A series of PDB-related databanks for everyday needs

Molecular simulations guide immobilization of lipase on nest-like ZIFs with regulatable hydrophilic/hydrophobic surface

The catalytic performance of immobilized lipase is greatly influenced by functional support, which attracts growing interest for designing supports to achieve their promotive catalytic activity. Many lipases bind strongly to hydrophobic surfaces where they undergo interfacial activation. Herein, the behavioral differences of lipases with distinct lid structures on interfaces of varying hydrophobicity levels were firstly investigated by molecular simulations. It was found that a reasonable hydrophilic/hydrophobic surface could facilitate the lipase to undergo interfacial activation. Building on these findings, a novel "nest"-like superhydrophobic ZIFs (ZIFN) composed of hydrophobic ligands was prepared for the first time and used to immobilize lipase from Aspergillus oryzae (AOL@ZIFN). The AOL@ZIFN exhibited 2.0-folds higher activity than free lipase in the hydrolysis of p-Nitrophenyl palmitate (p-NPP). Especially, the modification of superhydrophobic ZIFN with an appropriate amount of hydrophilic tannic acid can significantly improve the activity of the immobilized lipase (AOL@ZIFN-TA). The AOL@ZIFN-TA exhibited 30-folds higher activity than free lipase, and still maintained 82% of its initial activity after 5 consecutive cycles, indicating good reusability. These results demonstrated that nanomaterials with rational arrangement of the hydrophilic/hydrophobic surface could facilitate the lipase to undergo interfacial activation and improve its activity, displaying the potential of the extensive application.

PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures

Phosphorylation is indispensable in comprehending biological processes, while biological experimental methods for identifying phosphorylation sites are tedious and arduous. With the rapid growth of biotechnology, deep learning methods have made significant progress in site prediction tasks. Nevertheless, most existing predictors only consider protein sequence information, that limits the capture of protein spatial information. Building upon the latest advancement in protein structure prediction by AlphaFold2, a novel integrated deep learning architecture PhosAF is developed to predict phosphorylation sites in human proteins by integrating CMA-Net and MFC-Net, which considers sequence and structure information predicted by AlphaFold2. Here, CMA-Net module is composed of multiple convolutional neural network layers and multi-head attention is appended to obtaining the local and long-term dependencies of sequence features. Meanwhile, the MFC-Net module composed of deep neural network layers is used to capture the complex representations of evolutionary and structure features. Furthermore, different features are combined to predict the final phosphorylation sites. In addition, we put forward a new strategy to construct reliable negative samples via protein secondary structures. Experimental results on independent test data and case study indicate that our model PhosAF surpasses the current most advanced methods in phosphorylation site prediction.

Membrane specificity of the human cholesterol transfer protein STARD4

STARD4 regulates cholesterol homeostasis by transferring cholesterol between the plasma membrane and endoplasmic reticulum. The STARD4 structure features a helix-grip fold surrounding a large hydrophobic cavity holding the sterol. Its access is controlled by a gate formed by the Ω1 and Ω4 loops and the C-terminal α-helix. Little is known about the mechanisms by which STARD4 binds to membranes and extracts/releases cholesterol. All available structures of STARD4 are without a bound sterol and display the same closed conformation of the gate. The cholesterol transfer activity of the mouse STARD4 is enhanced in the presence of anionic lipids, and in particular of phosphatidylinositol biphosphates (PIP2) for which two binding sites were proposed on the mouse STARD4 surface. Yet only one of these sites is conserved in human STARD4. We here report the results of a liposome microarray-based assay and microseconds-long molecular dynamics simulations of human STARD4 with complex lipid bilayers mimicking the composition of the donor and acceptor membranes. We show that the binding of apo form of human STARD4 is sensitive to the presence of PIP2 through two specific binding sites, one of which was not identified on mouse STARD4. We report two novel conformations of the gate in holo-STARD4: a yet-unobserved close conformation and an open conformation of Ω4 shedding light on the opening/closure mechanism needed for cholesterol uptake/release. Overall, the modulation of human STARD4 membrane-binding by lipid composition, and by the presence of the cargo supports the capacity of human STARD4 to achieve directed transfer between specific organelle membranes.

AlphaFold2-guided description of CoBaHMA, a novel family of bacterial domains within the heavy-metal-associated superfamily

Three-dimensional (3D) structure information, now available at the proteome scale, may facilitate the detection of remote evolutionary relationships in protein superfamilies. Here, we illustrate this with the identification of a novel family of protein domains related to the ferredoxin-like superfold, by combining (i) transitive sequence similarity searches, (ii) clustering approaches, and (iii) the use of AlphaFold2 3D structure models. Domains of this family were initially identified in relation with the intracellular biomineralization of calcium carbonates by Cyanobacteria. They are part of the large heavy-metal-associated (HMA) superfamily, departing from the latter by specific sequence and structural features. In particular, most of them share conserved basic amino acids  (hence their name CoBaHMA for Conserved Basic residues HMA), forming a positively charged surface, which is likely to interact with anionic partners. CoBaHMA domains are found in diverse modular organizations in bacteria, existing in the form of monodomain proteins or as part of larger proteins, some of which are membrane proteins involved in transport or lipid metabolism. This suggests that the CoBaHMA domains may exert a regulatory function, involving interactions with anionic lipids. This hypothesis might have a particular resonance in the context of the compartmentalization observed for cyanobacterial intracellular calcium carbonates.

Systematic evaluation of lecithin:Cholesterol acyltransferase binding sites in apolipoproteins via peptide based nanodiscs: Regulatory role of charged residues at positions 4 and 7

Lecithin:cholesterol acyltransferase (LCAT) exhibits α-activity on high-density and β-activity on low-density lipoproteins. However, the molecular determinants governing LCAT activation by different apolipoproteins remain elusive. Uncovering these determinants would offer the opportunity to design and explore advanced therapies against dyslipidemias. Here, we have conducted coarse-grained and all-atom molecular dynamics simulations of LCAT with nanodiscs made with α-helical amphiphilic peptides either derived from apolipoproteins A1 and E (apoA1 and apoE) or apoA1 mimetic peptide 22A that was optimized to activate LCAT. This study aims to explore what drives the binding of peptides to our previously identified interaction site in LCAT. We hypothesized that this approach could be used to screen for binding sites of LCAT in different apolipoproteins and would provide insights to differently localized LCAT activities. Our screening approach was able to discriminate apoA1 helixes 4, 6, and 7 as key contributors to the interaction with LCAT supporting the previous research data. The simulations provided detailed molecular determinants driving the interaction with LCAT: the formation of hydrogen bonds or salt bridges between peptides E4 or D4 and LCAT S236 or K238 residues. Additionally, salt bridging between R7 and D73 was observed, depending on the availability of R7. Expanding our investigation to diverse plasma proteins, we detected novel LCAT binding helixes in apoL1, apoB100, and serum amyloid A. Our findings suggest that the same binding determinants, involving E4 or D4 -S236 and R7-D73 interactions, influence LCAT β-activity on low-density lipoproteins, where apoE and or apoB100 are hypothesized to interact with LCAT.

