Improved prediction of protein-protein interactions using AlphaFold2

DiffPaSS-high-performance differentiable pairing of protein sequences using soft scores

MOTIVATION

Identifying interacting partners from two sets of protein sequences has important applications in computational biology. Interacting partners share similarities across species due to their common evolutionary history, and feature correlations in amino acid usage due to the need to maintain complementary interaction interfaces. Thus, the problem of finding interacting pairs can be formulated as searching for a pairing of sequences that maximizes a sequence similarity or a coevolution score. Several methods have been developed to address this problem, applying different approximate optimization methods to different scores.

RESULTS

We introduce Differentiable Pairing using Soft Scores (DiffPaSS), a differentiable framework for flexible, fast, and hyperparameter-free optimization for pairing interacting biological sequences, which can be applied to a wide variety of scores. We apply it to a benchmark prokaryotic dataset, using mutual information and neighbor graph alignment scores. DiffPaSS outperforms existing algorithms for optimizing the same scores. We demonstrate the usefulness of our paired alignments for the prediction of protein complex structure. DiffPaSS does not require sequences to be aligned, and we also apply it to nonaligned sequences from T-cell receptors.

AVAILABILITY AND IMPLEMENTATION

A PyTorch implementation and installable Python package are available at https://github.com/Bitbol-Lab/DiffPaSS.

High-Throughput Screening for Enhanced Thermal Stability of Inherently Salt-Tolerant l-Glutaminase and Its Efficient Expression in Bacillus licheniformis

In addressing the challenges posed by extended fermentation cycles and high-salt conditions in high-salt liquid-state fermentation soy sauce (HLFSS) production, a high-throughput screening method was devised to identify thermally stable l-glutaminase mutants. This study yielded mutants A146D and A51D, exhibiting enhanced thermal stability. Computer-aided analysis revealed that these mutations introduced additional forces, compacting the protein structure and lowering the Gibbs free energy, thereby improving thermostability. Furthermore, the incorporation of aspartic acid augmented the negative surface charge, contributing to superior salt tolerance compared to the wild type (WT). Notably, in a 25% NaCl buffer, A146D and A51D demonstrated half-lives of 72.57 and 71.31 day, respectively, surpassing the WT's 59.08 day. In a 5 L bioreactor, the optimal mutant A146D achieved a remarkable enzymatic activity of 2800.78 ± 98.1 U/mL in recombinant Bacillus licheniformis fermentation broth, setting a new benchmark. This research offers valuable insights and a foundation for the modification and application of l-glutaminase in the food industry, particularly in HLFSS brewing.

Identification of novel FosX family determinants from diverse environmental samples

OBJECTIVES

This study aimed to identify novel fosfomycin resistance genes across diverse environmental samples, ranging in levels of anthropogenic pollution. We focused on fosfomycin resistance, and given its increasing clinical importance, explored the prevalence of these genes within different environmental contexts.

METHODS

Metagenomic DNA was extracted from wastewater and sediment samples collected from sites in India, Sweden, and Antarctica. Class 1 integron gene cassette libraries were prepared, and resistant clones were selected on fosfomycin-supplemented media. Long-read sequencing was performed followed by bioinformatics analysis to identify novel fosfomycin resistance genes. The genes were cloned and functionally characterized in E. coli, and the impact of phosphonoformate on the enzymes was assessed.

RESULTS

Four novel fosfomycin resistance genes were identified. Phylogenetic analysis placed these genes within the FosX family, a group of metalloenzymes that hydrolyse fosfomycin without thiol conjugation. The genes were subsequently renamed fosE2, fosI2, fosI3, and fosP. Functional assays confirmed that these genes conferred resistance to fosfomycin in E. coli, with MIC ranging from 32 μg/ml to 256 μg/ml. Unlike FosA/B enzymes, these FosX-like proteins were resistant to phosphonoformate inhibitory action. A fosI3 homolog was identified in Pseudomonas aeruginosa, highlighting potential clinical relevance.

CONCLUSIONS

This study expands the understanding of fosfomycin resistance by identifying new FosX family members across diverse environments. The lack of phosphonoformate inhibition underscores the clinical importance of these poorly studied enzymes, which warrant further investigation, particularly in pathogenic contexts.

Accurate predictions of SARS-CoV-2 infectivity from comprehensive analysis

An unprecedented amount of SARS-CoV-2 data has been accumulated compared with previous infectious diseases, enabling insights into its evolutionary process and more thorough analyses. This study investigates SARS-CoV-2 features as it evolved to evaluate its infectivity. We examined viral sequences and identified the polarity of amino acids in the receptor binding motif (RBM) region. We detected an increased frequency of amino acid substitutions to lysine (K) and arginine (R) in variants of concern (VOCs). As the virus evolved to Omicron, commonly occurring mutations became fixed components of the new viral sequence. Furthermore, at specific positions of VOCs, only one type of amino acid substitution and a notable absence of mutations at D467 were detected. We found that the binding affinity of SARS-CoV-2 lineages to the ACE2 receptor was impacted by amino acid substitutions. Based on our discoveries, we developed APESS, an evaluation model evaluating infectivity from biochemical and mutational properties. In silico evaluation using real-world sequences and in vitro viral entry assays validated the accuracy of APESS and our discoveries. Using Machine Learning, we predicted mutations that had the potential to become more prominent. We created AIVE, a web-based system, accessible at https://ai-ve.org to provide infectivity measurements of mutations entered by users. Ultimately, we established a clear link between specific viral properties and increased infectivity, enhancing our understanding of SARS-CoV-2 and enabling more accurate predictions of the virus.

Benzoylmesaconine alters the native structure and activity of hen egg white lysozyme: revealing possible mechanism of aconitum-induced toxicity

PURPOSE

This study examines the interaction between benzoylmesaconine (BMA) and hen egg white lysozyme (HEWL) under various physiological conditions, aiming to determine how BMA affects the HEWL's structure and function.

METHODS

Several analytical techniques were used, including tryptophan assay, light scattering, thioflavin T (ThT)-binding assay, dynamic light scattering, 8-anilino-1-naphthalenesulfonic acid (ANS)-binding assay, circular dichroism (CD) spectroscopy, enzyme activity assay, and molecular docking.

RESULTS

The tryptophan assay displayed a concentration-dependent decrease in tryptophan fluorescence, showing an interaction between BMA and HEWL. Light scattering and ThT-binding assays confirmed increased protein aggregation and amyloid fibril formation, while the ANS-binding assay demonstrated altered exposed hydrophobic regions, implying structural changes. CD spectroscopy showed a reduction in α-helix content, indicating conformational alterations, and enzyme activity assays showed a loss of lytic function due to structural distortion. Finally, molecular docking identified significant bonds and hydrophobic interactions between BMA and HEWL residues.

CONCLUSIONS

BMA binding induces structural changes in proteins, forming small oligomers and amyloid fibrils that decrease HEWL enzymatic activity and disrupt functional integrity.

