Molecular crowding facilitates assembly of spidroin-like proteins through phase separation

Gaining insights into the processes that transform dispersed biopolymers into well-ordered structures, such as soluble spidroin-proteins to spider silk threads, is essential for attempts to understand their biological function and to mimic their unique properties. One of these processes is liquid-liquid phase separation, which can act as an intermediate step for molecular assembly. We have shown that a self-coacervation step that occurs at a very high protein concentration (> 200 gl(-1)) is crucial for the fiber assembly of an engineered triblock silk-like molecule. In this study, we demonstrate that the addition of a crowding agent lowers the concentration at which coacervation occurs by almost two orders of magnitude. Coacervates induced by addition of a crowding agent are functional in terms of fiber formation, and the crowding agent appears to affect the process solely by increasing the effective concentration of the protein. Furthermore, induction at lower concentrations allows us to study the thermodynamics of the system, which provides insights into the coacervation mechanism. We suggest that this approach will be valuable for studies of biological coacervating systems in general.Peer reviewe

Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning

Multiclass classification is of great interest for various applications, for example, it is a common task in computer vision, where one needs to categorize an image into three or more classes. Here we propose a quantum machine learning approach based on quantum convolutional neural networks for solving the multiclass classification problem. The corresponding learning procedure is implemented via TensorFlowQuantum as a hybrid quantum-classical (variational) model, where quantum output results are fed to the softmax activation function with the subsequent minimization of the cross entropy loss via optimizing the parameters of the quantum circuit. Our conceptional improvements here include a new model for a quantum perceptron and an optimized structure of the quantum circuit. We use the proposed approach to solve a 4-class classification problem for the case of the MNIST dataset using eight qubits for data encoding and four ancilla qubits; previous results have been obtained for 3-class classification problems. Our results show that the accuracy of our solution is similar to classical convolutional neural networks with comparable numbers of trainable parameters. We expect that our findings will provide a new step toward the use of quantum neural networks for solving relevant problems in the NISQ era and beyond

Improving the performance of fermionic neural networks with the Slater exponential Ansatz

In this work, we propose a technique for the use of fermionic neural networks (FermiNets) with the Slater exponential Ansatz for electron-nuclear and electron-electron distances, which provides faster convergence of target ground-state energies due to a better description of the interparticle interaction in the vicinities of the coalescence points. Analysis of learning curves indicates on the possibility to obtain accurate energies with smaller batch sizes using arguments of the bagging approach. In order to obtain even more accurate results for the ground-state energies, we suggest an extrapolation scheme, which estimates Monte Carlo integrals in the limit of an infinite number of points. Numerical tests for a set of molecules demonstrate a good agreement with the results of original FermiNets (achieved with larger batch sizes than required by our approach) and with results of coupled-cluster singles and doubles with perturbative triples (CCSD(T)) method, calculated in the complete basis set (CBS) limit.Comment: 16 pages, 2 figures, 1 tabl

Enabling Precision Agriculture Through Embedded Sensing With Artificial Intelligence

Artificial intelligence (AI) has smoothly penetrated in a number of monitoring and control applications, including agriculture. However, research efforts toward low-power sensing devices with fully functional AI on board are still fragmented. In this article, we present an embedded system enriched with AI, ensuring the continuous analysis and in situ prediction of the growth dynamics of plant leaves. The embedded solution is grounded on a low-power embedded sensing system with a graphics processing unit (GPU) and is able to run the neural network-based AI on board. We use a recurrent neural network (RNN) called the long short-term memory network (LSTM) as a core of AI in our system. The proposed approach guarantees the system autonomous operation for 180 days using a standard Li-ion battery. We rely on the state-of-the-art mobile graphical chips for “smart” analysis and control of autonomous devices. This pilot study opens up wide vista for a variety of intelligent monitoring applications, especially in the agriculture domain. In addition, we share with the research community the Tomato Growth data set

Size-Dependent Biodistribution of Fluorescent Furano-Allocolchicinoid-Chitosan Formulations in Mice

The aim of this study was to compare the biodistribution in mice of functionalized rhodamine B (Rh) labeled colchicine derivative furano-allocolchicinoid (AC, 6) either conjugated to 40 kDa chitosan (AC-Chi, 8) or encapsulated into chitosan nanoparticles (AC-NPs). AC-NPs were formed by ionotropic gelation and were 400–450 nm in diameter as estimated in mice by dynamic light scattering and confocal microscopy. AC-Chi and AC-NPs preserved the specific colchicine activity in vitro. AC preparations were once IV injected into C75BL/6 mice; muscles, spleen, kidney, liver, lungs, blood cells and serum were collected at 30 min, 2, 5, 10, and 20 h post injection. To analyze the distribution of the furano-allocolchicinoid preparations in body liquids and tissues, Rh was measured directly in sera or extracted by acidic ethanol from tissue homogenates. Preliminary Rh extraction rate was estimated in vitro in tissue homogenates and was around 25–30% from total quantity added. After in vivo injection, AC-NPs were accumulated more in liver and spleen, while less in kidney and lungs in comparison with free AC and AC-Chi. Therefore, incorporation of colchicine derivatives as well as other hydrophobic substances into nano/micro sized carriers may help redistribute the drug to different organs and, possibly, improve antitumor accumulation

Analyzing the weak dimerization of a cellulose binding module by analytical ultracentrifugation

