The equilibrium stability of a protein is determined by its amino acid sequence and the solution conditions, such as temperature, pH and presence of chemical denaturant. The stability of a single protein in two identical solutions can nonetheless differ if other macromolecules, termed cosolutes or crowders, are present in one of the solutions at concentrations high enough to occupy a substantial fraction of the solution volume. This effect, due to the presence of the crowders, decreases or increases the stability depending on the interactions between the protein and crowders. Hard-core steric repulsions, which are responsible for the reduction in free volume, are expected to entropically stabilize the protein while attractive interactions can be destabilizing. Here we use a coarse-grained protein model to assess the impact of different types of crowder-protein interactions on the stability of a 35-amino acid model sequence folding into a helical bundle. We find that, for the same interaction strength and concentration, spherical crowders with a hydrophobic character are more destabilizing than crowders interacting nonspecifically with the protein. However, the two types of interactions differ in the degree of association between crowders and protein. At an interaction strength for which the attractive interactions roughly counteracts the stabilizing hard-core repulsions, the nonspecific interactions lead to much stronger crowder-protein association than the hydrophobic interactions. Additionally, we study crowders in the form of polypeptide chains, which are capable of hydrogen bonding with the protein. These peptide crowders have a destabilizing effect even at relatively low crowder concentrations, especially if the sequence of the peptide crowders includes hydrophobic amino acids. Our findings emphasize the importance of the interplay between different types of attractive crowder-protein interactions and entropic effects in determining the net effect on protein stability.

This study leverages a graph-based genetic algorithm (GB-GA) for the design of efficient nitrogen-fixing catalysts as alternatives to the Schrock catalyst, with the aim to improve the energetics of key reaction steps. Despite the abundance of nitrogen in the atmosphere, it remains largely inaccessible due to its inert nature. The Schrock catalyst, a molybdenum-based complex, offered a breakthrough but its practical application is limited due to low turnover numbers and energetic bottlenecks. The genetic algorithm in our study explores the chemical space for viable modifications of the Schrock catalyst, evaluating each modified catalyst’s fitness based on reaction energies of key catalytic steps and synthetic accessibility. Through a series of selection and optimization processes, we obtained fully converged catalytic cycles for 20 molecules at the B3LYP level of theory. From these results, we identified three promising molecules, each demonstrating unique advantages in different aspects of the catalytic cycle. This study offers valuable insights into the potential of generative models for catalyst design. Our results can help guide future work on catalyst discovery for the challenging nitrogen fixation process.

Ignition delay times (IDT) for stoichiometric propane (C3H8) diluted with nitrogen were measured in a shock tube facility under reflected shock wave conditions at pressures ranging from 1 to 10 atm and temperatures between 850 and 1500 K. The experiments were limited to a maximum pressure of 10 atm due to the facility’s constraints. In addition, numerical simulations were conducted using several detailed kinetic mechanisms at pressures from 1 to 30 atm and three equivalence ratios (φ = 0.5, 1, and 2) to provide comparative insights. The results indicated that IDT decreases as pressure increases, with a more significant reduction observed between 1 and 10 atm compared to 10 to 30 atm. While most models exhibited similar trends and minimal discrepancies, the GRI Mech 3.0 mechanism demonstrated a slower prediction of ignition delay times at temperatures below 1250 K. In contrast, the POLIMI model exhibited a relatively faster prediction at temperatures above 1250 K, with the deviation between the two models becoming more pronounced as pressure increased. A comparative analysis revealed that the experimental predictions of propane autoignition behavior were in good agreement with the results obtained using the ARAMCO 3.0 mechanism. To further understand the chemistry governing the autoignition process of C3H8, a sensitivity analysis was performed for a stoichiometric mixture at three distinct temperatures (850 K, 1200 K, and 1550 K).

Synthetic biology aims to engineer biological circuits, which often involve gene expression. A particularly promising group of regulatory elements are riboswitches because of their versatility with respect to their targets, but early synthetic designs were not as attractive because of a reduced dynamic range with respect to protein regulators. Only recently, the creation of toehold switches helped overcome this obstacle by also providing an unprecedented degree of orthogonality. However, a lack of automated design and optimization tools prevents the widespread and effective use of toehold switches in high throughput experiments. To address this, we developed Toeholder, a comprehensive open-source software for toehold design and in silico comparison. Toeholder takes into consideration sequence constraints from experimentally tested switches, as well as data derived from molecular dynamics simulations of a toehold switch. We describe the software and its in silico validation results, as well as its potential applications and impacts on the management and design of toehold switches.

A high priority of the World Health Organization (WHO) is the study of drugs against Pseudomonas aeruginosa, which has developed antibiotic resistance. In this order, recent research is analyzing biomaterials and metal oxide nanoparticles, such as chitosan (QT) and TiO2 (NT), which can transport molecules with biological activity against bacteria, to propose them as drug carrier candidates. In the present work, 10 modified benzofuran-isatin molecules were studied through computational simulation using density functional theory (DFT) and molecular docking assays against Hfq and LpxC (proteins of P. aeruginosa). The results show that the ligand efficiency of commercial drugs C-CP and C-AZI against Hfq is low compared with the best-designed molecule MOL-A. However, we highlight that the influence of NT promotes a better interaction of some molecules, where MOL-E generates a better interaction by 0.219 kcal/mol when NT is introduced in Hfq, forming the system Hfq-NT (Target-NT). Similar behavior is observed in the LpxC target, in which MOL-J is better at 0.072 kcal/mol. Finally, two pharmacophoric models for Hfq and LpxC implicate hydrophobic and aromatic-hydrophobic fragments.