Dissecting Cytophagalysin: Structural and Biochemical Studies of a Bacterial Pappalysin-Family Metallopeptidase

Cytophaga is a genus of Gram-negative bacteria occurring in soil and the gut microbiome. It is closely related to pathogenic Flavobacterium spp. that cause severe diseases in fish. Cytophaga strain L43-1 secretes cytophagalysin (CPL1), a 137 kDa peptidase with reported collagenolytic and gelatinolytic activity. We performed highly-confident structure prediction calculations for CPL1, which identified 11 segments and domains, including a signal peptide for secretion, a prosegment (PS) for latency, a metallopeptidase (MP)-like catalytic domain (CD), and eight immunoglobulin (Ig)-like domains (D3-D10). In addition, two short linkers were found at the D8-D9 and D9-D10 junctions, and the structure would be crosslinked by four disulfide bonds. The CPL1 CD was found closest to ulilysin from Methanosarcina acetivorans, which assigns CPL1 to the lower-pappalysin family within the metzincin clan of MPs. Based on the structure predictions, we aimed to produce constructs spanning the full-length enzyme, as well as PS+CD, PS+CD+D3, and PS+CD+D3+D4. However, we were successful only with the latter three constructs. We could activate recombinant CPL1 by PS removal employing trypsin, and found that both zymogen and mature CPL1 were active in gelatin zymography and against a fluorogenic gelatin variant. This activity was ablated in a mutant, in which the catalytic glutamate described for lower pappalyins and other metzincins was replaced by alanine, and by a broad-spectrum metal chelator. Overall, these results proved that our recombinant CPL1 is a functional active MP, thus supporting the conclusions derived from the structure predictions.

ProBID-Net: a deep learning model for protein-protein binding interface design

Protein-protein interactions are pivotal in numerous biological processes. The computational design of these interactions facilitates the creation of novel binding proteins, crucial for advancing biopharmaceutical products. With the evolution of artificial intelligence (AI), protein design tools have swiftly transitioned from scoring-function-based to AI-based models. However, many AI models for protein design are constrained by assuming complete unfamiliarity with the amino acid sequence of the input protein, a feature most suited for de novo design but posing challenges in designing protein-protein interactions when the receptor sequence is known. To bridge this gap in computational protein design, we introduce ProBID-Net. Trained using natural protein-protein complex structures and protein domain-domain interface structures, ProBID-Net can discern features from known target protein structures to design specific binding proteins based on their binding sites. In independent tests, ProBID-Net achieved interface sequence recovery rates of 52.7%, 43.9%, and 37.6%, surpassing or being on par with ProteinMPNN in binding protein design. Validated using AlphaFold-Multimer, the sequences designed by ProBID-Net demonstrated a close correspondence between the design target and the predicted structure. Moreover, the model's output can predict changes in binding affinity upon mutations in protein complexes, even in scenarios where no data on such mutations were provided during training (zero-shot prediction). In summary, the ProBID-Net model is poised to significantly advance the design of protein-protein interactions.

Prediction of protein interactions between pine and pine wood nematode using deep learning and multi-dimensional feature fusion

Pine Wilt Disease (PWD) is a devastating forest disease that has a serious impact on ecological balance ecological. Since the identification of plant-pathogen protein interactions (PPIs) is a critical step in understanding the pathogenic system of the pine wilt disease, this study proposes a Multi-feature Fusion Graph Attention Convolution (MFGAC-PPI) for predicting plant-pathogen PPIs based on deep learning. Compared with methods based on single-feature information, MFGAC-PPI obtains more 3D characterization information by utilizing AlphaFold and combining protein sequence features to extract multi-dimensional features via Transform with improved GCN. The performance of MFGAC-PPI was compared with the current representative methods of sequence-based, structure-based and hybrid characterization, demonstrating its superiority across all metrics. The experiments showed that learning multi-dimensional feature information effectively improved the ability of MFGAC-PPI in plant and pathogen PPI prediction tasks. Meanwhile, a pine wilt disease PPI network consisting of 2,688 interacting protein pairs was constructed based on MFGAC-PPI, which made it possible to systematically discover new disease resistance genes in pine trees and promoted the understanding of plant-pathogen interactions.

ESM-scan-A tool to guide amino acid substitutions

Protein structure prediction and (re)design have gone through a revolution in the last 3 years. The tremendous progress in these fields has been almost exclusively driven by readily available machine learning algorithms applied to protein folding and sequence design problems. Despite these advancements, predicting site-specific mutational effects on protein stability and function remains an unsolved problem. This is a persistent challenge, mainly because the free energy of large systems is very difficult to compute with absolute accuracy and subtle changes to protein structures are hard to capture with computational models. Here, we describe the implementation and use of ESM-Scan, which uses the ESM zero-shot predictor to scan entire protein sequences for preferential amino acid changes, thus enabling in silico deep mutational scanning experiments. We benchmark ESM-Scan on its predictive capabilities for stability and functionality of sequence changes using three publicly available datasets and proceed by experimentally testing the tool's performance on a challenging test case of a blue-light-activated diguanylate cyclase from Methylotenera species (MsLadC), where it accurately predicted the importance of a highly conserved residue in a region involved in allosteric product inhibition. Our experimental results show that the ESM-zero shot model is capable of inferring the effects of a set of amino acid substitutions in their correlation between predicted fitness and experimental results. ESM-Scan is publicly available at https://huggingface.co/spaces/thaidaev/zsp.

Helicobacter pylori East Asian type CagA hijacks more SHIP2 by its EPIYA-D motif to potentiate the oncogenicity

CagA is a significant oncogenic factor injected into host cells by Helicobacter pylori, which is divided into two subtypes: East Asian type (CagAE), characterized by the EPIYA-D motif, and western type (CagAW), harboring the EPIYA-C motif. CagAE has been reported to have higher carcinogenicity than CagAW, although the underlying reason is not fully understood. SHIP2 is an intracellular phosphatase that can be recruited by CagA to perturb the homeostasis of intracellular signaling pathways. In this study, we found that SHIP2 contributes to the higher oncogenicity of CagAE. Co-Immunoprecipitation and Pull-down assays showed that CagAE bind more SHIP2 than CagAW. Immunofluorescence staining showed that a higher amount of SHIP2 recruited by CagAE to the plasma membrane catalyzes the conversion of PI(3,4,5)P3 into PI(3,4)P2. This alteration causes higher activation of Akt signaling, which results in enhanced IL-8 secretion, migration, and invasion of the infected cells. SPR analysis showed that this stronger interaction between CagAE and SHIP2 stems from the higher affinity between the EPIYA-D motif of CagAE and the SH2 domain of SHIP2. Structural analysis revealed the crucial role of the Phe residue at the Y + 5 position in EPIYA-D. After mutating Phe of CagAE into Asp (the corresponding residue in the EPIYA-C motif) or Ala, the activation of downstream Akt signaling was reduced and the malignant transformation of infected cells was alleviated. These findings revealed that CagAE hijacks SHIP2 through its EPIYA-D motif to enhance its carcinogenicity, which provides a better understanding of the higher oncogenic risk of H. pylori CagAE.

