The recent AI breakthrough of AlphaFold2 has revolutionized 3D protein structural modeling, proving crucial for protein design and variant effects prediction. However, intrinsically disordered regions-known for their lack of well-defined structure and lower sequence conservation-often yield low-confidence models. The latest Variant Effect Predictor (VEP), AlphaMissense, leverages AlphaFold2 models, achieving over 90% sensitivity and specificity in predicting variant effects. However, the effectiveness of tools for variants in disordered regions, which account for 30% of the human proteome, remains unclear.

In this study, we found that predicting pathogenicity for variants in disordered regions is less accurate than in ordered regions, particularly for mutations at the first N-Methionine site. Investigations into the efficacy of variant effect predictors on intrinsically disordered regions (IDRs) indicated that mutations in IDRs are predicted with lower sensitivity and the gap between sensitivity and specificity is largest in disordered regions, especially for AlphaMissense and VARITY.

The prevalence of IDRs within the human proteome, coupled with the increasing repertoire of biological functions they are known to perform, necessitated an investigation into the efficacy of state-of-the-art VEPs on such regions. This analysis revealed their consistently reduced sensitivity and differing prediction performance profile to ordered regions, indicating that new IDR-specific features and paradigms are needed to accurately classify disease mutations within those regions.

The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein-protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field.

With the ability to cause massive epidemics that have consequences on millions of individuals globally, the Chikungunya virus (CHIKV) emerges as a severe menace. Developing an effective vaccine is urgent as no effective therapeutics are available for such viral infections. Therefore, we designed a novel mRNA vaccine against CHIKV with a combination of highly antigenic and potential MHC-I, MHC-II, and B-cell epitopes from the structural polyprotein. The vaccine demonstrated well-characterized physicochemical properties, indicating its solubility and potential functional stability within the body (GRAVY score of -0.639). Structural analyses of the vaccine revealed a well-stabilized secondary and tertiary structure (Ramachandran score of 82.8% and a Z-score of -4.17). Docking studies of the vaccine with TLR-2 (-1027.7 KJ/mol) and TLR-4 (-1212.4 KJ/mol) exhibited significant affinity with detailed hydrogen bond interactions. Molecular dynamics simulations highlighted distinct conformational dynamics among the vaccine, "vaccine-TLR-2" and "vaccine-TLR-4" complexes. The vaccine's ability to elicit both innate and adaptive immune responses, including the presence of memory B-cells and T-cells, persistent B-cell immunity for a year, and the activation of TH cells leading to the release of IFN-γ and IL-2, has significant implications for its potential effectiveness. The CHIKV vaccine developed in this study shows promise as a potential candidate for future vaccine production against CHIKV, suggesting its suitability for further clinical advancement, including in vitro and in vivo experiments.

Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying DTIs necessitate computational tools for automated prediction and comprehension of DTIs. Despite recent advancements, current methods fail to fully leverage the hierarchical information in DTIs.

Here, we introduce H2GnnDTI, a novel two-level hierarchical heterogeneous graph learning model to predict DTIs, by integrating the structures of drugs and proteins via a low-level view GNN and a high-level view GNN. The hierarchical graph consists of high-level heterogeneous nodes representing drugs and proteins, connected by edges representing known DTIs. Each drug or protein node is further detailed in a low-level graph, where nodes represent molecules within each drug or amino acids within each protein, accompanied by their respective chemical descriptors. Two distinct low-level graph neural networks are first deployed to capture structural and chemical features specific to drugs and proteins from these low-level graphs. Subsequently, a high-level graph encoder (GE) is used to comprehensively capture and merge interactive features pertaining to drugs and proteins from the high-level graph. The high-level encoder incorporates a structure and attribute information fusion module designed to explicitly integrate representations acquired from both a feature encoder and a GE, facilitating consensus representation learning. Extensive experiments conducted on three benchmark datasets have shown that our proposed H2GnnDTI model consistently outperforms state-of-the-art deep learning methods.

The codes are freely available at https://github.com/LiminLi-xjtu/H2GnnDTI.

Shigellosis remains a major global health concern, particularly in regions with poor sanitation and limited access to clean water. This study used immunoinformatics and reverse vaccinology to design a potential mRNA vaccine targeting Shigella pathotypes out of 4071 proteins from Shigella sonnei str. Ss046, 4 key antigenic candidates were identified: putative outer membrane protein (Q3YZL0), PapC-like porin protein (Q3YZM5), putative fimbrial-like protein (Q3Z3I2), and lipopolysaccharide (LPS)-assembly protein LptD (Q3Z5V5), ensuring broad pathotype coverage. A multitope vaccine was designed incorporating cytotoxic T lymphocyte, helper T lymphocyte, and B-cell epitopes, linked with suitable linkers and adjuvants to enhance immunogenicity. Computational analyses predicted vaccine's favorable antigenicity, solubility, and stability, while molecular docking and dynamic simulations demonstrated strong binding affinity and stability with Toll-like receptor 4 (TLR-4), indicating potential for robust immune activation. Immune simulations predicted strong humoral and cellular immune responses, characterized by significant cytokine production and long-term immune memory. Structural evaluations of the complex, including radius of gyration, root mean square deviation, root mean square fluctuation, and solvent accessibility, confirmed the vaccine's structural integrity, and stability under physiological conditions. This research contributes to the ongoing effort to alleviate the global burden of Shigella infections, providing a foundation for future wet laboratory investigations aimed at vaccine development.