Probing Enzymatic PET Degradation: Molecular Dynamics Analysis of Cutinase Adsorption and Stability

Understanding the mechanisms influencing poly(ethylene terephthalate) (PET) biodegradation is crucial for developing innovative strategies to accelerate the breakdown of this persistent plastic. In this study, we employed all-atom molecular dynamics simulation to investigate the adsorption process of the LCC-ICCG cutinase enzyme onto the PET surface. Our results revealed that hydrophobic, π-π, and H bond interactions, specifically involving aliphatic, aromatic, and polar uncharged amino acids, were the primary driving forces for the adsorption of the cutinase enzyme onto PET. Additionally, we observed a negligible change in the enzyme's tertiary structure during the interaction with PET (RMSD = 1.35 Å), while its secondary structures remained remarkably stable. Quantitative analysis further demonstrated that there is about a 24% decrease in the number of enzyme-water hydrogen bonds upon adsorption onto the PET surface. The significance of this study lies in unraveling the molecular intricacies of the adsorption process, providing valuable insights into the initial steps of enzymatic PET degradation.

Bioinformatics leading to conveniently accessible, helix enforcing, bicyclic ASX motif mimics (BAMMs)

Helix mimicry provides probes to perturb protein-protein interactions (PPIs). Helical conformations can be stabilized by joining side chains of non-terminal residues (stapling) or via capping fragments. Nature exclusively uses capping, but synthetic helical mimics are heavily biased towards stapling. This study comprises: (i) creation of a searchable database of unique helical N-caps (ASX motifs, a protein structural motif with two intramolecular hydrogen-bonds between aspartic acid/asparagine and following residues); (ii) testing trends observed in this database using linear peptides comprising only canonical L-amino acids; and, (iii) novel synthetic N-caps for helical interface mimicry. Here we show many natural ASX motifs comprise hydrophobic triangles, validate their effect in linear peptides, and further develop a biomimetic of them, Bicyclic ASX Motif Mimics (BAMMs). BAMMs are powerful helix inducing motifs. They are synthetically accessible, and potentially useful to a broad section of the community studying disruption of PPIs using secondary structure mimics.

DrugMap: A quantitative pan-cancer analysis of cysteine ligandability

Cysteine-focused chemical proteomic platforms have accelerated the clinical development of covalent inhibitors for a wide range of targets in cancer. However, how different oncogenic contexts influence cysteine targeting remains unknown. To address this question, we have developed "DrugMap," an atlas of cysteine ligandability compiled across 416 cancer cell lines. We unexpectedly find that cysteine ligandability varies across cancer cell lines, and we attribute this to differences in cellular redox states, protein conformational changes, and genetic mutations. Leveraging these findings, we identify actionable cysteines in NF-κB1 and SOX10 and develop corresponding covalent ligands that block the activity of these transcription factors. We demonstrate that the NF-κB1 probe blocks DNA binding, whereas the SOX10 ligand increases SOX10-SOX10 interactions and disrupts melanoma transcriptional signaling. Our findings reveal heterogeneity in cysteine ligandability across cancers, pinpoint cell-intrinsic features driving cysteine targeting, and illustrate the use of covalent probes to disrupt oncogenic transcription-factor activity.

Functional in vitro diversity of an intrinsically disordered plant protein during freeze-thawing is encoded by its structural plasticity

Intrinsically disordered late embryogenesis abundant (LEA) proteins play a central role in the tolerance of plants and other organisms to dehydration brought upon, for example, by freezing temperatures, high salt concentration, drought or desiccation, and many LEA proteins have been found to stabilize dehydration-sensitive cellular structures. Their conformational ensembles are highly sensitive to the environment, allowing them to undergo conformational changes and adopt ordered secondary and quaternary structures and to participate in formation of membraneless organelles. In an interdisciplinary approach, we discovered how the functional diversity of the Arabidopsis thaliana LEA protein COR15A found in vitro is encoded in its structural repertoire, with the stabilization of membranes being achieved at the level of secondary structure and the stabilization of enzymes accomplished by the formation of oligomeric complexes. We provide molecular details on intra- and inter-monomeric helix-helix interactions, demonstrate how oligomerization is driven by an α-helical molecular recognition feature (α-MoRF) and provide a rationale that the formation of noncanonical, loosely packed, right-handed coiled-coils might be a recurring theme for homo- and hetero-oligomerization of LEA proteins.

Structure-Based Rational and General Strategy for Stabilizing Single-Chain T-Cell Receptors to Enhance Affinity

The T-cell receptor (TCR) is a crucial molecule in cellular immunity. The single-chain T-cell receptor (scTCR) is a potential format in TCR therapeutics because it eliminates the possibility of αβ-TCR mispairing. However, its poor stability and solubility impede the in vitro study and manufacturing of therapeutic applications. In this study, some conserved structural motifs are identified in variable domains regardless of germlines and species. Theoretical analysis helps to identify those unfavored factors and leads to a general strategy for stabilizing scTCRs by substituting residues at exact IMGT positions with beneficial propensities on the consensus sequence of germlines. Several representative scTCRs are displayed to achieve stability optimization and retain comparable binding affinities with the corresponding αβ-TCRs in the range of μM to pM. These results demonstrate that our strategies for scTCR engineering are capable of providing the affinity-enhanced and specificity-retained format, which are of great value in facilitating the development of TCR-related therapeutics.

Computational Insights into the Conformational Dynamics of HIV-1 Vpr in a Lipid Bilayer for Ion Channel Modeling

HIV-1 Vpr is a multifunctional accessory protein consisting of 96 amino acids that play a critical role in viral pathogenesis. Among its diverse range of activities, Vpr can create a cation-selective ion channel within the plasma membrane. However, the oligomeric state of this channel has not yet been elucidated. In this study, we investigated the conformational dynamics of Vpr helices to model the ion channel topology. First, we employed a series of multiscale simulations to investigate the specific structure of monomeric Vpr in a membrane model. During the lipid bilayer self-assembly coarse grain simulation, the C-terminal helix (residues 56-77) effectively formed the transmembrane region, while the N-terminal helix exhibited an amphipathic nature by associating horizontally with a single leaflet. All-atom molecular dynamics (MD) simulations of full-length Vpr inside a phospholipid bilayer show that the C-terminal helix remains very stable inside the bilayer core in a vertical orientation. Subsequently, using the predicted C-terminal helix orientation and conformation, various oligomeric states (ranging from tetramer to heptamer) possibly forming the Vpr ion channel were built and further evaluated. Among these models, the pentameric form exhibited consistent stability in MD simulations and displayed a compatible conformation for a water-assisted ion transport mechanism. This study provides structural insights into the ion channel activity of the Vpr protein and the foundation for developing therapeutics against HIV-1 Vpr-related conditions.

