Search Results (22)

Plutella xylostella, a major pest of cruciferous vegetables, depends predominantly on chemoreception to locate host plants. Legs are crucial in insect chemical perception, particularly during close-range and contact chemoreception. However, the molecular basis underlying the chemosensory repertoire in P. xylostella legs remains elusive. To address this, we sequenced chemosensory-related genes in diamondback moth legs. Sequencing identified 32 odorant binding protein (OBP), 18 chemosensory protein (CSP), 26 odorant receptor (OR), 20 gustatory receptor (GR), 15 ionotropic receptor (IR), and 3 sensory neuron membrane protein (SNMP) genes. Comparative analysis with antennal transcriptome data revealed three CSPs, seven ORs, and two GRs newly identified in the legs. Transcriptome analysis showed higher fragments per kilobase of transcript per million mapped reads values for CSPs than for other chemosensory-related gene families. Furthermore, qRT-PCR confirmed the highest expression of PxylCSP9 in the legs, suggesting its role in perceiving external compounds. Fluorescent binding assays revealed high binding affinity of PxylCSP9 for several host plant semiochemicals. Molecular docking predicted a hydrophobic binding pocket in PxylCSP9 with Met11, Leu13, and Leu43 frequently participating in ligand interactions. Our findings indicate that leg-enriched PxylCSP9 is pivotal for host plant recognition during close-range chemoreception, suggesting its potential as a molecular target for precision management through behavior-based strategies. Full article

Human growth hormone (GH) exerts its pleiotropic effects by binding to its receptor (GHR), leading to receptor dimerization and activation. We combined structural, evolutionary, and genetic analyses to elucidate the critical determinants of GH-GHR interaction and the impact of disease-causing mutations. Protein contact analysis revealed the specific amino acid residues involved in two distinct binding interfaces between GH and two chains of GHR. ConSurf analysis demonstrated significant sequence conservation in the receptor-binding regions of GH across species, highlighting their functional importance. A comprehensive list of known disease-causing mutations in GH was compiled and mapped to these binding interfaces and conserved regions. Computational site-directed mutagenesis (SDM) analysis predicted the impact of several mutations on protein stability, revealing both stabilizing and destabilizing effects. Sequence comparisons with orthologs from various species further supported the evolutionary conservation of key functional residues. Integrated analysis of contact residues between GH and GHR showed a strong correlation between receptor-binding residues, evolutionary conservation, and the occurrence of disease-associated mutations. These findings underscore the critical role of specific GH residues in mediating high-affinity interactions with its receptor and how mutations in these conserved contact points can disrupt binding affinity and/or protein stability, ultimately leading to growth disorders. This multi-faceted approach provides valuable insights into the molecular mechanisms underlying growth hormone deficiency and related syndromes. Full article

Alpha helical transmembrane proteins are a specific type of membrane proteins that consist of helices spanning the entire cell membrane. They make up almost a third of all transmembrane (TM) proteins and play a significant role in various cellular activities. The structural prediction of these proteins is crucial in understanding how they behave inside the cell and thus in identifying their functions. Despite their importance, only a small portion of TM proteins have had their structure determined experimentally. Inter-helical residue contact is one of the most successful computational approaches for reducing the TM protein fold search space and generating an acceptable 3D structure. Most current TM protein residue contact predictors use features extracted only from protein sequences to predict residue contacts. However, these features alone deliver a low-accuracy contact map and, as a result, a poor 3D structure. Although there are models that explore leveraging features extracted from protein 3D structures in order to produce a better representative contact model, such an approach remains theoretical, assuming the structure features are available, whereas in reality they are only available in the training data, but not in the test data, whose structure is what needs to be predicted. This presents a brand new transfer learning paradigm: training examples contain two sets of features, but test examples contain only one set of the less informative features. In this work, we propose a novel approach that can train a model with training examples that contain both sequence features and atomic features and apply the model on the test data that contain only sequence features but not atomic features, while still improving contact prediction rather than using sequence features alone. Specifically, our method, AT-TSVM, employs Active Transfer for Transductive Support Vector Machines, which is augmented with transfer, active learning and conventional transductive learning to enhance contact prediction accuracy. Results from a benchmark dataset show that our method can boost contact prediction accuracy by an average of 5 to 6% over the inductive classifier and 2.5 to 4% over the transductive classifier. Full article

