Gating mechanisms of voltage-gated proton channels.

Hv1 is a voltage-gated proton-selective channel that plays critical parts in host defense, sperm motility, and cancer progression. Hv1 contains a conserved voltage-sensor domain (VSD) that is shared by a large family of voltage-gated ion channels, but it lacks a pore domain. Voltage sensitivity and proton conductivity are conferred by a unitary VSD that consists of four transmembrane helices. The architecture of Hv1 differs from that of cation channels that form a pore in the center among multiple subunits (as in most cation channels) or homologous repeats (as in voltage-gated sodium and calcium channels). Hv1 forms a dimer in which a cytoplasmic coiled coil underpins the two protomers and forms a single, long helix that is contiguous with S4, the transmembrane voltage-sensing segment. The closed-state structure of Hv1 was recently solved using X-ray crystallography. In this article, we discuss the gating mechanism of Hv1 and focus on cooperativity within dimers and their sensitivity to metal ions.

Exploiting enzyme evolution for computational protein design.

Recent years have seen an explosion of interest in understanding the physicochemical parameters that shape enzyme evolution, as well as substantial advances in computational enzyme design. This review discusses three areas where evolutionary information can be used as part of the design process: (i) using ancestral sequence reconstruction (ASR) to generate new starting points for enzyme design efforts; (ii) learning from how nature uses conformational dynamics in enzyme evolution to mimic this process in silico; and (iii) modular design of enzymes from smaller fragments, again mimicking the process by which nature appears to create new protein folds. Using showcase examples, we highlight the importance of incorporating evolutionary information to continue to push forward the boundaries of enzyme design studies.

Lactylation prediction models based on protein sequence and structural feature fusion.

Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread attention due to the Warburg effect in tumor cells. In this work, we first design a natural language processing method to automatically extract the 3D structural features of Kla sites, avoiding potential biases caused by manually designed structural features. Then, we establish two Kla prediction frameworks, Attention-based feature fusion Kla model (ABFF-Kla) and EBFF-Kla, to integrate the sequence features and the structure features based on the attention layer and embedding layer, respectively. The results indicate that ABFF-Kla and Embedding-based feature fusion Kla model (EBFF-Kla), which fuse features from protein sequences and spatial structures, have better predictive performance than that of models that use only sequence features. Our work provides an approach for the automatic extraction of protein structural features, as well as a flexible framework for Kla prediction. The source code and the training data of the ABFF-Kla and the EBFF-Kla are publicly deposited at: https://github.com/ispotato/Lactylation_model.

The impact of farming on prehistoric culinary practices throughout Northern Europe.

To investigate changes in culinary practices associated with the arrival of farming, we analysed the organic residues of over 1,000 pottery vessels from hunter-gatherer-fisher and early agricultural sites across Northern Europe from the Lower Rhine Basin to the Northeastern Baltic. Here, pottery was widely used by hunter-gatherer-fishers prior to the introduction of domesticated animals and plants. Overall, there was surprising continuity in the way that hunter-gatherer-fishers and farmers used pottery. Both aquatic products and wild plants remained prevalent, a pattern repeated consistently across the study area. We argue that the rapid adaptation of farming communities to exploit coastal and lagoonal resources facilitated their northerly expansion, and in some cases, hunting, gathering, and fishing became the most dominant subsistence strategy. Nevertheless, dairy products frequently appear in pottery associated with the earliest farming groups often mixed with wild plants and fish. Interestingly, we also find compelling evidence of dairy products in hunter-gatherer-fisher Ertebølle pottery, which predates the arrival of domesticated animals. We propose that Ertebølle hunter-gatherer-fishers frequently acquired dairy products through exchange with adjacent farming communities prior to the transition. The continuity observed in pottery use across the transition to farming contrasts with the analysis of human remains which shows substantial demographic change through ancient DNA and, in some cases, a reduction in marine consumption through stable isotope analysis. We postulate that farmers acquired the knowledge and skills they needed to succeed from local hunter-gatherer-fishers but without substantial admixture.

