The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES.

Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.

Model-based design of gradient elution in liquid-liquid chromatography: Application to the separation of cannabinoids.

Liquid-liquid chromatography (LLC) is a separation technique that utilizes a biphasic solvent system as the mobile and stationary phases. The components are separated solely due to their different distributions between the two liquid phases. Gradient change in the mobile phase composition during the chromatographic process is a powerful method for improving the resolution of separation or shortening the process time. Gradient elution readily applies to LLC with biphasic solvent systems in which the stationary phase composition remains nearly constant when the mobile phase composition changes. This work proposes a model-based approach to optimize gradients in LLC and circumvent tedious trial-and-error experiments. The solutes' distribution constant depends on the mobile phase composition. Thus, the distribution constants were described as a function of the content of one of the solvents (= modifier) in the mobile phase. The dispersive and mass-transfer effects in the tubing and the column are modeled with a stage model. Only a few experiments are required to determine the model parameters. After the validation of the model and its parameters, the model can be used for LLC gradient optimization. The proposed approach was demonstrated for a gradient LLC separation of a mixture of four cannabinoids. Two different gradient shapes, one-step and linear gradient, were considered. For a pre-selected minimal purity requirement, the gradient was optimized for maximum process efficiency, defined as the product of productivity and yield. An experiment conducted with the optimized gradient conditions was in good agreement with the simulation, showing the potential of the proposed method.

Adsorption-desorption of 241Am(â ¢) on montmorillonite colloids and quartz sand: Effects of pH, ionic strength, colloid concentration and grain size.

Clay colloids in the subsurface environment have a strong adsorption capacity for radionuclides, and the mobile colloids will carry the nuclides for migration, which would promote the movability of radionuclides in the groundwater environment and pose a threat to the ecosphere. The investigations of the adsorption/desorption behaviors of radionuclides in colloids and porous media are significant for the evaluation of the geological disposal of radioactive wastes. To illustrate the adsorption/desorption behaviors of 241Am(Ⅲ) in Na-montmorillonite colloid and/or quartz sand systems at different pH (5, 7 and 9), ionic strengths (0, 0.1 and 5 mM), colloid concentrations (300 and 900 mg/L), nuclide concentrations (500, 800, 1100 and 1400 Bq/mL) and grain sizes (40 and 60 mesh), a series of batch sorption-desorption experiments were conducted. Combining the analysis of the physical and chemical properties of Na-montmorillonite with the Freundlich model, the influencing mechanism of different controlling factors is discussed. The experimental results show that the adsorption/desorption behaviors of 241Am(Ⅲ) in Na-montmorillonite colloid and/or quartz sand strongly are influenced by the pH value and ionic strength of a solution, the colloid concentration as well as quartz sand grain size. The adsorption and desorption isotherms within all the experimental conditions could be well-fitted by the Freundlich model and the correlation coefficients (R2) are bigger than 0.9. With the increase in pH, the adsorption partition coefficient (Kd) at 241Am(Ⅲ)-Na-montmorillonite colloid two-phase system and 241Am(Ⅲ)-Na-montmorillonite colloid-quartz sand three-phase system presents a trend which increases firstly followed by decreasing, due to the changes in the morphology of Am with pH. The Kd of 241Am(Ⅲ) adsorption on montmorillonite colloid and quartz sand decreases with increasing in ionic strength, which is mainly attributed to the competitive adsorption, surface complexation and the reduction of surface zeta potential. Additionally, the Kd increases with increasing colloid concentrations because of the increase in adsorption sites. When the mean grain diameter changes from 0.45 to 0.3 mm, the adsorption variation trends of 241Am(Ⅲ) remain basically unchanged. The research results obtained in this work are meaningful and helpful in understanding the migration behaviors of radionuclides in the underground environment.

Cotransport of aged biochar colloids and thallium(I) in water-saturated porous media: Impact of the ionic strength, pH and aging degree.

