The Protein Journal最新文献

It is demonstrated, for the first time, that a mass spectrometry approach (known as phylonumerics) can be successfully implemented for structural phylogenetics investigations to chart the evolution of a protein’s structure and function. Illustrated for the compact globular protein myoglobin, peptide masses produced from the proteolytic digestion of the protein across animal species generate trees congruent to the sequence tree counterparts. Single point mutations calculated during the same mass tree building step can be followed along interconnected branches of the tree and represent a viable structural metric. A mass tree built for 15 diverse animal species, easily resolve the birds from mammal species, and the ruminant mammals from the remainder of the animals. Mutations within helix-spanning peptide segments alter both the mass and structure of the protein in these segments. Greater evolution is found in the B-helix over the A, E, F, G and H helices. A further mass tree study, of six more closely related primate species, resolves gorilla from the other primates based on a P22S mutation within the B-helix. The remaining five primates are resolved into two groups based on whether they contain a glycine or serine at position 23 in the same helix. The orangutan is resolved from the gibbon and siamang by its G-helix C110S mutation, while homo sapiens are resolved from chimpanzee based on the Q116H mutation. All are associated with structural perturbations in such helices. These structure altering mutations can be tracked along interconnecting branches of a mass tree, to follow the protein’s structure and evolution, and ultimately the evolution of the species in which the proteins are expressed. Those that have the greatest impact on a protein’s structure, its function, and ultimately the evolution of the species, can be selectively tracked or monitored.

Nitric oxide (NO) induces protein posttranslational modification (PTM), known as S-nitrosylation, which has started to gain attention as a critical regulator of thousands of substrate proteins. However, our understanding of the biological consequences of this emerging PTM is incomplete because of the limited number of identified S-nitrosylated proteins (S–NO proteins). Recent advances in detection methods have effectively contributed to broadening the spectrum of discovered S–NO proteins. This article briefly reviews the progress in S–NO protein detection methods and discusses how these methods are involved in characterizing the biological consequences of this PTM. Additionally, we provide insight into S–NO protein-related diseases, focusing on the role of these proteins in mitigating the severity of infectious diseases.

Graphical Abstract

A recent study showed that just one point mutation F33 to Y in the complementarity-determining region 1 of heavy chain (H-CDR1) could lead to the auto-antibody losing its DNA binding ability. However, the potential molecular mechanisms have not been well elucidated. In this study, we investigated how the antibody lost the DNA binding ability caused by mutation F33 to Y in the H-CDR1. We found that the electrostatic force was not the primary driving force for the interaction between anti-DNA antibodies and the antigen single strand DNA (ssDNA), and that the H-CDR2 largely contributed to the binding of antigen ssDNA, even larger than H-CDR1. The H-F33Y mutation could increase the hydrogen-bond interaction but impair the pi-pi stacking interaction between the antibody and ssDNA. We further found that F33_H, W98_H and Y95_L in the wiletype antibody could form the stable pi-pi stacking interaction with the nucleotide bases of ssDNA. However, the Y33 in mutant could not form the parallel sandwich pi-pi stacking interaction with the ssDNA. To further confirm the importance of pi-pi stacking, the wildtype antibody and the mutants (F33Y_H, F33A_H, W98A_H and Y95A_L) were experimentally expressed in CHO cells and purified, and the results from ELISA clearly showed that all the mutants lost the ssDNA binding ability. Taken together, our findings may not only deepen the understanding of the underlying interaction mechanism between autoantibody and antigen, but also broad implications in the field of antibody engineer.

Lectins are sugar interacting proteins which bind specific glycans reversibly and have ubiquitous presence in all forms of life. They have diverse biological functions such as cell signaling, molecular recognition, etc. C-type lectins (CTL) are a group of proteins from the lectin family which have been studied extensively in animals and are reported to be involved in immune functions, carcinogenesis, cell signaling, etc. The carbohydrate recognition domain (CRD) in CTL has a highly variable protein sequence and proteins carrying this domain are also referred to as C-type lectin domain containing proteins (CTLD). Because of this low sequence homology, identification of CTLD from hypothetical proteins in the sequenced genomes using homology based programs has limitations. Machine learning (ML) tools use characteristic features to identify homologous sequences and it has been used to develop a tool for identification of CTLD. Initially 500 sequences of well annotated CTLD and 500 sequences of non CTLD were used in developing the machine learning model. The classifier program Linear SVC from sci kit library of python was used and characteristic features in CTLD sequences like dipeptide and tripeptide composition were used as training attributes in various classifiers. A precision, recall and multiple correlation coefficient (MCC) value of 0.92, 0.91 and 0.82 respectively were obtained when tested on external test set. On fine tuning of the parameters like kernel, C value, gamma, degree and increasing number of non CTLD sequences there was improvement in precision, recall and MCC and the corresponding values were 0.99, 0.99 and 0.96. New CTLD have also been identified in the hypothetical segment of human genome using the trained model. The tool is available on our local server for interested users.