The Mpox DNA polymerase (DNA pol) plays a crucial role in the viral replication process, making it an ideal target for antiviral therapies. It facilitates the synthetic process of viral DNA, which is an integral stage in the life of a virus. The inhibition of the operation of Mpox DNA pol would interfere with the multiplication of the virus and help manage the disease. Peptides have emerged as a possible therapeutic alternative against viruses due to their distinct characteristics. Peptides have broad-spectrum antiviral activity, being effective against a variety of viruses. Using computational techniques, we attempted to explore the molecular details of the interaction between antiviral peptides and Mpox DNA pol. Two databases of antiviral peptides were screened in this study. This study used molecular docking, followed by molecular dynamics (MD) simulation and post-simulation binding energy predictions. From the 19 selected peptides with activity against DNA polymerases, two peptides-DRAVPe01393 and DRAVPe01399-were identified as particularly promising candidates. These peptides exhibited stable interactions with Mpox DNA pol and demonstrated good cell penetration potential as evident from the MD simulation studies. Notably, the peptides DRAVPe01399 and DRAVPe01393 have a better binding affinity of - 60.86 kcal/mol and - 47.92 kcal/mol respectively than the control ligand Cidofovir diphosphate (- 10.79 kcal/mol). These findings could lead to the development of innovative antiviral treatments to prevent monkeypox, helping global efforts to battle this emerging infectious disease.

Unlike most other eukaryotes, where mitochondria continuously fuse and divide, the mitochondrion of trypanosome cells forms a single and continuously interconnected network that divides only during cytokinesis. However, the machinery governing mitochondrial remodeling and interconnection of trypanosome mitochondrion remain largely unknown. We functionally characterize a new member of the dynamin superfamily protein (DSP) from T. brucei (TbMfnL), which shares similarity with a family of homologs present in various eukaryotic and prokaryotic phyla but not in opisthokonts like mammals and budding yeast. The sequence and domain organization of TbMfnL is distinct, and it is phylogenetically very distant from the yeast and mammalian dynamin-related proteins involved in mitochondrial fusion/fission dynamics, such as optic atrophy 1 (Opa1) and mitofusin (Mfn). TbMfnL localizes to the inner mitochondrial membrane facing the matrix and, upon overexpression, induces a strong increase in the interconnection and branching of mitochondrial filaments in a GTPase-dependent manner. TbMfnL is a component of a novel membrane remodeling machinery with an unprecedented matrix-side localization that is able to modulate the degree of inter-mitochondrial connections.

Protein-protein interactions (PPIs) are pivotal in cellular processes, influencing a wide range of functions, from metabolism to immune responses. Despite the advancements in experimental techniques for PPI detection, their inherent limitations, such as high false-positive rates and significant resource demands, necessitate the development of computational approaches. This study presents a novel computational model named MFPIC (Multi-Feature Protein Interaction Classifier) for predicting PPIs, integrating enhanced sequence-based features, including a novel spaced conjoint triad (SCT) and amino acid pairwise distance (AAPD), with existing methods such as position-specific scoring matrices (PSSM) and AAindex-based features. The SCT captures complex sequence motifs by considering non-adjacent amino acid interactions, while AAPD provides critical spatial information about amino acid residues within protein sequences. The proposed model was evaluated across three benchmark datasets-Saccharomyces cerevisiae, Helicobacter pylori, and human proteins-demonstrating superior performance in comparison to state-of-the-art models. The results underscore the efficacy of integrating diverse and complementary features, achieving significant improvements in predictive accuracy, with the model achieving 95.90%, 99.33%, and 90.95% accuracy on the Saccharomyces cerevisiae, Helicobacter pylori, and human dataset, respectively. This approach not only enhances our understanding of PPI mechanisms but also offers valuable insights for the development of targeted therapeutic strategies.

A comprehensive study of the genome and genetics of superior germplasms is fundamental for crop improvement. As a widely adapted protein crop with high yield potential, the improvement in breeding and development of the seeds industry of faba bean have been greatly hindered by its giant genome size and high outcrossing rate.

To fully explore the genomic diversity and genetic basis of important agronomic traits, we first generate a de novo genome assembly and perform annotation of a special short-wing petal faba bean germplasm (VF8137) exhibiting a low outcrossing rate. Comparative genome and pan-genome analyses reveal the genome evolution characteristics and unique pan-genes among the three different faba bean genomes. In addition, the genome diversity of 558 accessions of faba bean germplasm reveals three distinct genetic groups and remarkable genetic differences between the southern and northern germplasms. Genome-wide association analysis identifies several candidate genes associated with adaptation- and yield-related traits. We also identify one candidate gene related to short-wing petals by combining quantitative trait locus mapping and bulked segregant analysis. We further elucidate its function through multiple lines of evidence from functional annotation, sequence variation, expression differences, and protein structure variation.

Our study provides new insights into the genome evolution of Leguminosae and the genomic diversity of faba bean. It offers valuable genomic and genetic resources for breeding and improvement of faba bean.

Signal recognition particle (SRP) system is critical for protein translocation across membranes in all domains of life. In archaea, this pathway relies on two GTPase proteins, SRP54 and FtsY, which interact with SRP RNA to facilitate the targeting of nascent proteins to the membrane. Although the SRP components in eukaryotes and bacteria are well characterized, the mechanisms underlying SRP-dependent membrane targeting in archaea remain poorly understood, particularly concerning the role of the FtsY N-terminal domains. This study provides an in-depth exploration of the archaeal SRP system, focusing on the N-terminal domains of the FtsY protein and their role in the formation and functionality of the targeting complex (TC). We characterized the minimal structural domains of FtsY required for SRP54 binding and membrane association, demonstrating the critical involvement of the A domain and N-terminal alpha helices in facilitating these processes. The deletion of these domains led to a progressive reduction in the affinity between SRP54 and FtsY, disrupting TC formation and compromising its catalytic efficiency. Molecular dynamics simulations and thermodynamic analyses corroborated these experimental findings, revealing that the A domain is integral to stabilizing TC and enhancing reciprocal GTP hydrolysis. Furthermore, the study showed that membrane association, mediated by the orientation of the A domain and the αN1 helix, is essential for stabilizing the interaction between SRP and the membrane. These results shed light on the molecular basis of SRP assembly and membrane targeting in archaea, marking an important advancement in our understanding of the archaeal SRP machinery.

