CoDockPP: A Multistage Approach for Global and Site-Specific Protein–Protein Docking

Molecular recognition of ITIM/ITSM domains with SHP2 and their allosteric effect

Src homology 2-domain-containing tyrosine phosphatase 2 (SHP2) is a non-receptor protein tyrosine phosphatase that is widely expressed in a variety of cells and regulates the immune response of T cells through the PD-1 pathway. However, the activation mechanism and allosteric effects of SHP2 remain unclear, hindering the development of small molecule inhibitors. For the first time, in this study, the complex structure formed by the intact PD-1 tail and SHP2 was modeled. The molecular recognition and conformational changes of inactive/active SHP2 versus ITIM/ITSM were compared based on prolonged MD simulations. The relative flexibility of the two SH2 domains during MD simulations contributes to the recruitment of ITIM/ITSM and supports the subsequent conformational change of SHP2. The binding free energy calculation shows that inactive SHP2 has a higher affinity for ITIM/ITSM than active SHP2, mainly because the former's N-SH2 refers to the α-state. In addition, a significant decrease in the contribution to the binding energy of certain residues (e.g., R32, S34, K35, T42, and K55) of conformationally transformed SHP2 contributes to the above result. These detailed changes during conformational transition will provide theoretical guidance for the molecular design of subsequent novel anticancer drugs.

Recent Advances and Challenges in Protein Structure Prediction

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

NLRP6 potentiates PI3K/AKT signalling by promoting autophagic degradation of p85α to drive tumorigenesis

The PI3K/AKT pathway plays an essential role in tumour development. NOD-like receptors (NLRs) regulate innate immunity and are implicated in cancer, but whether they are involved in PI3K/AKT pathway regulation is poorly understood. Here, we report that NLRP6 potentiates the PI3K/AKT pathway by binding and destabilizing p85α, the regulatory subunit of PI3K. Mechanistically, NLRP6 recruits the E3 ligase RBX1 to p85α and ubiquitinates lysine 256 on p85α, which is recognized by the autophagy cargo receptor OPTN, causing selective autophagic degradation of p85α and subsequent activation of the PI3K/AKT pathway by reducing PTEN stability. We further show that loss of NLRP6 suppresses cell proliferation, colony formation, cell migration, and tumour growth in glioblastoma cells in vitro and in vivo. Disruption of the NLRP6/p85α interaction using the Pep9 peptide inhibits the PI3K/AKT pathway and generates potent antitumour effects. Collectively, our results suggest that NLRP6 promotes p85α degradation via selective autophagy to drive tumorigenesis, and the interaction between NLRP6 and p85α can be a promising therapeutic target for tumour treatment.

Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence

Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.

Immune subtype identification and multi-layer perceptron classifier construction for breast cancer

Introduction

Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer.

Methods

In this study, we calculated the relative proportion of tumor infiltrating immune cells (TIICs) in the breast cancer TME, and used the consensus clustering algorithm to cluster the breast cancer subtypes. We also developed a multi-layer perceptron (MLP) classifier based on a deep learning framework to detect breast cancer subtypes, which 70% of the breast cancer research cohort was used for the model training and 30% for validation.

Results

By performing the K-means clustering algorithm, the research cohort was clustered into two subtypes. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Finally, we identified a 38-gene signature and developed a multilayer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis.

Conclusion

Identification of breast cancer subtypes based on the immune signature in the tumor microenvironment can assist clinicians to effectively and accurately assess the progression of breast cancer and formulate different treatment strategies for different subtypes.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

Screening and Identification of HBV Epitopes Restricted by Multiple Prevalent HLA-A Allotypes

Although host T cell immune responses to hepatitis B virus (HBV) have been demonstrated to have important influences on the outcome of HBV infection, the development of T cell epitope-based vaccine and T cell therapy and the clinical evaluation of specific T cell function are currently hampered markedly by the lack of validated HBV T cell epitopes covering broad patients. This study aimed to screen T cell epitopes spanning overall HBsAg, HBeAg, HBx and HBpol proteins and presenting by thirteen prevalent human leukocyte antigen (HLA)-A allotypes which gather a total gene frequency of around 95% in China and Northeast Asia populations. 187 epitopes were in silico predicted. Of which, 62 epitopes were then functionally validated as real-world HBV T cell epitopes by ex vivo IFN-γ ELISPOT assay and in vitro co-cultures using peripheral blood mononuclear cells (PBMCs) from HBV infected patients. Furthermore, the HLA-A cross-restrictions of each epitope were identified by peptide competitive binding assay using transfected HMy2.CIR cell lines, and by HLA-A/peptide docking as well as molecular dynamic simulation. Finally, a peptide library containing 105 validated epitopes which cross-binding by 13 prevalent HLA-A allotypes were used in ELISPOT assay to enumerate HBV-specific T cells for 116 patients with HBV infection. The spot forming units (SFUs) was significantly correlated with serum HBsAg level as confirmed by multivariate linear regression analysis. This study functionally validated 62 T cell epitopes from HBV main proteins and elucidated their HLA-A restrictions and provided an alternative ELISPOT assay using validated epitope peptides rather than conventional overlapping peptides for the clinical evaluation of HBV-specific T cell responses.