Thrombosis is the formation of abnormal blood clots in the blood vessels that obstruct blood flow and lead to thrombosis. Current treatments for thrombosis are associated with serious side effects. Therefore there is a need for alternative natural therapy. A fibrinolytic protease was isolated from fresh leaves of Moringa oleifera Lam. and characterized for its potential to solubilize blood clots and hydrolyse fibrin under in-vitro conditions. The isolated protease showed a single protein band on native-PAGE. It showed optimum fibrinolytic activity at pH 8.0, 37 ^oC with 50 µg protein. The fibrinolytic activity of isolated protease was also confirmed by fibrin zymography. K_m and V_max of isolated protease were determined by the Lineweaver Burk plot. The isolated protease could solubilize 96.41% of blood clots by 96 h under in-vitro conditions. In-vitro fibrin hydrolysis and blood clot solubilization activities shown by an isolated protease from leaves of Moringa oleifera Lam. suggest its fibrinolytic potential to dissolve blood clots. Being a natural molecule and from a dietary plant it can be explored as an alternative natural therapy against thrombosis.

Antimicrobial peptides have gradually gained advantages over small molecule inhibitors for their multifunctional effects, synthesising accessibility and target specificity. The current study aims to determine an antimicrobial peptide to inhibit PknB, a serine/threonine protein kinase (STPK), by binding efficiently at the helically oriented hinge region. A library of 5626 antimicrobial peptides from publicly available repositories has been prepared and categorised based on the length. Molecular docking using ADCP helped to find the multiple conformations of the subjected peptides. For each peptide served as input the tool outputs 100 poses of the subjected peptide. To maintain an efficient binding for relatively a longer duration, only those peptides were chosen which were seen to bind constantly to the active site of the receptor protein over all the poses observed. Each peptide had different number of constituent amino acid residues; the peptides were classified based on the length into five groups. In each group the peptide length incremented upto four residues from the initial length form. Five peptides were selected for Molecular Dynamic simulation in Gromacs based on higher binding affinity. Post-dynamic analysis and the frame comparison inferred that neither the shorter nor the longer peptide but an intermediate length of 15 mer peptide bound well to the receptor. Residual substitution to the selected peptides was performed to enhance the targeted interaction. The new complexes considered were further analysed using the Elastic Network Model (ENM) for the functional site’s intrinsic dynamic movement to estimate the new peptide’s role. The study sheds light on prospects that besides the length of peptides, the combination of constituent residues equally plays a pivotal role in peptide-based inhibitor generation. The study envisages the challenges of fine-tuned peptide recovery and the scope of Machine Learning (ML) and Deep Learning (DL) algorithm development. As the study was primarily meant for generation of therapeutics for Tuberculosis (TB), the peptide proposed by this study demands meticulous invitro analysis prior to clinical applications.

Graphical Abstract

Protein conformation is affected by interaction of several small molecules resulting either stabilization or disruption depending on the nature of the molecules. In our earlier communication, Hg²⁺ was known to disrupt the native structure of α-Cgn A leading to aggregation (Ansari, N.K., Rais, A. & Naeem, A. Methotrexate for Drug Repurposing as an Anti-Aggregatory Agent to Mercuric Treated α-Chymotrypsinogen-A. Protein J (2024). https://doi.org/10.1007/s10930-024-10187-z). Accumulation of β-rich aggregates in the living system is found to be linked with copious number of disorders. Here, we have investigated the effect of varying concentration of doxorubicin (DOX) i.e. 0-100 µM on the preformed aggregates of α-Cgn A upon incubation with 120 µM Hg²⁺. The decrease in the intrinsic fluorescence and enzyme activity with respect to increase in the Hg²⁺ concentration substantiate the formation of aggregates. The DOX showed the dose dependent decrease in the ThT fluorescence, turbidity and RLS measurements endorsing the dissolution of aggregates which were consistent with red shift in ANS, confirming the breakdown of aggregates. The α-Cgn A has 30% α-helical content which decreases to 3% in presence of Hg²⁺. DOX increased the α-helicity to 28% confirming its anti-aggregatory potential. The SEM validates the formation of aggregates with Hg²⁺ and their dissolution upon incubation with the DOX. Hemolysis assay checked the cytotoxicity of α-Cgn A aggregates. Docking revealed that the DOX interacted Lys203, Cys201, Cys136, Ser159, Leu10, Trp207, Val137 and Thr134 of α-Cgn A through hydrophobic interactions and Gly133, Thr135 and Lys202 forms hydrogen bonds.