The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies—BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10—and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein using multiscale modeling, which combined molecular simulations with the ensemble-based mutational scanning of the binding interfaces and binding free energy computations. A central theme emerging from this work is that the unique binding strength and resilience to immune escape of the BD55-1205 antibody are determined by leveraging a broad epitope footprint and distributed hotspot architecture, additionally supported by backbone-mediated specific interactions, which are less sensitive to amino acid substitutions and together enable exceptional tolerance to mutational escape. In contrast, BD-604 and OMI-42 exhibit localized binding modes with strong dependence on side-chain interactions, rendering them particularly vulnerable to escape mutations at K417N, L455M, F456L and A475V. Similarly, P5S-1H1 and P5S-2B10 display intermediate behavior—effective in some contexts but increasingly susceptible to antigenic drift due to narrower epitope coverage and concentrated hotspots. Our computational predictions show strong agreement with experimental deep mutational scanning data, validating the accuracy of the models and reinforcing the value of binding hotspot mapping in predicting antibody vulnerability. This work highlights that neutralization breadth and durability are not solely dictated by epitope location, but also by how binding energy is distributed across the interface. The results provide atomistic insight into mechanisms driving resilience to immune escape for broadly neutralizing antibodies targeting the ACE2 binding interface—which stems from cumulative effects of structural diversity in binding contacts, redundancy in interaction patterns and reduced vulnerability to mutation-prone positions. Full article

Background: Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterised by the accumulation of neurotoxic substances in the brain, ultimately leading to progressive cognitive decline. The complex aetiology and involvement of multiple molecular targets in AD pathogenesis have made discovering effective therapeutic agents particularly challenging. Targeting multiple proteins simultaneously with a single therapeutic agent may offer a promising strategy to address the disease’s multifaceted nature. Methods: This study employed advanced computational methodologies to perform multitargeted molecular docking of FDA-approved drugs against four key AD-associated proteins implicated in disease progression. Among the screened compounds, Rebamipide—a drug conventionally used for treating gastrointestinal disorders—demonstrated notable binding affinities across all targets. Pharmacokinetic predictions, interaction fingerprinting, WaterMap analysis, density functional theory (DFT) calculations, and 100 ns MD simulations were performed for each protein–ligand complex to evaluate its multitarget potential. Results: Rebamipide bound effectively to the NR1 ligand-binding core, suggesting modulation of glutamatergic signalling while reducing β-secretase production and regulating neurotransmitter homeostasis through inhibiting monoamine oxidase-A. Furthermore, Rebamipide enhanced cholinergic neurotransmission by inhibiting human acetylcholinesterase, potentially improving cognitive function. Pharmacokinetic analyses confirmed favourable drug-like properties. Molecular interaction fingerprints revealed consistent hydrogen bonding, hydrophobic contacts, and π-π stacking interactions. WaterMap analysis indicated thermodynamically favourable water displacement upon binding, enhancing ligand affinity. DFT analysis of Rebamipide showed a 4.24 eV HOMO-LUMO gap, with ESP values ranging from −6.63 × 10⁻² to +6.63 × 10⁻² A.U., indicating reactive sites. TDDFT predicted strong UV absorption at 314 nm with a peak intensity of ~6500 L mol⁻¹ cm⁻¹. MD simulations over 100 ns demonstrated minimal structural deviations and stable ligand–protein complexes, reinforcing its multitarget efficacy. Conclusions: The comprehensive in silico investigation highlights Rebamipide as a promising multitargeted therapeutic candidate for Alzheimer’s disease. Its ability to modulate multiple pathogenic pathways simultaneously underscores its potential utility; however, these computational findings warrant further experimental validation to confirm its efficacy and therapeutic relevance in AD. Full article

Most life activities of organisms are realized through protein–protein interactions, and these interactions are mainly achieved through residue–residue contact between monomer proteins. Consequently, studying residue–residue contact at the protein interaction interface can contribute to a deeper understanding of the protein–protein interaction mechanism. In this paper, we focus on the research of the trimer protein interface residue pair. Firstly, we utilize the amino acid k-interval product factor descriptor ($\mathrm{A}\mathrm{A}\mathrm{I}\mathrm{P}\mathrm{F}\left(\mathrm{k}\right)$) to integrate the positional information and physicochemical properties of amino acids, combined with the electric properties and geometric shape features of residues, to construct an 8 × 16 multi-feature map. This multi-feature map represents a sample composed of two residues on a trimer protein. Secondly, we construct a CNN-GRU deep learning framework to predict the trimer protein interface residue pair. The results show that when each dimer protein provides 10 prediction results and two protein–protein interaction interfaces of a trimer protein needed to be accurately predicted, the accuracy of our proposed method is 60%. When each dimer protein provides 10 prediction results and one protein–protein interaction interface of a trimer protein needs to be accurately predicted, the accuracy of our proposed method is 93%. Our results can provide experimental researchers with a limited yet precise dataset containing correct trimer protein interface residue pairs, which is of great significance in guiding the experimental resolution of the trimer protein three-dimensional structure. Furthermore, compared to other computational methods, our proposed approach exhibits superior performance in predicting residue–residue contact at the trimer protein interface. Full article

