Dye-decolorizing peroxidases (DyPs), a type of heme-containing oxidoreductase enzymes, catalyze the peroxide-dependent oxidation of various industrial dyes as well as lignin and lignin model compounds. In our previous work, we have recently reported the crystal structures of class A-type DyP from Bacillus subtilis at pH 7.0 (BsDyP7), exposing the location of three binding sites for small substrates and high redox-potential substrates. The biochemical studies revealed the optimum acidic pH for enzyme activity. In the present study, the crystal structure of BsDyP at acidic pH (BsDyP4) reveals two-monomer units stabilized by intermolecular salt bridges and a hydrogen bond network in a homo-dimeric unit. Based on the monomeric structural comparison of BsDyP4 and BsDyP7, minor differences were observed in the loop regions, that is, LI (Ala64-Gln71), LII (Glu96-Lys108), LIII (Pro117-Leu124), and LIV (Leu295-Asp303). Despite these differences, BsDyP4 adopts similar heme architecture as well as three substrate-binding sites to BsDyP7. In BsDyP4, a shift in Asp187, heme pocket residue discloses the plausible reason for optimal acidic pH for BsDyP activity. This study provides insight into the structural changes in BsDyP at acidic pH, where BsDyP is biologically active.

The protein secondary structure (SS) prediction plays an important role in the characterization of general protein structure and function. In recent years, a new generation of algorithms for SS prediction based on embeddings from protein language models (pLMs) is emerging. These algorithms reach state-of-the-art accuracy without the need for time-consuming multiple sequence alignment (MSA) calculations. Long short-term memory (LSTM)-based SPOT-1D-LM and NetSurfP-3.0 are the latest examples of such predictors. We present the ProteinUnetLM model using a convolutional Attention U-Net architecture that provides prediction quality and inference times at least as good as the best LSTM-based models for 8-class SS prediction (SS8). Additionally, we address the issue of the heavily imbalanced nature of the SS8 problem by extending the loss function with the Matthews correlation coefficient, and by proper assessment using previously introduced adjusted geometric mean (AGM) metric. ProteinUnetLM achieved better AGM and sequence overlap score than LSTM-based predictors, especially for the rare structures 310-helix (G), beta-bridge (B), and high curvature loop (S). It is also competitive on challenging datasets without homologs, free-modeling targets, and chameleon sequences. Moreover, ProteinUnetLM outperformed its previous MSA-based version ProteinUnet2, and provided better AGM than AlphaFold2 for 1/3 of proteins from the CASP14 dataset, proving its potential for making a significant step forward in the domain. To facilitate the usage of our solution by protein scientists, we provide an easy-to-use web interface under https://biolib.com/SUT/ProteinUnetLM/.

Interactions between the meiosis-expressed gene 1 (MEIG1) and Parkin co-regulated gene (PACRG) protein are critical in the formation of mature sperm cells. Targeting either MEIG1 or PACRG protein could be a contraceptive strategy. The W50A and Y68A mutations on MEIG1 are known to interrupt the MEIG1-PACRG interactions resulting in defective sperm cells. However, the details about how the mutants disrupt the protein-protein binding are not clear. In this study, we reveal insights on MEIG1 and PACRG protein dynamics by applying Gaussian-accelerated molecular dynamics (GaMD) simulations and post-GaMD analysis. Our results show that the mutations destabilize the protein-protein interfacial interaction. The effect of the Y68A mutation is more significant than W50A as Y68 forms stronger polar interactions with PACRG. Because both human and mouse models demonstrate similar dynamic properties, the findings from mouse proteins can be applied to the human system. Moreover, we report a potential ligand binding pocket on the MEIG1 and PACRG interaction surface that could be a target for future drug design to inhibit the MEIG1-PACRG interaction. PACRG shows more qualified pockets along the protein-protein interface, implying that it is a better target than MEIG1. Our work provides a fundamental understanding of MEIG1 and PACRG protein dynamics, paving the way for drug discovery in male-based contraception.

Escherichia coli NfsA and NfsB are founding members of two flavoprotein families that catalyze the oxygen-insensitive reduction of nitroaromatics and quinones by NAD(P)H. This reduction is required for the activity of nitrofuran antibiotics and the enzymes have also been proposed for use with nitroaromatic prodrugs in cancer gene therapy and biocatalysis, but the roles of the proteins in vivo in bacteria are not known. NfsA is NADPH-specific whereas NfsB can also use NADH. The crystal structures of E. coli NfsA and NfsB and several analogs have been determined previously. In our crystal trials, we unexpectedly observed NfsA bound to fumarate. We here present the X-ray structure of the E. coli NfsA-fumarate complex and show that fumarate acts as a weak inhibitor of NfsA but not of NfsB. The structural basis of this differential inhibition is conserved in the two protein families and occurs at fumarate concentrations found in vivo, so impacting the efficacy of these proteins.

Heparin and heparan sulfate are important glycosaminoglycans that can regulate the activities of many vital proteins, especially the fibroblast growth factor (FGF) family. Because FGF7 (KGF) has an important role in tissue repair and maintaining the integrity of the mucosal barrier, recombinant human keratinocyte growth factor (rhKGF, palifermin) has been approved for the treatment of wound healing and oral cavity. Due to heparin plays an important role in the KGF signaling pathway, a more detailed study of the drug-drug interactions (DDIs) between rhKGF and heparin at the atomic level and investigating their synergistic effect on each other in terms of biology, especially in silico, is necessary for a better understanding of DDIs. In this study, DDIs between rhKGF and low-molecular weight heparin types (LMWH) were investigated. In this regard, scrutiny of the influence of the synergistic heparin types on the structure and biostability of rhKGF is accomplished using computational methods such as molecular docking and molecular dynamic simulations (MDs). Subsequently, the motion behavior of rhKGF in interaction with LMWHs was evaluated based on eigenvectors by using principal component analysis (PCA). Also, the binding free energies of rhKGF-LMWH complexes were calculated by the molecular mechanics/Poisson-Boltzmann surface area (MM-BPSA) method. The result showed that rhKGF-idraparinux (-6.9 kcal/mol) and rhKGF-heparin (-6.0 kcal/mol) complexes had significant binding affinity as well as they had a more stable binding to rhKGF than to other LMWH during 100 ns simulation. However, in order to confirm the curative effect of these drugs, clinical trials must be done.