Photon capture by chlorophylls and other chromophores in light-harvesting complexes and photosystems is the driving force behind the light reactions of photosynthesis. Excitation of photosystem II allows it to receive electrons from the water-oxidizing oxygen-evolution complex and to transfer them to an electron-transport chain that generates a transmembrane electrochemical gradient and ultimately reduces plastocyanin, which donates its electron to photosystem I. Subsequently, excitation of photosystem I leads to electron transfer to a ferredoxin which can either reduce plastocyanin again (in so-called “cyclical electron-flow”) and release energy for the maintenance of the electrochemical gradient, or reduce NADP+ to NADPH. Although photons in the far-red (700–750 nm) portion of the solar spectrum carry enough energy to enable the functioning of the photosynthetic electron-transfer chain, most extant photosystems cannot usually take advantage of them due to only absorbing light with shorter wavelengths. In this work, we used computational methods to characterize the spectral and redox properties of 49 chlorophyll derivatives, with the aim of finding suitable candidates for incorporation into synthetic organisms with increased ability to use far-red photons. The data offer a simple and elegant explanation for the evolutionary selection of chlorophylls a, b, c, and d among all easily-synthesized singly-substituted chlorophylls, and identified one novel candidate (2,12-diformyl chlorophyll a) with an absorption peak shifted 79 nm into the far-red (relative to chlorophyll a) with redox characteristics fully suitable to its possible incorporation into photosystem I (though not photosystem II). chlorophyll d is shown by our data to be the most suitable candidate for incorporation into far-red utilizing photosystem II, and several candidates were found with red-shifted Soret bands that allow the capture of larger amounts of blue and green light by light harvesting complexes.

Carnosine (CAR) and anserine (ANS) are histidine-containing dipeptides that show the therapeutic properties and protective abilities against diabetes and cognitive deficit. Both dipeptides are rich in meat products and have been used as a supplement. However, in humans, both compounds have a short half-life due to the rapid degradation by dizinc carnosinase 1 (CN1) which is a hurdle for its therapeutic application. To date, a comparative study of carnosine- and anserine-CN1 complexes is limited. Thus, in this work, molecular dynamics (MD) simulations were performed to explore the binding of carnosine and anserine to CN1. CN1 comprises 2 chains (Chains A and B). Both monomers are found to work independently and alternatingly. The displacement of Zn2+ pair is found to disrupt the substrate binding. CN1 employs residues from the neighbour chain (H235, T335, and T337) to form the active site. This highlights the importance of a dimer for enzymatic activity. Anserine is more resistant to CN 1 than carnosine because of its bulky and dehydrated imidazole moiety. Although both dipeptides can direct the peptide oxygen to the active Zn2+ which can facilitate the catalytic reaction, the bulky methylated imidazole on anserine promotes various poses that can retard the hydrolytic activity in contrast to carnosine. Anserine is likely to be the temporary competitive inhibitor by retarding the carnosine catabolism.

Protein engineers conventionally use tools such as Directed Evolution to find new proteins with better functionalities and traits. More recently, computational techniques and especially machine learning approaches have been recruited to assist Directed Evolution, showing promising results. In this article, we propose POET, a computational Genetic Programming tool based on evolutionary computation methods to enhance screening and mutagenesis in Directed Evolution and help protein engineers to find proteins that have better functionality. As a proof-of-concept, we use peptides that generate MRI contrast detected by the Chemical Exchange Saturation Transfer contrast mechanism. The evolutionary methods used in POET are described, and the performance of POET in different epochs of our experiments with Chemical Exchange Saturation Transfer contrast are studied. Our results indicate that a computational modeling tool like POET can help to find peptides with 400% better functionality than used before.

Measurements of nonlinear optical (NLO) properties of different binary mixtures having carbon disulfide (CS2) as the common component, namely CS2-acetone, CS2-cyclopentanone, CS2-toluene, and CS2-carbon tetrachloride (CCl4), are carried out by using the z-scan technique. Open-aperture z-scan (OAZS) and close-aperture z-scan (CAZS) experiments are performed to determine the nonlinear absorption coefficient (β) and nonlinear refractive index (n2) of all binary liquid mixtures at various compositions of the components by employing a pulsed, high repetition rate (HRR) femtosecond laser. Also, we were able to use the flowing liquid to measure NLO properties in the CS2-acetone binary mixture to remove the cumulative thermal effects produced due to the pulsed HRR laser light. Nonlinear refractive index (n2) values are found to be influenced by the weak dipole-induced dipole intermolecular interactions between the nonpolar CS2 and polar acetone as well as cyclopentanone of the respective binary mixtures. On the contrary n2 values are not found to be affected by the intermolecular interactions in CS2-toluene and CS2-CCl4 binary mixtures. In comparison, the nonlinear absorption coefficient (β) values are not found to be affected by the same in all different sets of binary mixtures.

We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions on uni- and bimolecular reactions extracted from the barrier dataset developed by Grambow, Pattanaik & Green (2020). For unimolecular reactions the meta-MD simulations identify 25 of the 26 products found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional four reactions due to an overestimation of the reaction energies or estimated barrier heights relative to DFT. In addition, our approach identifies 36 reactions not found by Grambow, Pattanaik & Green (2020), 10 of which are <30 kcal/mol. For bimolecular reactions the meta-MD simulations identify 19 of the 20 reactions found by Grambow, Pattanaik & Green (2020), while the subsequent semiempirical screening eliminates an additional reaction. In addition, we find 34 new low-barrier reactions. For bimolecular reactions we found that it is necessary to “encourage” the reactants to go to previously undiscovered products, by including products found by other MD simulations when computing the biasing potential as well as decreasing the size of the molecular cavity in which the MD occurs, until a reaction is observed. We also show that our methodology can find the correct products for two reactions that are more representative of those encountered in synthetic organic chemistry. The meta-MD hyperparameters used in this study thus appear to be generally applicable to finding low-barrier reactions.