Biochemistry and genetics are coming together to improve our understanding of genotype to phenotype relationships

Since genome sequencing became accessible, determining how specific differences in genotypes lead to complex phenotypes such as disease has become one of the key goals in biomedicine. Predicting effects of sequence variants on cellular or organismal phenotype faces several challenges. First, variants simultaneously affect multiple protein properties and predicting their combined effect is complex. Second, effects of changes in a single protein propagate through the cellular network, which we only partially understand. In this review, we emphasize the importance of both biochemistry and genetics in addressing these challenges. Moreover, we highlight work that blurs the distinction between biochemistry and genetics fields to provide new insights into the genotype-to-phenotype relationships.

Determining key residues of engineered scFv antibody variants with improved MMP-9 binding using deep sequencing and machine learning

Given the crucial role of specific matrix metalloproteinases (MMPs) in the extracellular matrix, an imbalance in the regulation of activation of matrix metalloproteinase-9 (MMP-9) zymogen and inhibition of the enzyme can result in various diseases, such as cancer, neurodegenerative, and gynecological diseases. Thus, developing novel therapeutics that target MMP-9 with single-chain antibody fragments (scFvs) is a promising approach. We used fluorescent-activated cell sorting (FACS) to screen a synthetic scFv antibody library displayed on yeast for enhanced binding to MMP-9. The screened scFv mutants demonstrated improved binding to MMP-9 compared to the natural inhibitor of MMPs, tissue inhibitor of metalloproteinases (TIMPs). To identify the molecular determinants of these engineered scFv variants that affect binding to MMP-9, we used next-generation DNA sequencing and computational protein structure analysis. Additionally, a deep-learning language model was trained on the screened scFv library of variants to predict the binding affinities of scFv variants based on their CDR-H3 sequences.

New Insights into Bioactive Peptides: Design, Synthesis, Structure-Activity Relationship

In recent decades, peptides have attracted significant attention not only from Academia but also from big Pharma as novel potential therapeutic compounds [...].

Mass spectrometry-complemented molecular modeling predicts the interaction interface for a camelid single-domain antibody targeting the Plasmodium falciparum circumsporozoite protein's C-terminal domain

Background

Bioanalytical methods that enable rapid and high-detail characterization of binding specificities and strengths of protein complexes with low sample consumption are highly desired. The interaction between a camelid single domain antibody (sdAbCSP1) and its target antigen (PfCSP-Cext) was selected as a model system to provide proof-of-principle for the here described methodology.

Research design and methods

The structure of the sdAbCSP1 - PfCSP-Cext complex was modeled using AlphaFold2. The recombinantly expressed proteins, sdAbCSP1, PfCSP-Cext, and the sdAbCSP1 - PfCSP-Cext complex, were subjected to limited proteolysis and mass spectrometric peptide analysis. ITEM MS (Intact Transition Epitope Mapping Mass Spectrometry) and ITC (Isothermal Titration Calorimetry) were applied to determine stoichiometry and binding strength.

Results

The paratope of sdAbCSP1 mainly consists of its CDR3 (aa100-118). PfCSP-Cext's epitope is assembled from its α-helix (aa40-52) and opposing loop (aa83-90). PfCSP-Cext's GluC cleavage sites E46 and E58 were shielded by complex formation, confirming the predicted epitope. Likewise, sdAbCSP1's tryptic cleavage sites R105 and R108 were shielded by complex formation, confirming the predicted paratope. ITEM MS determined the 1:1 stoichiometry and the high complex binding strength, exemplified by the gas phase dissociation reaction enthalpy of 50.2 kJ/mol. The in-solution complex dissociation constant is 5 × 10-10 M.

Conclusions

Combining AlphaFold2 modeling with mass spectrometry/limited proteolysis generated a trustworthy model for the sdAbCSP1 - PfCSP-Cext complex interaction interface.

Recent advances and challenges in protein complex model accuracy estimation

Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.

Anti-viral defence by an mRNA ADP-ribosyltransferase that blocks translation

Host-pathogen conflicts are crucibles of molecular innovation1,2. Selection for immunity to pathogens has driven the evolution of sophisticated immunity mechanisms throughout biology, including in bacterial defence against bacteriophages3. Here we characterize the widely distributed anti-phage defence system CmdTAC, which provides robust defence against infection by the T-even family of phages4. Our results support a model in which CmdC detects infection by sensing viral capsid proteins, ultimately leading to the activation of a toxic ADP-ribosyltransferase effector protein, CmdT. We show that newly synthesized capsid protein triggers dissociation of the chaperone CmdC from the CmdTAC complex, leading to destabilization and degradation of the antitoxin CmdA, with consequent liberation of the CmdT ADP-ribosyltransferase. Notably, CmdT does not target a protein, DNA or structured RNA, the known targets of other ADP-ribosyltransferases. Instead, CmdT modifies the N6 position of adenine in GA dinucleotides within single-stranded RNAs, leading to arrest of mRNA translation and inhibition of viral replication. Our work reveals a novel mechanism of anti-viral defence and a previously unknown but broadly distributed class of ADP-ribosyltransferases that target mRNA.

Protein subcellular localization prediction tools

Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.

Studying protein-protein interactions: Latest and most popular approaches

PPIs, or protein-protein interactions, are essential for many biological processes. According to the findings, abnormal PPIs have been linked to several diseases, such as cancer and infectious and neurological disorders. Consequently, focusing on PPIs is a path toward disease treatment and a crucial tool for producing novel medications. Many methods exist to investigate PPIs, including low- and high-throughput studies. Since many PPIs have been discovered using in vitro and in vivo experimental approaches, the use of computational methods to predict PPIs has grown due to the expanding scale of PPI data and the intrinsic complexity of interacting mechanisms. Recognizing PPI networks offers a systematic means of predicting protein functions, and pathways that are included. These investigations can help uncover the underlying molecular mechanisms of complex phenotypes and clarify the biological processes related to health and diseases. Therefore, our goal in this study is to provide an overview of the latest and most popular approaches for investigating PPIs. We also overview some important clinical approaches based on the PPIs and how these interactions can be targeted.

Exploration of the hierarchical assembly space of collagen-like peptides beyond the triple helix

The de novo design of self-assembling peptides has garnered significant attention in scientific research. While alpha-helical assemblies have been extensively studied, exploration of polyproline type II helices, such as those found in collagen, remains relatively limited. In this study, we focus on understanding the sequence-structure relationship in hierarchical assemblies of collagen-like peptides, using defense collagen Surfactant Protein A as a model. By dissecting the sequence derived from Surfactant Protein A and synthesizing short collagen-like peptides, we successfully construct a discrete bundle of hollow triple helices. Amino acid substitution studies pinpoint hydrophobic and charged residues that are critical for oligomer formation. These insights guide the de novo design of collagen-like peptides, resulting in the formation of diverse quaternary structures, including discrete and heterogenous bundled oligomers, two-dimensional nanosheets, and pH-responsive nanoribbons. Our study represents a significant advancement in the understanding and harnessing of collagen higher-order assemblies beyond the triple helix.