Natural flavonoid hesperetin blocks amyloid β-protein fibrillogenesis, depolymerizes preformed fibrils and alleviates cytotoxicity caused by amyloids

The aggregation of β-amyloid (Aβ) peptides to form amyloid plaques is one of the primary hallmarks for Alzheimer's disease (AD). Dietary flavonoid supplements containing hesperetin have an ability to decline the risk of developing AD, but the molecular mechanism is still unclear. In this work, hesperetin, a flavanone abundant in citrus fruits, has been proven to prevent the formation of Aβ aggregates and depolymerized preformed fibrils in a concentration-dependent fashion. Hesperetin inhibited the conformational conversion from the natural structure to a β-sheet-rich conformation. It was found that hesperetin significantly reduced the cytotoxicity and relieved oxidative stress eventuated by Aβ aggregates in a concentration-dependent manner. Additionally, the beneficial effects of hesperetin were confirmed in Caenorhabditis elegans, including the inhibition of the formation and deposition of Aβ aggregates and extension of their lifespan. Finally, the results of molecular dynamics simulations showed that hesperetin directly interacted with an Aβ42 pentamer mainly through strong non-polar and electrostatic interactions, which destroyed the structural stability of the preformed pentamer. To summarize, hesperetin exhibits great potential as a prospective dietary supplement for preventing and improving AD.

Self-incompatibility phenotypes of SRK mutants can be predicted with high accuracy

Only very limited information is available on why some non-synonymous variants severely alter gene function while others have no effect. To identify the characteristic features of mutations that strongly influence gene function, this study focused on S-locus receptor kinase, SRK, which encodes a highly polymorphic receptor kinase expressed in stigma papillary cells that underlies a female determinant of self-incompatibility in Brassicaceae. A set of 299 Arabidopsis thaliana transformants expressing mutated SRKb from A. lyrata was constructed and analyzed to determine the genotype and self-incompatibility phenotype of each transformant. Almost all the transformants showing the self-incompatibility defect contained mutations in AlSRKb that altered localization to the plasma membrane. The observed mutations occurred in amino acid residues that were highly conserved across S haplotypes and whose predicted locations were in the interior of the protein. These mutations were likely to underlie the self-incompatibility defect as they caused significant changes to amino acid properties. Such findings suggested that mutations causing the self-incompatibility defect were more likely to result from changes to AlSRKb biosynthesis than from loss of function. In addition, this study showed the RandomForest and Extreme Gradient Boosting methods could predict self-incompatibility phenotypes of SRK mutants with high accuracy.

Molecular insights on the coronavirus MERS-CoV interaction with the CD26 receptor

The Middle East respiratory syndrome (MERS) is a severe respiratory disease with high fatality rates, caused by the Middle East respiratory syndrome coronavirus (MERS-CoV). The virus initiates infection by binding to the CD26 receptor (also known as dipeptidyl peptidase 4 or DPP4) via its spike protein. Although the receptor-binding domain (RBD) of the viral spike protein and the complex between RBD and the extracellular domain of CD26 have been studied using X-ray crystallography, conflicting studies exist regarding the importance of certain amino acids outside the resolved RBD-CD26 complex interaction interface. To gain atomic-level knowledge of the RBD-CD26 complex, we employed computational simulations to study the complex's dynamic behavior as it evolves from its crystal structure to a conformation stable in solution. Our study revealed previously unidentified interaction regions and interacting amino acids within the complex, determined a novel comprehensive RBD-binding domain of CD26, and by that expanded the current understanding of its structure. Additionally, we examined the impact of a single amino acid substitution, E513A, on the complex's stability. We discovered that this substitution disrupts the complex through an allosteric domino-like mechanism that affects other residues. Since MERS-CoV is a zoonotic virus, we evaluated its potential risk of human infection via animals, and suggest a low likelihood for possible infection by cats or dogs. The molecular structural information gleaned from our insights into the RBD-CD26 complex pre-dissociative states may be proved useful not only from a mechanistic view but also in assessing inter-species transmission and in developing anti-MERS-CoV antiviral therapeutics.

Influence of Aza-Glycine Substitution on the Internalization of Penetratin

The cell-penetrating peptide (CPP) penetratin has gained much attention over many years due to its potential role as a transporter for a broad range of cargo into cells. The modification of penetratin has been extensively investigated too. Aza-peptides are peptide analogs in which one or more of the amino residues are replaced by a semicarbazide. This substitution results in conformational restrictions and modifications in hydrogen bonding properties, which affect the structure and may lead to enhanced activity and selectivity of the modified peptide. In this work, the Trp residues of penetratin were substituted by aza-glycine or glycine residues to examine the effect of these modifications on the cellular uptake and the internalization mechanism. The substitution of Trp48 or Trp48,56 dramatically reduced the internalization, showing the importance of Trp48 in cellular uptake. Interestingly, while aza-glycine in the position of Trp56 increased the cellular uptake, Gly reduced it. The two Trp-modified derivatives showed altered internalization pathways, too. Based on our knowledge, this is the first study about the effect of aza-amino acid substitution on the cell entry of CPPs. Our results suggest that aza-amino acid insertion is a useful modification to change the internalization of a CPP.

The thylakoid proton antiporter KEA3 regulates photosynthesis in response to the chloroplast energy status

Plant photosynthesis contains two functional modules, the light-driven reactions in the thylakoid membrane and the carbon-fixing reactions in the chloroplast stroma. In nature, light availability for photosynthesis often undergoes massive and rapid fluctuations. Efficient and productive use of such variable light supply requires an instant crosstalk and rapid synchronization of both functional modules. Here, we show that this communication involves the stromal exposed C-terminus of the thylakoid K+-exchange antiporter KEA3, which regulates the ΔpH across the thylakoid membrane and therefore pH-dependent photoprotection. By combining in silico, in vitro, and in vivo approaches, we demonstrate that the KEA3 C-terminus senses the energy state of the chloroplast in a pH-dependent manner and regulates transport activity in response. Together our data pinpoint a regulatory feedback loop by which the stromal energy state orchestrates light capture and photoprotection via multi-level regulation of KEA3.

Microphase Separation Produces Interfacial Environment within Diblock Biomolecular Condensates

The phase separation of intrinsically disordered proteins is emerging as an important mechanism for cellular organization. However, efforts to connect protein sequences to the physical properties of condensates, i.e., the molecular grammar, are hampered by a lack of effective approaches for probing high-resolution structural details. Using a combination of multiscale simulations and fluorescence lifetime imaging microscopy experiments, we systematically explored a series of systems consisting of diblock elastin-like polypeptides (ELP). The simulations succeeded in reproducing the variation of condensate stability upon amino acid substitution and revealed different microenvironments within a single condensate, which we verified with environmentally sensitive fluorophores. The interspersion of hydrophilic and hydrophobic residues and a lack of secondary structure formation result in an interfacial environment, which explains both the strong correlation between ELP condensate stability and interfacial hydrophobicity scales, as well as the prevalence of protein-water hydrogen bonds. Our study uncovers new mechanisms for condensate stability and organization that may be broadly applicable.

DeepSS2GO: protein function prediction from secondary structure

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond

Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.

Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions

The burgeoning comprehension of protein phase separation (PS) has ushered in a wealth of bioinformatics tools for the prediction of phase-separating proteins (PSPs). These tools often skew towards PSPs with a high content of intrinsically disordered regions (IDRs), thus frequently undervaluing potential PSPs without IDRs. Nonetheless, PS is not only steered by IDRs but also by the structured modular domains and interactions that aren't necessarily reflected in amino acid sequences. In this work, we introduce PSPire, a machine learning predictor that incorporates both residue-level and structure-level features for the precise prediction of PSPs. Compared to current PSP predictors, PSPire shows a notable improvement in identifying PSPs without IDRs, which underscores the crucial role of non-IDR, structure-based characteristics in multivalent interactions throughout the PS process. Additionally, our biological validation experiments substantiate the predictive capacity of PSPire, with 9 out of 11 chosen candidate PSPs confirmed to form condensates within cells.

Oxidative stress induced conformational changes of human serum albumin

Oxidative stress, generated by reactive oxygen species (ROS), is responsible for the loss of structure and functionality of proteins and is associated with several aging-related diseases. Here, we report an in vitro study to gauge the effect of ROS on the structural rearrangement of human serum albumin (HSA), a plasma protein, through metal-catalyzed oxidation (MCO) at physiological temperature through various biophysical techniques like UV-vis absorption, circular dichroism (CD), differential scanning calorimetry (DSC), MALDI-TOF, FTIR, and Raman spectroscopy. The UV-vis spectra of oxidized HSA show an early blueshift, signifying the unfolding of the protein because of ROS followed by the broadening of the absorption peak at a longer time. The DSC data corroborate the observation, revealing an exothermic transition for the oxidized sample at a longer time, suggesting in situ aggregation. The CD and FTIR spectra indicate the associated secondary structural changes occurring with time, depicting the variation of the helical content of HSA. The amide-III analysis of Raman data also complements the structural changes, and MALDI-TOF data show the mass distribution with time. Overall, this work might help determine the effect of oxidation on the biological activity of serum albumin as it can impact the physiological properties of HSA.

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes

MOTIVATION

The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes.

RESULTS

We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments.

AVAILABILITY AND IMPLEMENTATION

Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Utilizing the scale-invariant feature transform algorithm to align distance matrices facilitates systematic protein structure comparison

MOTIVATION

Protein structure comparison is pivotal for deriving homological relationships, elucidating protein functions, and understanding evolutionary developments. The burgeoning field of in-silico protein structure prediction now yields billions of models with near-experimental accuracy, necessitating sophisticated tools for discerning structural similarities among proteins, particularly when sequence similarity is limited.

RESULTS

In this article, we have developed the align distance matrix with scale (ADAMS) pipeline, which synergizes the distance matrix alignment method with the scale-invariant feature transform algorithm, streamlining protein structure comparison on a proteomic scale. Utilizing a computer vision-centric strategy for contrasting disparate distance matrices, ADAMS adeptly alleviates challenges associated with proteins characterized by a high degree of structural flexibility. Our findings indicate that ADAMS achieves a level of performance and accuracy on par with Foldseek, while maintaining similar speed. Crucially, ADAMS overcomes certain limitations of Foldseek in handling structurally flexible proteins, establishing it as an efficacious tool for in-depth protein structure analysis with heightened accuracy.

AVAILABILITY

ADAMS can be download and used as a python package from Python Package Index (PyPI): adams · PyPI. Source code and other materials are available from young55775/ADAMS-developing (github.com). An online server is available: Bseek Search Server (cryonet.ai).

The crystal structure of mycothiol disulfide reductase (Mtr) provides mechanistic insight into the specific low-molecular-weight thiol reductase activity of Actinobacteria

Low-molecular-weight (LMW) thiols are involved in many processes in all organisms, playing a protective role against reactive species, heavy metals, toxins and antibiotics. Actinobacteria, such as Mycobacterium tuberculosis, use the LMW thiol mycothiol (MSH) to buffer the intracellular redox environment. The NADPH-dependent FAD-containing oxidoreductase mycothiol disulfide reductase (Mtr) is known to reduce oxidized mycothiol disulfide (MSSM) to MSH, which is crucial to maintain the cellular redox balance. In this work, the first crystal structures of Mtr are presented, expanding the structural knowledge and understanding of LMW thiol reductases. The structural analyses and docking calculations provide insight into the nature of Mtrs, with regard to the binding and reduction of the MSSM substrate, in the context of related oxidoreductases. The putative binding site for MSSM suggests a similar binding to that described for the homologous glutathione reductase and its respective substrate glutathione disulfide, but with distinct structural differences shaped to fit the bulkier MSSM substrate, assigning Mtrs as uniquely functioning reductases. As MSH has been acknowledged as an attractive antitubercular target, the structural findings presented in this work may contribute towards future antituberculosis drug development.

Accelerating Antimicrobial Peptide Discovery for WHO Priority Pathogens through Predictive and Interpretable Machine Learning Models

The escalating menace of multidrug-resistant (MDR) pathogens necessitates a paradigm shift from conventional antibiotics to innovative alternatives. Antimicrobial peptides (AMPs) emerge as a compelling contender in this arena. Employing in silico methodologies, we can usher in a new era of AMP discovery, streamlining the identification process from vast candidate sequences, thereby optimizing laboratory screening expenditures. Here, we unveil cutting-edge machine learning (ML) models that are both predictive and interpretable, tailored for the identification of potent AMPs targeting World Health Organization's (WHO) high-priority pathogens. Furthermore, we have developed ML models that consider the hemolysis of human erythrocytes, emphasizing their therapeutic potential. Anchored in the nuanced physical-chemical attributes gleaned from the three-dimensional (3D) helical conformations of AMPs, our optimized models have demonstrated commendable performance-boasting an accuracy exceeding 75% when evaluated against both low-sequence-identified peptides and recently unveiled AMPs. As a testament to their efficacy, we deployed these models to prioritize peptide sequences stemming from PEM-2 and subsequently probed the bioactivity of our algorithm-predicted peptides vis-à-vis WHO's priority pathogens. Intriguingly, several of these new AMPs outperformed the native PEM-2 in their antimicrobial prowess, thereby underscoring the robustness of our modeling approach. To elucidate ML model outcomes, we probe via Shapley Additive exPlanations (SHAP) values, uncovering intricate mechanisms guiding diverse actions against bacteria. Our state-of-the-art predictive models expedite the design of new AMPs, offering a robust countermeasure to antibiotic resistance. Our prediction tool is available to the public at https://ai-meta.chem.ncu.edu.tw/amp-meta.