DeepHomo2.0: improved protein-protein contact prediction of homodimers by transformer-enhanced deep learning.

Protein-protein interactions play an important role in many biological processes. However, although structure prediction for monomer proteins has achieved great progress with the advent of advanced deep learning algorithms like AlphaFold, the structure prediction for protein-protein complexes remains an open question. Taking advantage of the Transformer model of ESM-MSA, we have developed a deep learning-based model, named DeepHomo2.0, to predict protein-protein interactions of homodimeric complexes by leveraging the direct-coupling analysis (DCA) and Transformer features of sequences and the structure features of monomers. DeepHomo2.0 was extensively evaluated on diverse test sets and compared with eight state-of-the-art methods including protein language model-based, DCA-based and machine learning-based methods. It was shown that DeepHomo2.0 achieved a high precision of >70% with experimental monomer structures and >60% with predicted monomer structures for the top 10 predicted contacts on the test sets and outperformed the other eight methods. Moreover, even the version without using structure information, named DeepHomoSeq, still achieved a good precision of >55% for the top 10 predicted contacts. Integrating the predicted contacts into protein docking significantly improved the structure prediction of realistic Critical Assessment of Protein Structure Prediction homodimeric complexes. DeepHomo2.0 and DeepHomoSeq are available at http://huanglab.phys.hust.edu.cn/DeepHomo2/.

Freeprotmap: waiting-free prediction method for protein distance map.

BACKGROUND: Protein residue-residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue-residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed. RESULT: In this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue-residue distances in tens of milliseconds and has higher precision than the best structure prediction method. CONCLUSION: Several groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue-residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

Mutation of a distal gating residue modulates NADH binding in NADH:Quinone oxidoreductase from Pseudomonas aeruginosa PAO1.

Enzymes require flexible regions to adopt multiple conformations during catalysis. The mobile regions of enzymes include gates that modulate the passage of molecules in and out of the enzyme's active site. The enzyme PA1024 from Pseudomonas aeruginosa PA01 is a recently discovered flavin-dependent NADH:quinone oxidoreductase (NQO, EC 1.6.5.9). Q80 in loop 3 (residues 75-86) of NQO is ∼15 Å away from the flavin and creates a gate that seals the active site through a hydrogen bond with Y261 upon NADH binding. In this study, we mutated Q80 to glycine, leucine, or glutamate to investigate the mechanistic significance of distal residue Q80 in NADH binding in the active site of NQO. The UV-visible absorption spectrum reveals that the mutation of Q80 minimally affects the protein microenvironment surrounding the flavin. The anaerobic reductive half-reaction of the NQO-mutants yields a ≥25-fold increase in the Kd value for NADH compared to the WT enzyme. However, we determined that the kred value was similar in the Q80G, Q80L, and wildtype enzymes and only ∼25% smaller in the Q80E enzyme. Steady-state kinetics with NQO-mutants and NQO-WT at varying concentrations of NADH and 1,4-benzoquinone establish a ≤5-fold decrease in the kcat/KNADH value. Moreover, there is no significant difference in the kcat/KBQ (∼1 × 106 M-1s-1) and kcat (∼24 s-1) values in NQO-mutants and NQO-WT. These results are consistent with the distal residue Q80 being mechanistically essential for NADH binding to NQO with minimal effect on the quinone binding to the enzyme and hydride transfer from NADH to flavin.

A conserved tryptophan in the acylated segment of RTX toxins controls their ß2 integrin-independent cell penetration.