The Use of Molecular Dynamics Simulation Method to Quantitatively Evaluate the Affinity between HBV Antigen T Cell Epitope Peptides and HLA-A Molecules

Chronic hepatitis B virus (HBV), a potentially life-threatening liver disease, makes people vulnerable to serious diseases such as cancer. T lymphocytes play a crucial role in clearing HBV virus, while the pathway depends on the strong binding of T cell epitope peptide and HLA. However, the experimental identification of HLA-restricted HBV antigenic peptides is extremely time-consuming. In this study, we provide a novel prediction strategy based on structure to assess the affinity between the HBV antigenic peptide and HLA molecule. We used residue scanning, peptide docking and molecular dynamics methods to obtain the molecular docking model of HBV peptide and HLA, and then adopted the MM-GBSA method to calculate the binding affinity of the HBV peptide–HLA complex. Overall, we collected 59 structures of HLA-A from Protein Data Bank, and finally obtained 352 numerical affinity results to figure out the optimal bind choice between the HLA-A molecules and 45 HBV T cell epitope peptides. The results were highly consistent with the qualitative affinity level determined by the competitive peptide binding assay, which confirmed that our affinity prediction process based on an HLA structure is accurate and also proved that the homologous modeling strategy for HLA-A molecules in this study was reliable. Hence, our work highlights an effective way by which to predict and screen for HLA-peptide binding that would improve the treatment of HBV infection.

HLA3D: an integrated structure-based computational toolkit for immunotherapy

Motivation

The human major histocompatibility complex (MHC), also known as human leukocyte antigen (HLA), plays an important role in the adaptive immune system by presenting non-self-peptides to T cell receptors. The MHC region has been shown to be associated with a variety of diseases, including autoimmune diseases, organ transplantation and tumours. However, structural analytic tools of HLA are still sparse compared to the number of identified HLA alleles, which hinders the disclosure of its pathogenic mechanism.

Result

To provide an integrative analysis of HLA, we first collected 1296 amino acid sequences, 256 protein data bank structures, 120 000 frequency data of HLA alleles in different populations, 73 000 publications and 39 000 disease-associated single nucleotide polymorphism sites, as well as 212 modelled HLA heterodimer structures. Then, we put forward two new strategies for building up a toolkit for transplantation and tumour immunotherapy, designing risk alignment pipeline and antigenic peptide prediction pipeline by integrating different resources and bioinformatic tools. By integrating 100 000 calculated HLA conformation difference and online tools, risk alignment pipeline provides users with the functions of structural alignment, sequence alignment, residue visualization and risk report generation of mismatched HLA molecules. For tumour antigen prediction, we first predicted 370 000 immunogenic peptides based on the affinity between peptides and MHC to generate the neoantigen catalogue for 11 common tumours. We then designed an antigenic peptide prediction pipeline to provide the functions of mutation prediction, peptide prediction, immunogenicity assessment and docking simulation. We also present a case study of hepatitis B virus mutations associated with liver cancer that demonstrates the high legitimacy of our antigenic peptide prediction process. HLA3D, including different HLA analytic tools and the prediction pipelines, is available at http://www.hla3d.cn/.