Elucidating the reversible and irreversible self-assembly mechanisms of low-complexity aromatic-rich kinked peptides and steric zipper peptides

Many RNA-binding proteins such as fused-in sarcoma (FUS) can self-assemble into reversible liquid droplets and fibrils through the self-association of their low-complexity (LC) domains. Recent experiments have revealed that SYG-rich segments in the FUS LC domains play critical roles in the reversible self-assembly behaviors of FUS. These FUS LC segments alone can self-assemble into reversible kinked fibrils, which are markedly different from the canonical irreversible steric zipper β-sheet fibrils. However, the molecular determinants underlying the reversible and irreversible self-assembly are poorly understood. Herein we conducted extensive all-atom and coarse-grained molecular dynamics simulations of four representative hexapeptides: two low-complexity aromatic-rich kinked peptides from the amyotrophic lateral sclerosis-related FUS protein, FUS37-42 (SYSGYS) and FUS54-59 (SYSSYG); and two steric zipper peptides from Alzheimer's-associated Aβ and Tau proteins, Aβ16-21 (KLVFFA) and Tau306-311 (VQIVYK). We dissected their reversible and irreversible self-assembly dynamics, predicted their phase separation behaviors, and elucidated the underpinning molecular interactions. Our simulations showed that alternating stickers (Tyr) and spacers (Gly and Ser) in FUS37-42 and FUS54-59 facilitate the formation of highly dynamic coil-rich oligomers and lead to reversible self-assembly, while consecutive hydrophobic residues of LVFF in Aβ16-21 and IVY in Tau306-311 act as hydrophobic patches, favoring the formation of stable β-sheet-rich oligomers and driving the irreversible self-assembly. Intriguingly, we found that FUS37-42 and FUS54-59 peptides, possessing the same amino acid composition and the same number of sticker and spacer residues, display differential self-assembly propensities. This finding suggests that the self-assembly behaviors of FUS peptides are fine-tuned by the site-specific patterning of spacer residues (Ser and Gly). This study provides significant mechanistic insights into reversible and irreversible peptide self-assembly, which would be helpful for understanding the molecular mechanisms underlying the formation of biological liquid condensates and pathological solid amyloid fibrils.

Cellular targets and lysine selectivity of the HERC5 ISG15 ligase

ISG15 is a type I interferon-induced ubiquitin-like modifier that functions in innate immune responses. The major human ISG15 ligase is hHERC5, a ribosome-associated HECT E3 that broadly ISGylates proteins cotranslationally. Here, we characterized the hHERC5-dependent ISGylome and identified over 2,000 modified lysines in over 1,100 proteins in IFN-β-stimulated cells. In parallel, we compared the substrate selectivity hHERC5 to the major mouse ISG15 ligase, mHERC6, and analysis of sequences surrounding ISGylation sites revealed that hHERC5 and mHERC6 have distinct preferences for amino acid sequence context. Several features of the datasets were consistent with ISGylation of ribosome-tethered nascent chains, and mHERC6, like hHERC5, cotranslationally modified nascent polypeptides. The ISGylome datasets presented here represent the largest numbers of protein targets and modification sites attributable to a single Ub/Ubl ligase and the lysine selectivities of the hHERC5 and mHERC6 enzymes may have implications for the activities of HECT domain ligases, generally.

In silico approaches to investigate enzyme immobilization: a comprehensive systematic review

Enzymes are popular catalysts with many applications, especially in industry. Biocatalyst usage on a large scale is facing some limitations, such as low operational stability, low recyclability, and high enzyme cost. Enzyme immobilization is a beneficial strategy to solve these problems. Bioinformatics tools can often correctly predict immobilization outcomes, resulting in a cost-effective experimental phase with the least time consumed. This study provides an overview of in silico methods predicting immobilization processes via a comprehensive systematic review of published articles till 11 December 2022. It also mentions the strengths and weaknesses of the processes and explains the computational analyses in each method that are required for immobilization assessment. In this regard, Web of Science and Scopus databases were screened to gain relevant publications. After screening the gathered documents (n = 3873), 60 articles were selected for the review. The selected papers have applied in silico procedures including only molecular dynamics (MD) simulations (n = 20), parallel tempering Monte Carlo (PTMC) and MD simulations (n = 3), MD and docking (n = 1), density functional theory (DFT) and MD (n = 1), only docking (n = 11), metal ion binding site prediction (MIB) server and docking (n = 2), docking and DFT (n = 1), docking and analysis of enzyme surfaces (n = 1), only DFT (n = 1), only MIB server (n = 2), analysis of an enzyme structure and surface (n = 12), rational design of immobilized derivatives (RDID) software (n = 3), and dissipative particle dynamics (DPD; n = 2). In most included studies (n = 51), enzyme immobilization was investigated experimentally in addition to in silico evaluation.

Studies on the selectivity of the SARS-CoV-2 papain-like protease reveal the importance of the P2' proline of the viral polyprotein

The SARS-CoV-2 papain-like protease (PLpro) is an antiviral drug target that catalyzes the hydrolysis of the viral polyproteins pp1a/1ab, so releasing the non-structural proteins (nsps) 1-3 that are essential for the coronavirus lifecycle. The LXGG↓X motif in pp1a/1ab is crucial for recognition and cleavage by PLpro. We describe molecular dynamics, docking, and quantum mechanics/molecular mechanics (QM/MM) calculations to investigate how oligopeptide substrates derived from the viral polyprotein bind to PLpro. The results reveal how the substrate sequence affects the efficiency of PLpro-catalyzed hydrolysis. In particular, a proline at the P2' position promotes catalysis, as validated by residue substitutions and mass spectrometry-based analyses. Analysis of PLpro catalyzed hydrolysis of LXGG motif-containing oligopeptides derived from human proteins suggests that factors beyond the LXGG motif and the presence of a proline residue at P2' contribute to catalytic efficiency, possibly reflecting the promiscuity of PLpro. The results will help in identifying PLpro substrates and guiding inhibitor design.

Enzyme engineering of choline oxidase for improving stability

Functioning as a flavoprotein, choline oxidase facilitates the transformation of choline into glycine betaine. Notably, choline oxidase and its resultant product, glycine betaine, find extensive applications across various industries and fields of study. However, its high sensitivity and tendency to lose functional activity at high temperatures reduces its industrial usage. MD simulation and mutation studies have revealed the role of certain residues responsible for the enzyme's thermal instability. This study focuses on inducing thermal stability to choline oxidase of A. globiformis through computational approaches at a maximum temperature of 60 °C. MD simulation analysis showed that Trp 331, Val 464 and Ser 101 contribute to structural instability, leading to the instability at 60 °C. Mutation of these residues with phenylalanine residues and simulation of the mutated enzyme at 60 °C exhibited thermostability and insignificant residual fluctuation. The re-docking and MM/GBSA analyses further validated the mutated enzyme's binding affinity and catalytic activity.Communicated by Ramaswamy H. Sarma.

Reconciling ASPP-p53 binding mode discrepancies through an ensemble binding framework that bridges crystallography and NMR data

ASPP2 and iASPP bind to p53 through their conserved ANK-SH3 domains to respectively promote and inhibit p53-dependent cell apoptosis. While crystallography has indicated that these two proteins employ distinct surfaces of their ANK-SH3 domains to bind to p53, solution NMR data has suggested similar surfaces. In this study, we employed multi-scale molecular dynamics (MD) simulations combined with free energy calculations to reconcile the discrepancy in the binding modes. We demonstrated that the binding mode based solely on a single crystal structure does not enable iASPP's RT loop to engage with p53's C-terminal linker-a verified interaction. Instead, an ensemble of simulated iASPP-p53 complexes facilitates this interaction. We showed that the ensemble-average inter-protein contacting residues and NMR-detected interfacial residues qualitatively overlap on ASPP proteins, and the ensemble-average binding free energies better match experimental KD values compared to single crystallgarphy-determined binding mode. For iASPP, the sampled ensemble complexes can be grouped into two classes, resembling the binding modes determined by crystallography and solution NMR. We thus propose that crystal packing shifts the equilibrium of binding modes towards the crystallography-determined one. Lastly, we showed that the ensemble binding complexes are sensitive to p53's intrinsically disordered regions (IDRs), attesting to experimental observations that these IDRs contribute to biological functions. Our results provide a dynamic and ensemble perspective for scrutinizing these important cancer-related protein-protein interactions (PPIs).