Biochar colloids entering the soil undergo aging over time and exhibit strong capabilities in adsorbing and transporting pollutants. Therefore, investigating the cotransport of aged biochar colloids and thallium (Tl(I)) in quartz sand media is crucial for understanding Tl(I) migration in underground environments. This study investigated the migration of biochar colloids with two different aging degrees and Tl(I) in quartz sand media at various pH and ionic strengths (ISs). The results revealed that under all ISs and pH, 30%AWB (biochar aged with 30 % (w/w) HNO3) inhibited Tl(I) migration in media. This inhibition primarily arose from the introduction of hydroxyl and carboxyl groups during aging, which significantly enhanced colloid adsorption onto Tl(I). At lower ISs, 30%AWB colloids exhibited greater inhibition of Tl(I) migration due to their increased adsorption capacity. Additionally, aging promoted the migration of biochar colloids in the media. Greater biochar aging notably enhanced this promotion, potentially owing to reduced colloidal particle size and the formation of biochar derivatives. Moreover, 50%AWB (biochar aged with 50 % (w/w) HNO3) inhibited Tl(I) migration under low ISs but had almost no impact under high ISs. Nonetheless, at high pH, 50%AWB colloids facilitated Tl(I) migration. This phenomenon might be attributed to the inhibitory effect of aged biochar colloids on Tl(I) adsorption onto media at a high pH, as well as the stable binding between Tl(I) and aged biochar colloids. This study discusses the cotransport of biochar with various degrees of aging and Tl(I) in media, providing insights into remediating soils contaminated with Tl.

Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis.

Synthesis planning of new pharmaceutical compounds is a well-known bottleneck in modern drug design. Template-free methods, such as transformers, have recently been proposed as an alternative to template-based methods for single-step retrosynthetic predictions. Here, we trained and evaluated a transformer model, called the Chemformer, for retrosynthesis predictions within drug discovery. The proprietary data set used for training comprised ∼18 M reactions from literature, patents, and electronic lab notebooks. Chemformer was evaluated for the purpose of both single-step and multistep retrosynthesis. We found that the single-step performance of Chemformer was especially good on reaction classes common in drug discovery, with most reaction classes showing a top-10 round-trip accuracy above 0.97. Moreover, Chemformer reached a higher round-trip accuracy compared to that of a template-based model. By analyzing multistep retrosynthesis experiments, we observed that Chemformer found synthetic routes, leading to commercial starting materials for 95% of the target compounds, an increase of more than 20% compared to the template-based model on a proprietary compound data set. In addition to this, we discovered that Chemformer suggested novel disconnections corresponding to reaction templates, which are not included in the template-based model. These findings were further supported by a publicly available ChEMBL compound data set. The conclusions drawn from this work allow for the design of a synthesis planning tool where template-based and template-free models work in harmony to optimize retrosynthetic recommendations.

Low-temperature NH3 abatement via selective oxidation over a supported copper catalyst with high Cu+ abundance.

Selective catalytic NH3-to-N2 oxidation (NH3-SCO) is highly promising for abating NH3 emissions slipped from stationary flue gas after-treatment devices. Its practical application, however, is limited by the non-availability of low-cost catalysts with high activity and N2 selectivity. Here, using defect-rich nitrogen-doped carbon nanotubes (NCNT-AW) as the support, we developed a highly active and durable copper-based NH3-SCO catalyst with a high abundance of cuprous (Cu+) sites. The obtained Cu/NCNT-AW catalyst demonstrated outstanding activity with a T50 (i.e. the temperature to reach 50% NH3 conversion) of 174°C in the NH3-SCO reaction, which outperformed not only the Cu catalyst supported on N-free O-functionalized CNTs (OCNTs) or NCNT with less surface defects, but also those most active Cu catalysts in open literature. Reaction kinetics measurements and temperature-programmed surface reactions using NH3 as a probe molecule revealed that the NH3-SCO reaction on Cu/NCNT-AW follows an internal selective catalytic reaction (i-SCR) route involving nitric oxide (NO) as a key intermediate. According to mechanistic investigations by X-ray photoelectron spectroscopy, Raman spectroscopy, and X-ray absorption spectroscopy, the superior NH3-SCO performance of Cu/NCNT-AW originated from a synergy of surface defects and N-dopants. Specifically, surface defects promoted the anchoring of CuO nanoparticles on N-containing sites and, thereby, enabled efficient electron transfer from N to CuO, increasing significantly the fraction of SCR-active Cu+ sites in the catalyst. This study puts forward a new idea for manipulating and utilizing the interplay of defects and N-dopants on carbon surfaces to fabricate Cu+-rich Cu catalysts for efficient abatement of slip NH3 emissions via selective oxidation.

Improving benzene catalytic oxidation on Ag/Co3O4 by regulating the chemical states of Co and Ag.

