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Register a new userStructural changes in barley protein LTP1 isoforms at air-water interfaces
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We use a coarse-grained model to study the conformational changes in two barley proteins, LTP1 and its ligand adduct isoform LTP1b, that result from their adsorption to the air-water interface. The model introduces the interface through hydropathy indices. We justify the model by all-atom simulations. The choice of the proteins is motivated by making attempts to understand formation and stability of foam in beer. We demonstrate that both proteins flatten out at the interface and can make a continuous stabilizing and denser film. We show that the degree of the flattening depends on the protein -- the layers of LTP1b should be denser than those of LTP1 -- and on the presence of glycation. It also depends on the number ($le 4$) of the disulfide bonds in the proteins. The geometry of the proteins is sensitive to the specificity of the absent bonds. We provide estimates of the volume of cavities of the proteins when away from the interface.
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We study the behavior of five proteins at the air-water and oil-water interfaces by all-atom molecular dynamics. The proteins are found to get distorted when pinned to the interface. This behavior is consistent with the phenomenological way of introducing the interfaces in a coarse-grained model through a force that depends on the hydropathy indices of the residues. Proteins couple to the oil-water interface stronger than to the air- water one. They diffuse slower at the oil-water interface but do not depin from it, whereas depinning events are observed at the other interface. The reduction of the disulfide bonds slows the diffusion down.
We consider multi-chain protein native structures and propose a criterion that determines whether two chains in the system are entangled or not. The criterion is based on the behavior observed by pulling at both temini of each chain simultaneously in the two chains. We have identified about 900 entangled systems in the Protein Data Bank and provided a more detailed analysis for several of them. We argue that entanglement enhances the thermodynamic stability of the system but it may have other functions: burying the hydrophobic residues at the interface, and increasing the DNA or RNA binding area. We also study the folding and stretching properties of the knotted dimeric proteins MJ0366, YibK and bacteriophytochrome. These proteins have been studied theoretically in their monomer
Normal mode analysis offers an efficient way of modeling the conformational flexibility of protein structures. Simple models defined by contact topology, known as elastic network models, have been used to model a variety of systems, but the validation is typically limited to individual modes for a single protein. We use anisotropic displacement parameters from crystallography to test the quality of prediction of both the magnitude and directionality of conformational variance. Normal modes from four simple elastic network model potentials and from the CHARMM forcefield are calculated for a data set of 83 diverse, ultrahigh resolution crystal structures. While all five potentials provide good predictions of the magnitude of flexibility, the methods that consider all atoms have a clear edge at prediction of directionality, and the CHARMM potential produces the best agreement. The low-frequency modes from different potentials are similar, but those computed from the CHARMM potential show the greatest difference from the elastic network models. This was illustrated by computing the dynamic correlation matrices from different potentials for a PDZ domain structure. Comparison of normal mode results with anisotropic temperature factors opens the possibility of using ultrahigh resolution crystallographic data as a quantitative measure of molecular flexibility. The comprehensive evaluation demonstrates the costs and benefits of using normal mode potentials of varying complexity. Comparison of the dynamic correlation matrices suggests that a combination of topological and chemical potentials may help identify residues in which chemical forces make large contributions to intramolecular coupling.
The formation of smart emulsions or foams whose stability can be controlled on-demand by switching external parameters is of great interest for basic research and applications. An emerging group of smart stabilizers are microgels, which are nano- and micro-sized, three-dimensional polymer networks that are swollen by a good solvent. In the last decades, the influence of various external stimuli on the two-dimensional phase behavior of microgels at air- and oil-water interfaces has been studied. However, the impact of the top-phase itself has been barely considered. Here, we present data that directly address the influence of the top-phase on the microgel properties at interfaces. The dimensions of pNIPAM microgels are measured after deposition from two interfaces, i.e., air- and decane-water. While the total in-plane size of the microgel increases with increasing interfacial tension, the portions or fractions of the microgels situated in the aqueous phase are not affected. We correlate the area microgels occupy to the surface tensions of the interfaces, which allows to estimate an elastic modulus. In comparison to nanoindentation measurements, we observe a larger elastic modulus for the microgels. By combining compression, deposition, and visualization, we show that the two-dimensional phase behavior of the microgel monolayers is not altered, although the microgels have a larger total in-plane size at higher interfacial tension. A peer reviewed and extended version of this preprint and the electronic supplementary information can be found under S.~Bochenek, A.~Scotti, W.~Richtering, textit{Soft Matter}, 2020, DOI: 10.1039/d0sm01774d.
Motivation: Bridging the exponentially growing gap between the number of unlabeled and labeled proteins, a couple of works have adopted semi-supervised learning for protein sequence modeling. They pre-train a model with a substantial amount of unlabeled data and transfer the learned representations to various downstream tasks. Nonetheless, the current pre-training methods mostly rely on a language modeling task and often show limited performances. Therefore, a complementary protein-specific task for pre-training is necessary to better capture the information contained within unlabeled protein sequences. Results: In this paper, we introduce a novel pre-training scheme called PLUS, which stands for Protein sequence representations Learned Using Structural information. PLUS consists of masked language modeling and a complementary protein-specific pre-training task, namely same family prediction. PLUS can be used to pre-train various model architectures. In this work, we mainly use PLUS to pre-train a recurrent neural network (RNN) and refer to the resulting model as PLUS-RNN. It advances state-of-the-art pre-training methods on six out of seven tasks, i.e., (1) three protein(-pair)-level classification, (2) two protein-level regression, and (3) two amino-acid-level classification tasks. Furthermore, we present results from our ablation studies and interpretation analyses to better understand the strengths of PLUS-RNN. Availability: The codes and pre-trained models are available at https://github.com/mswzeus/PLUS/
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