Development of new thieno[2,3-d]pyrimidines as dual EGFR and STAT3 inhibitors endowed with anticancer and pro-apoptotic activities

In part due to the resilience of cellular feedback pathways that develop therapeutic resistance to targeting the EGFR alone, using EGFR inhibitors alone was demonstrated to be unsuccessful in clinical trials. The over-activation of the signal transducer/activator of transcription 3 (STAT3) during the administration of an EGFR inhibitor is expected to play a substantial part in the failure and resistance of EGFR inhibitor treatment. Therein, we proposed a hypothesis that induced STAT3-mediated resistance to EGFR inhibition therapy could be addressed by a dual inhibition of EGFR and STAT3 method. To this end, we tried to discover new thieno[2,3-d]pyrimidine derivatives "5a-o". Results from the screening on A549 and MCF7 cancer cell lines revealed that compounds 5j and 5k showed two-digit nanomolar with appropriate safety towards the WI-38 cell line. The best molecules, 5j and 5k, were subjected to γ-radiation, and their cytotoxic efficacy didn't change after irradiation, demonstrating that not having to use it avoided its side effects. Compounds 5j and 5k demonstrated the highest inhibition when their potency was tested as dual inhibitors on EGFR 67 and 41 nM, respectively, and STAT3 5.52 and 3.34 nM, respectively, proved with in silico molecular docking and dynamic simulation. In light of the results presented above, the capacity of both powerful compounds to alter the cell cycle and initiate the apoptotic process in breast cancer MCF7 cells was investigated. Caspase-8, Bcl-2, Bax and Caspase-9 apoptotic indicators were studied.

Emerging role of carbonyl-carbonyl interactions in the classification of beta turns

Carbonyl-carbonyl interactions in peptides and proteins attracted considerable interest in recent years. Here, we report a survey of carbonyl-carbonyl interactions in cyclic peptides, depsipeptides, peptoids and discuss the relationship between backbone torsion angles and CO∙∙∙CO distances. In general, φ values in the range between -40° and -90° and between 40° and 90° correspond to CO∙∙∙CO distances below 3.22 Å. By extending the analysis of carbonyl-carbonyl interactions in different types of beta turns in proteins, we also highlight the role of direct or reciprocal carbonyl-carbonyl interactions in stabilizing the beta turn conformation for each specific type. We confirmed the new type II beta turn, detected by Dunbrack and coworkers, and named Pa, and detect the presence of a direct carbonyl-carbonyl interaction between the second and third residues of the turn. We also evidenced the existence of another new type II beta turn, named pA (following Dunbrack's notation), which represents the alternative conformation of Pa with opposite φ and ψ values and is characterized by a direct carbonyl-carbonyl interaction between the second and third residues of the turn. Finally, we show that the occurrence of CO∙∙∙CO interactions could be also advocated to explain from a chemical point of view the diversity of turn types.

Generation of antibodies to an extracellular region of the transporters Glut1/Glut4 by immunization with a designed antigen

Monoclonal antibodies are one of the fastest growing class of drugs. Nevertheless, relatively few biologics target multispanning membrane proteins because of technical challenges. To target relatively small extracellular regions of multiple membrane-spanning proteins, synthetic peptides, which are composed of amino acids corresponding to an extracellular region of a membrane protein, are often utilized in antibody discovery. However, antibodies to these peptides often do not recognize parental membrane proteins. In this study, we designed fusion proteins in which an extracellular helix of the membrane protein glucose transporter 1 (Glut1) was grafted onto the scaffold protein Adhiron. In the initial design, the grafted fragment did not form a helical conformation. Molecular dynamics simulations of full-length Glut1 suggested the importance of intramolecular interactions formed by surrounding residues in the formation of the helical conformation. A fusion protein designed to maintain such intramolecular interactions did form the desired helical conformation in the grafted region. We then immunized an alpaca with the designed fusion protein and obtained VHH (variable region of heavy-chain antibodies) using the phage display method. The binding of these VHH antibodies to the recombinant Glut1 protein was evaluated by surface plasmon resonance, and their binding to Glut1 on the cell membrane was further validated by flow cytometry. Furthermore, we also succeeded in the generation of a VHH against another integral membrane protein, glucose transporter 4 (Glut4) with the same strategy. These illustrates that our combined biochemical and computational approach can be applied to designing other novel fusion proteins for generating site-specific antibodies.

Anillin-related Mid1 as an adaptive and multimodal contractile ring anchoring protein: A simulation study

Cytokinesis of animal and fungi cells depends crucially on the anillin scaffold proteins. Fission yeast anillin-related Mid1 anchors cytokinetic ring precursor nodes to the membrane. However, it is unclear if both of its Pleckstrin Homology (PH) and C2 C-terminal domains bind to the membrane as monomers or dimers, and if one domain plays a dominant role. We studied Mid1 membrane binding with all-atom molecular dynamics near a membrane with yeast-like lipid composition. In simulations with the full C terminal region started away from the membrane, Mid1 binds through the disordered L3 loop of C2 in a vertical orientation, with the PH away from the membrane. However, a configuration with both C2 and PH initially bound to the membrane remains associated with the membrane. Simulations of C2-PH dimers show extensive asymmetric membrane contacts. These multiple modes of binding may reflect Mid1's multiple interactions with membranes, node proteins, and ability to sustain mechanical forces.

Functional and structural analysis of a cyclization domain in a cyclic β-1,2-glucan synthase

Cyclic β-1,2-glucan synthase (CGS) is a key enzyme in production of cyclic β-1,2-glucans (CβGs) which are involved in bacterial infection or symbiosis to host organisms. Nevertheless, a mechanism of cyclization, the final step in the CGS reaction, has not been fully understood. Here we performed functional and structural analyses of the cyclization domain of CGS alone from Thermoanaerobacter italicus (TiCGSCy). We first found that β-glucosidase-resistant compounds are produced by TiCGSCy with linear β-1,2-glucans as substrates. The 1H-NMR analysis revealed that these products are CβGs. Next, action pattern analyses using β-1,2-glucooligosaccharides revealed a unique reaction pattern: exclusive transglycosylation without hydrolysis and a hexasaccharide being the minimum length of the substrate. These analyses also showed that longer substrate β-1,2-glucooligosaccharides are preferred, being consistent with the fact that CGSs generally produce CβGs with degrees of polymerization of around 20. Finally, the overall structure of the cyclization domain of TiCGSCy was found to be similar to those of β-1,2-glucanases in phylogenetically different groups. Meanwhile, the identified catalytic residues indicated clear differences in the reaction pathways between these enzymes. Overall, we propose a novel reaction mechanism of TiCGSCy. Thus, the present group of CGSs defines a new glycoside hydrolase family, GH189. KEY POINTS: • It was clearly evidenced that cyclization domain alone produces cyclic β-1,2-glucans. • The domain exclusively catalyzes transglycosylation without hydrolysis. • The present catalytic domain defines as a new glycoside hydrolase family 189.

