Large Molecules Research Articles

Synthesis and Stability of Biomolecules in C-H-O-N Fluids under Earth's Upper Mantle Conditions.

How life started on Earth is an unsolved mystery. There are various hypotheses for the location ranging from outer space to the seafloor, subseafloor, or potentially deeper. Here, we applied extensive ab initio molecular dynamics simulations to study chemical reactions between NH3, H2O, H2, and CO at pressures (P) and temperatures (T) approximating the conditions of Earth's upper mantle (i.e., 10-13 GPa, 1000-1400 K). Contrary to the previous assumptions that large organic molecules might readily disintegrate in aqueous solutions at extreme P-T conditions, we found that many organic compounds formed without any catalysts and persisted in C-H-O-N fluids under these extreme conditions, including glycine, ribose, urea, and uracil-like molecules. Particularly, our free-energy calculations showed that the C-N bond is thermodynamically stable at 10 GPa and 1400 K. Moreover, while the pyranose (six-membered ring) form of ribose is more stable than the furanose (five-membered ring) form at ambient conditions, we found that the formation of the five-membered-ring form of ribose is thermodynamically more favored at extreme conditions, which is consistent with the exclusive incorporation of β-d-ribofuranose in RNA. We have uncovered a previously unexplored pathway through which the crucial biomolecules could be abiotically synthesized from geofluids in the deep interior of Earth and other planets, and these formed biomolecules could potentially contribute to the early stage of the emergence of life.

Generation of binder-format-payload conjugate-matrices by antibody chain-exchange

The generation of antibody-drug conjugates with optimal functionality depends on many parameters. These include binder epitope, antibody format, linker composition, conjugation site(s), drug-to-antibody ratio, and conjugation method. The production of matrices that cover all possible parameters is a major challenge in identifying optimal antibody-drug conjugates. To address this bottleneck, we adapted our Format Chain Exchange technology (FORCE), originally established for bispecific antibodies, toward the generation of binder-format-payload matrices (pair-FORCE). Antibody derivatives with exchange-enabled Fc-heterodimers are combined with payload-conjugated Fc donors, and subsequent chain-exchange transfers payloads to antibody derivatives in different formats. The resulting binder-format-conjugate matrices can be generated with cytotoxic payloads, dyes, haptens, and large molecules, resulting in versatile tools for ADC screening campaigns. We show the relevance of pair-FORCE for identifying optimal HER2-targeting antibody-drug conjugates. Analysis of this matrix reveals that the notion of format-defines-function applies not only to bispecific antibodies, but also to antibody-drug conjugates.

Toward Efficient Modeling of Nonradiative Decay in Extended INVEST: Overcoming Computational Challenges in Quantum Dynamics Simulations.

In recent years, an increasing number of fully organic molecules capable of thermally activated delayed fluorescence (TADF) have been reported, often with very small or even inverted singlet-triplet (INVEST) energy gaps. These molecules typically exhibit complex photophysics due to the close energy levels of multiple singlet and triplet states, which create various transition pathways toward emission. A predictive model for the rates of these transitions is thus essential for assessing the suitability of new materials for light-emitting devices. Quantum Dynamics (QD) calculations are ideal for this purpose, as they include quantum effects, without the limitations of first-order perturbative approaches, also allowing taking into account more than two electronic states at once. However, the huge computational demands of QD methodologies, especially for large molecules, currently limit their use as a standard tool. To address this problem, we here employ a strategy that allows us to include almost the whole set of the vibrational coordinates by selecting the key elements of the Hilbert space that significantly impact dynamics, thereby hugely reducing the computational burden. Application of this protocol to two relatively large INVEST molecules reveals that internal conversion in these systems is very fast, making indirect emissive pathways a possible channel for the population of the S1 state. More importantly, this study demonstrates that the dynamics can be accurately described even with a significantly reduced vibrational space, thus allowing quantum dynamics calculations that yield accurate transition rates in a few minutes of computational time.

Biology, function and structure of the calcium homeostasis modulator family.

Calcium homeostasis modulators (CALHMs) are the most recently discovered members of the large-pore channel family. They mediate the conductance of ions and larger molecules, such as ATP, and play critical roles in pathways related to Alzheimer's disease, neuroinflammation, neuromodulation, taste perception and innate immune responses. Since the inaugural report on CALHM1 in 2008, significant breakthroughs have revealed their biological roles, ion and ATP channel functions, and structures, positioning the field for further advancements. In this review, we discuss the overall progress and recent developments in understanding the biological roles, functions and molecular structures of CALHM proteins.