Silver (9 wt.%) was loaded on Co3O4-nanofiber using reduction and impregnation methods, respectively. Due to the stronger electronegativity of silver, the ratios of surface Co3+/Co2+ on Ag/Co3O4 were higher than on Co3O4, which further led to more adsorbed oxygen species as a result of the charge compensation. Moreover, the introducing of silver also obviously improved the reducibility of Co3O4. Hence the Ag/Co3O4 showed better catalytic performance than Co3O4 in benzene oxidation. Compared with the Ag/Co3O4 synthesized via impregnation method, the one prepared using reduction method (named as AgCo-R) exhibited higher contents of surface Co3+ and adsorbed oxygen species, stronger reducibility, as well as more active surface lattice oxygen species. Consequently, AgCo-R showed lowest T90 value of 183°C, admirable catalytic stability, largest normalized reaction rate of 1.36 × 10-4 mol/(h·m2) (150°C), and lowest apparent activation energy (Ea) of 63.2 kJ/mol. The analyzing of in-situ DRIFTS indicated benzene molecules were successively oxidized to phenol, o-benzoquinone, small molecular intermediates, and finally to CO2 and water on the surface of AgCo-R. At last, potential reaction pathways including five detailed steps were proposed.

Improving photochemical indicators for attributing ozone sensitivities in source apportionment analysis.

Comprehensive Air Quality Model with extensions (CAMx)-Decoupled Direct Method (DDM) simulations of first-order ozone (O3) sensitivity to nitrogen oxides (NOx) and volatile organic compounds (VOCs) emissions were performed and combined with modelled [Formula: see text] ratios to obtain a range of thresholds for determining O3-sensitivity regimes for different areas of China. Utilising the new threshold ranges for photochemical indicators, the method for determining O3 formation in the Ozone Source Apportionment Technology (OSAT) module within CAMx was improved by a dynamically varied threshold of [Formula: see text] ratio. The O3 concentration contributions in the newly added transition regime were apportioned to NOx and VOCs emissions in proportion to the relationship between the [Formula: see text] ratio and first-order O3 sensitivity. The source contributions of O3 concentrations from different emission sectors from June to September 2019 were compared using the original and improved CAMx-OSAT. The results showed that the O3 concentration contributions changed significantly in the NOx-limited regime, with a maximum decrease of 21.89%, while the contributions increased by up to 7.57% in the VOC-limited regime, and were within 15 µg/m3 in the transition regime. The modified OSAT module enabled a more sophisticated attribution of O3 to precursor emissions and may have far-reaching implications for informing O3 pollution control policy.

Degradation of o-dichlorobenzene by DBD-NTP co-modified titanium gel catalyst.

In order to study the degradation process of dioxins in industrial flue gas, the decomposition of o-dichlorobenzene (o-DCB) in a DBD plasma catalytic reactor was investigated. The results showed that an NTP-catalyzed system, especially using the CuMnTiOx catalyst, had better o-DCB degradation performance compared to plasma alone. The combination of the CuMnTiOx catalyst with NTP can achieve a degradation efficiency of up to 97.2% for o-DCB; the selectivity of CO and CO2 and the carbon balance were 40%, 45%, and 85%, respectively. The dielectric constant and electrical property results indicated that the surface discharge capacity of the catalysts played a major role in the degradation of o-DCB, and a higher dielectric constant could suppress the plasma expansion and enhance the duration of the plasma discharge per discharge cycle. According to the O1s XPS and O2-TPD results, the conversion of CO to CO2 follows the M-v-K mechanism; thus, the active species on the catalyst surface play an important role. Moreover, the CuMnTiOx and NTP mixed system exhibited excellent stability, which is probably because Cu doping improved the lifetime of the catalyst. This work can provide an experimental and theoretical basis for research in the degradation of o-DCB by plasma catalyst systems.

Michaelis-Menten kinetics during dry etching processes.

The chemical etching of germanium in Br2 environment at elevated temperatures is described by the Michaelis-Menten equation. The validity limit of Michaelis-Menten kinetics is subjected to the detailed analysis. The steady-state etching rate requires synergy of two different process parameters. High purity gas should be directed to the substrate on which intermediate reaction product does not accumulate. Theoretical calculations indicate that maximum etching rate is maintained when 99.89% of the germanium surface is covered by the reaction product, and 99.9999967% of the incident Br2 molecules are reflected from the substrate surface. Under these conditions, single GeBr2 molecule is formed after 30 million collisions of Br2 molecules with the germanium surface.

Viscosities and Densities of Binary and Ternary Mixtures of Aliphatic and Polyaromatic Hydrocarbons: Pyrene +1-Methylnaphthalene + Dodecane at T = (293.15 to 343.15) K. Experiment and Modeling.

