Structural Features Of Proteins Research Articles

Dynamic Roles of Insect Carboxyl/Cholinesterases in Chemical Adaptation.

Insects have evolved several intricate defense mechanisms to adapt to their chemical environment. Due to their versatile capabilities in hydrolytic biotransformation, insect carboxyl/cholinesterases (CCEs) play vital roles in the development of pesticide resistance, facilitating the adaptation of insects to their host plants, and manipulating insect behaviors through the olfaction system. CCEs confer insecticide resistance through the mechanisms of qualitative or quantitative changes of CCE-mediated enhanced metabolism or target-site insensitivity, and may contribute to the host plant adaptation. CCEs represent the first odorant-degrading enzymes (ODEs) discovered to degrade insect pheromones and plant odors and remain the most promising ODE candidates. Here, we summarize insect CCE classification, currently characterized insect CCE protein structure characteristics, and the dynamic roles of insect CCEs in chemical adaptation.

Revealing the potential regulatory relationship between HSP70, HSP90 and HSF genes under temperature stress

Heat Shock Protein (HSPs) gene family members play fundamental roles in different environmental stress tolerances, protect the structure and function of cells, and perform a significant task in cellular homeostasis. In this study, we conducted a genome-wide identification, evolutionary relationship analysis and gene expression analysis of the HSP70, HSP90, and HSF gene families in Ruditapes philippinarum. We identified 83 RpHSP70, 6 RpHSP90, and 3 RpHSF genes in R. philippinarum. The structural characteristics, chromosomal localization, and the gene structure map were constructed to reveal the characteristics of protein structures. Furthermore, the expression profiling of transcriptome data showed the expression pattern of HSP70, HSP90 and HSF genes in Manila clam from different populations, and under high and low temperature stress. In addition, we performed protein–protein interaction network analysis between HSP70, HSP90, and HSF gene family which enabled us to recognize the regulatory relationship between the two HSP gene families and the HSF gene family. Furthermore, the predicted sub-cellular location revealed a diversified subcellular distribution of HSP70, HSP90, and HSF proteins, which may be directly or indirectly associated with functional diversification under heat stress condition.

Structures of honeybee-infecting Lake Sinai virus reveal domain functions and capsid assembly with dynamic motions

Understanding the structural diversity of honeybee-infecting viruses is critical to maintain pollinator health and manage the spread of diseases in ecology and agriculture. We determine cryo-EM structures of T = 4 and T = 3 capsids of virus-like particles (VLPs) of Lake Sinai virus (LSV) 2 and delta-N48 LSV1, belonging to tetraviruses, at resolutions of 2.3–2.6 Å in various pH environments. Structural analysis shows that the LSV2 capsid protein (CP) structural features, particularly the protruding domain and C-arm, differ from those of other tetraviruses. The anchor loop on the central β-barrel domain interacts with the neighboring subunit to stabilize homo-trimeric capsomeres during assembly. Delta-N48 LSV1 CP interacts with ssRNA via the rigid helix α1’, α1’–α1 loop, β-barrel domain, and C-arm. Cryo-EM reconstructions, combined with X-ray crystallographic and small-angle scattering analyses, indicate that pH affects capsid conformations by regulating reversible dynamic particle motions and sizes of LSV2 VLPs. C-arms exist in all LSV2 and delta-N48 LSV1 VLPs across varied pH conditions, indicating that autoproteolysis cleavage is not required for LSV maturation. The observed linear domino-scaffold structures of various lengths, made up of trapezoid-shape capsomeres, provide a basis for icosahedral T = 4 and T = 3 architecture assemblies. These findings advance understanding of honeybee-infecting viruses that can cause Colony Collapse Disorder.

The development of molecular docking and molecular dynamics and their application in the field of chemistry and computer simulation

With the rapid development of modern life science, computational Molecular docking has gradually become one of the core disciplines and methods of modern life science research. Computational docking studies the relationship between the structure and pharmacodynamics of biological macromolecules and the interaction between biological macromolecules and ligands. It promotes the development of protein engineering, protein design, and computer-aided drug design with powerful and various docking software in predicting the three-dimensional structure and dynamic characteristics of proteins from protein sequences. Nowadays, this computing power can be provided by the GPU through the use of a general-purpose computing model on GPUs. This article presents two approaches to parallelizing the descriptive algorithms on the GPU to solve the molecular docking problem and then evaluating them in terms of the computation time achieved. The proposed approaches are effective in accelerating molecular docking on GPUs compared to a single-core or multicore CPU. Besides introducing parallelization approaches, we propose a new descriptive algorithm based on the bee swarm algorithm to solve the molecular docking problem as an alternative to traditional descriptive algorithms such as the genetic algorithm.