Osteoporosis is a systemic microstructural degradation of bone tissue, often accompanied by fractures, pain, and other complications, resulting in a decline in patients' life quality. In response to the increased incidence of osteoporosis, related drug discovery has attracted more and more attention, but it is often faced with challenges due to long development cycle and high cost. Deep learning with powerful data processing capabilities has shown significant advantages in the field of drug discovery. With the development of technology, it is more and more applied to all stages of drug discovery. In particular, large models, which have been developed rapidly recently, provide new methods for understanding disease mechanisms and promoting drug discovery because of their large parameters and ability to deal with complex tasks. This review introduces the traditional models and large models in the deep learning domain, systematically summarizes their applications in each stage of drug discovery, and analyzes their application prospect in osteoporosis drug discovery. Finally, the advantages and limitations of large models are discussed in depth, in order to help future drug discovery.

We review MELD, an accelerator of Molecular Dynamics simulations of biomolecules. MELD (Modeling Employing Limited Data) integrates molecular dynamics (MD) with a variety of types of structural information through Bayesian inference, generating ensembles of protein and DNA structures having proper Boltzmann populations. MELD minimizes the computational sampling of irrelevant regions of phase space by applying energetic penalties to areas that conflict with the available data. MELD is effective in refining protein structures using NMR or cryo-EM data or predicting protein-ligand binding poses. As a plugin for OpenMM, MELD is interoperable with other enhanced sampling methods, offering a versatile tool for structural determination in computational chemistry and biophysics.

The vital cell cycle machinery is tightly regulated and alterations of its central signaling hubs are a hallmark of cancer. The activity of CDK6 is controlled by interaction with several partners including cyclins and INK4 proteins, which have been shown to mainly bind to the amino-terminal lobe. We analyzed the impact of CDK6's C-terminus on its functions in a leukemia model, revealing a central role in promoting proliferation. C-terminally truncated Cdk6 (Cdk6 ΔC) shows reduced nuclear translocation and therefore chromatin interaction and fails to enhance proliferation and disease progression. The combination of proteomic analysis and protein modeling highlights that Cdk6's C-terminus is essential for protein flexibility and for its binding potential to cyclin D, p27Kip1 and INK4 proteins but not cyclin B. We demonstrate that the C-terminus is a unique and essential part of the CDK6 protein, regulating interaction partner binding and therefore CDK6's functionality.

Tamarillo is widely grown in Yunnan Province, China, and has been found that it can be used in cheese-making with a distinctive fruity flavour. However, this primary component responsible for curdling milk remains unclear. This study aimed to identify the main component in tamarillo responsible for curdling milk using proteomics and ammonium sulfate (AS) precipitation. Herein, 3199 proteins were identified in tamarillo, of which 546 exhibited hydrolase activity. In particular, a novel serine protease with milk-clotting activity (MCA) and a molecular weight of 79.1 kDa, named "MCP746", was isolated from tamarillo. The milk-clotting proteases (MCPs) from tamarillo exhibited the highest MCA at 80 °C and stability under incubation temperatures below 70 °C, pH range of 5-8, and NaCl concentrations below 1 mol/L. This study revealed that serine protease is the primary MCPs of tamarillo along with a characterization of its milk-clotting characteristics, providing valuable insights into its potential application in cheese-making.

Echinococcosis is a significant parasitic zoonotic disease with severe implications for human and animal health. To date, there has been no effective vaccine candidate available for echinococcosis. Therefore, we employed computational approaches to develop a multiepitope-based vaccine using the most potent epitopes of MHC-I, MHC-II, and B-cell derived from the Ag5 protein of Echinococcus spp. The final vaccine construct containing the epitopes, linkers, and adjuvant exhibited potent antigenicity (score > 0.1) with no evidence of allergenicity (score < 0) and toxicity (score < 0) in several computational platforms. The vaccine also exhibited favorable physicochemical characteristics such as being highly soluble (SOLpro score of 0.781243) and hydrophilic (Grand average of hydropathy of -0.433). Moreover, the tertiary structure of the vaccine was also found to be structurally stable, with a Z score of -5.71. Further, the molecular docking analysis confirmed the vaccine's significant binding affinity to the RP-105 (docking score of -1252.7) and TLR-9 (docking score of -970.9). The molecular dynamic simulations confirmed the structural stability of the docked complexes under a virtual physiological system. The negative ΔTOTAL values derived from the MM-PBSA and MM-GBSA analyses confirmed a spontaneous and thermodynamically favorable binding process between the vaccine and receptors. Moreover, the vaccine demonstrated high potentiality to elicit both innate (natural killer cell, dendritic and macrophage) and adaptive (B-cell, helper T cell and cytotoxic T cell) immune responses with sustained humoral immune responses evidenced by increased IFN-γ and IL-2 levels. Following codon optimization and in silico cloning, the vaccine was successfully expressed (CAI value of 0.9607 and average GC content of 52.34%) after being inserted into the pET-28a (+) plasmid of E. coli. These findings highlight the potential of the designed vaccine candidate to combat echinococcosis and lay the groundwork for future preclinical and clinical studies.