This work presents new experimental viscosity and density data for aromatic and polyaromatic compounds in binary and ternary pyrene, 1-methylnaphthalene, and dodecane mixtures. The lack of experimental viscosity data for these mixtures requires the development of a new database, which is vital for understanding the behavior of mixtures in more complex systems, such as asphaltenes and fuels. The mixtures proposed in this work have been measured over a temperature range of (293.15 to 343.15) K at atmospheric pressure. Several mixture compositions have been studied at these conditions: 1.0, 2.5, 5.0, 7.5, 10.0, 12.5, and 15.0% pyrene mass fraction. The concentration of pyrene correlates with an increase in the viscosity and density values. At the lowest temperature in binary mixtures, the corresponding values reach 4.4217 mPa·s for viscosity and 1.0447 × 103 kg·m-3 for density, respectively. In ternary mixtures, the introduction of dodecane leads to the lowest maximum values of 3.5555 mPa·s for viscosity and 1.0112 × 103 kg·m-3 for density at the same temperature. The experimental data have been employed for the specific modification of viscosity models. These modifications could facilitate the prediction of the viscosity of mixtures that are more complex than those presented in this work. Various viscosity models have been employed, such as Linear, Ratcliff and Khan, modified UNIFAC-Visco, and Krieger-Dougherty. The settings in the models used reliably reproduce the experiment reliably. However, the Ratcliff model agrees excellently with the experiment, having a low standard deviation (2.0%) compared to other models. Furthermore, a model based on the equation of state of Guo is proposed to predict the viscosity values by modifying the specific parameters and adjusting them to the mixtures proposed in this work. The results from this study are compared to previous work, where pyrene, toluene, and heptane mixtures were analyzed. In this case, we find that the decrease of aggregation grade in the present systems is predicted by the model fixed in this work.

The Adipokinetic Hormone (AKH) and the Adipokinetic Hormone/Corazonin-Related Peptide (ACP) Signalling Systems of the Yellow Fever Mosquito Aedes aegypti: Chemical Models of Binding.

Neuropeptides are the main regulators of physiological, developmental, and behavioural processes in insects. Three insect neuropeptide systems, the adipokinetic hormone (AKH), corazonin (Crz), and adipokinetic hormone/corazonin-related peptide (ACP), and their cognate receptors, are related to the vertebrate gonadotropin (GnRH) system and form the GnRH superfamily of peptides. In the current study, the two signalling systems, AKH and ACP, of the yellow fever mosquito, Aedes aegypti, were comparatively investigated with respect to ligand binding to their respective receptors. To achieve this, the solution structure of the hormones was determined by nuclear magnetic resonance distance restraint methodology. Atomic-scale models of the two G protein-coupled receptors were constructed with the help of homology modelling. Thereafter, the binding sites of the receptors were identified by blind docking of the ligands to the receptors, and models were derived for each hormone system showing how the ligands are bound to their receptors. Lastly, the two models were validated by comparing the computational results with experimentally derived data available from the literature. This mostly resulted in an acceptable agreement, proving the models to be largely correct and usable. The identification of an antagonist versus a true agonist may, however, require additional testing. The computational data also explains the exclusivity of the two systems that bind only the cognate ligand. This study forms the basis for further drug discovery studies.

A systematic study on the chemical model of polycyclic aromatic hydrocarbons formation from nutrients (glucose, amino acids, fatty acids) in food.

Polycyclic aromatic hydrocarbons (PAHs), prominent carcinogens formed during food processing, pose health risks through long-term consumption. This study focuses on 16 priority PAHs in the European Union, investigating their formation during pyrolysis. Glucose, amino acids and fatty acids are important food nutrients. To further explore whether these nutrients in food form PAHs during heating, a single chemical model method was used to heat these nutrients respectively, and GC-MS/MS was used to identify and quantify the obtained components. Glucose is the most basic nutrient in food, so the influence of water, pH, temperature and other factors on the formation of PAHs was studied in the glucose model. At the same time, the models of amino acids and fatty acids were used to assist in improving the entire nutrient research system. According to our results, some previously reported mechanisms of PAHs formation by fatty acids heating were confirmed. In addition, glucose and amino acids could also produce many PAHs after heating, and some conclusions were improved by comparing the intermediates of PAHs from three types of nutrients.

Lagrangian and Eulerian modelling of 106Ru atmospheric transport in 2017 over northern hemisphere.