In Silico Investigation on the Interaction of Chiral Phytochemicals from Opuntia ficus-indica with SARS-CoV-2 Mpro

Opuntia ficus-indica is a cactaceous plant native to America but, nowadays, widely found worldwide, having been the most common domesticated species of cactus grown as a crop plant in semiarid and arid parts of the globe, including several Mediterranean basin countries. Opuntia ficus-indica can be regarded as a medicinal plant, being source of numerous bioactive phytochemicals such as vitamins, polyphenols, and amino acids. The urgent need for therapeutic treatments for the COronaVIrus Disease 19 (COVID-19), caused by the Severe Acute Respiratory Syndrome (SARS)-Coronavirus (CoV)-2, justifies the great attention currently being paid not only to repurposed antiviral drugs, but also to natural products and herbal medications. In this context, the anti-COVID-19 utility of Opuntia ficus-indica as source of potential antiviral drugs was investigated in this work on the basis of the activity of some of its phytochemical constituents. The antiviral potential was evaluated in silico in docking experiments with Mpro, i.e., the main protease of SARS-CoV-2, that is one of the most investigated protein targets of therapeutic strategies for COVID-19. By using two web-based molecular docking programs (1-Click Mcule and COVID-19 Docking Server), we found, for several flavonols and flavonol glucosides isolated from Opuntia ficus-indica, good binding affinities for Mpro, and in particular, binding energies lower than −7.0 kcal/mol were predicted for astragalin, isorhamnetin, isorhamnetin 3-O-glucoside, 3-O-caffeoyl quinic acid, and quercetin 5,4′-dimethyl ether. Among these compounds, the chiral compound astragalin showed in our in silico studies the highest affinity for Mpro (−8.7 kcal/mol) and also a low toxicity profile, emerging, thus, as an interesting protease inhibitor candidate for anti-COVID-19 strategies.

MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm

Deep learning is an important branch of artificial intelligence that has been successfully applied into medicine and two-dimensional ligand design. The three-dimensional (3D) ligand generation in the 3D pocket of protein target is an interesting and challenging issue for drug design by deep learning. Here, the MolAICal software is introduced to supply a way for generating 3D drugs in the 3D pocket of protein targets by combining with merits of deep learning model and classical algorithm. The MolAICal software mainly contains two modules for 3D drug design. In the first module of MolAICal, it employs the genetic algorithm, deep learning model trained by FDA-approved drug fragments and Vinardo score fitting on the basis of PDBbind database for drug design. In the second module, it uses deep learning generative model trained by drug-like molecules of ZINC database and molecular docking invoked by Autodock Vina automatically. Besides, the Lipinski's rule of five, Pan-assay interference compounds (PAINS), synthetic accessibility (SA) and other user-defined rules are introduced for filtering out unwanted ligands in MolAICal. To show the drug design modules of MolAICal, the membrane protein glucagon receptor and non-membrane protein SARS-CoV-2 main protease are chosen as the investigative drug targets. The results show MolAICal can generate the various and novel ligands with good binding scores and appropriate XLOGP values. We believe that MolAICal can use the advantages of deep learning model and classical programming for designing 3D drugs in protein pocket. MolAICal is freely for any nonprofit purpose and accessible at https://molaical.github.io.

Bioinformatic Analysis of Genome-Predicted Bat Cathelicidins

Bats are unique in their potential to serve as reservoir hosts for intracellular pathogens. Recently, the impact of COVID-19 has relegated bats from biomedical darkness to the frontline of public health as bats are the natural reservoir of many viruses, including SARS-Cov-2. Many bat genomes have been sequenced recently, and sequences coding for antimicrobial peptides are available in the public databases. Here we provide a structural analysis of genome-predicted bat cathelicidins as components of their innate immunity. A total of 32 unique protein sequences were retrieved from the NCBI database. Interestingly, some bat species contained more than one cathelicidin. We examined the conserved cysteines within the cathelin-like domain and the peptide portion of each sequence and revealed phylogenetic relationships and structural dissimilarities. The antibacterial, antifungal, and antiviral activity of peptides was examined using bioinformatic tools. The peptides were modeled and subjected to docking analysis with the region binding domain (RBD) region of the SARS-CoV-2 Spike protein. The appearance of multiple forms of cathelicidins verifies the complex microbial challenges encountered by these species. Learning more about antiviral defenses of bats and how they drive virus evolution will help scientists to investigate the function of antimicrobial peptides in these species.