The discovery of Wza, an octomeric helical barrel integral bacterial outer-transmembrane protein, has challenged the widely held understanding that all integral outer-membrane proteins of Gram-negative bacteria are closed β-barrels composed of transmembrane β- strands. Wza is a member of the Outer-Membrane Polysaccharide Exporter family and our bioinformatics analysis suggests that other members of the family may also contain outer-membrane transmembrane segments that are helical. A review of the literature indicates that in addition to Wza, outer-membrane core complex proteins of the type IV secretion systems also contain transmembrane segments that are helical.

N-Methylation of the peptide backbone confers pharmacologically beneficial characteristics to peptides that include greater membrane permeability and resistance to proteolytic degradation. The borosin family of ribosomally synthesized and post-translationally modified peptides offer a post-translational route to install amide backbone α-N-methylations. Previous work has elucidated the substrate scope and engineering potential of two examples of type I borosins, which feature autocatalytic precursors that encode N-methyltransferases that methylate their own C-termini in trans. We recently reported the first discrete N-methyltransferase and precursor peptide from Shewanella oneidensis MR-1, a minimally iterative, type IV borosin that allowed the first detailed kinetic analyses of borosin N-methyltransferases. Herein, we characterize the substrate scope and resilient regiospecificity of this discrete N-methyltransferase by comparison of relative rates and methylation patterns of over 40 precursor peptide variants along with structure analyses of nine enzyme-substrate complexes. Sequences critical to methylation are identified and demonstrated in assaying minimal peptide substrates and non-native peptide sequences for assessment of secondary structure requirements and engineering potential. This work grants understanding towards the mechanism of substrate recognition and iterative activity by discrete borosin N-methyltransferases.

Flaviviruses, including West Nile virus, Zika virus, and Dengue virus, pose global health challenges due to their distribution, pathogenicity, and lack of effective treatments or vaccines. This study investigated the antiviral activity of novel truncated peptides derived from the two-peptide plantaricins PLNC8 αβ, PlnEF, PlnJK, and PlnA. The antiviral potential was predicted using machine learning tools, followed by in vitro evaluation against the Kunjin virus using plaque reduction assays in Vero cells. Molecular docking assessed peptide interactions with KUNV and ZIKV. Full-length and truncated peptides from PlnA, PlnE, PlnF, PlnJ, and PlnK demonstrated limited antiviral efficacy against KUNV in vitro, despite in silico predictions suggesting antiviral potential for PlnA, PlnE, and PlnJ. Large discrepancies were observed between the predicted and experimentally determined activities. However, complementary two-peptide plantaricins PlnEF and PlnJK exhibited significant synergistic effects. Furthermore, the truncated peptides PLNC8 α1-15 and PLNC8 β1-20 reduced KUNV viral load by over 90%, outperforming their full-length counterparts. Molecular docking revealed interactions of PLNC8 α and PLNC8 β, and their truncated variants, with KUNV and ZIKV, suggesting a mechanism involving viral envelope disruption. These findings highlight the potential of plantaricin-derived peptides as promising antiviral candidates against flaviviruses, warranting further investigation into their mechanisms and applications.

Severe COVID-19 can trigger a cytokine storm, leading to acute respiratory distress syndrome (ARDS) with similarities to superantigen-induced toxic shock syndrome. An outstanding question is whether SARS-CoV-2 protein sequences can directly induce inflammatory responses. In this study, we identify a region in the SARS-CoV-2 S2 spike protein with sequence homology to bacterial super-antigens (termed P3). Computational modeling predicts P3 binding to sites on MHC class I/II and the TCR that partially overlap with sites for the binding of staphylococcal enterotoxins B and H. Like SEB and SEH derived peptides, P3 stimulated 25-40% of human CD4+ and CD8 + T-cells, increasing IFN-γ and granzyme B production. viSNE and SPADE profiling identified overlapping and distinct IFN-γ+ and GZMB+ subsets. The super-antigenic properties of P3 were further evident by its selective expansion of T-cells expressing specific TCR Vα and Vβ chain repertoires. In vivo experiments in mice revealed that the administration of P3 led to a significant upregulation of proinflammatory cytokines IL-1β, IL-6, and TNF-α. While the clinical significance of P3 in COVID-19 remains unclear, its homology to other mammalian proteins suggests a potential role for this peptide family in human inflammation and autoimmunity.

N-utilization substance A (NusA) is a regulatory factor with pleiotropic functions in gene expression in bacteria. Archaea encode two conserved small proteins, NusA1 and NusA2, with domains orthologous to the two RNA binding K Homology (KH) domains of NusA. Here, we report the crystal structures of NusA2 from Sulfolobus acidocaldarius and Saccharolobus solfataricus obtained at 3.1 Å and 1.68 Å, respectively. NusA2 comprises an N-terminal zinc finger followed by two KH-like domains lacking the GXXG signature. Despite the loss of the GXXG motif, NusA2 binds single-stranded RNA. Mutations in the zinc finger domain compromise the structural integrity of NusA2 at high temperatures and molecular dynamics simulations indicate that zinc binding provides an energy barrier preventing the domain from reaching unfolded states. A structure-guided phylogenetic analysis of the KH-like domains supports the notion that the NusA2 clade is ancestral to the ribosomal protein eS7 in eukaryotes, implying a potential role of NusA2 in translation.

