hairpin sequence.

Adsorption of enzymes on solid surfaces may lead to conformational changes that reduce their catalytic conversion activity and are thus detrimental to the efficiency of biotechnology or biosensing applications. This work is a joint theoretical and experimental endeavor in which we identify and quantify the conformational changes that chymotrypsin undergoes when in contact with the surface of amorphous silica nanoparticles. For this purpose, we use circular dichroism spectroscopy, standard molecular dynamics, and advanced-sampling methods. Only the combination of these techniques allowed us to pinpoint a destabilization effect of silica on specific structural motifs of chymotrypsin. They are linked by the possibility of theoretically predicting CD spectra, allowing us to elucidate the source of the experimentally observed spectral changes. We find that chymotrypsin loses part of its helical content upon adsorption, with minor perturbation of its overall tertiary structure, associated with changes in the aromatic interactions. We demonstrate that the C-terminal helical fragment is unfolded as an isolated oligopeptide in pure water, folded as an α-helix as terminus of chymotrypsin in solution, and again partly disordered when the protein is adsorbed on silica. We believe that the joint methodology introduced in this manuscript has a direct general applicability to investigate any biomolecule-inorganic surface system. Methods to theoretically predict circular dichroism spectra from atomistic simulations were compared and improved. The drawbacks of the approaches are discussed; in particular, the limited capability of advanced-sampling MD schemes to explore the conformational phase space of large proteins and the dependency of the predicted ellipticity bands on the choice of calculation parameters.

A fully quantitative theory connecting protein conformation and optical spectroscopy would facilitate deeper insights into biophysical and simulation studies of protein dynamics and folding. The web server DichroCalc (http://comp.chem.nottingham.ac.uk/dichrocalc) allows one to compute from first principles the electronic circular dichroism spectrum of a (modeled or experimental) protein structure or ensemble of structures. The regular, repeating, chiral nature of secondary structure elements leads to intense bands in the far-ultraviolet (UV). The near-UV bands are much weaker and have been challenging to compute theoretically. We report some advances in the accuracy of calculations in the near-UV, realized through the consideration of the vibrational structure of the electronic transitions of aromatic side chains. The improvements have been assessed over a set of diverse proteins. We illustrate them using bovine pancreatic trypsin inhibitor and present a new, detailed analysis of the interactions which are most important in determining the near-UV circular dichroism spectrum., (Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.)

We consider lead discovery as active search in a space of labelled graphs. In particular, we extend our recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an αv integrin, the target protein that belongs to a group of Arg-Gly-Asp integrin receptors. This group of integrin receptors is thought to play a key role in idiopathic pulmonary fibrosis, a chronic lung disease of significant pharmaceutical interest. As an in silico proxy of the binding affinity, we use a molecular docking score to an experimentally determined αvβ6 protein structure. The search is driven by a probabilistic surrogate of the activity of all molecules from that space. As the process evolves and the algorithm observes the activity scores of the previously designed molecules, the hypothesis of the activity is refined. The algorithm is guaranteed to converge in probability to the best hypothesis from an a priori specified hypothesis space. In our empirical evaluations, the approach achieves a large structural variety of designed molecular structures for which the docking score is better than the desired threshold. Some novel molecules, suggested to be active by the surrogate model, provoke a significant interest from the perspective of medicinal chemistry and warrant prioritization for synthesis. Moreover, the approach discovered 19 out of the 24 active compounds which are known to be active from previous biological assays., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Antimicrobial resistance represents a growing global health problem. The emergence of novel resistance mechanisms necessitates the development of alternative approaches to investigate the molecular fundamentals of resistance, leading ultimately to new strategies for counteracting them. To gain deeper insight into antibiotic-target interactions, the binding of the frontline anti-tuberculosis drug isoniazid (INH) to a target enzyme, InhA, from Mycobacterium tuberculosis was studied using ultrafast two-dimensional infrared (2D-IR) spectroscopy and molecular simulations. Comparing wild-type InhA with a series of single point mutations, it was found that binding of the INH-NAD inhibitor to susceptible forms of the enzyme increased the vibrational coupling between residues located in the Rossmann fold co-factor binding site of InhA and suppressed dynamic fluctuations of the enzyme structure. The effect correlated with biochemical assay data, being reduced in the INH-resistant S94A mutant and absent in the biochemically-inactive P193A control. Molecular dynamics simulations and calculations of inter-residue couplings indicate that the changes in coupling and dynamics are not localised to the co-factor binding site, but permeate much of the protein. We thus propose that the resistant S94A mutation circumvents subtle changes in global structural dynamics caused by INH upon binding to the wild-type enzyme that may impact upon the formation of important protein-protein complexes in the fatty acid synthase pathway of M. tuberculosis .