The evolutionary analysis of complement component C5 and the gene co-expression network and putative interaction between C5a and C5a anaphylatoxin receptor (C5AR/CD88) in human and two Cyprinid fish

The complement system is a complex network of soluble and membrane-associated serum proteins that regulate immune response. Activation of the complement C5 generates C5a and C5b which generate chemoattractive effect on myeloid cells and initiate the membrane attack complex (MAC) assembly. However, the study of evolutionary process and systematic function of C5 are still limited. In this study, we performed an evolutionary analysis of C5. Phylogeny analysis indicated that C5 sequences underwent complete divergence in fish and non-fish vertebrate. It was found that codon usage bias improved and provided evolution evidence of C5 in species. Notably, the codon usage bias of grass carp was evolutionarily closer to the zebrafish genome compared with humans and stickleback. This suggested that the zebrafish cell line may provide an alternative environment for heterologous protein expression of grass carp. Sequence comparison showed a higher similarity between human and mouse, grass carp, and zebrafish. Moreover, selective pressure analysis revealed that the C5 genes in fish and non-fish vertebrates exhibited different evolutionary patterns. To study the function of C5, gene co-expression networks of human and zebrafish were built which revealed the complexity of C5 function networks in different species. The protein structure simulation of C5 indicated that grass carp and zebrafish are more similar than to human, however, differences between species in C5a proteins are extremely smaller. Spatial conformations of C5a-C5AR (CD88) protein complex were constructed, which showed that possible interaction may exist between C5a and CD88 proteins. Furthermore, the protein docking sites/residues were measured and calculated according to the minimum distance for all atoms from C5a and CD88 proteins. In summary, this study provides insights into the evolutionary history, function and potential regulatory mechanism of C5 in fish immune responses.

Protective role of serpina3c as a novel thrombin inhibitor against atherosclerosis in mice

Abnormal vascular smooth muscle cell (VSMC) proliferation is a critical step in the development of atherosclerosis. Serpina3c is a serine protease inhibitor (serpin) that plays a key role in metabolic diseases. The present study aimed to investigate the role of serpina3c in atherosclerosis and regulation of VSMC proliferation and possible mechanisms. Serpina3c is down-regulated during high-fat diet (HFD)-induced atherosclerosis. An Apoe-/-/serpina3c-/--double-knockout mouse model was used to determine the role of serpina3c in atherosclerosis after HFD for 12 weeks. Compared with Apoe-/- mice, the Apoe-/-/serpina3c-/- mice developed more severe atherosclerosis, and the number of VSMCs and macrophages in aortic plaques was significantly increased. The present study revealed serpina3c as a novel thrombin inhibitor that suppressed thrombin activity. In circulating plasma, thrombin activity was high in the Apoe-/-/serpina3c-/- mice, compared with Apoe-/- mice. Immunofluorescence staining showed thrombin and serpina3c colocalization in the liver and aortic cusp. In addition, inhibition of thrombin by dabigatran in serpina3c-/- mice reduced neointima lesion formation due to partial carotid artery ligation. Moreover, an in vitro study confirmed that thrombin activity was also decreased by serpina3c protein, supernatant and cell lysate that overexpressed serpina3c. The results of experiments showed that serpina3c negatively regulated VSMC proliferation in culture. The possible mechanism may involve serpina3c inhibition of ERK1/2 and JNK signaling in thrombin/PAR-1 system-mediated VSMC proliferation. Our results highlight a protective role for serpina3c as a novel thrombin inhibitor in the development of atherosclerosis, with serpina3c conferring protection through the thrombin/PAR-1 system to negatively regulate VSMC proliferation through ERK1/2 and JNK signaling.

Differential seminal plasma proteome signatures of boars with high and low resistance to hypothermic semen preservation at 5°C

BACKGROUND

Hypothermic storage at 5°C has been investigated as an alternative to promote the prudent use of antibiotics for boar artificial insemination doses. However, this temperature is challenging for some ejaculates or boars.

OBJECTIVE

The present study aimed to identify putative biomarkers for semen resistance to hypothermic storage at 5°C by comparing the seminal plasma proteomes of boars with high and low seminal resistance to preservation at 5°C.

MATERIALS AND METHODS

From an initial group of 34 boars, 15 were selected based on the following criteria: ejaculate with ≤20% abnormal spermatozoa and at least 70% progressive motility at 120 hours of storage at 17°C. Then, based on the response to semen hypothermic storage at 5°C, boars were classified into two categories: high resistance-progressive motility of >75% in the three collections (n = 3); and low resistance-progressive motility of <75% in the three collections (n = 3). Seminal plasma proteins were analyzed in pools, and differential proteomics was performed using Multidimensional Protein Identification Technology.