A computational study and analysis of Variational Quantum Eigensolver over multiple parameters for molecules and ions

AbstractMaterial discovery is a phenomenon practiced since the evolution of the world. The discovery of materials has led to significant development in varied fields such as Science, Engineering and Technology. Computationally simulating molecules has been an area of interest in the industry as well as academia. However, simulating large molecules can be computationally expensive in terms of computing power and complexity. Quantum computing is a recent development that can improve the efficiency in predicting properties of atoms and molecules which will be useful for material design. The Variational Quantum Eigensolver (VQE) is one such quantum algorithm used to calculate the ground state energy of molecules or ions. In this study, we have done a comparative analysis of the parameters that constitute the VQE algorithm. This includes components such as basis, qubit mapping, ansatz, and optimizers used. We have also developed a database consisting of 79 single atoms and their variations of oxidation states and 33 molecules with the data of their Hamiltonian and ground state energy and dipole moment.

Controlled enzyme cargo loading in engineered bacterial microcompartment shells.

Bacterial microcompartments (BMCs) are nanometer-scale organelles with a protein-based shell that serve to co-localize and encapsulate metabolic enzymes. They may provide a range of benefits to improve pathway catalysis, including substrate channeling and selective permeability. Several groups are working toward using BMC shells as a platform for enhancing engineered metabolic pathways. The microcompartment shell of Haliangium ochraceum (HO) has emerged as a versatile and modular shell system that can be expressed and assembled outside its native host and with non-native cargo. Further, the HO shell has been modified to use the engineered protein conjugation system SpyCatcher-SpyTag for non-native cargo loading. Here, we used a model enzyme, triose phosphate isomerase (Tpi), to study non-native cargo loading into four HO shell variants and begin to understand maximal shell loading levels. We also measured activity of Tpi encapsulated in the HO shell variants and found that activity was determined by the amount of cargo loaded and was not strongly impacted by the predicted permeability of the shell variant to large molecules. All shell variants tested could be used to generate active, Tpi-loaded versions, but the simplest variants assembled most robustly. We propose that the simple variant is the most promising for continued development as a metabolic engineering platform.

Implantable Microarray Patch: Engineering at the Nano and Macro Scale for Sustained Therapeutic Release via Synthetic Biodegradable Polymers

AbstractTransdermal drug delivery has been an increasingly studied alternative drug administration route owing to the large surface area of the skin, enabling ease of access and application. Among the various transdermal drug delivery technologies that have been developed so far, microarray patches (MAPs) incorporating microneedle technology (MN) have garnered much attention. MAPs can easily penetrate the stratum corneum, facilitating the delivery of desired substances directly into the skin, exhibiting a higher delivery efficiency compared to alternative transdermal drug delivery approaches. The effectiveness of MAPs in delivering therapeutics is affected by a multitude of factors, one of which is the selection of materials employed in their fabrication. Among various materials that have been used to fabricate MAPs, synthetic biodegradable polymers such as poly(lactic‐co‐glycolic acid) (PLGA) and polylactic acid (PLA) have been exploited for sustained and controlled release with excellent biocompatibility. PLGA and PLA can be used to construct the entire needle shaft or drug‐loaded microparticles which are then incorporated into needle layer. The combination of MN technology in conjunction with biodegradable polymers has revolutionized sustained and controlled transdermal delivery of small and large molecules.

AI-Driven Electrolyte Additive Selection to Boost Aqueous Zn-Ion Batteries Stability.

In tackling the stability challenge of aqueous Zn-ion batteries (AZIBs) for large-scale energy storage, the adoption of electrolyte additive emerges as a practical solution. Unlike current trial-and-error methods for selecting electrolyte additives, a data-driven strategy is proposed using theoretically computed surface free energy as a stability descriptor, benchmarked against experimental results. Numerous additives are calculated from existing literature, forming a database for machine learning (ML) training. Importantly, this ML model relies solely on experimental values, effectively addressing the challenge of large solvent molecule models that are difficult to handle with quantum chemistry computation. The interpretable linear regression algorithm identifies the number of heavy atoms in the additive molecule and the liquid surface tension as key factors. Artificial intelligence (AI) clustering categorizes additive molecules, identifying regions with the most significant impact on enhancing battery stability. Experimental verification successfully confirms the exceptional performance of 1,2,3-butanetriol and acetone in the optimal region. This integrated methodology, combining theoretical models, data-driven ML, and experimental validation, provides insights into the rational design of battery electrolyte additives.