The secondary structures (SSs) and supersecondary structures (SSSs) underlie the three-dimensional structure of proteins. Prediction of the SSs and SSSs from protein sequences enjoys high levels of use and finds numerous applications in the development of a broad range of other bioinformatics tools. Numerous sequence-based predictors of SS and SSS were developed and published in recent years. We survey and analyze 45 SS predictors that were released since 2018, focusing on their inputs, predictive models, scope of their prediction, and availability. We also review 32 sequence-based SSS predictors, which primarily focus on predicting coiled coils and beta-hairpins and which include five methods that were published since 2018. Substantial majority of these predictive tools rely on machine learning models, including a variety of deep neural network architectures. They also frequently use evolutionary sequence profiles. We discuss details of several modern SS and SSS predictors that are currently available to the users and which were published in higher impact venues.

Carrion's disease, caused by the bacterium Bartonella bacilliformis, is a serious public health problem in Peru, Ecuador and Colombia. Currently there is no available vaccine against B. bacilliformis. While antibiotics are the standard treatment, resistant strains have been reported, and there is a potential spread of the vector that transmits the bacteria. This study aimed to design a multi-epitope vaccine candidate against the causative agent of Carrion's disease using immunoinformatics tools. Predictions of B-cell epitopes, as well as CD4+ and CD8+T cell epitopes, were performed from the entire proteome of B. bacilliformis KC583 using the most frequent alleles from Peru, Ecuador, Colombia, and worldwide. B-cell epitopes and T-cell nested epitopes from outer membrane and virulence-associated proteins were selected. Epitopes were filtered out based on promiscuity, non-allergenicity, conservation, non-homology and non-toxicity. Two vaccine constructs were assembled using linkers. The tertiary structure of the constructs was predicted, and their stability was evaluated through molecular dynamics simulations. The most stable construct was selected for molecular docking with the TLR4 receptor. This study proposes a vaccine construct evaluated in silico as a potential vaccine candidate against Bartonella bacilliformis.

DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibility, intrinsic disorder, and signal peptides, and putative annotations of residues that interact with proteins, peptides and nucleic acids. These data can be used to elucidate protein functions, to support efforts to develop therapeutics, and to develop and evaluate future predictors of protein structure and function. DescribePROT includes 7.8 billion predictions for 1.4 million proteins from 83 complete proteomes of popular model organisms. This information can be downloaded at multiple levels of scope (entire database, specific organisms, and individual proteins) and can be interacted with using a graphical interface that simultaneously displays data on multiple descriptors. We describe the contents of this resource, provide directions on how to use its interface, and offer instructions on how to obtain and interact with the underlying data. Moreover, we briefly discuss plans for a future expansion of this database. DescribePROT is available at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/ .

Protein secondary structure prediction is useful for many applications. It can be considered a language translation problem, that is, translating a sequence of 20 different amino acids into a sequence of secondary structure symbols (e.g., alpha helix, beta strand, and coil). Here, we develop a novel protein secondary structure predictor called TransPross based on the transformer network and attention mechanism widely used in natural language processing to directly extract the evolutionary information from the protein language (i.e., raw multiple sequence alignment [MSA] of a protein) to predict the secondary structure. The method is different from traditional methods that first generate a MSA and then calculate expert-curated statistical profiles from the MSA as input. The attention mechanism used by TransPross can effectively capture long-range residue-residue interactions in protein sequences to predict secondary structures. Benchmarked on several datasets, TransPross outperforms the state-of-art methods. Moreover, our experiment shows that the prediction accuracy of TransPross positively correlates with the depth of MSAs, and it is able to achieve the average prediction accuracy (i.e., Q3 score) above 80% for hard targets with few homologous sequences in their MSAs. TransPross is freely available at https://github.com/BioinfoMachineLearning/TransPro .

Cryo-electron microscopy (cryo-EM) has become a powerful tool for determining the structures of macromolecules, such as proteins and DNA/RNA complexes. While high-resolution cryo-EM maps are increasingly available, there is still a substantial number of maps determined at intermediate or low resolution. These maps present challenges when it comes to extracting structural information. In response to this, two computational methods, Emap2sec and Emap2sec+, have been developed by our group to address these challenges and benefit the analysis of cryo-EM maps. In this chapter, we describe how to use the web servers of two of our structure analysis software for cryo-EM, Emap2sec and Emapsec+. Both methods identify local structures in medium-resolution EM maps of 5-10 Å to help find and fit protein and DNA/RNA structures in EM maps. Emap2sec identifies the secondary structures of proteins, while Emap2sec+ also identifies DNA/RNA locations in cryo-EM maps. As cryo-electron tomogram (cryo-ET) has started to produce data of this resolution, these methods would be useful for cryo-ET, too. Both methods are available in the form of webservers and source code at https://kiharalab.org/emsuites/ .

Mucormycosis is an invasive fungal infection with high mortality rate in immunocompromised individuals. Due to COVID-19 pandemic, the disease has resurfaced recently and lack of appropriate antifungals resulted in a poor outcome in patients. The iron uptake mechanism in Rhizopus delemar, the predominant causal agent, is crucial for its survival and pathogenesis in human host. The current study is first of its kind to focus on structural dynamics of high affinity iron permease (Ftr1), a virulence factor for Mucormycosis. Ftr1 is a transmembrane protein which is responsible for transport of Fe3+ ion from extracellular milieu to cytoplasm under iron starving conditions in Rhizopus. In this work, the three-dimensional modelling of Ftr1 was carried out. The Ftr1 possessed seven transmembrane helices with N- & C-termini in extracellular and intracellular regions respectively. Moreover, the present study delineates interaction of glutamic acid residues, found in the REGLE motif of fourth transmembrane helix with Fe3+. The molecular dynamics simulation study revealed that the glycine present in the motif destabilizes the helix thereby bringing E157 closer to positively charged ion. Understanding the interaction between Fe3+ ion and Ftr1 would be helpful in designing effective small molecule drugs against this novel therapeutic target for treating mucormycosis.