RESULTS

Progressive motility was lower at 120 hours of storage in low resistance, compared to high resistance boars (P < .05). Acrosome and plasma membrane integrity were not affected by the boar category, storage time, or their interaction (P ≥ .104). Sixty-five proteins were considered for differential proteomics. Among the differentially expressed and exclusive proteins, the identification of proteins such cathepsin B, legumain, and cystatin B suggests significant changes in key enzymes (eg, metalloproteinases) involved in spermatogenesis, sperm integrity, and fertilizing potential.

DISCUSSION AND CONCLUSION

Differences in the seminal plasma suggest that proteins involved in the proteolytic activation of metalloproteinases and proteins related to immune response modulation could disrupt key cellular pathways during spermatogenesis and epididymal maturation, resulting in altered resistance to chilling injury. Further in vivo studies focusing on the immunological crosstalk between epithelial cells and gametes might explain how the immune regulators influence sperm resistance to hipothermic storage.

COVID-19 Docking Server: a meta server for docking small molecules, peptides and antibodies against potential targets of COVID-19

Motivation

The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019. Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target-ligand interactions represents a key challenge. Herein, we introduce COVID-19 Docking Server, a web server that predicts the binding modes between COVID-19 targets and the ligands including small molecules, peptides and antibodies.

Results

Structures of proteins involved in the virus life cycle were collected or constructed based on the homologs of coronavirus, and prepared ready for docking. The meta platform provides a free and interactive tool for the prediction of COVID-19 target-ligand interactions and following drug discovery for COVID-19.

Availability

http://ncov.schanglab.org.cn

Supplementary information

Fish complement C4 gene evolution and gene/protein regulatory network analyses and simulated stereo conformation of C4-MASP-2 protein complex

Complement C4 is a central protein by acting as pivotal molecule in the activation of the complement system. More than a decade ago, C4 gene duplication had been found in several species including fish, revealing the evolutionary origin of C4 gene. However, the evolutionary pattern and systematic function of C4 are still limited. In this study, C4 D and H types in different species groups were completely diverged. The codon usage of C4 H type in higher vertebrates were much closer to their own genome environment, in contrast to lower vertebrates, suggesting that the evolution may provide the dynamic for homogeneous codon usage between specific gene and genome. Multiple C4 sequence alignment showed that the sequences were conserved among different species. However, sequence similarity was obviously different between species C4 D and H type. Negative selection pressure was found on C4 gene evolution and it may be one of the possible reasons for the sequence broad similarity and conservation among interspecies. Proteins from C4 protein-protein interaction (PPI) network were enriched in more hematopoiesis, infections, diseases and immune-related pathways in human than zebrafish. The result suggested that the functional complexities of C4 isotypes are distinct in species from different evolutionary positions. The simulated C4 protein structures between human and grass carp shared structural similarity and the stereo structures of grass carp C4-MASP-2 protein complexes were further simulated according to a study of human. These results suggested that the interaction between C4 and MASP-2 proteins may also exist in grass carp. Our results can provide an insight for the evolutionary process of C4 and better understanding to the potential mechanism of interaction between C4 and MASP-2 in fish species.

Peptide modelling and screening against human ACE2 and spike glycoprotein RBD of SARS-CoV-2

Outbreak of Coronavirus Disease 2019 (COVID-19) has become a great challenge for scientific community globally. Virus enters cell through spike glycoprotein fusion with ACE2 (Angiotensin-Converting Enzyme 2) human receptor. Hence, spike glycoprotein of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a potential target for diagnostics, vaccines, and antibodies. Also, virus entry can be prevented by blocking ACE2 thus, ACE2 can be considered potential target for therapeutics. As being highly specific, safe and efficacious, peptides hold their place in therapeutics. In present study, we retrieved sequence of 70 peptides from Antiviral Peptide Database (AVPdb), modelled them using 3D structure predicting web tool and docked them with receptor binding domain (RBD) of spike protein and human host receptor ACE2 using peptide-protein docking. It was observed that peptides have more affinity towards ACE2 in comparison with spike RBD. Interestingly it was noticed that most of the peptides bind to RBM (residue binding motif) which is responsible for ACE2 binding at the interface of RBD while, for ACE2, peptides prefer to bind the core cavity rather than RBD binding interface. To further investigate how peptides at the interface of RBD or ACE2 alter the binding between RBD and ACE2, protein-protein docking of RBD and ACE2 with and without peptides was performed. Peptides, AVP0671 at RBD and AVP1244 at ACE2 interfaces significantly reduce the binding affinity and change the orientation of RBD and ACE2 binding. This finding suggests that peptides can be used as a drug to inhibit virus entry in cells to stop COVID-19 pandemic in the future after experimental evidences.