Tuning the Sensitivity of Photonic Sensors Toward Alkanes Through the Textural Properties of Hybrid Xerogel Coatings

AbstractThis work exemplifies how incorporating organosilane modifiers into silica matrices allows for tuning the optical response of reflection photonic sensors through customizing the textural properties of hybrid xerogel sensing films. Xerogels with propyl molar percentages 0, 5, and 10% are used to construct photonic probes (OFS0pTEOS, OFS5pTEOS and OFS10pTEOS, respectively) by dip‐coating upon optimizing film deposition parameters. The time response of these probes toward a battery of volatile organic compounds (VOCs) comprising species with different functionality, size‐shape, and polarity is systematically analyzed through ON/OFF experiments, revealing that a low propyl content makes the poor‐responding OFS0pTEOS film highly sensitive toward non‐aromatic, large molecules with low‐polar or non‐polar character in OFS5pTEOS. This sensor is particularly sensitive toward alkanes, with globular cyclohexane (cyHex) outperforming elongated n‐hexane. Variable‐temperature calibration curves obtained from step‐by‐step experiments and adsorption–desorption cycles corroborate these observations and allow hysteresis to be quantified. The response to cyHex closely follows VOC concentration changes with the most stable signal among analytes, leading to well‐defined curves with low‐to‐negligible hysteresis. The isosteric enthalpies of cyHex adsorption are obtained for both the bulk material and the sensor, demonstrating labile adsorbate‐adsorbent interactions ruling the sensor response and becoming more exothermic for larger VOC concentrations.

Enhancing Th17 cells drainage through meningeal lymphatic vessels alleviate neuroinflammation after subarachnoid hemorrhage

BackgroundSubarachnoid hemorrhage (SAH) is a severe cerebrovascular disorder primarily caused by the rupture of aneurysm, which results in a high mortality rate and consequently imposes a significant burden on society. The occurrence of SAH initiates an immune response that further exacerbates brain damage. The acute inflammatory reaction subsequent to SAH plays a crucial role in determining the prognosis. Th17 cells, a subset of T cells, are related to the brain injury following SAH, and it is unclear how Th17 cells are cleared in the brain. Meningeal lymphatic vessels are a newly discovered intracranial fluid transport system that has been shown to drain large molecules and immune cells to deep cervical lymph nodes. There is limited understanding of the role of the meningeal lymphatic system in SAH. The objective of this research is to explore the impact and underlying mechanism of drainage Th17 cells by meningeal lymphatics on SAH.MethodsTreatments to manipulate meningeal lymphatic function and the CCR7-CCL21 pathway were administered, including laser ablation, injection of VEGF-C geneknockout, and protein injection. Mouse behavior was assessed using the balance beam experiment and the modified Garcia scoring system. Flow cytometry, enzyme-linked immunosorbent assays (ELISA), and immunofluorescence staining were used to study the impact of meningeal lymphatic on SAH drainage. Select patients with unruptured and ruptured aneurysms in our hospital as the control group and the SAH group, with 7 cases in each group. Peripheral blood and cerebrospinal fluid (CSF) samples were assessed by ELISA and flow cytometry.ResultsMice with SAH showed substantial behavioral abnormalities and brain damage in which immune cells accumulated in the brain. Laser ablation of the meningeal lymphatic system or knockout of the CCR7 gene leads to Th17 cell aggregation in the meninges, resulting in a decreased neurological function score and increased levels of inflammatory factors. Injection of VEGF-C or CCL21 protein promotes Th17 cell drainage to lymph nodes, an increased neurological function score, and decreased levels of inflammatory factors. Clinical blood and CSF results showed that inflammatory factors in SAH group were significantly increased. The number of Th17 cells in the SAH group was significantly higher than the control group. Clinical results confirmed Th17 cells aggravated the level of neuroinflammation after SAH.ConclusionThis study shows that improving the drainage of Th17 cells by meningeal lymphatics via the CCR7-CCL21 pathway can reduce brain damage and improve behavior in the SAH mouse model. This could lead to new treatment options for SAH.