Pseudosymmetric hetero-oligomers with three or more unique subunits with overall structural (but not sequence) symmetry play key roles in biology, and systematic approaches for generating such proteins de novo would provide new routes to controlling cell signaling and designing complex protein materials. However, the de novo design of protein hetero-oligomers with three or more distinct chains with nearly identical structures is a challenging unsolved problem because it requires the accurate design of multiple protein-protein interfaces simultaneously. Here, we describe a divide-and-conquer approach that breaks the multiple-interface design challenge into a set of more tractable symmetric single-interface redesign tasks, followed by structural recombination of the validated homo-oligomers into pseudosymmetric hetero-oligomers. Starting from de novo designed circular homo-oligomers composed of 9 or 24 tandemly repeated units, we redesigned the inter-subunit interfaces to generate 19 new homo-oligomers and structurally recombined them to make 24 new hetero-oligomers, including ABC heterotrimers, A2B2 heterotetramers, and A3B3 and A2B2C2 heterohexamers which assemble with high structural specificity. The symmetric homo-oligomers and pseudosymmetric hetero-oligomers generated for each system have identical or nearly identical backbones, and hence are ideal building blocks for generating and functionalizing larger symmetric and pseudosymmetric assemblies.

Heterozygous deleterious null alleles and specific missense variants in the DNA-binding domain of the ETS2 repressor factor (ERF) cause craniosynostosis, while the recurrent p.(Tyr89Cys) missense variant is associated with Chitayat syndrome. Exome and whole transcriptome sequencing revealed the ERF de novo in-frame indel c.911_913del selectively removing the serine of the FSF motif, which interacts with the extracellular signal-regulated kinases (ERKs), in a 10-year-old girl with microcephaly, multiple congenital joint dislocations, generalized joint hypermobility, and Pierre-Robin sequence. Three additional cases with developmental delay variably associated with microcephaly, Pierre-Robin sequence and minor skeletal anomalies were detected carrying heterozygous de novo non-truncating alleles (two with c.911_913del and one with the missense c.907 T > A change) in the same FSF motif. Protein affinity maps, co-immunoprecipitation experiments and subcellular distribution showed that both the variants impair the interaction between ERF and activated ERK1/2 and increase ERF nuclear localization, affecting ERF repressor activity that may lead to developmental defects. Our work expands the phenotypic spectrum of ERF-related disorders to a pleiotropic condition with microcephaly, developmental delay and skeletal anomalies, that we termed MIDES syndrome, and adds to the understanding of the relevance of the ERF-ERK interaction in human development and disease.

DNA replication represents a series of precisely regulated events performed by a complex protein machinery that guarantees accurate duplication of the genetic information. Since DNA replication is permanently faced by a variety of exogenous and endogenous stressors, DNA damage response, repair and replication must be closely coordinated to maintain genomic integrity. HROB has been identified recently as a binding partner and activator of the Mcm8/9 helicase involved in DNA interstrand crosslink (ICL) repair. We identified HROB independently as a nuclear protein whose expression is co-regulated with various DNA replication factors. Accordingly, the HROB protein level showed a maximum in S phase and a downregulation in quiescence. Structural prediction and homology searches revealed that HROB is a largely intrinsically disordered protein bearing a helix-rich region and a canonical oligonucleotide/oligosaccharide-binding-fold motif that originated early in eukaryotic evolution. Employing a flow cytometry Förster resonance energy transfer (FRET) assay, we detected associations between HROB and proteins of the DNA replication machinery. Moreover, ectopic expression of HROB protein led to an almost complete shutdown of DNA replication. The available data imply a function for HROB during DNA replication across barriers such as ICLs.

Borna disease virus 1 (BoDV-1) is an emerging zoonotic RNA virus that can cause severe acute encephalitis with high mortality. Currently, there are no effective countermeasures, and the potential risk of a future outbreak requires urgent attention. To address this challenge, the complete genome sequence of BoDV-1 was utilized, and immunoinformatics was applied to identify antigenic peptides suitable for vaccine development.

Immunoinformatics and antigenicity-focused protein screening were employed to predict B-cell linear epitopes, B-cell conformational epitopes, and cytotoxic T lymphocyte (CTL) epitopes. Only overlapping epitopes with antigenicity greater than 1 and non-toxic, non-allergenic properties were selected for subsequent vaccine construction. The epitopes were linked using GPGPG linkers, incorporating β-defensins at the N-terminus to enhance immune response, and incorporating Hit-6 at the C-terminus to improve protein solubility and aid in protein purification. Computational tools were used to predict the immunogenicity, physicochemical properties, and structural stability of the vaccine. Molecular docking was performed to predict the stability and dynamics of the vaccine in complex with Toll-like receptor 4 (TLR-4) and major histocompatibility complex I (MHC I) receptors. The vaccine construct was cloned through in silico restriction to create a plasmid for expression in a suitable host.

Among the six BoDV-1 proteins analyzed, five exhibited high antigenicity scores. From these, eight non-toxic, non-allergenic overlapping epitopes with antigenicity scores greater than 1 were selected for vaccine development. Computational predictions indicated favorable immunogenicity, physicochemical properties, and structural stability. Molecular docking analysis showed that the vaccine remained stable in complex with TLR-4 and MHC I receptors, suggesting strong potential for immune recognition. A plasmid construct was successfully generated, providing a foundation for the experimental validation of vaccines in future pandemic scenarios.