Cellulose binding modules (CBMs) are found widely in different proteins that act on cellulose. Because they allow a very easy way of binding recombinant proteins to cellulose, they have become widespread in many biotechnological applications involving cellulose. One commonly used variant is the CBMCipA from Clostridium thermocellum. Here we studied the oligomerization behavior of CBMCipA, as such solution association may have an impact on its use. As the principal approach, we used sedimentation velocity and sedimentation equilibrium analytical ultracentrifugation. To enhance our understanding of the possible interactions, we used molecular dynamics simulations. By analysis of the sedimentation velocity data by a discrete model genetic algorithm and by building a binding isotherm based on weight average sedimentation coefficient and by global fitting of sedimentation equilibrium data we found that the CBMCipA shows a weak dimerization interaction with a dissociation constant KD of 90 ± 30 μM. As the KD of CBMCipA binding to cellulose is below 1 μM, we conclude that the dimerization is unlikely to affect cellulose binding. However, at high concentrations used in some applications of the CBMCipA, its dimerization is likely to have a marked effect on its solution behavior.Peer reviewe

Self-assembly of silk-like protein into nanoscale bicontinuous networks under phase separation conditions

Liquid–liquid phase separation of biomacromolecules is crucial in various inter- and extracellular biological functions. This includes formation of condensates to control, e.g., biochemical reactions and structural assembly. The same phenomenon is also found to be critically important in protein-based high-performance biological materials. Here, we use a well-characterized model triblock protein system to demonstrate the molecular level formation mechanism and structure of its condensate. Large-scale molecular modeling supported by analytical ultracentrifuge characterization combined with our earlier high magnification precision cryo-SEM microscopy imaging leads to deducing that the condensate has a bicontinuous network structure. The bicontinuous network rises from the proteins having a combination of sites with stronger mutual attraction and multiple weakly attractive regions connected by flexible, multiconfigurational linker regions. These attractive sites and regions behave as stickers of varying adhesionstrength. For the examined model triblock protein construct, the β-sheet-rich end units are the stronger stickers, while additional weaker stickers, contributing to the condensation affinity, rise from spring-like connections in the flexible middle region of the protein. The combination of stronger and weaker sticker-like connections and the flexible regions between the stickers result in a versatile, liquid-like, self-healing structure. This structure also explains the high flexibility, easy deformability, and diffusion of the proteins, decreasing only 10–100 times in the bicontinuous network formed in the condensate phase in comparison to dilute protein solution. The here demonstrated structure and condensation mechanism of a model triblock protein construct via a combination of the stronger binding regions and the weaker, flexible sacrificial-bond-like network as well as its generalizability via polymer sticker models provide means to not only understand intracellular organization, regulation, and cellular function but also to identify direct control factors for and to enable engineering improved protein and polymer constructs to enhance control of advanced fiber materials, smart liquid biointerfaces, or self-healing matrices for pharmaceutics or bioengineering materials.Peer reviewe

pH dependence of the assembly mechanism and properties of poly(l-lysine) and poly(l-glutamic acid) complexes

Funding Information: This work is supported by the National Science Centre, Poland (grant no. 2018/31/D/ST5/01866) (P. Ba.), the Academy of Finland through its Centres of Excellence Programme (2022-2029, LIBER) under project no. 346111 (M. S.) and 346105 (M. L.) and project no. 309324 (M. S.), Novo Nordisk Foundation under project no. NNF22OC0074060 (M. S.), Finnish Cultural Foundation (T. K.), and U.S. National Science Foundation under grant no. 1905732 (J. L. L.). We are grateful for the support by FinnCERES Materials Bioeconomy Ecosystem. M. Morga thanks the European Union Erasmus+ programme (project no. 2019-1-PL01-KA103-061592) for providing financial support for the mobility and training in Aalto University, Finland. Computational resources by CSC IT Centre for Finland, Poland's high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH), grant no. PLG/2023/016229, and RAMI – RawMatters Finland Infrastructure are also gratefully acknowledged. Publisher Copyright: © 2023 The Royal Society of Chemistry.We show by extensive experimental characterization combined with molecular simulations that pH has a major impact on the assembly mechanism and properties of poly(l-lysine) (PLL) and poly(l-glutamic acid) (PGA) complexes. A combination of dynamic light scattering (DLS) and laser Doppler velocimetry (LDV) is used to assess the complexation, charge state, and other physical characteristics of the complexes, isothermal titration calorimetry (ITC) is used to examine the complexation thermodynamics, and circular dichroism (CD) is used to extract the polypeptides’ secondary structure. For enhanced analysis and interpretation of the data, analytical ultracentrifugation (AUC) is used to define the precise molecular weights and solution association of the peptides. Molecular dynamics simulations reveal the associated intra- and intermolecular binding changes in terms of intrinsic vs. extrinsic charge compensation, the role of hydrogen bonding, and secondary structure changes, aiding in the interpretation of the experimental data. We combine the data to reveal the pH dependency of PLL/PGA complexation and the associated molecular level mechanisms. This work shows that not only pH provides a means to control complex formation but also that the associated changes in the secondary structure and binding conformation can be systematically used to control materials assembly. This gives access to rational design of peptide materials via pH control.Peer reviewe

Synthesis of Chlorin-(Arylamino)quinazoline Hybrids as Models for Multifunctional Drug Development

A series of multifunctional conjugates each consisting of a fluorescent chlorin photosensitizer and an (arylamino)quinazoline-based epidermal growth factor receptor/vascular endothelial growth factor receptor ligand, potentially useful in site-selective photodynamic antitumor therapy, were prepared and their photochemical properties were investigated