The development of optical multidimensional spectroscopic techniques has opened up new possibilities for the study of biological processes. Recently, ultrafast two-dimensional ultraviolet spectroscopy experiments have determined the rates of tryptophan → heme electron transfer and excitation energy transfer for the two tryptophan residues in myoglobin (Consani et al., Science, 2013, 339, 1586). Here, we show that accurate prediction of these rates can be achieved using Marcus theory in conjunction with time-dependent density functional theory. Key intermediate residues between the donor and acceptor are identified, and in particular the residues Val68 and Ile75 play a critical role in calculations of the electron coupling matrix elements. Our calculations demonstrate how small changes in structure can have a large effect on the rates, and show that the different rates of electron transfer are dictated by the distance between the heme and tryptophan residues, while for excitation energy transfer the orientation of the tryptophan residues relative to the heme is important. © 2017 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc., (© 2017 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.)

The ability to compute from first principles the infrared spectrum of a protein in solution phase representing a biological system would provide a useful connection to atomistic models of protein structure and dynamics. Indeed, such calculations are a vital complement to 2DIR experimental measurements, allowing the observed signals to be interpreted in terms of detailed structural and dynamical information. In this article, we have studied nine structurally and spectroscopically well-characterized proteins, representing a range of structural types. We have simulated the equilibrium conformational dynamics in an explicit point charge water model. Using the resulting trajectories based on MD simulations, we have computed the one and two dimensional infrared spectra in the Amide I region, using an exciton approach, in which a local mode basis of carbonyl stretches is considered. The role of solvent in shifting the Amide I band (by 30 to 50 cm -1 ) is clearly evident. Similarly, the conformational dynamics contribute to the broadening of peaks in the spectrum. The inhomogeneous broadening in both the 1D and 2D spectra reflects the significant conformational diversity observed in the simulations. Through the computed 2D cross-peak spectra, we show how different pulse schemes can provide additional information on the coupled vibrations. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc., (© 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.)

Vibrational structure in the near-UV circular dichroism (CD) spectra of proteins is an important source of information on protein conformation and can be exploited to study structure and folding. A fully quantitative theory of the relationship between protein conformation and optical spectroscopy would facilitate deeper interpretation of and insight into biophysical and simulation studies of protein dynamics and folding. We have developed new models of the aromatic side chain chromophores toluene, p -cresol and 3-methylindole, which incorporate ab initio calculations of the Franck-Condon effect into first principles calculations of CD using an exciton approach. The near-UV CD spectra of 40 proteins are calculated with the new parameter set and the correlation between the computed and the experimental intensity from 270 to 290 nm is much improved. The contribution of individual chromophores to the CD spectra has been calculated for several mutants and in many cases helps rationalize changes in their experimental spectra. Considering conformational flexibility by using families of NMR structures leads to further improvements for some proteins and illustrates an informative level of sensitivity to side chain conformation. In several cases, the near-UV CD calculations can distinguish the native protein structure from a set of computer-generated misfolded decoy structures.