Investigating the Effect of Various Sous-Vide Cooking Conditions on Protein Structure and Texture Characteristics of Tilapia Fillet Using Synchrotron Radiation-Based FTIR.

The effects of various sous-vide (SV) cooking conditions (50-60℃, 30-60 min) on physicochemical properties related to the texture characteristics, protein structure/degradation, and sensory acceptability of tilapia fillet (Oreochromis niloticus) were investigated. With an increasing temperature and processing time of SV cooking, protein degradation (of both myofibrils and connective tissue) was more pronounced, as evaluated by the decrease in water- and salt-soluble proteins, total collagen, as well as the changes in the ratio of secondary protein structures (α-helix, β-sheet, β-turn, etc.), which were determined by synchrotron-FTIR (SR-FTIR). These degradations were associated with the improvement of meat tenderness, as estimated by shear force and texture profile analyzer (TPA) results. Among all SV conditions, using 60 ℃ for 45 min seems to be the optimal condition for tilapia meat, since it delivered the best results for texture characteristics and acceptability (p < 0.05). Moreover, principal component analysis (PCA) results clearly demonstrated that the highest texture-liking score of this condition was well associated with the intensity of β-sheets, which seem to be the crucial component that affected the texture of SV-cooked tilapia more so than other parameters. The findings demonstrated the potential of SR-FTIR to decipher the biomolecular structure, particularly the secondary protein structure, of SV-cooked tilapia. This technique provided essential information for a better understanding of the changes in biomolecules related to the textural characteristics of this product.

Revisiting the Ramachandran plot based on statistical analysis of static and dynamic characteristics of protein structures

Ramachandran plots, which describe protein structures by plotting the dihedral angle pairs of the backbone on a two-dimensional plane, have played an important role in structural biology over the past few decades. However, despite continued discovery of new protein structures to date, the Ramachandran plot is still constructed by only a small number of data points, and further it cannot reflect the steric information of proteins. Here, we investigated the secondary structure of proteins in terms of static and dynamic characteristics. As for static feature, the Ramachandran plot was revisited for the dataset consisting of 9,148 non-redundant high-resolution protein structures released in the protein data bank until April 1, 2022. By calculating amino acid propensities, it was found that the proportion of secondary structures with respect to residue depth is directly related to their hydrophobicity. As for dynamic feature, normal mode analysis (NMA) based on an elastic network model (ENM) was carried out for the dataset using our KOSMOS web server (http://bioengineering.skku.ac.kr/kosmos/). All ENM-based NMA results were stored in the KOSMOS database, allowing researchers to use them in various ways. In this process, it was commonly found that high B-factors appeared at the edge of the alpha helix region, which was elucidated by introducing residue depth. In addition, by investigating the change in dihedral angle, it was possible to quantitatively survey the contribution of structural change of protein on the Ramachandran plot. In conclusion, our statistical analysis of protein characteristics will provide insight into a range of protein structural studies.

Helicobacter pylori strain 26695 versus J99: Analysis on the protein structure and physicochemical characteristics

Helicobacter pylori strain 26695 versus J99: Analysis on the protein structure and physicochemical characteristics

Characterization and epitope mapping of monoclonal antibodies against PEDV N protein

Porcine epidemic diarrhea virus (PEDV) causes acute diarrhea, vomiting, dehydration and high mortality in neonatal piglets. The nucleocapsid (N) protein of PEDV is a highly conserved protein with strong immunogenicity and palys an important role in PEDV diagnosis. However, epitopes on the PEDV N protein have not yet been well characterized. Here, 32 monoclonal antibodies (mAbs) against the PEDV N protein were produced and identified. Six new epitopes were first identified by using a high-throughput epitope mapping method named AbMap. Sequence analysis revealed that among the six epitopes five epitopes were highly conserved among different PEDV strains. We also confirmed that the mAbs derived from the six epitopes of PEDV N protein, have no cross-reactivity with transmissible gastro enteritis virus or porcine delta coronavirus. These mAbs and their defined epitopes will help to understand the N protein structure and immunological characteristics, and to develop a rapid, accurate PEDV diagnosis method.

The Nature of Functional Features of Different Classes of G-Protein-Coupled Receptors.