The acylated Repeats in ToXins (RTX) leukotoxins, the adenylate cyclase toxin (CyaA) or α-hemolysin (HlyA), bind ß2 integrins of leukocytes but also penetrate cells lacking these receptors. We show that the indoles of conserved tryptophans in the acylated segments, W876 of CyaA and W579 of HlyA, are crucial for ß2 integrin-independent membrane penetration. Substitutions of W876 by aliphatic or aromatic residues did not affect acylation, folding, or the activities of CyaA W876L/F/Y variants on cells expressing high amounts of the ß2 integrin CR3. However, toxin activity of CyaA W876L/F/Y on cells lacking CR3 was strongly impaired. Similarly, a W579L substitution selectively reduced HlyA W579L cytotoxicity towards cells lacking ß2 integrins. Intriguingly, the W876L/F/Y substitutions increased the thermal stability (Tm) of CyaA by 4 to 8 °C but locally enhanced the accessibility to deuteration of the hydrophobic segment and of the interface of the two acylated loops. W876Q substitution (showing no increase in Tm), or combination of W876F with a cavity-filling V822M substitution (this combination decreasing the Tm closer to that of CyaA), yielded a milder defect of toxin activity on erythrocytes lacking CR3. Furthermore, the activity of CyaA on erythrocytes was also selectively impaired when the interaction of the pyrrolidine of P848 with the indole of W876 was ablated. Hence, the bulky indoles of residues W876 of CyaA, or W579 of HlyA, rule the local positioning of the acylated loops and enable a membrane-penetrating conformation in the absence of RTX toxin docking onto the cell membrane by ß2 integrins.

PDB-BRE: A ligand-protein interaction binding residue extractor based on Protein Data Bank.

Proteins typically exert their biological functions by interacting with other biomolecules or ligands. The study of ligand-protein interactions is crucial in elucidating the biological mechanisms of proteins. Most existing studies have focused on analyzing ligand-protein interactions, and they ignore the additional situational of inserted and modified residues. Besides, the resources often support only a single ligand type and cannot obtain satisfied results in analyzing novel complexes. Therefore, it is important to develop a general analytical tool to extract the binding residues of ligand-protein interactions in complexes fully. In this study, we propose a ligand-protein interaction binding residue extractor (PDB-BRE), which can be used to automatically extract interacting ligand or protein-binding residues from complex three-dimensional (3D) structures based on the RCSB Protein Data Bank (RCSB PDB). PDB-BRE offers a notable advantage in its comprehensive support for analyzing six distinct types of ligands, including proteins, peptides, DNA, RNA, mixed DNA and RNA entities, and non-polymeric entities. Moreover, it takes into account the consideration of inserted and modified residues within complexes. Compared to other state-of-the-art methods, PDB-BRE is more suitable for massively parallel batch analysis, and can be directly applied for downstream tasks, such as predicting binding residues of novel complexes. PDB-BRE is freely available at http://bliulab.net/PDB-BRE.

iNucRes-ASSH: Identifying nucleic acid-binding residues in proteins by using self-attention-based structure-sequence hybrid neural network.

Interaction between proteins and nucleic acids is crucial to many cellular activities. Accurately detecting nucleic acid-binding residues (NABRs) in proteins can help researchers better understand the interaction mechanism between proteins and nucleic acids. Structure-based methods can generally make more accurate predictions than sequence-based methods. However, the existing structure-based methods are sensitive to protein conformational changes, causing limited generalizability. More effective and robust approaches should be further explored. In this study, we propose iNucRes-ASSH to identify nucleic acid-binding residues with a self-attention-based structure-sequence hybrid neural network. It improves the generalizability and robustness of NABR prediction from two levels: residue representation and prediction model. Experimental results show that iNucRes-ASSH can predict the nucleic acid-binding residues even when the experimentally validated structures are unavailable and outperforms five competing methods on a recent benchmark dataset and a widely used test dataset.

Interfacial residues in protein-protein complexes are in the eyes of the beholder.

Interactions between proteins are vital in almost all biological processes. The characterization of protein-protein interactions helps us understand the mechanistic basis of biological processes, thereby enabling the manipulation of proteins for biotechnological and clinical purposes. The interface residues of a protein-protein complex are assumed to have the following two properties: (a) they always interact with a residue of a partner protein, which forms the basis for distance-based interface residue identification methods, and (b) they are solvent-exposed in the isolated form of the protein and become buried in the complex form, which forms the basis for Accessible Surface Area (ASA)-based methods. The study interrogates this popular assumption by recognizing interface residues in protein-protein complexes through these two methods. The results show that a few residues are identified uniquely by each method, and the extent of conservation, propensities, and their contribution to the stability of protein-protein interaction varies substantially between these residues. The case study analyses showed that interface residues, unique to distance, participate in crucial interactions that hold the proteins together, whereas the interface residues unique to the ASA method have a potential role in the recognition, dynamics, and specificity of the complex and can also be a hotspot. Overall, the study recommends applying both distance and ASA methods so that some interface residues missed by either method but crucial to the stability, recognition, dynamics, and function of protein-protein complexes are identified in a complementary manner.