In September 2017, numerous measurement stations recorded large surface concentrations of Ru106 in Europe. This event was well recorded by various monitoring stations worldwide and offer a valuable framework to compare the modelling strategies deployed to quickly evaluate where the plume goes and with what concentrations. In general, the source and its intensity are not known and hypotheses have to be done. Models have to be fast and accurate: Lagrangian and Eulerian are often used but rarely compared. In this study, the FLEXPART Lagrangian model and the WRF-CHIMERE Eulerian models are used to simulate the emissions, transport and deposition of this source of Ru106. First, it is shown that the hypothesis of location, timing and intensity of the source is realistic, by comparison to surface measurements. Second, sensitivity analysis performed with the Eulerian model and several transport scheme showed that this model may provide better results than the Lagrangian one. It opens the door to further development, including chemistry and mixing with other pollutants during these specific events.

Oxidation pathways and kinetics of the 1,1,2,3-tetrafluoropropene (CF2CF-CH2F) reaction with Cl-atoms and subsequent aerial degradation of its product radicals in the presence of NO.

To give a comprehensive account of the environmental acceptability of 1,1,2,3-tetrafluoropropene (CF2CF-CH2F) in the troposphere, we have examined the oxidation reaction pathways and kinetics of CF2CF-CH2F initiated by Cl-atoms using the second-order Møller-Plesset perturbation (MP2) theory along with the 6-31+G(d,p) basis set. We also performed single-point energy calculations to further refine the energies at the CCSD(T) level along with the basis sets 6-31+G(d,p) and 6-311++G(d,p). The estimation of the relative energies and thermodynamic parameters of the CF2CF-CH2F + Cl reaction clearly shows that Cl-atom addition reaction pathways are more dominant compared to H-abstraction reaction pathways. The value of the rate coefficient for each reaction channel is calculated using the conventional transition state theory (TST) over the temperature range of 200-1000 K at 1 atm. The estimated overall rate coefficients for the title reaction are found to be 1.10 × 10-12, 1.21 × 10-10, and 1.13 × 10-8 cm3 per molecule per s via the respective calculation methods viz. MP2/6-31+G(d,p), CCSD(T)//MP2/6-31+G(d,p), and CCSD(T)/6-311++G(d,p)//MP2/6-31+G(d,p), at 298.15 K. Moreover, the calculated rate coefficients and percentage branching ratio values suggest that the Cl-atom addition reaction at the ß-carbon atom is more preferable to that of the α-carbon addition to CF2CF-CH2F. Based on the rate coefficient values calculated by the three different methods, the atmospheric lifetime for the title reaction at 298.15 K is estimated. The radiative efficiency (RE) and Global Warming Potential (GWP) results of the title molecule show that its GWP would be negligible. Further, we have explored the degradation of its product radicals in the presence of O2 and NO. From the degradation results, we have found that CF2(Cl)COF, FCOCH2F, FCFO and FCOCl are formed as stable end products along with various radicals such as ˙CF2Cl and ˙CH2F. Therefore, these findings of kinetic and mechanistic data can be applied to the development and implementation of a novel CFC replacement.

New insights into the mechanism and kinetics of the addition reaction of unsaturated Criegee intermediates to CF3COOH and tropospheric implications.

In this work, we have investigated the mechanism, thermochemistry and kinetics of the reaction of syn-cis-CH2RzCRyCO+O- (where Rz, Ry = H, CH3-) unsaturated Criegee intermediates (CIs) with CF3COOH using quantum chemical methods. The rate coefficients for the barrierless reactions were calculated using variable reaction coordinate variational transition state theory (VRC-VTST). For the syn-cis-CH2RzCRyCO+O- conformation in which conjugated CC and CO double bonds are aligned with each other, we propose a new pathway for the unidirectional addition of an OC-OH molecule (CF3COOH) to the CC double bond of syn-cis-CH2RzCRyCO+O-. The rate coefficient for the 1,4-CC addition reaction at 298 K is ∼10-10 to 10-11 cm3 s-1, resulting in the formation of CF3C(O)OCH2CRzRyCOOH trifluoroacetate alkyl allyl hydroperoxide (TFAAAH) as a new transitory adduct. It can act as a precursor for the formation of secondary organic aerosols (SOAs). This novel TFAAAH hydroperoxide was identified through a detailed quantum chemical study of the 1,4-addition mechanism and will provide new insights into the significance of the 1,4-addition reaction of unsaturated Cls with trace tropospheric gases on -CRzCH2 vinyl carbon atoms.

Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective.