The molecular interactions between the Ce IV -substituted Keggin anion [PW 11 O 39 Ce(OH 2 ) 4 ] 3- (CeK) and hen egg-white lysozyme (HEWL) were investigated by molecular dynamics simulations. The analysis of CeK was compared with the Ce IV -substituted Keggin dimer [(PW 11 O 39 ) 2 Ce] 10- (CeK 2 ) and the Zr IV -substituted Lindqvist anion [W 5 O 18 Zr(OH 2 )(OH)] 3- (ZrL) to understand how POM features such as shape, size, charge, or type of incorporated metal ion influence the POM⋅⋅⋅protein interactions. Simulations revealed two regions of the protein in which the CeK anion interacts strongly: cationic sites formed by Arg21 and by Arg45 and Arg68. The POMs chiefly interact with the side chains of the positively charged (arginines, lysines) and the polar uncharged residues (tyrosines, serines, aspargines) via electrostatic attraction and hydrogen bonding with the oxygen atoms of the POM framework. The CeK anion shows higher protein affinity than the CeK 2 and ZrL anions, because it is less hydrophilic and it has the right size and shape for establishing interactions with several residues simultaneously. The larger, more negatively charged CeK 2 anion has a high solvent-accessible surface, which is sub-optimal for the interaction, while the smaller ZrL anion is highly hydrophilic and cannot efficiently interact with several residues simultaneously., (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Nonlinear two-dimensional infrared spectroscopy (2DIR) is most commonly simulated within the framework of the exciton method. The key parameters for these calculations include the frequency of the oscillators within their molecular environments and coupling constants that describe the strength of coupling between the oscillators. It is shown that these quantities can be obtained directly from harmonic frequency calculations by exploiting a procedure that localizes the normal modes. This approach is demonstrated using the amide I modes of polypeptides. For linear and cyclic diamides and hexapeptide Z-Aib-L-Leu-(Aib)2-Gly-Aib-OtBu, the computed parameters are compared with those from existing schemes, and the resulting 2DIR spectra are consistent with experimental observations. The incorporation of conformational averaging of structures from molecular dynamics simulations is discussed, and a hybrid scheme wherein the Hamiltonian matrix from the quantum chemical local-mode approach is combined with fluctuations from empirical schemes is shown to be consistent with experiment. The work demonstrates that localized vibrational modes can provide a foundation for the calculation of 2DIR spectra that does not rely on extensive parametrization and can be applied to a wide range of systems. For systems that are too large for quantum chemical harmonic frequency calculations, the local-mode approach provides a convenient platform for the development of site frequency and coupling maps.

Lanthionine antibiotics are an important class of naturally-occurring antimicrobial peptides. The best-known, nisin, is a commercial food preservative. However, structural and mechanistic details on nisin-lipid II membrane complexes are currently lacking. Recently, we have developed empirical force-field parameters to model lantibiotics. Docking and molecular dynamics (MD) simulations have been used to study the nisin2:lipid II complex in bacterial membranes, which has been put forward as the building block of nisin/lipid II binary membrane pores. An Ile1Trp mutation of the N-terminus of nisin has been modelled and docked onto lipid II models; the computed binding affinity increased compared to wild-type. Wild-type nisin was also docked onto three different lipid II structures and a stable 2:1 nisin:lipid II complex formed. This complex was inserted into a membrane. Six independent MD simulations revealed key interactions in the complex, specifically the N-terminal engagement of nisin with lipid II at the pyrophosphate and C-terminus of the pentapeptide chain. Nisin(2) inserts into the membrane and we propose this as the first step in pore formation, mediated by the nisin N-terminus-lipid II pentapeptide hydrogen bond. The lipid II undecaprenyl chain adopted different conformations in the presence of nisin, which may also have implications for pore formation.

Preschool classrooms with varying levels of structural quality requirements across the state of West Virginia were investigated for differences in measured structural and process quality. Quality was measured using group size, child-to-teacher/staff ratio, teacher education, and the Early Childhood Environmental Rating Scale-Revised (ECERS-R; Harms, T., Clifford, R. M., & Cryer, D. (2005). The early childhood environment rating scale-revised . New York, NY: Teachers College Press). Thirty-six classrooms with less structural quality requirements and 136 with more structural quality requirements were measured. There were significant differences between classroom type, with classrooms with more structural quality requirements having significantly higher teacher education levels and higher environmental rating scores on the ECERS-R subscales of Space and Furnishings, Activities, and Program Structure. Results support previous research that stricter structural state regulations are correlated with higher measured structural and process quality in preschool classrooms. Implications for preschool state quality standards are discussed., Competing Interests: Disclosure statement No potential conflict of interest was reported by the authors.