An Efficient RI-MP2 Algorithm for Distributed Many-GPU Architectures.

Second-order Møller-Plesset perturbation theory (MP2) using the Resolution of the Identity approximation (RI-MP2) is a widely used method for computing molecular energies beyond the Hartree-Fock mean-field approximation. However, its high computational cost and lack of efficient algorithms for modern supercomputing architectures limit its applicability to large molecules. In this paper, we present the first distributed-memory many-GPU RI-MP2 algorithm explicitly designed to utilize hundreds of GPU accelerators for every step of the computation. Our novel algorithm achieves near-peak performance on GPU-based supercomputers through the development of a distributed memory algorithm for forming RI-MP2 intermediate tensors with zero internode communication, except for a single asynchronous broadcast, and a distributed memory algorithm for the energy reduction step, capable of sustaining near-peak performance on clusters with several hundred GPUs. Comparative analysis shows our implementation outperforms state-of-the-art quantum chemistry software by over 3.5 times in speed while achieving an 8-fold reduction in computational power consumption. Benchmarking on the Perlmutter supercomputer, our algorithm achieves 11.8 PFLOP/s (83% of peak performance) performing and the RI-MP2 energy calculation on a 314-water cluster with 7850 primary and 30,144 auxiliary basis functions in 4 min on 180 nodes and 720 A100 GPUs. This performance represents a substantial improvement over traditional CPU-based methods, demonstrating significant time-to-solution and power consumption benefits of leveraging modern GPU-accelerated computing environments for quantum chemistry calculations.

NINJ1 regulates plasma membrane fragility under mechanical tension.

Plasma membrane integrity is vital not only for cell survival but also nearly all aspects of cell functioning. Mechanical stress can cause plasma membrane damage, but it is not known whether there are large molecules (proteins) that control plasma membrane integrity. Here we constructed a 384-well cellular stretch system that delivers precise, reproducible mechanical strain to adherent cells. Using the system, we screened 10,843 siRNAs targeting 2,726 multi-pass transmembrane proteins for stretch-induced membrane permeability changes. The screen identified NINJ1, a protein recently proposed to regulate pyroptosis and other lytic cell death, as the top hit. We demonstrate that NINJ1 is a critical regulator for mechanical force-induced plasma membrane rupture (PMR), without the need of stimulating any cell death programs. Low NINJ1 expression renders the membrane more resistant to stretching, while high expression of NINJ1 lowers the threshold of PMR under mechanical strain. NINJ1 level on the plasma membrane is inversely correlated to tension required to rupture the membrane. In the pyroptosis context, NINJ1 on its own is not sufficient to fully rupture the membrane, and additional mechanical stress is required for full PMR. Our work establishes that NINJ1 functions as a bona fide determinant of membrane biomechanical properties. Our study also suggests that PMR across tissues of distinct mechanical environments is subjected to fine tuning by differences in NINJ1 expression and external mechanical forces.

Thawed Gaussian Wavepacket Dynamics with Δ-Machine-Learned Potentials.

A method for performing variable-width (thawed) Gaussian wavepacket (GWP) variational dynamics on machine-learned potentials is presented. Instead of fitting the potential energy surface (PES), the anharmonic correction to the global harmonic approximation (GHA) is fitted using kernel ridge regression─this is a Δ-machine learning approach. The training set consists of energy differences between ab initio electronic energies and values given by the GHA. The learned potential is subsequently used to propagate a single thawed GWP by using the time-dependent variational principle to compute the autocorrelation function, which provides direct access to vibronic spectra via its Fourier transform. We applied the developed method to simulate the photoelectron spectrum of ammonia and found excellent agreement between theoretical and experimental spectra. We show that fitting the anharmonic corrections requires a smaller training set as compared to fitting total electronic energies. We also demonstrate that our approach allows to reduce the dimensionality of the nuclear space used to scan the PES when constructing the training set. Thus, only the degrees of freedom associated with large-amplitude motions need to be treated with Δ-machine learning, which paves a way for reliable simulations of vibronic spectra of large floppy molecules.

Isolation and purification of DNA double-strand break repair intermediates for understanding complex molecular mechanisms.