Decoding the secrets: how conformational and structural regulators inhibit the human 20S proteasome

Acquired resistance to drugs that modulate specific protein functions, such as the human proteasome, presents a significant challenge in targeted therapies. This underscores the importance of devising new methodologies to predict drug binding and potential resistance due to specific protein mutations. In this work, we conducted an extensive computational analysis to ascertain the effects of selected mutations (Ala49Thr, Ala50Val, and Cys52Phe) within the active site of the human proteasome. Specifically, we sought to understand how these mutations might disrupt protein function either by altering protein stability or by impeding interactions with a clinical administered drug. Leveraging molecular dynamics simulations and molecular docking calculations, we assessed the effect of these mutations on protein stability and ligand affinity. Notably, our results indicate that the Cys52Phe mutation critically impacts protein-ligand binding, providing valuable insights into potential proteasome inhibitor resistance.

NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes

The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100 000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.

ATLAS: protein flexibility description from atomistic molecular dynamics simulations

Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.

The crystal structure of insecticidal protein Txp40 from Xenorhabdus nematophila reveals a two-domain unique binary toxin with homology to the toxin-antitoxin (TA) system

Txp40 is a ubiquitous, conserved, and novel toxin from Xenorhabdus and Photorhabdus bacteria, toxic to a wide range of insect pests. However, the three-dimensional structure and toxicity mechanism for Txp40 or any of its sequence homologs are not yet known. Here, we are reporting the crystal structure of the insecticidal protein Txp40 from Xenorhabdus nematophila at 2.08 Å resolution. The Txp40 was structurally distinct from currently known insecticidal proteins. Txp40 consists of two structurally different domains, an N-terminal domain (NTD) and a C-terminal domain (CTD), primarily joined by a 33-residue long linker peptide. Txp40 displayed proteolytic propensity. Txp40 gets proteolyzed, removing the linker peptide, which is essential for proper crystal packing. NTD adopts a novel fold composed of nine amphipathic helices and has no shared sequence or structural homology to any known proteins. CTD has structural homology with RNases of type II toxin-antitoxin (TA) complex belonging to the RelE/ParE toxin domain superfamily. NTD and CTD were individually toxic to Galleria mellonella larvae. However, maximal toxicity was observed when both domains were present. Our results suggested that the Txp40 acts as a two-domain binary toxin, which is unique and different from any known binary toxins and insecticidal proteins. Txp40 is also unique because it belongs to the prokaryotic RelE/ParE toxin family with a toxic effect on eukaryotic organisms, in contrast to other members of the same family. Broad insect specificity and unique binary toxin complex formation make Txp40 a viable candidate to overcome the development of resistance in insect pests.

Outer membrane β-barrel structure prediction through the lens of AlphaFold2

Most proteins found in the outer membrane of gram-negative bacteria share a common domain: the transmembrane β-barrel. These outer membrane β-barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ββ-hairpins. This is part of the reason why predicting their three-dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close-to-experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB-like folds of various topologies using an in-house-developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane β-barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane β-barrel topologies identified in high-throughput OMBB annotation studies or designed de novo.

Molecular determinants of ligand efficacy and potency in GPCR signaling

Heterotrimeric guanine nucleotide-binding protein (G protein)-coupled receptors (GPCRs) bind to extracellular ligands and drugs and modulate intracellular responses through conformational changes. Despite their importance as drug targets, the molecular origins of pharmacological properties such as efficacy (maximum signaling response) and potency (the ligand concentration at half-maximal response) remain poorly understood for any ligand-receptor-signaling system. We used the prototypical adrenaline-β2 adrenergic receptor-G protein system to reveal how specific receptor residues decode and translate the information encoded in a ligand to mediate a signaling response. We present a data science framework to integrate pharmacological and structural data to uncover structural changes and allosteric networks relevant for ligand pharmacology. These methods can be tailored to study any ligand-receptor-signaling system, and the principles open possibilities for designing orthosteric and allosteric compounds with defined signaling properties.

Effects of Hydrogen Peroxide on the Hydrogen Bonding Structure and Dynamics of Water and Its Influence on the Aqueous Solvation of the Insulin Monomer

The hydrogen bond structure and dynamics of water and hydrogen peroxide (H2O2) in their binary mixtures have been studied at 298 K by classical molecular dynamics simulations. Twelve different concentrations of aqueous-H2O2 solutions are considered for this study. We have analyzed the interactions between water and H2O2 by site-site pair correlation functions and observed that the probability of formation of OW···HP hydrogen bonds are higher compared to OP···HW. The second solvation shell of water is strongly affected by increasing H2O2 concentrations (XP > 0.50), which signifies the destruction of the tetrahedral network structure of water. The translational and rotational dynamics of water and H2O2 do not significantly change up to 25% of H2O2 in aqueous mixtures. The hydrogen bond lifetime of water-water, water-H2O2, and H2O2-H2O2 in the aqueous-H2O2 solutions shows a very minimal change with increasing H2O2 concentrations. In addition to this, we also investigated the effect of H2O2 on the insulin monomer and observed that higher concentrations of H2O2 (XP = 0.10) change the secondary structure. The influence of H2O2 is more on chain-B than that on chain-A in the insulin monomer. The H2O2 occupancy at the protein surface is higher for negatively charged (GLU) and polar (ASN and THR) amino acid residues compared with that for positively charged and neutral residues in the solutions.

The functional mutational landscape of the lacZ gene

The lacZ gene of Escherichia coli encodes β-galactosidase (β-gal), a lactose metabolism enzyme of the lactose operon. Previous chemical modification or site-directed mutagenesis experiments have identified 21 amino acids that are essential for β-gal catalytic activity. We have assembled over 10,000 lacZ mutations from published studies that were collected using a positive selection assay to identify mutations in lacZ that disrupted β-gal function. We analyzed 6,465 independent lacZ mutations that resulted in 2,732 missense mutations that impaired β-gal function. Those mutations affected 492 of the 1,023 lacZ codons, including most of the 21 previously known residues critical for catalytic activity. Most missense mutations occurred near the catalytic site and in regions important for subunit tetramerization. Overall, our work provides a comprehensive and detailed map of the amino acid residues affecting the structure and catalytic activity of the β-gal enzyme.

PoSSuM v.3: A Major Expansion of the PoSSuM Database for Finding Similar Binding Sites of Proteins

Information on structures of protein-ligand complexes, including comparisons of known and putative protein-ligand-binding pockets, is valuable for protein annotation and drug discovery and development. To facilitate biomedical and pharmaceutical research, we developed PoSSuM (https://possum.cbrc.pj.aist.go.jp/PoSSuM/), a database for identifying similar binding pockets in proteins. The current PoSSuM database includes 191 million similar pairs among almost 10 million identified pockets. PoSSuM drug search (PoSSuMds) is a resource for investigating ligand and receptor diversity among a set of pockets that can bind to an approved drug compound. The enhanced PoSSuMds covers pockets associated with both approved drugs and drug candidates in clinical trials from the latest release of ChEMBL. Additionally, we developed two new databases: PoSSuMAg for investigating antibody-antigen interactions and PoSSuMAF to simplify exploring putative pockets in AlphaFold human protein models.