PCSK9 inhibitors as safer therapeutics for atherosclerotic cardiovascular disease (ASCVD): Pharmacophore design and molecular dynamics analysis.

Cardiovascular disorders are still challenging and are among the deadly diseases. As a major risk factor for atherosclerotic cardiovascular disease, dyslipidemia, and high low-density lipoprotein cholesterol in particular, can be prevented primary and secondary by lipid-lowering medications. Therefore, insights are still needed into designing new drugs with minimal side effects. Proprotein convertase subtilisin/kexin 9 (PCSK9) enzyme catalyses protein-protein interactions with low-density lipoprotein, making it a critical target for designing promising inhibitors compared to statins. Therefore, we screened for potential compounds using a redesigned PCSK9 conformational behaviour to search for a significantly extensive chemical library and investigated the inhibitory mechanisms of the final compounds using integrated computational methods, from ligand essential functional group screening to all-atoms MD simulations and MMGBSA-based binding free energy. The inhibitory mechanisms of the screened compounds compared with the standard inhibitor. K31 and K34 molecules showed stronger interactions for PCSK9, having binding energy (kcal/mol) of -33.39 and -63.51, respectively, against -27.97 of control. The final molecules showed suitable drug-likeness, non-mutagenesis, permeability, and high solubility values. The C-α atoms root mean square deviation and root mean square fluctuation of the bound-PCSK9 complexes showed stable and lower fluctuations compared to apo PCSK9. The findings present a model that unravels the mechanism by which the final molecules proposedly inhibit the PCSK9 function and could further improve the design of novel drugs against cardiovascular diseases.

Severe Combined Immunodeficiency from a Homozygous DNA Ligase 1 Mutant with Reduced Catalytic Activity but Increased Ligation Fidelity.

A cell's ability to survive and to evade cancer is contingent on its ability to retain genomic integrity, which can be seriously compromised when nucleic acid phosphodiester bonds are disrupted. DNA Ligase 1 (LIG1) plays a key role in genome maintenance by sealing single-stranded nicks that are produced during DNA replication and repair. Autosomal recessive mutations in a limited number of individuals have been previously described for this gene. Here we report a homozygous LIG1 mutation (p.A624T), affecting a universally conserved residue, in a patient presenting with leukopenia, neutropenia, lymphopenia, pan-hypogammaglobulinemia, and diminished in vitro response to mitogen stimulation. Patient fibroblasts expressed normal levels of LIG1 protein but exhibited impaired growth, poor viability, high baseline levels of gamma-H2AX foci, and an enhanced susceptibility to DNA-damaging agents. The mutation reduced LIG1 activity by lowering its affinity for magnesium 2.5-fold. Remarkably, it also increased LIG1 fidelity > 50-fold against 3' end 8-Oxoguanine mismatches, exhibiting a marked reduction in its ability to process such nicks. This is expected to yield increased ss- and dsDNA breaks. Molecular dynamic simulations, and Residue Interaction Network studies, predicted an allosteric effect for this mutation on the protein loops associated with the LIG1 high-fidelity magnesium, as well as on DNA binding within the adenylation domain. These dual alterations of suppressed activity and enhanced fidelity, arising from a single mutation, underscore the mechanistic picture of how a LIG1 defect can lead to severe immunological disease.

Fluorine-19 labeling of the tryptophan residues in the G protein-coupled receptor NK1R using the 5-fluoroindole precursor in Pichia pastoris expression.