A potent and selective anti-glutathione peroxidase 4 nanobody as a ferroptosis inducer

Glutathione peroxidase 4 (GPX4) plays a crucial role in the ferroptosis pathway, emerging as a potential drug target in the treatment of refractory tumors. Unfortunately, the development of GPX4-targeted treatment has been very limited due to the poor selectivity and drug-like properties of current GPX4 inhibitors. Here, we report a proof-of-concept study of potent anti-GPX4 nanobodies, successfully identified through immunizing Bactrian camels and constructing a phage library. Utilizing a cell-penetrating peptide fusion strategy, these nanobodies with high affinities to GPX4 efficiently internalized in cells and formed the basis for further applications. In particular, 12E significantly inhibited cellular GPX4 and consequently induced remarkable ferroptosis in cancer cells. Furthermore, 12E could impair zebrafish dorsal organizer formation in vivo, as evidenced by a phenotype comparable to that observed in zebrafish with the gpx4b gene knocked out. The new GPX4-inhibiting nanobody described here exhibits superior proteome-wide selectivity and a vastly improved safety profile compared to existing GPX4 inhibitors. These incredible features of 12E, as an anti-GPX4 nanobody, may not only contribute to ferroptosis-related anticancer treatment but also establish a new paradigm for nanobodies in drug development for traditionally undruggable targets.

Uncoupling FRUITFULL's functions through modification of a protein motif identified by co-ortholog analysis

Many plant transcription factors (TFs) are multifunctional and regulate growth and development in more than one tissue. These TFs can generally associate with different protein partners depending on the tissue type, thereby regulating tissue-specific target gene sets. However, how interaction specificity is ensured is still largely unclear. Here, we examine protein-protein interaction specificity using subfunctionalized co-orthologs of the FRUITFULL (FUL) subfamily of MADS-domain TFs. In Arabidopsis, FUL is multifunctional, playing important roles in flowering and fruiting, whereas these functions have partially been divided in the tomato co-orthologs FUL1 and FUL2. By linking protein sequence and function, we discovered a key amino acid motif that determines interaction specificity of MADS-domain TFs, which in Arabidopsis FUL determines the interaction with AGAMOUS and SEPALLATA proteins, linked to the regulation of a subset of targets. This insight offers great opportunities to dissect the biological functions of multifunctional MADS TFs.

The structural and functional impacts of rationally designed cyclic peptides on self-assembly-mediated functionality

Compared with their linear counterparts, cyclic peptides, characterized by their unique topologies, offer superior stability and enhanced functionality. In this review article, the rational design of cyclic peptide primary structures and their significant influence on self-assembly processes and functional capabilities are comprehensively reviewed. We emphasize how strategically modifying amino acid sequences and ring sizes critically dictate the formation and properties of peptide nanotubes (PNTs) and complex assemblies, such as rotaxanes. Adjusting the number of amino acid residues and side chains allows researchers to tailor the diameter, surface properties, and functions of PNTs precisely. In addition, we discuss the complex host-guest chemistry of cyclic peptides and their ability to form rotaxanes, highlighting their potential in the development of mechanically interlocked structures with novel functionalities. Moreover, the critical role of computational methods for accurately predicting the solution structures of cyclic peptides is also highlighted, as it enables the design of novel peptides with tailored properties for a range of applications. These insights set the stage for groundbreaking advances in nanotechnology, drug delivery, and materials science, driven by the strategic design of cyclic peptide primary structures.

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

Zfp260 choreographs the early stage osteo-lineage commitment of skeletal stem cells

The initial fine-tuning processes are crucial for successful bone regeneration, as they guide skeletal stem cells through progenitor differentiation toward osteo- or chondrogenic fate. While fate determination processes are well-documented, the mechanisms preceding progenitor commitment remain poorly understood. Here, we identified a transcription factor, Zfp260, as pivotal for stem cell maturation into progenitors and directing osteogenic differentiation. Zfp260 is markedly up-regulated as cells transition from stem to progenitor stages; its dysfunction causes lineage arrest at the progenitor stage, impairing bone repair. Zfp260 is required for maintaining chromatin accessibility and regulates Runx2 expression by forming super-enhancer complexes. Furthermore, the PKCα kinase phosphorylates Zfp260 at residues Y173, S182, and S197, which are essential for its functional activity. Mutations at these residues significantly impair its functionality. These findings position Zfp260 as a vital factor bridging stem cell activation with progenitor cell fate determination, unveiling a element fundamental to successful bone regeneration.

ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction

MOTIVATION

Accurately predicting the degradation capabilities of proteolysis-targeting chimeras (PROTACs) for given target proteins and E3 ligases is important for PROTAC design. The distinctive ternary structure of PROTACs presents a challenge to traditional drug-target interaction prediction methods, necessitating more innovative approaches. While current state-of-the-art (SOTA) methods using graph neural networks (GNNs) can discern the molecular structure of PROTACs and proteins, thus enabling the efficient prediction of PROTACs' degradation capabilities, they rely heavily on limited crystal structure data of the POI-PROTAC-E3 ternary complex. This reliance underutilizes rich PROTAC experimental data and neglects intricate interaction relationships within ternary complexes.

RESULTS

In this study, we propose a model based on cross-modal strategy and ternary attention technology, ET-PROTACs, to predict the targeted degradation capabilities of PROTACs. Our model capitalizes on the strengths of cross-modal methods by using equivariant GNN graph neural networks to process the graph structure and spatial coordinates of PROTAC molecules concurrently while utilizing sequence-based methods to learn the protein sequence information. This integration of cross-modal information is cohesively harnessed and channeled into a ternary attention mechanism, specially tailored for the unique structure of PROTACs, enabling the congruent modeling of both PROTAC and protein modalities. Experimental results demonstrate that the ET-PROTACs model outperforms existing SOTA methods. Moreover, visualizing attention scores illuminates crucial residues and atoms pivotal in specific POI-PROTAC-E3 interactions, thus offering invaluable insights and guidance for future pharmaceutical research.

AVAILABILITY AND IMPLEMENTATION

The codes of our model are available at https://github.com/GuanyuYue/ET-PROTACs.

AlphaFold2 enables accurate deorphanization of ligands to single-pass receptors

Secreted proteins play crucial roles in paracrine and endocrine signaling; however, identifying ligand-receptor interactions remains challenging. Here, we benchmarked AlphaFold2 (AF2) as a screening approach to identify extracellular ligands to single-pass transmembrane receptors. Key to the approach is the optimization of AF2 input and output for screening ligands against receptors to predict the most probable ligand-receptor interactions. The predictions were performed on ligand-receptor pairs not used for AF2 training. We demonstrate high discriminatory power and a success rate of close to 90% for known ligand-receptor pairs and 50% for a diverse set of experimentally validated interactions. Further, we show that screen accuracy does not correlate linearly with prediction of ligand-receptor interaction. These results demonstrate a proof of concept of a rapid and accurate screening platform to predict high-confidence cell-surface receptors for a diverse set of ligands by structural binding prediction, with potentially wide applicability for the understanding of cell-cell communication.

PepCARES: A Comprehensive Advanced Refinement and Evaluation System for Peptide Design and Affinity Screening

Peptides are crucial in vaccine research, and their remarkable specificity and efficacy make them a promising potential drug class. However, designing and screening these peptides computationally is challenging. Here, we present the comprehensive advanced refinement and evaluation system (PepCARES), a program utilizing our novel model called PeptideMPNN and score evaluation for peptide design and affinity screening. PeptideMPNN, built on ProteinMPNN with transfer learning, significantly enhances sequence recovery (by 26.26%) and reduces perplexity (by 0.536) in a sequence generation task. We designed peptides targeting two HLA alleles and, using MHCfovea and PDBePISA, identified candidates with high potential. From 20 designed peptides, 14 and 7 peptides were selected, respectively. Our research provides a method for designing and screening peptides, making an important step toward the development of peptide-based vaccines.