G-protein-coupled receptors (GPCRs) are a critical family in the human proteome and are involved in various physiological processes. They are also the most important drug target, with approximately 30% of approved drugs acting on such receptors. The members of the family are divided into six classes based on their structural and functional characteristics. Understanding their structural-functional relationships will benefit us in future drug development. In this article, we investigate the features of protein function, structure, and energy that describe the dynamics of the GPCR activation process between different families. GPCRs straddle the cell membrane and transduce signals from outside the membrane into the cell. During the process, the conformational change in GPCRs that is activated by the binding of signal molecules is essential. During the binding process, different types of signal molecules result in different signal transfer efficiencies. Therefore, the GPCR classes show a variety of structures and activation processes. Based on the experimental crystal structures, we modeled the activation process of the β2 adrenergic receptor (β2AR), glucagon receptor (GCGR), and metabotropic glutamate receptor 2 (mGluR2), which represent class A, B, and C GPCRs, respectively. We calculated their activation free-energy landscapes and analyzed the structure-energy-function relationship. The results show a consistent picture of the activation mechanisms between different types of GPCRs. This could also provide us a way to understand other signal transduction proteins.

Improved model quality assessment using sequence and structural information by enhanced deep neural networks.

Protein model quality assessment plays an important role in protein structure prediction, protein design and drug discovery. In this work, DeepUMQA2, a substantially improved version of DeepUMQA for protein model quality assessment, is proposed. First, sequence features containing protein co-evolution information and structural features reflecting family information are extracted to complement model-dependent features. Second, a novel backbone network based on triangular multiplication update and axial attention mechanism is designed to enhance information exchange between inter-residue pairs. On CASP13 and CASP14 datasets, the performance of DeepUMQA2 increases by 20.5 and 20.4% compared with DeepUMQA, respectively (measured by top 1 loss). Moreover, on the three-month CAMEO dataset (11 March to 04 June 2022), DeepUMQA2 outperforms DeepUMQA by 15.5% (measured by local AUC0,0.2) and ranks first among all competing server methods in CAMEO blind test. Experimental results show that DeepUMQA2 outperforms state-of-the-art model quality assessment methods, such as ProQ3D-LDDT, ModFOLD8, and DeepAccNet and DeepUMQA2 can select more suitable best models than state-of-the-art protein structure methods, such as AlphaFold2, RoseTTAFold and I-TASSER, provided themselves.

BepiPred-3.0: Improved B-cell epitope prediction using protein language models.

B-cell epitope prediction tools are of great medical and commercial interest due to their practical applications in vaccine development and disease diagnostics. The introduction of protein language models (LMs), trained on unprecedented large datasets of protein sequences and structures, tap into a powerful numeric representation that can be exploited to accurately predict local and global protein structural features from amino acid sequences only. In this paper, we present BepiPred-3.0, a sequence-based epitope prediction tool that, by exploiting LM embeddings, greatly improves the prediction accuracy for both linear and conformational epitope prediction on several independent test sets. Furthermore, by carefully selecting additional input variables and epitope residue annotation strategy, performance was further improved, thus achieving unprecedented predictive power. Our tool can predict epitopes across hundreds of sequences in minutes. It is freely available as a web server and a standalone package at https://services.healthtech.dtu.dk/service.php?BepiPred-3.0 with a user-friendly interface to navigate the results.

The Integration of Proteome-Wide PTM Data with Protein Structural and Sequence Features Identifies Phosphorylations that Mediate 14-3-3 Interactions

14-3-3s are abundant proteins that regulate essentially all aspects of cell biology, including cell cycle, motility, metabolism, and cell death. 14-3-3s work by docking to phosphorylated Ser/Thr residues on a large network of client proteins and modulating client protein function in a variety of ways. In recent years, aided by improvements in proteomics, the discovery of 14-3-3 client proteins has far outpaced our ability to understand the biological impact of individual 14-3-3 interactions. The rate-limiting step in this process is often the identification of the individual phospho-serines/threonines that mediate 14-3-3 binding, which are difficult to distinguish from other phospho-sites by sequence alone. Furthermore, trial-and-error molecular approaches to identify these phosphorylations are costly and can take months or years to identify even a single 14-3-3 docking site phosphorylation. To help overcome this challenge, we used machine learning to analyze predictive features of 14-3-3 binding sites. We found that accounting for intrinsic protein disorder and the unbiased mass spectrometry identification rate of a given phosphorylation significantly improves the identification of 14-3-3 docking site phosphorylations across the proteome. We incorporated these features, coupled with consensus sequence prediction, into a publicly available web app, called “14-3-3 site-finder”. We demonstrate the strength of this approach through its ability to identify 14-3-3 binding sites that do not conform to the loose consensus sequence of 14-3-3 docking phosphorylations, which we validate with 14-3-3 client proteins, including TNK1, CHEK1, MAPK7, and others. In addition, by using this approach, we identify a phosphorylation on A-kinase anchor protein-13 (AKAP13) at Ser2467 that dominantly controls its interaction with 14-3-3.