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate the design of novel reactions, optimize existing ones for higher yields, and discover new pathways for synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning models, it is imperative to derive robust and informative representations or engage in feature engineering using extensive data sets of reactions. This work aims to provide a comprehensive review of established reaction featurization approaches, offering insights into the selection of representations and the design of features for a wide array of tasks. The advantages and limitations of employing SMILES, molecular fingerprints, molecular graphs, and physics-based properties are meticulously elaborated. Solutions to bridge the gap between different representations will also be critically evaluated. Additionally, we introduce a new frontier in chemical reaction pretraining, holding promise as an innovative yet unexplored avenue.

Analysis of Poly(ethylene terephthalate) degradation kinetics of evolved IsPETase variants using a surface crowding model.

Poly(ethylene terephthalate) (PET) is a major plastic polymer utilized in the single-use and textile industries. The discovery of PET-degrading enzymes (PETases) has led to an increased interest in the biological recycling of PET in addition to mechanical recycling. IsPETase from Ideonella sakaiensis is a candidate catalyst, but little is understood about its structure-function relationships with regards to PET degradation. To understand the effects of mutations on IsPETase productivity, we develop a directed evolution assay to identify mutations beneficial to PET film degradation at 30 °C. IsPETase also displays enzyme concentration-dependent inhibition effects, and surface crowding has been proposed as a causal phenomenon. Based on total internal reflectance fluorescence microscopy and adsorption experiments, IsPETase is likely experiencing crowded conditions on PET films. Molecular dynamics simulations of IsPETase variants reveal a decrease in active site flexibility in free enzymes and reduced probability of productive active site formation in substrate-bound enzymes under crowding. Hence, we develop a surface crowding model to analyze the biochemical effects of three hit mutations (T116P, S238N, S290P) that enhanced ambient temperature activity and/or thermostability. We find that T116P decreases susceptibility to crowding, resulting in higher PET degradation product accumulation despite no change in intrinsic catalytic rate. In conclusion, we show that a macromolecular crowding-based biochemical model can be used to analyze the effects of mutations on properties of PETases and that crowding behavior is a major property to be targeted for enzyme engineering for improved PET degradation.

Leveraging DFT and Molecular Fragmentation for Chemically Accurate pKa Prediction Using Machine Learning.

We present a quantum mechanical/machine learning (ML) framework based on random forest to accurately predict the pKas of complex organic molecules using inexpensive density functional theory (DFT) calculations. By including physics-based features from low-level DFT calculations and structural features from our connectivity-based hierarchy (CBH) fragmentation protocol, we can correct the systematic error associated with DFT. The generalizability and performance of our model are evaluated on two benchmark sets (SAMPL6 and Novartis). We believe the carefully curated input of physics-based features lessens the model's data dependence and need for complex deep learning architectures, without compromising the accuracy of the test sets. As a point of novelty, our work extends the applicability of CBH, employing it for the generation of viable molecular descriptors for ML.

Making Use of Averaging Methods in MODELLER for Protein Structure Prediction.

Recent advances in protein structure prediction, driven by AlphaFold 2 and machine learning, demonstrate proficiency in static structures but encounter challenges in capturing essential dynamic features crucial for understanding biological function. In this context, homology-based modeling emerges as a cost-effective and computationally efficient alternative. The MODELLER (version 10.5, accessed on 30 November 2023) algorithm can be harnessed for this purpose since it computes intermediate models during simulated annealing, enabling the exploration of attainable configurational states and energies while minimizing its objective function. There have been a few attempts to date to improve the models generated by its algorithm, and in particular, there is no literature regarding the implementation of an averaging procedure involving the intermediate models in the MODELLER algorithm. In this study, we examined MODELLER's output using 225 target-template pairs, extracting the best representatives of intermediate models. Applying an averaging procedure to the selected intermediate structures based on statistical potentials, we aimed to determine: (1) whether averaging improves the quality of structural models during the building phase; (2) if ranking by statistical potentials reliably selects the best models, leading to improved final model quality; (3) whether using a single template versus multiple templates affects the averaging approach; (4) whether the "ensemble" nature of the MODELLER building phase can be harnessed to capture low-energy conformations in holo structures modeling. Our findings indicate that while improvements typically fall short of a few decimal points in the model evaluation metric, a notable fraction of configurations exhibit slightly higher similarity to the native structure than MODELLER's proposed final model. The averaging-building procedure proves particularly beneficial in (1) regions of low sequence identity between the target and template(s), the most challenging aspect of homology modeling; (2) holo protein conformations generation, an area in which MODELLER and related tools usually fall short of the expected performance.