In NMR spectroscopy of biomolecular systems, the use of fluorine-19 probes benefits from a clean background and high sensitivity. Therefore, 19F-labeling procedures are of wide-spread interest. Here, we use 5-fluoroindole as a precursor for cost-effective residue-specific introduction of 5-fluorotryptophan (5F-Trp) into G protein-coupled receptors (GPCRs) expressed in Pichia pastoris. The method was successfully implemented with the neurokinin 1 receptor (NK1R). The 19F-NMR spectra of 5F-Trp-labeled NK1R showed one well-separated high field-shifted resonance, which was assigned by mutational studies to the "toggle switch tryptophan". Residue-selective labeling thus enables site-specific investigations of this functionally important residue. The method described here is inexpensive, requires minimal genetic manipulation and can be expected to be applicable for yeast expression of GPCRs at large.

NMR analysis of a loop-bulge structure of UUCGA pentaloop.

Although structures of many RNA loops, such as GNRA and UNCG tetraloops, were well known, it is still possible to find more RNA structures. In the present study, solution structure of an RNA fragment having UUCGA pentaloop was analyzed by NMR spectroscopy. It was found that the UUCG tetraloop is formed and the adenosine residue at the 3' side of the tetraloop is bulged out. The characteristic motif of the loop-bulge structure has also been found in other RNAs including CUUGU and CUGGC pentaloops. Along with the recently found T-hairpin structure with a UUUGAUU loop, in which UUUGA pentaloop and UU bulge are formed, the loop-bulge structures can be categorized as an RNA motif and it may be called as the integrated structure loop, I-loop.

Epitranscriptome insights into Riccia fluitans L. (Marchantiophyta) aquatic transition using nanopore direct RNA sequencing.

BACKGROUND: Riccia fluitans, an amphibious liverwort, exhibits a fascinating adaptation mechanism to transition between terrestrial and aquatic environments. Utilizing nanopore direct RNA sequencing, we try to capture the complex epitranscriptomic changes undergone in response to land-water transition. RESULTS: A significant finding is the identification of 45 differentially expressed genes (DEGs), with a split of 33 downregulated in terrestrial forms and 12 upregulated in aquatic forms, indicating a robust transcriptional response to environmental changes. Analysis of N6-methyladenosine (m6A) modifications revealed 173 m6A sites in aquatic and only 27 sites in the terrestrial forms, indicating a significant increase in methylation in the former, which could facilitate rapid adaptation to changing environments. The aquatic form showed a global elongation bias in poly(A) tails, which is associated with increased mRNA stability and efficient translation, enhancing the plant's resilience to water stress. Significant differences in polyadenylation signals were observed between the two forms, with nine transcripts showing notable changes in tail length, suggesting an adaptive mechanism to modulate mRNA stability and translational efficiency in response to environmental conditions. This differential methylation and polyadenylation underline a sophisticated layer of post-transcriptional regulation, enabling Riccia fluitans to fine-tune gene expression in response to its living conditions. CONCLUSIONS: These insights into transcriptome dynamics offer a deeper understanding of plant adaptation strategies at the molecular level, contributing to the broader knowledge of plant biology and evolution. These findings underscore the sophisticated post-transcriptional regulatory strategies Riccia fluitans employs to navigate the challenges of aquatic versus terrestrial living, highlighting the plant's dynamic adaptation to environmental stresses and its utility as a model for studying adaptation mechanisms in amphibious plants.

Resolving coupled pH titrations using alchemical free energy calculations.

In a protein, nearby titratable sites can be coupled: the (de)protonation of one may affect the other. The degree of this interaction depends on several factors and can influence the measured p K a . Here, we derive a formalism based on double free energy differences ( Δ Δ G ) for quantifying the individual site p K a values of coupled residues. As Δ Δ G values can be obtained by means of alchemical free energy calculations, the presented approach allows for a convenient estimation of coupled residue p K a s in practice. We demonstrate that our approach and a previously proposed microscopic p K a formalism, can be combined with alchemical free energy calculations to resolve pH-dependent protein p K a values. Toy models and both, regular and constant-pH molecular dynamics simulations, alongside experimental data, are used to validate this approach. Our results highlight the insights gleaned when coupling and microstate probabilities are analyzed and suggest extensions to more complex enzymatic contexts. Furthermore, we find that naïvely computed p K a values that ignore coupling, can be significantly improved when coupling is accounted for, in some cases reducing the error by half. In short, alchemical free energy methods can resolve the p K a values of both uncoupled and coupled residues.