Branched DNA molecules are key intermediates in the molecular pathways of DNA replication, repair and recombination. Understanding their structural details, therefore, helps to envisage the mechanisms underlying these processes. While the configurations of DNA molecules can be effectively analysed in bulk using gel electrophoresis techniques, direct visualization provides a complementary single-molecule approach to investigating branched DNA structures. However, for microscopic examination, the sample needs to be free from impurities that could obscure the molecules of interest, and free from the bulk of unwanted non-specific DNA molecules that would otherwise dominate the field of view. Additionally, in the case of recombination intermediates, the length of the DNA molecules becomes an important factor to consider since the structures can be spread over a large distance on the chromosome in vivo. As a result, apart from sample purity, efficient isolation of large-sized DNA fragments without damaging their branched structures is crucial for further analysis. These factors are illustrated by the example of DNA double-strand break repair in the bacterium E. coli. In E. coli recombination intermediates may be spread over a distance of 40 kb which constitutes less than 1% of the 4.6 Mb genome. This study reveals ways to overcome some of the technical challenges that are associated with the isolation and purification of large and complex branched DNA structures using E. coli DNA double-strand break repair intermediates. High-molecular weight and branched DNA molecules do not run into agarose gels subjected to electrophoresis. However, they can be extracted from the wells of the gels if they are agarose embedded, by using β-agarase digestion, filtration, and concentration. Furthermore, a second round of gel electrophoresis followed by purification is recommended to enhance the purity of the specific DNA samples. These preliminary findings may prove to be pioneering for various single-molecule analyses of large and complex DNA molecules of DNA replication, repair and recombination.

Physicochemical Properties of Seed Oil Blends and Their Potential for the Creation of Synthetic Oleosomes with Modulated Polarities.

There is an increasing demand within the pharmaceutical and cosmetic industries for biofriendly lipid-based active ingredient delivery systems. Micelles, liposomes, and lipid nanoparticles are currently the most used systems despite their limitations. Oleosomes, also known as lipid droplets, are promising alternatives to the existing strategies. Oleosomes are typically found in plant cells and are characterized by a nonpolar triacylglycerol core surrounded by a phospholipid monolayer punctuated with the protein oleosin. Producing oleosomes synthetically allows the customization of their lipid content, size, protein content, and oil core characteristics, expanding their versatility. Herein we demonstrate a proof of concept for the use of synthetic oleosomes to sequester polar molecules by modulating their core polarity with blends of sunflower and castor oils. The physical properties (density, refractive index, and permittivity) of the oil blends are characterized and demonstrate ideal mixing of the oils, which is supported by molecular dynamics simulations. Spectroscopic examination of the oil blends using fluorescent probes shows that the polarity of oil blends increases as the fraction of castor oil increases. Finally, we show that the uptake of a polar fluorescent probe (NBD-glucose) into synthetic oleosomes is enhanced by increasing the polarity of the oil core, but large charged molecules are excluded from the core regardless of polarity. These experiments show that synthetic oleosomes with tunable oil cores can expand the range of molecules that can be loaded into a biofriendly package as desired for biotechnology applications.

Enhancing density-functional theory for static correlation in large molecules

Enhancing density-functional theory for static correlation in large molecules

A cobalt coordination complex binds on a unique binding site between domain-I and domain-III of serum albumin

Serum albumin binding influences physiological effects and pharmacokinetics of drugs. Major binding sites in serum albumin are sudlow sites I and II, located on domains IIA and IIIA, respectively. However, groove between domain I and III as a binding site is not well studied. In this work, we studied the binding of a cobalt coordination complex (DCoP) with bovine serum albumin (BSA) using multi-spectroscopic methods and molecular docking. DCoP strongly interacts with BSA with association constant of ∼7 × 105M−1. The binding was found to significantly alter protein's conformation and unfolding pathway. Results of this study shows that DCoP binds at the groove formed between domains III and I. We conclude that the groove is a rare binding site for large molecules and coordination complexes. The knowledge of unique binding site of serum albumin enlightens the comprehensive role of protein as a carrier biomacromolecule.

Optical, Photophysical, and Electroemission Characterization of Blue Emissive Polymers as Active Layer for OLEDs.