MFPSP: Identification of fungal species-specific phosphorylation site using offspring competition-based genetic algorithm

Protein phosphorylation is essential in various signal transduction and cellular processes. To date, most tools are designed for model organisms, but only a handful of methods are suitable for predicting task in fungal species, and their performance still leaves much to be desired. In this study, a novel tool called MFPSP is developed for phosphorylation site prediction in multi-fungal species. The amino acids sequence features were derived from physicochemical and distributed information, and an offspring competition-based genetic algorithm was applied for choosing the most effective feature subset. The comparison results shown that MFPSP achieves a more advanced and balanced performance to several state-of-the-art available toolkits. Feature contribution and interaction exploration indicating the proposed model is efficient in uncovering concealed patterns within sequence. We anticipate MFPSP to serve as a valuable bioinformatics tool and benefiting practical experiments by pre-screening potential phosphorylation sites and enhancing our functional understanding of phosphorylation modifications in fungi. The source code and datasets are accessible at https://github.com/AI4HKB/MFPSP/.

PRITrans: A Transformer-Based Approach for the Prediction of the Effects of Missense Mutation on Protein-RNA Interactions

Protein-RNA interactions are essential to many cellular functions, and missense mutations in RNA-binding proteins can disrupt these interactions, often leading to disease. To address this, we developed PRITrans, a specialized computational method aimed at predicting the effects of missense mutations on protein-RNA interactions, which is vital for understanding disease mechanisms and advancing molecular biology research. PRITrans is a novel deep learning model designed to predict the effects of missense mutations on protein-RNA interactions, which employs a Transformer architecture enhanced with multiscale convolution modules for comprehensive feature extraction. Its primary innovation lies in integrating protein language model embeddings with a deep feature fusion strategy, effectively handling high-dimensional feature representations. By utilizing multi-layer self-attention mechanisms, PRITrans captures nuanced, high-level sequence information, while multiscale convolutions extract features across various depths, thereby enhancing predictive accuracy. Consequently, this architecture enables significant improvements in ΔΔG prediction compared to traditional approaches. We validated PRITrans using three different cross-validation strategies on two newly reconstructed mutation datasets, S315 and S630 (containing 315 forward and 315 reverse mutations). The results consistently demonstrated PRITrans's strong performance on both datasets. PRITrans demonstrated strong predictive capability, achieving a Pearson correlation coefficient of 0.741 and a root mean square error (RMSE) of 1.168 kcal/mol on the S630 dataset. Moreover, its robust performance extended to independent test sets, achieving a Pearson correlation of 0.699 and an RMSE of 1.592 kcal/mol. These results underscore PRITrans's potential as a powerful tool for protein-RNA interaction studies. Moreover, when tested against existing prediction methods on an independent dataset, PRITrans showed improved predictive accuracy and robustness.

Structure of the yeast ceramide synthase

Ceramides are essential lipids involved in forming complex sphingolipids and acting as signaling molecules. They result from the N-acylation of a sphingoid base and a CoA-activated fatty acid, a reaction catalyzed by the ceramide synthase (CerS) family of enzymes. Yet, the precise structural details and catalytic mechanisms of CerSs have remained elusive. Here we used cryo-electron microscopy single-particle analysis to unravel the structure of the yeast CerS complex in both an active and a fumonisin B1-inhibited state. Our results reveal the complex's architecture as a dimer of Lip1 subunits bound to the catalytic subunits Lag1 and Lac1. Each catalytic subunit forms a hydrophobic crevice connecting the cytosolic site with the intermembrane space. The active site, located centrally in the tunnel, was resolved in a substrate preloaded state, representing one intermediate in ceramide synthesis. Our data provide evidence for competitive binding of fumonisin B1 to the acyl-CoA-binding tunnel.

Integration of molecular coarse-grained model into geometric representation learning framework for protein-protein complex property prediction

Structure-based machine learning algorithms have been utilized to predict the properties of protein-protein interaction (PPI) complexes, such as binding affinity, which is critical for understanding biological mechanisms and disease treatments. While most existing algorithms represent PPI complex graph structures at the atom-scale or residue-scale, these representations can be computationally expensive or may not sufficiently integrate finer chemical-plausible interaction details for improving predictions. Here, we introduce MCGLPPI, a geometric representation learning framework that combines graph neural networks (GNNs) with MARTINI molecular coarse-grained (CG) models to predict PPI overall properties accurately and efficiently. Extensive experiments on three types of downstream PPI property prediction tasks demonstrate that at the CG-scale, MCGLPPI achieves competitive performance compared with the counterparts at the atom- and residue-scale, but with only a third of computational resource consumption. Furthermore, CG-scale pre-training on protein domain-domain interaction structures enhances its predictive capabilities for PPI tasks. MCGLPPI offers an effective and efficient solution for PPI overall property predictions, serving as a promising tool for the large-scale analysis of biomolecular interactions.

On the Nature of the Interactions That Govern COV-2 Mutants Escape from Neutralizing Antibodies

The most fruitful prevention and treatment tools for the COVID-19 pandemic have proven to be vaccines and therapeutic antibodies, which have reduced the spread of the disease to manageable proportions. The search for the most effective antibodies against the widest set of COV-2 variants has required a long time and substantial resources. It would be desirable to have a tool that will enable us to understand the structural basis on which mutants escape at least some of the epitope-bound antibodies, a tool that may substantially reduce the time and resources invested in this effort. In this work, we applied a computational-based tool (employed previously by us to understand COV-2 spike binding to its cognate cell receptor) to the study of the effect of Delta and Omicron mutations on the escape tendencies. Our binding energy predictions agree extremely well with the experimentally observed escape tendencies. They have also allowed us to set forth a structural explanation for the results that could be used for the screening of antibodies. Lastly, our results explain the differences in molecular interactions that govern interaction of the spike variants with the receptor as opposed to those with antibodies.

CEP192 localises mitotic Aurora-A activity by priming its interaction with TPX2

Aurora-A is an essential cell-cycle kinase with critical roles in mitotic entry and spindle dynamics. These functions require binding partners such as CEP192 and TPX2, which modulate both kinase activity and localisation of Aurora-A. Here we investigate the structure and role of the centrosomal Aurora-A:CEP192 complex in the wider molecular network. We find that CEP192 wraps around Aurora-A, occupies the binding sites for mitotic spindle-associated partners, and thus competes with them. Comparison of two different Aurora-A conformations reveals how CEP192 modifies kinase activity through the site used for TPX2-mediated activation. Deleting the Aurora-A-binding interface in CEP192 prevents centrosomal accumulation of Aurora-A, curtails its activation-loop phosphorylation, and reduces spindle-bound TPX2:Aurora-A complexes, resulting in error-prone mitosis. Thus, by supplying the pool of phosphorylated Aurora-A necessary for TPX2 binding, CEP192:Aurora-A complexes regulate spindle function. We propose an evolutionarily conserved spatial hierarchy, which protects genome integrity through fine-tuning and correctly localising Aurora-A activity.