Phosphorylation is one of the most common protein post-translational modifications. The identification of phosphorylation sites serves as the cornerstone for protein-phosphorylation-related research. This paper proposes a protein-phosphorylation site-prediction model based on graph neural networks named GraphPhos, which combines sequence features with structure features. Sequence features are derived from manual extraction and the calculation of protein pre-trained language models, and the structure feature is the secondary structure contact map calculated from protein tertiary structure. These features are then innovatively applied to graph neural networks. By inputting the features of the entire protein sequence and its contact graph, GraphPhos achieves the goal of predicting phosphorylation sites along the entire protein. Experimental results indicate that GraphPhos improves the accuracy of serine, threonine, and tyrosine site prediction by at least 8%, 15%, and 12%, respectively, exhibiting an average 7% improvement in accuracy compared to individual amino acid category prediction models. Full article

Residue contact maps provide a condensed two-dimensional representation of three-dimensional protein structures, serving as a foundational framework in structural modeling but also as an effective tool in their own right in identifying inter-helical binding sites and drawing insights about protein function. Treating contact maps primarily as an intermediate step for 3D structure prediction, contact prediction methods have limited themselves exclusively to sequential features. Now that AlphaFold2 predicts 3D structures with good accuracy in general, we examine (1) how well predicted 3D structures can be directly used for deciding residue contacts, and (2) whether features from 3D structures can be leveraged to further improve residue contact prediction. With a well-known benchmark dataset, we tested predicting inter-helical residue contact based on AlphaFold2’s predicted structures, which gave an 83% average precision, already outperforming a sequential features-based state-of-the-art model. We then developed a procedure to extract features from atomic structure in the neighborhood of a residue pair, hypothesizing that these features will be useful in determining if the residue pair is in contact, provided the structure is decently accurate, such as predicted by AlphaFold2. Training on features generated from experimentally determined structures, we leveraged knowledge from known structures to significantly improve residue contact prediction, when testing using the same set of features but derived using AlphaFold2 structures. Our results demonstrate a remarkable improvement over AlphaFold2, achieving over 91.9% average precision for a held-out subset and over 89.5% average precision in cross-validation experiments. Full article

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields. Full article

Protein structure analysis is essential in various bioinformatics domains such as drug discovery, disease diagnosis, and evolutionary studies. Within structural biology, the classification of protein structures is pivotal, employing machine learning algorithms to categorize structures based on data from databases like the Protein Data Bank (PDB). To predict protein functions, embeddings based on protein sequences have been employed. Creating numerical embeddings that preserve vital information while considering protein structure and sequence presents several challenges. The existing literature lacks a comprehensive and effective approach that combines structural and sequence-based features to achieve efficient protein classification. While large language models (LLMs) have exhibited promising outcomes for protein function prediction, their focus primarily lies on protein sequences, disregarding the 3D structures of proteins. The quality of embeddings heavily relies on how well the geometry of the embedding space aligns with the underlying data structure, posing a critical research question. Traditionally, Euclidean space has served as a widely utilized framework for embeddings. In this study, we propose a novel method for designing numerical embeddings in Euclidean space for proteins by leveraging 3D structure information, specifically employing the concept of contact maps. These embeddings are synergistically combined with features extracted from LLMs and traditional feature engineering techniques to enhance the performance of embeddings in supervised protein analysis. Experimental results on benchmark datasets, including PDB Bind and STCRDAB, demonstrate the superior performance of the proposed method for protein function prediction. Full article