Spidroins under the Influence of Alcohol: Effect of Ethanol on Secondary Structure and Molecular Level Solvation of Silk-Like Proteins

Future sustainable materials based on designer biomolecules require control of the solution assembly, but also interfacial interactions. Alcohol treatments of protein materials are an accessible means to this, making understanding of the process at the molecular level of seminal importance. We focus here on the influence of ethanol on spidroins, the main proteins of silk. By large-scale atomistically detailed molecular dynamics (MD) simulations and interconnected experiments, we characterize the protein aggregation, secondary structure changes, molecular level origins of them, and solvation environment changes for the proteins, as induced by ethanol as a solvation additive. The MD and circular dichoroism (CD) findings jointly show that ethanol promotes ordered structure in the protein molecules, leading to an increase of helix content and turns but also increased aggregation, as revealed by dynamic light scattering (DLS) and light microscopy. The structural changes correlate at the molecular level with increased intramolecular hydrogen bonding. The simulations reveal that polar amino acids, such as glutamine and serine, are most influenced by ethanol, whereas glycine residues are most prone to be involved in the ethanol-induced secondary structure changes. Furthermore, ethanol engages in interactions with the hydrophobic alanine-rich regions of the spidroin, significantly decreasing the hydrophobic interactions of the protein with itself and its surroundings. The protein solutes also change the microstructure of water/ethanol mixtures, essentially decreasing the level of larger local clustering. Overall, the work presents a systematic characterization of ethanol effects on a widely used, common protein type, spidroins, and generalizes the findings to other intrinsically disordered proteins by pinpointing the general features of the response. The results can aid in designing effective alcohol treatments for proteins, but also enable design and tuning of protein material properties by a relatively controllable solvation handle, the addition of ethanol.

Revealing the interaction between peptide drugs and permeation enhancers in the presence of intestinal bile salts

Permeability enhancer-based formulations offer a promising approach to enhance the oral bioavailability of peptides. We used all-atom molecular dynamics simulations to investigate the interaction between two permeability enhancers (sodium caprate, and SNAC), and four different peptides (octreotide, hexarelin, degarelix, and insulin), in the presence of taurocholate, an intestinal bile salt. The permeability enhancers exhibited distinct effects on peptide release based on their properties, promoting hydrophobic peptide release while inhibiting water-soluble peptide release. Lowering peptide concentrations in the simulations reduced peptide-peptide interactions but increased their interactions with the enhancers and taurocholates. Introducing peptides randomly with enhancer and taurocholate molecules yielded dynamic molecular aggregation, and reduced peptide-peptide interactions and hydrogen bond formation compared to peptide-only systems. The simulations provided insights into molecular-level interactions, highlighting the specific contacts between peptide residues responsible for aggregation, and the interactions between peptide residues and permeability enhancers/taurocholates that are crucial within the mixed colloids. Therefore, our results can provide insights into how modifications of these critical contacts can be made to alter drug release profiles from peptide-only or mixed peptide-PE-taurocholate aggregates. To further probe the molecular nature of permeability enhancers and peptide interactions, we also analyzed insulin secondary structures using Fourier transform infrared spectroscopy. The presence of SNAC led to an increase in β-sheet formation in insulin. In contrast, both in the absence and presence of caprate, α-helices, and random structures dominated. These molecular-level insights can guide the design of improved permeability enhancer-based dosage forms, allowing for precise control of peptide release profiles near the intended absorption site.

A bivalent form of a RBD-specific synthetic antibody effectively neutralizes SARS-CoV-2 variants

Coronavirus Disease 2019 (COVID-19) pandemic is severely impacting the world, and tremendous efforts have been made to deal with it. Despite many advances in vaccines and therapeutics, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants remains an intractable challenge. We present a bivalent Receptor Binding Domain (RBD)-specific synthetic antibody, specific for the RBD of wild-type (lineage A), developed from a non-antibody protein scaffold composed of LRR (Leucine-rich repeat) modules through phage display. We further reinforced the unique feature of the synthetic antibody by constructing a tandem dimeric form. The resulting bivalent form showed a broader neutralizing activity against the variants. The in vivo neutralizing efficacy of the bivalent synthetic antibody was confirmed using a human ACE2-expressing mouse model that significantly alleviated viral titer and lung infection. The present approach can be used to develop a synthetic antibody showing a broader neutralizing activity against a multitude of SARS-CoV-2 variants.

Structure of phosphorylated-like RssB, the adaptor delivering σs to the ClpXP proteolytic machinery, reveals an interface switch for activation

In enterobacteria such as Escherichia coli, the general stress response is mediated by σs, the stationary phase dissociable promoter specificity subunit of RNA polymerase. σs is degraded by ClpXP during active growth in a process dependent on the RssB adaptor, which is thought to be stimulated by the phosphorylation of a conserved aspartate in its N-terminal receiver domain. Here we present the crystal structure of full-length RssB bound to a beryllofluoride phosphomimic. Compared to the structure of RssB bound to the IraD anti-adaptor, our new RssB structure with bound beryllofluoride reveals conformational differences and coil-to-helix transitions in the C-terminal region of the RssB receiver domain and in the interdomain segmented helical linker. These are accompanied by masking of the α4-β5-α5 (4-5-5) "signaling" face of the RssB receiver domain by its C-terminal domain. Critically, using hydrogen-deuterium exchange mass spectrometry, we identify σs-binding determinants on the 4-5-5 face, implying that this surface needs to be unmasked to effect an interdomain interface switch and enable full σs engagement and hand-off to ClpXP. In activated receiver domains, the 4-5-5 face is often the locus of intermolecular interactions, but its masking by intramolecular contacts upon phosphorylation is unusual, emphasizing that RssB is a response regulator that undergoes atypical regulation.

Programming co-assembled peptide nanofiber morphology via anionic amino acid type: Insights from molecular dynamics simulations

Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecular architecture. Here we use molecular dynamics simulations to quantify the effect of anionic amino acid type on co-assembly dynamics and nanofiber structure in binary CATCH(+/-) peptide systems. CATCH peptide sequences follow a general pattern: CQCFCFCFCQC, where all C's are either a positively charged or a negatively charged amino acid. Specifically, we investigate the effect of substituting aspartic acid residues for the glutamic acid residues in the established CATCH(6E-) molecule, while keeping CATCH(6K+) unchanged. Our results show that structures consisting of CATCH(6K+) and CATCH(6D-) form flatter β-sheets, have stronger interactions between charged residues on opposing β-sheet faces, and have slower co-assembly kinetics than structures consisting of CATCH(6K+) and CATCH(6E-). Knowledge of the effect of sidechain type on assembly dynamics and fibrillar structure can help guide the development of advanced biomaterials and grant insight into sequence-to-structure relationships.