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction

Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, as they represent the physical contacts and functional associations between proteins. Recent advances have witnessed the achievements of artificial intelligence (AI) methods aimed at predicting PPIs. However, these approaches often handle the intricate web of relationships and mechanisms among proteins, drugs, diseases, ribonucleic acid (RNA), and protein structures in a fragmented or superficial manner. This is typically due to the limitations of non-end-to-end learning frameworks, which can lead to sub-optimal feature extraction and fusion, thereby compromising the prediction accuracy. To address these deficiencies, this paper introduces a novel end-to-end learning model, the Knowledge Graph Fused Graph Neural Network (KGF-GNN). This model comprises three integral components: (1) Protein Associated Network (PAN) Construction: We begin by constructing a PAN that extensively captures the diverse relationships and mechanisms linking proteins with drugs, diseases, RNA, and protein structures. (2) Graph Neural Network for Feature Extraction: A Graph Neural Network (GNN) is then employed to distill both topological and semantic features from the PAN, alongside another GNN designed to extract topological features directly from observed PPI networks. (3) Multi-layer Perceptron for Feature Fusion: Finally, a multi-layer perceptron integrates these varied features through end-to-end learning, ensuring that the feature extraction and fusion processes are both comprehensive and optimized for PPI prediction. Extensive experiments conducted on real-world PPI datasets validate the effectiveness of our proposed KGF-GNN approach, which not only achieves high accuracy in predicting PPIs but also significantly surpasses existing state-of-the-art models. This work not only enhances our ability to predict PPIs with a higher precision but also contributes to the broader application of AI in Bioinformatics, offering profound implications for biological research and therapeutic development.

6-Phosphogluconate dehydrogenase 2 bridges the OPP and shikimate pathways to enhance aromatic amino acid production in plants

The oxidative pentose phosphate (OPP) pathway provides metabolic intermediates for the shikimate pathway and directs carbon flow to the biosynthesis of aromatic amino acids (AAAs), which serve as basic protein building blocks and precursors of numerous metabolites essential for plant growth. However, genetic evidence linking the two pathways is largely unclear. In this study, we identified 6-phosphogluconate dehydrogenase 2 (PGD2), the rate-limiting enzyme of the cytosolic OPP pathway, through suppressor screening of arogenate dehydrogenase 2 (adh2) in Arabidopsis. Our data indicated that a single amino acid substitution at position 63 (glutamic acid to lysine) of PGD2 enhanced its enzyme activity by facilitating the dissociation of products from the active site of PGD2, thus increasing the accumulation of AAAs and partially restoring the defective phenotype of adh2. Phylogenetic analysis indicated that the point mutation occurred in a well-conserved amino acid residue. Plants with different amino acids at this conserved site of PGDs confer diverse catalytic activities, thus exhibiting distinct AAAs producing capability. These findings uncover the genetic link between the OPP pathway and AAAs biosynthesis through PGD2. The gain-of-function point mutation of PGD2 identified here could be considered as a potential engineering target to alter the metabolic flux for the production of AAAs and downstream compounds.

A class of chemical compounds enhances clustering of muscle nicotinic acetylcholine receptor in cultured myogenic cells

Neuromuscular signal transmission is affected in various diseases including myasthenia gravis, congenital myasthenic syndromes, and sarcopenia. We used an ATF2-luciferase system to monitor the phosphorylation of MuSK in HEK293 cells introduced with MUSK and LRP4 cDNAs to find novel chemical compounds that enhanced agrin-mediated acetylcholine receptor (AChR) clustering. Four compounds with similar chemical structures carrying benzene rings and heterocyclic rings increased the luciferase activities 8- to 30-folds, and two of them showed continuously graded dose dependence. The effects were higher than that of disulfiram, a clinically available aldehyde dehydrogenase inhibitor, which we identified to be the most competent preapproved drug to enhance ATF2-luciferase activity in the same assay system. In C2C12 myotubes, all the compounds increased the area, intensity, length, and number of AChR clusters. Three of the four compounds increased the phosphorylation of MuSK, but not of Dok7, JNK. ERK, or p38. Monitoring cell toxicity using the neurite elongation of NSC34 neuronal cells as a surrogate marker showed that all the compounds had no effects on the neurite elongation up to 1 μM. Extensive docking simulation and binding structure prediction of the four compounds with all available human proteins using AutoDock Vina and DiffDock showed that the four compounds were unlikely to directly bind to MuSK or Dok7, and the exact target remained unknown. The identified compounds are expected to serve as a seed to develop a novel therapeutic agent to treat defective NMJ signal transmission.

AlphaFold-Multimer accurately captures interactions and dynamics of intrinsically disordered protein regions

Interactions mediated by intrinsically disordered protein regions (IDRs) pose formidable challenges in structural characterization. IDRs are highly versatile, capable of adopting diverse structures and engagement modes. Motivated by recent strides in protein structure prediction, we embarked on exploring the extent to which AlphaFold-Multimer can faithfully reproduce the intricacies of interactions involving IDRs. To this end, we gathered multiple datasets covering the versatile spectrum of IDR binding modes and used them to probe AlphaFold-Multimer's prediction of IDR interactions and their dynamics. Our analyses revealed that AlphaFold-Multimer is not only capable of predicting various types of bound IDR structures with high success rate, but that distinguishing true interactions from decoys, and unreliable predictions from accurate ones is achievable by appropriate use of AlphaFold-Multimer's intrinsic scores. We found that the quality of predictions drops for more heterogeneous, fuzzy interaction types, most likely due to lower interface hydrophobicity and higher coil content. Notably though, certain AlphaFold-Multimer scores, such as the Predicted Aligned Error and residue-ipTM, are highly correlated with structural heterogeneity of the bound IDR, enabling clear distinctions between predictions of fuzzy and more homogeneous binding modes. Finally, our benchmarking revealed that predictions of IDR interactions can also be successful when using full-length proteins, but not as accurate as with cognate IDRs. To facilitate identification of the cognate IDR of a given partner, we established "minD," which pinpoints potential interaction sites in a full-length protein. Our study demonstrates that AlphaFold-Multimer can correctly identify interacting IDRs and predict their mode of engagement with a given partner.

Applying Spatiotemporal Modeling of Cell Dynamics to Accelerate Drug Development

Cells act as physical computational programs that utilize input signals to orchestrate molecule-level protein-protein interactions (PPIs), generating and responding to forces, ultimately shaping all of the physiological and pathophysiological behaviors. Genome editing and molecule drugs targeting PPIs hold great promise for the treatments of diseases. Linking genes and molecular drugs with protein-performed cellular behaviors is a key yet challenging issue due to the wide range of spatial and temporal scales involved. Building predictive spatiotemporal modeling systems that can describe the dynamic behaviors of cells intervened by genome editing and molecular drugs at the intersection of biology, chemistry, physics, and computer science will greatly accelerate pharmaceutical advances. Here, we review the mechanical roles of cytoskeletal proteins in orchestrating cellular behaviors alongside significant advancements in biophysical modeling while also addressing the limitations in these models. Then, by integrating generative artificial intelligence (AI) with spatiotemporal multiscale biophysical modeling, we propose a computational pipeline for developing virtual cells, which can simulate and evaluate the therapeutic effects of drugs and genome editing technologies on various cell dynamic behaviors and could have broad biomedical applications. Such virtual cell modeling systems might revolutionize modern biomedical engineering by moving most of the painstaking wet-laboratory effort to computer simulations, substantially saving time and alleviating the financial burden for pharmaceutical industries.