The interaction between vascular endothelial growth factor A (VEGFA) and VEGF receptor 1(VEGFR1) is a central focus for drug development in pathological angiogenesis, where aberrant angiogenesis underlies various anomalies necessitating therapeutic intervention. Identifying hotspots of these proteins is crucial for developing new therapeutics. Although machine learning techniques have succeeded significantly in prediction tasks, they struggle to pinpoint hotspots linked to angiogenic activity accurately. This study involves the collection of diverse VEGFA and VEGFR1 protein sequences from various species via the UniProt database. Electron-ion interaction Potential (EIIP) values were assigned to individual amino acids and transformed into frequency-domain representations using discrete Fast Fourier Transform (FFT). A consensus spectrum emerged by consolidating FFT data from multiple sequences, unveiling specific characteristic frequencies. Subsequently, the Stockwell Transform (ST) was employed to yield the hotspots. The Resonant Recognition Model (RRM) identified a characteristic frequency of 0.128007 with an associated wavelength of 1570 nm and RRM-ST identified hotspots for VEGFA (Human 36, 46, 48, 67, 71, 74, 82, 86, 89, 93) and VEGFR1 (Human 224, 259, 263, 290, 807, 841, 877, 881, 885, 892, 894, 909, 913, 1018, 1022, 1026, 1043). These findings were cross-validated by Hotspots Wizard 3.0 webserver and Protein Data Bank (PDB). The study proposes using a 1570 nm wavelength for photo bio modulation to boost VEGFA/VEGFR1 interaction in the condition that is needed. It also aims to reduce VEGFA/VEGFR2 interaction, limiting harmful angiogenesis in conditions like diabetic retinopathy. Also, the identified hotspots assist in designing agonistic or antagonistic peptides tailored to specific medical requirements with abnormal angiogenesis.

Plasmodium falciparum (P. falciparum), which causes the most severe form of malaria, if left untreated, has 24 h window in which it can cause severe illness and even death. The aim of this study was to create the most comprehensive and informative secretory-proteome possible by combining high-accuracy and high-sensitivity protein identification technology. In this study, we used Plasmodium falciparum 3D7 (Pf3D7) as the model parasite to develop a label-free quantification proteomic strategy with the main goal of identifying Pf3D7 proteins that are supposed to be secreted outside the infected erythrocytes in the spent media culture during the in-vitro study. The spent culture media supernatant was subjected to differential and ultra-centrifugation steps followed by total protein extraction, estimation, and in-solution digestion using trypsin, digested peptides were analyzed using Nano-LC coupled with ESI for MS/MS. MS/MS spectra were processed using Maxquant software (v2.1.4.0.). Non-infected erythrocytes incubated spent cultured media supernatant were considered as control. Out of discovered 38 proteins, proteins belonging to P. falciparum spp. were EGF-like protein (C0H544), Endoplasmic reticulum chaperone GRP170 (C0H5H0), Small GTP-binding protein sar1 (Q8I1S0), Erythrocyte membrane protein 1, PfEMP1 (Q8I639), aldehyde reductase (Q8ID61), Conserved Plasmodium proteins (Q8IEH3, Q8ILD1), Antigen 332, DBL-like protein (Q8IHN4), Fe-S cluster assembly protein (Q8II78), identified and chosen for further in-depth investigation. This study highlights the value of secretory Plasmodium proteins play crucial roles in various aspects of the disease progression and host-pathogen interactions which can serve as diagnostic markers for malaria infection.

Coiled-coil domain-containing 124 protein is a multifunctional RNA-binding factor, and it was previously reported to interact with various biomolecular complexes localized at diverse subcellular locations, such as the ribosome, centrosome, midbody, and nucleoli. We aimed to better characterize the subcellular CCDC124 translocation by labelling this protein with a fluorescent tag, followed by laser scanning confocal microscopy methods. As traditional GFP-tagging of small proteins such as CCDC124 often faces limitations like potential structural perturbations of labeled proteins, and interference of the fluorescent-tag with their endogenous cellular functions, we aimed to label CCDC124 with the smallest possible split-GFP associated protein-tagging system (GFP11/GFP1-10) for better characterization of its subcellular localizations and its translocation dynamics. By recombinant DNA techniques we generated CCDC124-constructs labelled with either single of four tandem copies of GFP11 (GFP11 × ₁::CCDC124, GFP11 × ₄::CCDC124, or CCDC124::GFP11 × ₄). We then cotransfected U2OS cells with these split-GFP constructs (GFP11 × _{1(or X4)}::CCDC124/GFP1-10) and analyzed subcellular localization of CCDC124 protein by laser scanning confocal microscopy. Tagging CCDC124 with four tandem copies of a 16-amino acid short GFP-derived peptide-tag (GFP11_× 4::CCDC124) allowed better characterization of the subcellular localization of CCDC124 protein in our model human bone osteosarcoma (U2OS) cells. Thus, by this novel methodology we successfully identified GFP11 × ₄::CCDC124 molecules in G3BP1-overexpression induced stress-granules by live cell protein imaging for the first time. Our findings propose CCDC124 as a novel component of the stress granule which is a membraneless organelle involved in translational shut-down in response to cellular stress.