Styrene is a nonpolar organic compound used in very high volume for the industrial scale production of commercially important polymers such as polystyrene resins as well as copolymers like acrylonitrile butadiene styrene, latex, and rubber. These resins are widely used in the manufacturing of various products including single-use plastics such as disposable cups and containers, protective packaging, heat insulation, and so forth. The large-scale utilization leads to the over-accumulation of styrene waste in the environment causing deleterious health risks including cancer, neurological impairment, dysbiosis of central nervous system, and respiratory problems. To eliminate the accumulating waste. Microbial enzyme-based system represents the most environmental friendly and sustainable approach for elimination of styrene waste. However, comprehensive understanding of the enzyme-substrate interaction and associated pathways would be crucial for developing large-scale disposal systems. This study aims to understand the molecular interaction between the protein-ligand complexes of the styrene catabolic reactions by bacterial enzymes of sty operon. Molecular docking analysis for catalytic enzymes namely, styrene monooxygenase (SMO), styrene oxide isomerase (SOI), and phenylacetaldehyde dehydrogenase (PAD) of the bacterial sty operon was carried out with their individual substrates, that is, styrene, styrene oxide, and phenylacetic acid, respectively. The binding energy, amino acids forming binding cavity, and binding interactions between the protein-ligand binding sites were calculated for each case. The obtained binding energies showed a stable association of these complexes indicating the future scope of their utilization for large-scale bioremediation of styrene, and its commercially used polymers and copolymers.

The VPS13 protein family constitutes a novel class of bridge-like lipid transferases. Autosomal recessive inheritance of mutations in VPS13 genes is associated with the development of neurodegenerative diseases in humans. Bioinformatic approaches previously recognized the domain architecture of these proteins. In this study, we model the first ever full-length structures of the four human homologs VPS13A, VPS13B, VPS13C, and VPS13D in association with model membranes, to investigate their lipid transfer ability and potential structural association with membrane leaflets. We analyze the evolutionary conservation and physicochemical properties of these proteins, focusing on conserved C-terminal amphipathic helices that disturb organelle surfaces and that, adjoined, resemble a traditional Venetian gondola. The gondola domains share significant structural homology with lipid droplet surface-binding proteins. We introduce in silico protein-membrane models displaying the mode of association of VPS13A, VPS13B, VPS13C, and VPS13D to donor and target membranes, and present potential models of action for protein-mediated lipid transfer.

Transport of newly synthesized proteins from endoplasmic reticulum (ER) to Golgi is mediated by coat protein complex II (COPII). The assembly and disassembly of COPII vesicles is regulated by the molecular switch Sar1, which is a small GTPase and a component of COPII. Usually a small GTPase binds GDP (inactive form) or GTP (active form). Mammals have two Sar1 isoforms, Sar1a and Sar1b, that have approximately 90% sequence identity. Some experiments demonstrated that these two isoforms had distinct but overlapping functions. Here we found another instance of differing behavior: the alarmone ppGpp could bind to and inhibit the GTPase activity of human Sar1a but could not inhibit the GTPase activity of human Sar1b. The crystal structures of Sar1a⋅ppGpp and Sar1b⋅GDP have been determined. Superposition of the structures shows that ppGpp binds to the nucleotide-binding pocket, its guanosine base, ribose ring and 5'-diphosphate occupying nearly the same positions as for GDP. However, its 3'-diphosphate points away from the active site and, hence, away from the surface of the protein. The overall structure of Sar1a⋅ppGpp is more similar to Sar1b⋅GDP than to Sar1b⋅GTP. We also find that the Asp140-Arg138-water-ligand interaction net is important for the binding of ppGpp to Sar1a. This study provides further evidence showing that there are biochemical differences between the Sar1a and Sar1b isoforms of Sar1.

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins. However, exploring the flexibility of DNA-binding proteins and Coronavirus proteins through experiments or calculations is a difficult process. Since protein dihedral rotational motion can be used to predict protein structural changes, it provides key information about protein local conformation. Therefore, this paper introduces a method to improve the accuracy of protein flexibility prediction, DihProFle (Prediction of DNA-binding proteins and Coronavirus proteins flexibility introduces the calculated dihedral Angle information). Based on protein dihedral Angle information, protein evolution information, and amino acid physical and chemical properties, DihProFle realizes the prediction of protein flexibility in two cases on DNA-binding proteins and Coronavirus proteins, and assigns flexibility class to each protein sequence position. In this study, compared with the flexible prediction using sequence evolution information, and physicochemical properties of amino acids, the flexible prediction accuracy based on protein dihedral Angle information, sequence evolution information and physicochemical properties of amino acids improved by 2.2% and 3.1% in the nonstrict and strict conditions, respectively. And DihProFle achieves better performance than previous methods for protein flexibility analysis. In addition, we further analyzed the correlation of amino acid properties and protein dihedral angles with residues flexibility. The results show that the charged hydrophilic residues have higher proportion in the flexible region, and the rigid region tends to be in the angular range of the protein dihedral angle (such as the ψ angle of amino acid residues is more flexible than rigid in the range of 91°-120°). Therefore, the results indicate that hydrophilic residues and protein dihedral angle information play an important role in protein flexibility.