Alpha-1-B glycoprotein (A1BG) inhibits sterol-binding and export by CRISP2

Proteins belonging to the CAP superfamily are present in all kingdoms of life and have been implicated in various processes, including sperm maturation and cancer progression. They are mostly secreted glycoproteins and share a unique conserved CAP domain. The precise mode of action of these proteins, however, has remained elusive. Saccharomyces cerevisiae expresses three members of this protein family, which bind sterols in vitro and promote sterol secretion from cells. This sterol-binding and export function of yeast Pry proteins is conserved in the mammalian cysteine-rich secretory protein (CRISP) proteins and other CAP superfamily members. CRISP3 is an abundant protein of the human seminal plasma and interacts with alpha-1-B glycoprotein (A1BG), a human plasma glycoprotein that is upregulated in different types of cancers. Here, we examined whether the interaction between CRISP proteins and A1BG affects the sterol-binding function of CAP family members. Coexpression of A1BG with CAP proteins abolished their sterol export function in yeast and their interaction inhibits sterol-binding in vitro. We map the interaction between A1BG and CRISP2 to the third of five repeated immunoglobulin-like domains within A1BG. Interestingly, the interaction between A1BG and CRISP2 requires magnesium, suggesting that coordination of Mg2+ by the highly conserved tetrad residues within the CAP domain is essential for a stable interaction between the two proteins. The observation that A1BG modulates the sterol-binding function of CRISP2 has potential implications for the role of A1BG and related immunoglobulin-like domain containing proteins in cancer progression and the toxicity of reptile venoms containing CRISP proteins.

What Is the Crystallographic Resolution of Structural Models of Proteins Generated with AlphaFold2?

Recent advancements in AI-driven computational modeling, especially AlphaFold2, have revolutionized the prediction of biological macromolecule structures. AlphaFold2 enabled accurate predictions of structural domains and complex arrangements. However, computational models lack a clear metric for accuracy. This study explores whether computational models can match the crystallographic resolution of crystal structures. By comparing distances between atoms in models and crystal structures using t tests, it was found that AlphaFold2 models are comparable to high-resolution crystal structures (1.1 to 1.5 Å). While these models exhibit exceptional quality, their accuracy is lower than the crystal structure with resolutions better than 1 Å.

Discovery, characterization, and structure of a cofactor-independent histidine racemase from the oral pathogen Fusobacterium nucleatum

Fusobacterium nucleatum is an oral commensal bacterium that can act as an opportunistic pathogen and is implicated in diseases such as periodontitis, adverse pregnancy outcomes, colorectal cancer, and Alzheimer's disease. F. nucleatum synthesizes lanthionine for its peptidoglycan, rather than meso-2,6-diaminopimelic acid (DAP) used by most Gram-negative bacteria. Despite lacking the biosynthetic pathway for DAP, the genome of F. nucleatum ATCC 25586 encodes a predicted DAP epimerase. A recent study hypothesized that this enzyme may act as a lanthionine epimerase, but the authors found a very low turnover rate, suggesting that this enzyme likely has another more favored substrate. Here, we characterize this enzyme as a histidine racemase (HisR), and found that catalytic turnover is ∼10,000× faster with L-histidine than with L,L-lanthionine. Kinetic experiments suggest that HisR functions as a cofactor-independent racemase and that turnover is specific for histidine, while crystal structures of catalytic cysteine to serine mutants (C67S or C209S) reveal this enzyme in its substrate-unbound, open conformation. Currently, the only other reported cofactor-independent histidine racemase is CntK from Staphylococcus aureus, which is used in the biosynthesis of staphylopine, a broad-spectrum metallophore that increases virulence of S. aureus. However, CntK shares only 28% sequence identity with HisR, and their genes exist in different genomic contexts. Knockout of hisR in F. nucleatum results in a small but reproducible lag in growth compared to WT during exponential phase, suggesting that HisR may play a role in growth of this periodontal pathogen.

The Sorting and Transport of the Cargo Protein CcSnc1 by the Retromer Complex Regulate the Growth, Development, and Pathogenicity of Corynespora cassiicola

In eukaryotes, the retromer complex is critical for the transport of cargo proteins from endosomes to the trans-Golgi network (TGN). Despite its importance, there is a lack of research on the retromer-mediated transport of cargo proteins regulating the growth, development, and pathogenicity of filamentous fungi. In the present study, transcriptome analysis showed that the expression levels of the retromer complex (CcVPS35, CcVPS29 and CcVPS26) were significantly elevated during the early stages of Corynespora cassiicola invasion. Gene knockout and complementation analyses further highlighted the critical role of the retromer complex in C. cassiicola infection. Subcellular localization analysis showed that the retromer complex was mainly localized to the vacuolar membrane and partially to endosomes and the TGN. Further research found that the retromer core subunit CcVps35 can interact with the cargo protein CcSnc1. Subcellular localization showed that CcSnc1 is mainly located at the hyphal tip and partially in endosomes and the Golgi apparatus. Deletion of CcVPS35 resulted in the missorting of CcSnc1 into the vacuolar degradation pathway, indicating that the retromer can sort CcSnc1 from endosomes and transport it to the TGN. Additionally, gene knockout and complementation analyses demonstrated that CcSnc1 is critical for the growth, development, and pathogenicity of C. cassiicola. In summary, the vesicular transport pathway involving the retromer complex regulates the sorting and transport of the cargo protein CcSnc1, which is important for the growth, development and pathogenicity of C. cassiicola.

Effect of Direct and Water-Mediated Interactions on the Identification of Hotspots in Biomolecular Complexes with Multiple Subsystems

Identification of important residues in biochemical complexes is often a crucial step for many problems in molecular biology and biochemistry. A method is proposed to identify hotspots in biomolecular complexes based on a new metric, derived from networks representing molecular subunits (residues, bridging solvent molecules, ligands etc.) connected by interactions. A singular value decomposition of the weighted adjacency matrix is used to construct a scalar rank for each subunit that reflects its importance in the residue interaction network. This metric is called the singular value centrality. In addition, a new formalism is proposed to account for water-mediated interactions in the ranking of residues. Interactions for a residue network can be provided by various computational methods. In this work interactions are obtained from full quantum-mechanical calculations of protein-protein complexes using the fragment molecular orbital method. The ranking results are shown to be in good agreement with earlier computational and experimental studies. The developed method can be used to gain a deeper insight into the role of subunits in complex biomolecular systems.

A best-match approach for gene set analyses in embedding spaces

Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose an Algorithm for Network Data Embedding and Similarity (ANDES), a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein-protein interactions, can be used as a novel overrepresentation- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.