These findings demonstrate the potential of the immunoinformatics-designed multi-epitope vaccines for the prevention and treatment of BoDV-1. Relevant preparations were made in advance for possible future outbreaks and could be quickly utilized for experimental verification.

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore .

Engineered interleukin-18 (IL-18) has attracted interest as a cytokine-based treatment. However, knowledge-based mutagenesis of IL-18 has been reported for only a few regions of the protein structures, including binding sites I and II. When coupled with the binding region mutant (E6K), the non-binding residue of IL-18, Thr63 (T63), has been shown to increase the flexibility of the binding loop. Nevertheless, the function of Thr63 in conformational regulation is still unknown. Using homology modeling, molecular dynamics simulation, and structural analysis, we investigated the effects of Thr63 alteration coupling with E6K on conformational change pattern, binding loop flexibility, and the hydrogen bond network. The results indicate that the 63rd residue was significantly associated with hydrogen-bond relaxation at the core β-barrel binding sites I and II Glu85-Ile100 loop. This result provided conformational and flexible effects to binding sites I and III by switching their binding loops and stabilizing the 63rd residue cavity. These findings may pave the way for the conceptualization of a new design for IL-18 proteins by modifying non-binding residues for structure-based drug development.

Emerging evidence suggests that the localization of viral movement proteins (MPs) to both plasmodesmata (PD) and viral replication complexes (VRCs) is the key to viral cell-to-cell movement. However, the molecular mechanism that establishes the subcellular localization of MPs is not fully understood. Here, we investigated the PD localization pathway of red clover necrotic mosaic virus (RCNMV) MP and the functional regions of MP that are crucial for MP localization to PD and VRCs. Disruption analysis of the transport pathway suggested that RCNMV MP does not rely on the ER-Golgi pathway or the cytoskeleton for the localization to the PD. Furthermore, mutagenesis analysis identified amino acid residues within the alpha helix regions responsible for localization to the PD or VRCs. These α-helix regions were also essential for efficient viral cell-to-cell movement, highlighting the importance of these dynamic localization of the MPs for viral infection.

Lectins are ubiquitous proteins that selectively bind to carbohydrates, serving as vital models for understanding protein-carbohydrate interactions. While extensively distributed across various life forms, plant lectins, especially from the Leguminosae family, have garnered significant attention. However, limited research exists on lectins from the Caesalpinioideae subfamily, suggesting a source of untapped biotechnological potential. This underscores the imperative for further exploration, particularly in isolating lectins from the Bauhinia genus, which remains relatively understudied, despite harboring lectins with diverse characteristics and promising biotechnological activities. In this study, a novel lectin extracted from Bauhinia catingae Harms seeds (BCL) was isolated in three chromatographic steps. BCL exhibited affinity for galactose and derivatives, akin to other Bauhinia lectins, with SDS-PAGE confirming its molecular weight around 30 kDa. Notably, BCL demonstrated stability across temperature and pH ranges and lacked metalloprotein characteristics. Electrospray ionization mass spectrometry revealed a partial sequence covering 81 % of the total protein sequence with nearly 80 % identity to Bauhinia forficata. Structural analysis suggested a β-sheet-rich secondary structure similar to that of other lectins. Further structural elucidation of BCL is essential to unveil its full potential and applications.

MutS2 proteins are presumably involved in either control of recombination or translation quality control in bacteria. MutS2 homologs have been found in plants and some algae; however, their actual diversity in eukaryotes remains unknown. We found putative MutS2 homologs in various species of photosynthetic eukaryotes and performed a detailed analysis of the revealed amino acid sequences. Three groups of homologs were distinguished depending on their domain composition: MutS2 homologs with full set of specific domains, MutS2-like sequences without endonuclease Smr domain, and MutS2-like homologs lacking Smr and clamp in domain IV, the extreme form of which are proteins with only a complete ATPase domain. We clarified the information about amino acid composition and set of specific motifs in the conserved domains in MutS2 and MutS2-like sequences. The models of the predicted tertiary structure were obtained for each group of homologs. The phylogenetic analysis demonstrated that all eukaryotic sequences split into two large groups. The first group included homologs belonging to species of Archaeplastida and a subset of haptophyte homologs, while the second-sequences of organisms from CASH groups (cryptophytes, alveolates, stramenopiles, haptophytes) and chlorarachniophytes. The cyanobacterial MutS2 clustered together with the first group, and proteins belonging to Deltaproteobacteria (orders Myxococcales and Bradymonadales) showed phylogenetic affinity to the CASH-including group with strong support. The observed tree pattern did not support a clear differentiation of eukaryotes into lineages with red and green algae-derived plastids. The results are discussed in the context of current conceptions of serial endosymbioses and genetic mosaicism in algae with complex plastids.

Adenosine-to-inosine (A-to-I) RNA editing recodes the genome and confers flexibility for the organisms to adapt to the environment. It is believed that RNA recoding sites are well suited for facilitating adaptive evolution by increasing the proteomic diversity in a temporal-spatial manner. The function and essentiality of a few conserved recoding sites are recognized. However, the experimentally discovered functional sites only make up a small corner of the total sites, and there is still the need to expand the repertoire of such functional sites with bioinformatic approaches. In this study, we define a new category of RNA editing sites termed 'conserved editing with non-conserved recoding' and systematically identify such sites in Drosophila editomes, figuring out their selection pressure and signals of adaptation at inter-species and intra-species levels. Surprisingly, conserved editing sites with non-conserved recoding are not suppressed and are even slightly overrepresented in Drosophila. DNA mutations leading to such cases are also favoured during evolution, suggesting that the function of those recoding events in different species might be diverged, specialized, and maintained. Finally, structural prediction suggests that such recoding in potassium channel Shab might increase ion permeability and compensate the effect of low temperature. In conclusion, conserved editing with non-conserved recoding might be functional as well. Our study provides novel aspects in considering the adaptive evolution of RNA editing sites and meanwhile expands the candidates of functional recoding sites for future validation.