Fish complement C8 evolution, functional network analyses, and the theoretical interaction between C8 alpha chain and CD59

Complement C8, as a main component of the membrane attack complex, has only been identified in vertebrates. C8 comprises three subunits encoded by individual genes: C8a (alpha chain), C8b (beta chain), and C8g (gamma chain). However, in fish, there have been limited studies on the evolutionary history and systematic function of C8. In the present study, phylogenetic analysis indicated the complete divergence of C8 genes in different fish species. Codon usage bias analysis revealed the evolutionary complexity of C8 genes. Selective pressure analysis found that C8 genes have been affected by negative selection during evolution. Sequence alignment identified the sites that are under selective pressure. The systematic functions of C8 were revealed by gene co-expression and protein-protein interaction (PPI) network analyses. Notably, gene ontology enrichment analysis suggested that C8 proteins in zebrafish function mainly in the neuroendocrine system. Protein structural comparisons showed that putative functional residues and domains were conserved between the C8 subunits of human and grass carp. A preliminary study on the theoretical interaction between C8a and CD59 was performed according to the simulated protein stereo structure. The first functionally-related site was absent in the simulated conformation of the grass carp (Ctenopharyngodon idella) C8a-CD59 protein complex. We speculated that Tyr63 is involved in the functional loss of CD59 binding. The docking of CD59 to four potential sites (Met390, Ser391, Leu392, and Val405) in grass carp C8a was analyzed. The results of the present study provide a deeper understanding of the evolution and function of fish complement C8.

Novel Binding Mechanisms of Fusion Broad Range Anti-Infective Protein Ricin A Chain Mutant-Pokeweed Antiviral Protein 1 (RTAM-PAP1) against SARS-CoV-2 Key Proteins in Silico

The deadly pandemic named COVID-19, caused by a new coronavirus (SARS-CoV-2), emerged in 2019 and is still spreading globally at a dangerous pace. As of today, there are no proven vaccines, therapies, or even strategies to fight off this virus. Here, we describe the in silico docking results of a novel broad range anti-infective fusion protein RTAM-PAP1 against the various key proteins of SARS-CoV-2 using the latest protein-ligand docking software. RTAM-PAP1 was compared against the SARS-CoV-2 B38 antibody, ricin A chain, a pokeweed antiviral protein from leaves, and the lectin griffithsin using the special CoDockPP COVID-19 version. These experiments revealed novel binding mechanisms of RTAM-PAP1 with a high affinity to numerous SARS-CoV-2 key proteins. RTAM-PAP1 was further characterized in a preliminary toxicity study in mice and was found to be a potential therapeutic candidate. These findings might lead to the discovery of novel SARS-CoV-2 targets and therapeutic protein structures with outstanding functions.

Biomimetic polysaccharide-cloaked lipidic nanovesicles/microassemblies for improving the enzymatic activity and prolonging the action time for hyperuricemia treatment

The improvement and maintenance of enzymatic activities represent major challenges. However, to address these we developed novel biomimetic polysaccharide hyaluronan (Hn)-cloaked lipidic nanovesicles (BHLN) and microassemblies (BHLNM) as enzyme carriers that function by entrapping enzymes in the core or by tethering them to the inner/outer surfaces via covalent interactions. The effectiveness of these enzyme carriers was demonstrated through an evaluation of the enzymatic activity and anti-hyperuricemia bioactivity of urate oxidase (also called uricase, Uase). We showed that Uase was effectively loaded within the BHLN/BHLNM (UHLN/UHLNM) and maintained good enzymatic bioactivity through a range of effects, including isolation from the external environment due to the vesicle-carrying (shielding effect), avoidance of recognition by the reticuloendothelial system due to Hn-cloaking (long-term effect), production of beneficial conformational changes (allosteric effect) due to a favorable internal microenvironment of construction and vesicle loading, and stabilization due to the reversible conjugation of Uase or vesicle and serum albumin (deposit effect). UHLN/UHLNM had significantly increased bioavailability (∼533% and ∼331% compared to Uase) and demonstrated greatly improved efficacy, whereby the time required for UHLN/UHLNM to lower the plasma uric acid concentration to a normal level was much shorter than that for free Uase. The interactions of the therapeutic enzyme (Uase), biomimetic membrane components (Hn and phospholipid), and serum albumin were investigated with a fluorescent probe and computational simulations to help understand the superior properties of UHLN/UHLNM.