Targeting plant UBX proteins: AI-enhanced lessons from distant cousins.

Across all eukaryotic kingdoms, ubiquitin regulatory X (UBX) domain-containing adaptor proteins control the segregase cell division control protein 48 (CDC48), and thereby also control cellular proteostasis and adaptation. The structures and biological roles of UBX proteins in animals and fungi have garnered considerable attention. However, their counterparts in plants remain markedly understudied. Since 2021, the artificial intelligence (AI)-based algorithm AlphaFold has provided predictions of protein structural features that can be highly accurate. Predictions of the proteomes of all major model organisms are now freely accessible to the entire research community through user-friendly web interfaces. We propose that the combination of cross-kingdom comparison with AF analysis produces a wealth of testable hypotheses to inspire and guide experimental research on plant UBX domain-containing (PUX) proteins.

HBcompare: Classifying Ligand Binding Preferences with Hydrogen Bond Topology.

This paper presents HBcompare, a method that classifies protein structures according to ligand binding preference categories by analyzing hydrogen bond topology. HBcompare excludes other characteristics of protein structure so that, in the event of accurate classification, it can implicate the involvement of hydrogen bonds in selective binding. This approach contrasts from methods that represent many aspects of protein structure because holistic representations cannot associate classification with just one characteristic. To our knowledge, HBcompare is the first technique with this capability. On five datasets of proteins that catalyze similar reactions with different preferred ligands, HBcompare correctly categorized proteins with similar ligand binding preferences 89.5% of the time. Using only hydrogen bond topology, classification accuracy with HBcompare surpassed standard structure-based comparison algorithms that use atomic coordinates. As a tool for implicating the role of hydrogen bonds in protein function categories, HBcompare represents a first step towards the automatic explanation of biochemical mechanisms.

Curcumin and whey protein binding and structural characteristics of their complex evidenced by atomic force microscopy

Curcumin and whey protein binding and structural characteristics of their complex evidenced by atomic force microscopy

The Spatial Distribution Patterns, Physicochemical Properties, and Structural Characterization of Proteins in Oysters (Crassostrea hongkongensis).

Protein content, a vital component determining the nutritional quality of oysters, is unevenly distributed in different parts of oyster. In this study, the spatial distribution (visceral mass, mantle, gill, and adductor) patterns and structural characteristics of proteins, including water–soluble proteins (WSP), salt–soluble proteins (SSP), acid–soluble proteins (ASP) and alkali–soluble proteins (ALSP) of oysters (Crassostrea hongkongensis) were investigated with the amino acid analyzer, circular dichroism spectroscopy (CD), fourier transform infrared spectroscopy (FTIR), and fluorescence spectroscopy. The results showed that oyster proteins were mainly distributed in the visceral mass and mantle. The protein composition was WSP, SSP, ALSP, and ASP in descending order, which conformed to the ideal amino acid pattern. Variations in secondary structure, molecular weight distribution, and thermal denaturation temperatures of the oyster proteins were observed. SSP had wider bands (16–270 kDa) than those of ASP (30–37 kDa) and ALSP (66–270 kDa). Among the four proteins, the SSP of the mantle showed the highest thermal stability (87.4 °C), while ALSP of the adductor muscle had the lowest the lowest the peak denaturation temperature (Tm) (53.8 °C). The proportions of secondary structures in oyster proteins were different, with a higher proportion of solid protein β–folds, and the exposure of aromatic amino acid residues and disulfide bonds and the microenvironment in which they were located were also different.

Measuring the Radius of Gyration and Intrinsic Flexibility of Viral Proteins in Buffer Solution Using Small-Angle X-ray Scattering.

Measuring structural features of proteins dispersed in buffer solution, in contrast to crystal form, is indispensable in understanding morphological characteristics of the biomolecule in a native environment. We report on the structure and apparent viscosity of unfolded α and β variants of SARS-CoV-2 spike proteins dispersed in buffer solutions. The radius of gyration of the β variant is found to be larger than that of the α variant, while the ab initio computation of one of the possible particle-like bodies is consistent with the small-angle X-ray scattering (SAXS) profiles resembling a conformation similar to the three-dimensional structure of the folded state of the corresponding α and β spike variant. However, a smaller radius of gyration with respect to the predicted folded state of 2.4 and 2.7 is observed for both α and β variants, respectively. Our work complements the structural characterization of spike proteins using cryo-electron microscopy techniques. The measurement/analysis discussed here might be useful for quick and cost-effective evaluation of several protein structures, let alone mutated viral proteins, which is useful for drug discovery/development applications.