Accelerating protein-protein interaction screens with reduced AlphaFold-Multimer sampling

Motivation

Discovering new protein-protein interactions (PPIs) across entire proteomes offers vast potential for understanding novel protein functions and elucidate system properties within or between an organism. While recent advances in computational structural biology, particularly AlphaFold-Multimer, have facilitated this task, scaling for large-scale screenings remains a challenge, requiring significant computational resources.

Results

We evaluated the impact of reducing the number of models generated by AlphaFold-Multimer from five to one on the method's ability to distinguish true PPIs from false ones. Our evaluation was conducted on a dataset containing both intra- and inter-species PPIs, which included proteins from bacterial and eukaryotic sources. We demonstrate that reducing the sampling does not compromise the accuracy of the method, offering a faster, efficient, and environmentally friendly solution for PPI predictions.

Availability and implementation

The code used in this article is available at https://github.com/MIDIfactory/AlphaFastPPi. Note that the same can be achieved using the latest version of AlphaPulldown available at https://github.com/KosinskiLab/AlphaPulldown.

Structure, function, and assembly of PSI in thylakoid membranes of vascular plants

The photosynthetic apparatus is formed by thylakoid membrane-embedded multiprotein complexes that carry out linear electron transport in oxygenic photosynthesis. The machinery is largely conserved from cyanobacteria to land plants, and structure and function of the protein complexes involved are relatively well studied. By contrast, how the machinery is assembled in thylakoid membranes remains poorly understood. The complexes participating in photosynthetic electron transfer are composed of many proteins, pigments, and redox-active cofactors, whose temporally and spatially highly coordinated incorporation is essential to build functional mature complexes. Several proteins, jointly referred to as assembly factors, engage in the biogenesis of these complexes to bring the components together in a step-wise manner, in the right order and time. In this review, we focus on the biogenesis of the terminal protein supercomplex of the photosynthetic electron transport chain, PSI, in vascular plants. We summarize our current knowledge of the assembly process and the factors involved and describe the challenges associated with resolving the assembly pathway in molecular detail.

The human touch: Utilizing AlphaFold 3 to analyze structures of endogenous metabolons

Computational structural biology aims to accurately predict biomolecular complexes with AlphaFold 3 spearheading the field. However, challenges loom for structural analysis, especially when complex assemblies such as the pyruvate dehydrogenase complex (PDHc), which catalyzes the link reaction in cellular respiration, are studied. PDHc subcomplexes are challenging to predict, particularly interactions involving weaker, lower-affinity subcomplexes. Supervised modeling, i.e., integrative structural biology, will continue to play a role in fine-tuning this type of prediction (e.g., removing clashes, rebuilding loops/disordered regions, and redocking interfaces). 3D analysis of endogenous metabolic complexes continues to require, in addition to AI, precise and multi-faceted interrogation methods.

Exploring DIX-DIX Homo- and Hetero-Oligomers in Wnt Signaling with AlphaFold2

Wnt signaling is involved in embryo development and cancer. The binding between the DIX domains of Axin1/2, Dishevelled1/2/3, and Coiled-coil-DIX1 is essential for Wnt/β-catenin signaling. Structural and biological studies have revealed that DIX domains are polymerized through head-to-tail interface interactions, which are indispensable for activating β-catenin Wnt signaling. Although different isoforms of Dvl and Axin proteins display both redundant and specific functions in Wnt signaling, the specificity of DIX-mediated interactions remains unclear due to technical challenges. Using AlphaFold2(AF2), we predict the structures of 6 homodimers and 22 heterodimers of DIX domains without templates and compare them with the reported X-ray complex structures. PRODIGY is used to calculate the binding affinities of these DIX complexes. Our results show that the Axin2 DIX homodimer has a stronger binding affinity than the Axin1 DIX homodimer. Among Dishevelled (Dvl) proteins, the binding affinity of the Dvl1 DIX homodimer is stronger than that of Dvl2 and Dvl3. The Coiled-coil-DIX1(Ccd1) DIX homodimer shows weaker binding than the Axin1 DIX homodimer. Generally, heterodimer interactions tend to be stronger than those of homodimers. Our findings provide insights into the mechanism of the Wnt signaling pathway and highlight the potential of AF2 and PRODIGY for studying protein-protein interactions in signaling pathways.

Chebulinic acid isolated from aqueous extracts of Terminalia chebula Retz inhibits Helicobacter pylori infection by potential binding to Cag A protein and regulating adhesion

Background

Terminalia chebula Retz, known as the King of Tibet, is considered a functional food in China, celebrated for its antioxidant, immune-modulating, antibacterial, and anti-inflammatory properties. Chebulinic acid, derived from aqueous extracts of Terminalia chebula Retz, is known for its anti-inflammatory properties. However, its potential as an anti-Helicobacter pylori (HP) agent has not been fully explored.

Methods

Herein, we extracted the main compound from Terminalia chebula Retz using a semi-preparative liquid chromatography (LC) system and identified compound 5 as chebulinic acid through Ultra-high performance liquid chromatography-MS/MS (UPLC-MS/MS) and Nuclear Magnetic Resonance (NMR). To evaluate its role, we conducted minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assays, scanning electron microscope (SEM) imaging, inhibiting kinetics curves, urea fast test, cell counting kit-8 (CCK-8) assay, western blot analysis, griess reagent system, and molecular docking.

Results

Our results showed that chebulinic acid effectively inhibited the growth of the HP strain ATCC 700392, damaged the HP structure, and exhibited selective antimicrobial activity without affecting normal epithelial cells GES-1. Importantly, it suppressed the expression of Cytotoxin-associated gene A (Cag A) protein, a crucial factor in HP infection. Molecular docking analysis predicted a strong affinity (-9.7 kcal/mol) between chebulinic acid and Cag A protein.

Conclusion

Overall, our findings suggest that chebulinic acid acts as an anti-adhesive agent, disrupting the adhesion of HP to host cells, which is a critical step in HP infection. It also suppresses the Cag A protein. These results highlight the potential of chebulinic acid against HP infections.

Computational analysis of propeptide-containing proteins and prediction of their post-cleavage conformation changes

A propeptide is removed from a precursor protein to generate its active or mature form. Propeptides play essential roles in protein folding, transportation, and activation and are present in about 2.3% of reviewed proteins in the UniProt database. They are often found in secreted or membrane-bound proteins including proteolytic enzymes, hormones, and toxins. We identified a variety of globular and nonglobular Pfam domains in protein sequences designated as propeptides, some of which form intramolecular interactions with other domains in the mature proteins. Propeptide-containing enzymes mostly function as proteases, as they are depleted in other enzyme classes such as hydrolases acting on DNA and RNA, isomerases, and lyases. We applied AlphaFold to generate structural models for over 7000 proteins with propeptides having no less than 20 residues. Analysis of residue contacts in these models revealed conformational changes for over 300 proteins before and after the cleavage of the propeptide. Examples of conformation change occur in several classes of proteolytic enzymes in the families of subtilisins, trypsins, aspartyl proteases, and thermolysin-like metalloproteases. In most of the observed cases, cleavage of the propeptide releases the constraints imposed by the covalent bond between the propeptide and the mature protein, and cleavage enables stronger interactions between the propeptide and the mature protein. These findings suggest that post-cleavage propeptides could play critical roles in regulating the activity of mature proteins.