The σ1 receptor (σ1-R) is an enigmatic endoplasmic reticulum resident transmembrane protein implicated in a variety of central nervous system disorders and whose agonists have neuroprotective activity. In spite of σ1-R’s physio-pathological and pharmacological importance, two of the most important features required to fully understand σ1-R function, namely the receptor endogenous ligand(s) and the molecular mechanism of ligand access to the binding site, have not yet been unequivocally determined. In this work, we performed molecular dynamics (MD) simulations to help clarify the potential route of access of ligand(s) to the σ1-R binding site, on which discordant results had been reported in the literature. Further, we combined computational and experimental procedures (i.e., virtual screening (VS), electron density map fitting and fluorescence titration experiments) to provide indications about the nature of σ1-R endogenous ligand(s). Our MD simulations on human σ1-R suggested that ligands access the binding site through a cavity that opens on the protein surface in contact with the membrane, in agreement with previous experimental studies on σ1-R from Xenopus laevis. Additionally, steroids were found to be among the preferred σ1-R ligands predicted by VS, and 16,17-didehydroprogesterone was shown by fluorescence titration to bind human σ1-R, with significantly higher affinity than the prototypic σ1-R ligand pridopidine in the same essay. These results support the hypothesis that steroids are among the most important physiological σ1-R ligands. Full article

Mitragyna speciosa (Kratom) is a tropical narcotic plant native to Southeast Asia with unique pharmacological properties. Here, we report the first chromosome-scale assembly of the M. speciosa genome. We employed PacBio sequencing to obtain a preliminary assembly, which was subsequently scaffolded using the chromatin contact mapping technique (Hi-C) into 22 pseudomolecules. The final assembly was 692 Mb with a scaffold N50 of 26 Mb. We annotated a total of 39,708 protein-coding genes, and our gene predictions recovered 98.4% of the highly conserved orthologs based on the BUSCO analysis. The phylogenetic analysis revealed that M. speciosa diverged from the last common ancestors of Coffea arabica and Coffea canephora approximately 47.6 million years ago. Our analysis of the sequence divergence at fourfold-degenerate sites from orthologous gene pairs provided evidence supporting a genome-wide duplication in M. speciosa, agreeing with the report that members of the genus Mitragyna are tetraploid. The STRUCTURE and principal component analyses demonstrated that the 85 M. speciosa accessions included in this study were an admixture of two subpopulations. The availability of our high-quality chromosome-level genome assembly and the transcriptomic resources will be useful for future studies on the alkaloid biosynthesis pathway, as well as comparative phylogenetic studies in Mitragyna and related species. Full article

Electron cryo-microscopy (cryo-EM) has emerged as a powerful method by which to obtain three-dimensional (3D) structures of macromolecular complexes at atomic or near-atomic resolution. However, de novo building of atomic models from near-atomic resolution (3–5 Å) cryo-EM density maps is a challenging task, in particular because poorly resolved side-chain densities hamper sequence assignment by automatic procedures at a lower resolution. Furthermore, segmentation of EM density maps into individual subunits remains a difficult problem when the structure of the subunits is not known, or when significant conformational rearrangement occurs between the isolated and associated form of the subunits. To tackle these issues, we have developed a graph-based method to thread most of the C-$\alpha$ trace of the protein backbone into the EM density map. The EM density is described as a weighted graph such that the resulting minimum spanning tree encompasses the high-density regions of the map. A pruning algorithm cleans the tree and finds the most probable positions of the C-$\alpha$ atoms, by using side-chain density when available, as a collection of C-$\alpha$ trace fragments. By complementing experimental EM maps with contact predictions from sequence co-evolutionary information, we demonstrate that this approach can correctly segment EM maps into individual subunits and assign amino acid sequences to backbone traces to generate atomic models. Full article

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information. Full article

Protein–RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures involved in PRIs is important for unraveling such processes. Because of the expensive and time-consuming techniques required for experimental determination of complex protein–RNA structures, various computational methods have been developed to predict PRIs. However, most of these methods focus on predicting only RNA-binding regions in proteins or only protein-binding motifs in RNA. Methods for predicting entire residue–base contacts in PRIs have not yet achieved sufficient accuracy. Furthermore, some of these methods require the identification of 3D structures or homologous sequences, which are not available for all protein and RNA sequences. Here, we propose a prediction method for predicting residue–base contacts between proteins and RNAs using only sequence information and structural information predicted from sequences. The method can be applied to any protein–RNA pair, even when rich information such as its 3D structure, is not available. In this method, residue–base contact prediction is formalized as an integer programming problem. We predict a residue–base contact map that maximizes a scoring function based on sequence-based features such as k-mers of sequences and the predicted secondary structure. The scoring function is trained using a max-margin framework from known PRIs with 3D structures. To verify our method, we conducted several computational experiments. The results suggest that our method, which is based on only sequence information, is comparable with RNA-binding residue prediction methods based on known binding data. Full article