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1.
J Cheminform ; 16(1): 60, 2024 May 28.
Artigo em Inglês | MEDLINE | ID: mdl-38807181

RESUMO

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.

2.
Commun Chem ; 7(1): 102, 2024 May 08.
Artigo em Inglês | MEDLINE | ID: mdl-38720065

RESUMO

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.

3.
RSC Med Chem ; 15(4): 1392-1403, 2024 Apr 24.
Artigo em Inglês | MEDLINE | ID: mdl-38665844

RESUMO

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.

4.
J Chem Inf Model ; 64(1): 265-275, 2024 Jan 08.
Artigo em Inglês | MEDLINE | ID: mdl-38113509

RESUMO

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.


Assuntos
Produtos Biológicos , Excipientes , Humanos , Excipientes/química , Excipientes/metabolismo , Proteínas , Aminoácidos/química , Solventes
5.
Chem Commun (Camb) ; 59(99): 14713-14716, 2023 Dec 12.
Artigo em Inglês | MEDLINE | ID: mdl-37997814

RESUMO

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.


Assuntos
Aptâmeros de Nucleotídeos , Técnicas Biossensoriais , Cátions Bivalentes/química , Aptâmeros de Nucleotídeos/química , Dopamina , Serotonina , Simulação de Dinâmica Molecular
6.
J Chem Inf Model ; 63(10): 2895-2901, 2023 05 22.
Artigo em Inglês | MEDLINE | ID: mdl-37155346

RESUMO

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.


Assuntos
Laboratórios , Software , Eletrônica , Bases de Dados Factuais
7.
J Mol Graph Model ; 123: 108508, 2023 09.
Artigo em Inglês | MEDLINE | ID: mdl-37235902

RESUMO

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.


Assuntos
Antibacterianos , Penicilinas , Antibacterianos/farmacologia , Antibacterianos/química , Simulação de Acoplamento Molecular , Proteínas de Ligação às Penicilinas
8.
Digit Discov ; 2(2): 502-511, 2023 Apr 11.
Artigo em Inglês | MEDLINE | ID: mdl-37065679

RESUMO

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.

9.
Chemistry ; 29(16): e202202503, 2023 Mar 16.
Artigo em Inglês | MEDLINE | ID: mdl-36534955

RESUMO

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.


Assuntos
Cisteína , Proteínas , Cisteína/química , Proteínas/química , Peptídeos/química
10.
J Mol Graph Model ; 118: 108356, 2023 01.
Artigo em Inglês | MEDLINE | ID: mdl-36272195

RESUMO

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.


Assuntos
Aprendizado de Máquina , Fatores de Tempo , Temperatura
11.
Polym Chem ; 13(42): 6032-6045, 2022 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-36353599

RESUMO

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.

12.
J Chem Inf Model ; 62(6): 1458-1470, 2022 03 28.
Artigo em Inglês | MEDLINE | ID: mdl-35258972

RESUMO

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.


Assuntos
Proteínas Nucleares , Fatores de Transcrição , Entropia , Ligantes , Simulação de Dinâmica Molecular , Ligação Proteica , Termodinâmica
13.
J Chem Inf Model ; 62(3): 591-601, 2022 02 14.
Artigo em Inglês | MEDLINE | ID: mdl-35045248

RESUMO

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.


Assuntos
Proteínas Ferro-Enxofre , Ferro , Catálise , Humanos , Ferro/metabolismo , Proteínas Ferro-Enxofre/química , Oxirredução , Enxofre/química
14.
J Chem Inf Model ; 62(9): 2077-2092, 2022 05 09.
Artigo em Inglês | MEDLINE | ID: mdl-34699222

RESUMO

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.


Assuntos
Aprendizado de Máquina , Estudos Prospectivos
15.
Molecules ; 26(24)2021 Dec 10.
Artigo em Inglês | MEDLINE | ID: mdl-34946572

RESUMO

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.


Assuntos
Inibidores Enzimáticos/farmacologia , Histona Desmetilases/antagonistas & inibidores , Lisina/farmacologia , Aprendizado de Máquina , Bases de Dados Factuais , Avaliação Pré-Clínica de Medicamentos , Inibidores Enzimáticos/química , Histona Desmetilases/metabolismo , Humanos , Lisina/química , Estrutura Molecular
17.
J Phys Chem A ; 125(38): 8345-8346, 2021 09 30.
Artigo em Inglês | MEDLINE | ID: mdl-34533958
18.
Org Biomol Chem ; 19(25): 5632-5641, 2021 06 30.
Artigo em Inglês | MEDLINE | ID: mdl-34105560

RESUMO

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.


Assuntos
Proteínas de Ciclo Celular , Fatores de Transcrição
19.
J Phys Chem A ; 2021 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-34132093

RESUMO

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 S1 state with approximate vibronic coupling to the S2 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 S1 and S2 states is strong at the S0 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.

20.
Molecules ; 26(2)2021 Jan 13.
Artigo em Inglês | MEDLINE | ID: mdl-33451152

RESUMO

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.


Assuntos
Peptídeos/química , Dicroísmo Circular , Conformação Proteica , Espectrofotometria Ultravioleta
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