Polymers containing π-conjugated segments are a diverse group of large molecules with semiconducting and emissive properties, with strong potential for use as active layers in Organic Light-Emitting Diodes (OLEDs). Stable blue-emitting materials, which are utilized as emissive layers in solution-processed OLED devices, are essential for their commercialization. Achieving balanced charge injection is challenging due to the wide bandgap between the HOMO and LUMO energy levels. This study examines the optical and photophysical characteristics of blue-emitting polymers to contribute to the understanding of the fundamental mechanisms of color purity and its stability during the operation of OLED devices. The investigated materials are a novel synthesized lab scale polymer, namely poly[(2,7-di(p-acetoxystyryl)-9-(2-ethylhexyl)-9H-carbazole-4,4'-diphenylsulfone)-co-poly(2,6-diphenylpyrydine-4,4'-diphenylsulfone] (CzCop), as well as three commercially supplied materials, namely Poly(9,9-di-n-octylfluorenyl-2,7-diyl) (PFO), poly[9,9-bis(2'-ethylhexyl) fluorene-2,7-diyl] (PBEHF), and poly (9,9-n-dihexyl-2,7-fluorene-alt-9-phenyl-3,6-carbazole) (F6PC). The materials were compared to evaluate their properties using Spectroscopic Ellipsometry, Photoluminescence, and Atomic Force Microscopy (AFM). Additionally, the electrical characteristics of the OLED devices were investigated, as well as the stability of the electroluminescence emission spectrum during the device's operation. Finally, the determined optical properties, combined with their photo- and electro-emission characteristics, provided significant insights into the color stability and selectivity of each material.

Two-Photon Absorption Strengths of Small Molecules: Reference CC3 Values and Benchmarks.

We present a large dataset of highly accurate two-photon transition strengths (δTPA) determined for standard small molecules. Our reference values have been calculated using the quadratic response implementation of the third-order coupled cluster method including iterative triples (Q-CC3). The aug-cc-pVTZ atomic basis set is used for molecules with up to five non-hydrogen atoms, while larger molecules are assessed with aug-cc-pVDZ; the differences due to the basis sets are discussed. This dataset, encompassing 82 singlet transitions of various characters (Rydberg, valence, and double excitations), enables a comprehensive benchmark of smaller basis sets and alternative wavefunction methods when Q-CC3 calculations become beyond reach as well as time-dependent density functional theory (TD-DFT) approaches. The evaluated wavefunction methods include quadratic response and equation-of-motion CCSD approximations, Q-CC2, and second-order algebraic diagrammatic construction in its intermediate state representation (I-ADC2). In the TD-DFT framework, a set of five commonly used exchange-correlation functionals are evaluted. This extensive analysis provides a quantitative assessment of these methods, revealing how different system sizes, response intensities, and types of transitions affect their performances.

Accurate Geometries of Large Molecules at DFT Cost by Semiexperimental and Coupled Cluster Templating Fragments.

Accurate geometries of small semirigid molecules in the gas phase are available thanks to high-resolution spectroscopy and accurate quantum chemical approaches. These results can be employed for validating cheaper low-level quantum chemical models or correcting the corresponding structures of large molecules. On these grounds, in this work, a large panel of semiexperimental equilibrium structures already available in the literature is used to confirm the average error (1 mÅ for bond lengths and 2 mrad for valence angles) of a version of the Pisa composite schemes (PCS2), which is applicable to molecules containing up to about 20 atoms. Then, the geometries of 30 additional medium-sized systems were optimized at the PCS2 level to cover a more balanced chemical space containing moieties poorly represented in SE compilations. The final database is available on a public domain Web site (https://www.skies-village.it/databases/) and can be employed for correcting structures of larger molecules obtained by hybrid or double-hybrid density functionals in the framework of the templating molecule approach. Several examples show that corrections based on the structures of building blocks taken from this database reduce the error of the B3LYP geometrical parameters of large molecules by nearly an order of magnitude without increasing the computational cost. Furthermore, the results of different density functional theory (DFT) or wave function (e.g., MP2) models can be improved in the same way by simply computing both the whole molecule and suitable building blocks at the chosen level. Then, whenever reference structures of some building blocks containing up to about 20 atoms are not available, they can be purposely optimized at the PCS2 level by employing reasonable computer resources. Therefore, a new DFT-cost tool is now available for the accurate characterization of large molecules by experiment-oriented scientists.