Prediction of post-translational modification cross-talk and mutation within proteins via imbalanced learning

Post-translational modification (PTM) is crucial for various cell signaling pathways and biological processes. However, the interactions between PTMs (PTM cross-talk) or PTMs and other sites (PTM mutation) are still in there infancy, with many challenges remaining unexplored. Since PTMs play a fundamental role in cell biology, many mutations cause abnormalities in many PTM sites and affect many human diseases. In this research, we propose an algorithm to improve PTM sites and PTM sites (PTM cross-talk) prediction and to study the interaction between PTM sites and mutation sites (PTM mutation). In addition to the primary network features of three-dimensional protein structure and sequence features, the dynamic features based on ENMs (elastic network models) are also used to improve model performance. Through feature selection, we reserved 48 features and developed a predictor for predicting PTM cross-talk and mutation. According to the evaluation based on PTM cross-talk, the area under the curve of our best prediction model reaches 0.911, which exceeds the state-of-art-model PCTpred. Based on the evaluation of PTM mutation, our prediction model is highly reliable, with an AUC score of 0.935. Even with the removal of the distance, the performance of our model (which is the most excellent model of the nine models, Random Forest model) is relatively stable.

Structural signatures: a web server for exploring a database of and generating protein structural features from human cell lines and tissues.

Structural features of proteins provide powerful insights into biological function and similarity. Specifically, previous work has demonstrated that structural features of tissue and drug-treated cell line samples can be used to predict tissue type and characterize drug relationships, respectively. We have developed structural signatures, a web server for annotating and analyzing protein features from gene sets that are often found in transcriptomic and proteomic data. This platform provides access to a structural feature database derived from normal and disease human tissue samples. We show how analysis using this database can shed light on the relationship between states of single-cell RNA-sequencing lung cancer samples. These various structural feature signatures can be visualized on the server itself or downloaded for additional analysis. The structural signatures server tool is freely available at https://structural-server.kinametrix.com/.

Identification of the ubiquitin-proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network.

The major mechanism of proteolysis in the cytosol and nucleus is the ubiquitin–proteasome pathway (UPP). The highly controlled UPP has an effect on a wide range of cellular processes and substrates, and flaws in the system can lead to the pathogenesis of a number of serious human diseases. Knowledge about UPPs provide useful hints to understand the cellular process and drug discovery. The exponential growth in next-generation sequencing wet lab approaches have accelerated the accumulation of unannotated data in online databases, making the UPP characterization/analysis task more challenging. Thus, computational methods are used as an alternative for fast and accurate identification of UPPs. Aiming this, we develop a novel deep learning-based predictor named “2DCNN-UPP” for identifying UPPs with low error rate. In the proposed method, we used proposed algorithm with a two-dimensional convolutional neural network with dipeptide deviation features. To avoid the over fitting problem, genetic algorithm is employed to select the optimal features. Finally, the optimized attribute set are fed as input to the 2D-CNN learning engine for building the model. Empirical evidence or outcomes demonstrates that the proposed predictor achieved an overall accuracy and AUC (ROC) value using 10-fold cross validation test. Superior performance compared to other state-of-the art methods for discrimination the relations UPPs classification. Both on and independent test respectively was trained on 10-fold cross validation method and then evaluated through independent test. In the case where experimentally validated ubiquitination sites emerged, we must devise a proteomics-based predictor of ubiquitination. Meanwhile, we also evaluated the generalization power of our trained modal via independent test, and obtained remarkable performance in term of 0.862 accuracy, 0.921 sensitivity, 0.803 specificity 0.803, and 0.730 Matthews correlation coefficient (MCC) respectively. Four approaches were used in the sequences, and the physical properties were calculated combined. When used a 10-fold cross-validation, 2D-CNN-UPP obtained an AUC (ROC) value of 0.862 predicted score. We analyzed the relationship between UPP protein and non-UPP protein predicted score. Last but not least, this research could effectively analyze the large scale relationship between UPP proteins and non-UPP proteins in particular and other protein problems in general and our research work might improve computational biological research. Therefore, we could utilize the latest features in our model framework and Dipeptide Deviation from Expected Mean (DDE) -based protein structure features for the prediction of protein structure, functions, and different molecules, such as DNA and RNA.