Effect of Ni Sulfate Residue on Oxygen Evolution Reaction (OER) in Porous NiFe@NiFe Layered Double Hydroxide.

The development of cost-effective and high-performance oxygen evolution reaction (OER) catalysts is a significant challenge. This study presents the synthesis of binder-free NiFe@NiFe layered double hydroxide (NNF) via one-pot electrodeposition on carbon paper and Ni foam at high current densities. The presence of Ni sulfate residues on the prepared NNF is also investigated. The findings indicate that Ni sulfate significantly improves OER performance and durability. The sulfate content can be controlled by varying the method and duration of washing. NNF prepared through dipping (NNF-D) exhibits outstanding OER activity with a low overpotential of 241 mV, which is 25 mV lower than that of NNF washed for 60 s (NNF-W-60 s) at 10 mA cm-2 in 1 m KOH. Furthermore, density functional theory analyses indicate that the Ni sulfate residue helps modify the electronic structure, thereby optimizing the binding strength of *OOH. This synthetic strategy is expected to inspire the development of next-generation catalysts utilizing various adsorbates.

Additive Strategy Enhancing In Situ Polymerization Uniformity for High-Voltage Sodium Metal Batteries.

In situ polymerization to prepare quasi-solid electrolyte has attracted wide attentions for its advantage in achieving intimate electrode-electrolyte contact and the high process compatibility with current liquid batteries; however, gases can be generated during polymerization process and remained in the final electrolyte, severely impairing the electrolyte uniformity and electrochemical performance. In this work, an in situ polymerized poly(vinylene carbonate)-based quasi-solid electrolyte for high-voltage sodium metal batteries (SMBs) is demonstrated, which contains a novel multifunctional additive N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA). MSTFA as high-efficient plasticizer diminishes residual gases in electrolyte after polymerization; the softer and homogeneous electrolyte enables much faster ionic conduction. The HF/H2 O scavenge effect of MSTFA mitigates the corrosion of free acid to cathode and interfacial passivating layers, enhancing the cycle stability under high voltage. As a result, the 4.4 V Na||Na3 V2 (PO4 )2 F3 cell employing the optimized electrolyte possesses an initial discharge capacity of 112.0 mAh g-1 and a capacity retention of 91.3% after 100 cycles at 0.5C, obviously better than those of its counterparts without MSTFA addition. This work gives a pioneering study on the gas residue phenomenon in in situ polymerized electrolytes, and introduces a novel multifunctional silane additive that effectively enhances electrochemical performance in high-voltage SMBs, showing practical application significance.

iDRNA-ITF: identifying DNA- and RNA-binding residues in proteins based on induction and transfer framework.

Protein-DNA and protein-RNA interactions are involved in many biological activities. In the post-genome era, accurate identification of DNA- and RNA-binding residues in protein sequences is of great significance for studying protein functions and promoting new drug design and development. Therefore, some sequence-based computational methods have been proposed for identifying DNA- and RNA-binding residues. However, they failed to fully utilize the functional properties of residues, leading to limited prediction performance. In this paper, a sequence-based method iDRNA-ITF was proposed to incorporate the functional properties in residue representation by using an induction and transfer framework. The properties of nucleic acid-binding residues were induced by the nucleic acid-binding residue feature extraction network, and then transferred into the feature integration modules of the DNA-binding residue prediction network and the RNA-binding residue prediction network for the final prediction. Experimental results on four test sets demonstrate that iDRNA-ITF achieves the state-of-the-art performance, outperforming the other existing sequence-based methods. The webserver of iDRNA-ITF is freely available at http://bliulab.net/iDRNA-ITF.