Homology modeling is a widely used computational technique for predicting the three-dimensional (3D) structures of proteins based on known templates,evolutionary relationships to provide structural insights critical for understanding protein function, interactions, and potential therapeutic targets. However, existing tools often require significant expertise and computational resources, presenting a barrier for many researchers.

Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. Integrating Biopython for sequence alignment, BLAST for template identification, and ProMod3 for structure generation, Prostruc streamlines complex workflows into a user-friendly interface. The tool enables researchers to input protein sequences, identify homologous templates from databases such as the Protein Data Bank (PDB), and generate high-quality 3D structures with minimal computational expertise. Prostruc implements a two-stage vSquarealidation process: first, it uses TM-align for structural comparison, assessing Root Mean Deviations (RMSD) and TM scores against reference models. Second, it evaluates model quality via QMEANDisCo to ensure high accuracy.

The top five models are selected based on these metrics and provided to the user. Prostruc stands out by offering scalability, flexibility, and ease of use. It is accessible via a cloud-based web interface or as a Python package for local use, ensuring adaptability across research environments. Benchmarking against existing tools like SWISS-MODEL,I-TASSER and Phyre2 demonstrates Prostruc's competitive performance in terms of structural accuracy and job runtime, while its open-source nature encourages community-driven innovation.

Prostruc is positioned as a significant advancement in homology modeling, making high-quality protein structure prediction more accessible to the scientific community.

Multiple sequence alignment (MSA) has evolved into a fundamental tool in the biological sciences, playing a pivotal role in predicting molecular structures and functions. With broad applications in protein and nucleic acid modeling, MSAs continue to underpin advancements across a range of disciplines. MSAs are not only foundational for traditional sequence comparison techniques but also increasingly important in the context of artificial intelligence (AI)-driven advancements. Recent breakthroughs in AI, particularly in protein and nucleic acid structure prediction, rely heavily on the accuracy and efficiency of MSAs to enhance remote homology detection and guide spatial restraints. This review traces the historical evolution of MSA, highlighting its significance in molecular structure and function prediction. We cover the methodologies used for protein monomers, protein complexes, and RNA, while also exploring emerging AI-based alternatives, such as protein language models, as complementary or replacement approaches to traditional MSAs in application tasks. By discussing the strengths, limitations, and applications of these methods, this review aims to provide researchers with valuable insights into MSA's evolving role, equipping them to make informed decisions in structural prediction research.

Proteolytic processing of amyloid precursor protein (APP) plays a critical role in the pathogenesis of Azheimer's disease (AD). Sequential cleavage of APP by β and γ secretases leads to generation of Aβ40 (non-amyloidogenic) and Aβ42 (amyloidogenic) peptides. Despite intense studies, the biological function of these peptides and the mechanism of Aβ42 toxicity is poorly understood. In the previous publications we proposed that association of Aβ peptides with the endosomal membranes may have important implications for pathogenesis of AD (Kim and Bezprozvanny, IJMS, 2021, vol 22, 13600; Kim and Bezprozvanny, IJMS, 2023, vol 24, 2092). To understand potential biological importance of such interaction, we focused on the region of Aβ peptides involved in peri-membrane association (E682 to N698). We discovered that association of this region with the membranes is reminiscent of several known anti-microbial peptides (AMP) such as PA13, Aurein1.2 and BP100. Our analysis further revealed that energy of peri-membrane association of Aβ40 is significantly weaker than for Aβ42 or AMP peptides, but it can be increased in the presence of non-amyloidogenic FAD mutations or in the presence of cholesterol in the membrane. Based on similarity with established mechanism of action of AMP peptides, we propose that Aβ peptides affect the curvature of endosomal membranes and shift the balance between endosomal recycling to plasma membrane and late endosomal/lysosomal pathway. We further propose that these effects are enhanced as a result of non-amyloidogenic FAD mutations in the sequence of Aβ peptides or in the presence of cholesterol in the membrane. The proposed model provides potential mechanistic explanation to synaptic defects induced by increased levels of Aβ42, by non-amyloidogenic FAD mutations in APP and by age-related increase in the levels of cholesterol in the brain.

Streptococcus suis infection represents a major challenge in pig farming and public health due to its zoonotic potential and diverse serotypes, while existing vaccines lack effective cross-protection. This study employed reverse vaccinology and immunoinformatics to identify 8 conserved proteins across 11 prevalent serotypes of S. suis. 16 candidate epitopes were selected to design three multi-epitope antigens against S. suis (designated as MEASs), which fused with a dendritic cell-targeting peptide to improve antigen presentation in host. Purified MEASs displayed favorable cross-reactogenicity against 29 serotype-specific antiserums. Robust humoral and cellular immune responses can be induced by MEAS 1 and MEAS 3 in a mouse model, which provided substantial protection against virulent strains from two different serotypes. In particular, their immune serums exhibited positive opsonization effects within bloodstream and macrophage phagocytosis. Taken together, we identified two promising MEASs with excellent cross-protection, offering potential in preventing S. suis infections in a mouse model.