Ab initio modeling of small proteins by iterative TASSER simulations

PB-GPT: An innovative GPT-based model for protein backbone generation

With advanced computational methods, it is now feasible to modify or design proteins for specific functions, a process with significant implications for disease treatment and other medical applications. Protein structures and functions are intrinsically linked to their backbones, making the design of these backbones a pivotal aspect of protein engineering. In this study, we focus on the task of unconditionally generating protein backbones. By means of codebook quantization and compression dictionaries, we convert protein backbone structures into a distinctive coded language and propose a GPT-based protein backbone generation model, PB-GPT. To validate the generalization performance of the model, we trained and evaluated the model on both public datasets and small protein datasets. The results demonstrate that our model has the capability to unconditionally generate elaborate, highly realistic protein backbones with structural patterns resembling those of natural proteins, thus showcasing the significant potential of large language models in protein structure design.

Experimental and molecular dynamics simulation studies on the physical properties of three HBc-VLP derivatives as nanoparticle protein vaccine candidates

Self-assembling virus-like particles (VLPs) are promising platforms for vaccine development. However, the unpredictability of the physical properties, such as self-assembly capability, hydrophobicity, and overall stability in engineered protein particles fused with antigens, presents substantial challenges in their downstream processing. We envision that these challenges can be addressed by combining more precise computer-aided molecular dynamics (MD) simulations with experimental studies on the modified products, with more to-date forcefield descriptions and larger models closely resembling real assemblies, realized by rapid advancement in computing technology. In this study, three chimeric designs based on the hepatitis B core (HBc) protein as model vaccine candidates were constructed to study and compare the influence of inserted epitopes as well as insertion strategy on HBc modifications. Large partial VLP models containing 17 chains for the HBc chimeric model vaccines were constructed based on the wild-type (wt) HBc assembly template. The findings from our simulation analysis have demonstrated good consistency with experimental results, pertaining to the surface hydrophobicity and overall stability of the chimeric vaccine candidates. Furthermore, the different impact of foreign antigen insertions on the HBc scaffold was investigated through simulations. It was found that separately inserting two epitopes into the HBc platform at the N-terminal and the major immunogenic regions (MIR) yields better results compared to a serial insertion at MIR in terms of protein structural stability. This study substantiates that an MD-guided design approach can facilitate vaccine development and improve its manufacturing efficiency by predicting products with extreme surface hydrophobicity or structural instability.

Binding structures of SERF1a with NT17-polyQ peptides of huntingtin exon 1 revealed by SEC-SWAXS, NMR and molecular simulation

The aberrant fibrillization of huntingtin exon 1 (Httex1) characterized by an expanded polyglutamine (polyQ) tract is a defining feature of Huntington's disease, a neurodegenerative disorder. Recent investigations underscore the involvement of a small EDRK-rich factor 1a (SERF1a) in promoting Httex1 fibrillization through interactions with its N terminus. By establishing an integrated approach with size-exclusion-column-based small- and wide-angle X-ray scattering (SEC-SWAXS), NMR, and molecular simulations using Rosetta, the analysis here reveals a tight binding of two NT17 fragments of Httex1 (comprising the initial 17 amino acids at the N terminus) to the N-terminal region of SERF1a. In contrast, examination of the complex structure of SERF1a with a coiled NT17-polyQ peptide (33 amino acids in total) indicates sparse contacts of the NT17 and polyQ segments with the N-terminal side of SERF1a. Furthermore, the integrated SEC-SWAXS and molecular-simulation analysis suggests that the coiled NT17 segment can transform into a helical conformation when associated with a polyQ segment exhibiting high helical content. Intriguingly, NT17-polyQ peptides with enhanced secondary structures display diminished interactions with SERF1a. This insight into the conformation-dependent binding of NT17 provides clues to a catalytic association mechanism underlying SERF1a's facilitation of Httext1 fibrillization.

Accurate Structure Prediction for Cyclic Peptides Containing Proline Residues with High-Temperature Molecular Dynamics

Cyclic peptides (CPs) are emerging as promising drug candidates. Numerous natural CPs and their analogs are effective therapeutics against various diseases. Notably, many of them contain peptidyl cis-prolyl bonds. Due to the high rotational barrier of peptide bonds, conventional molecular dynamics simulations struggle to effectively sample the cis/trans-isomerization of peptide bonds. Previous studies have highlighted the high accuracy of the residue-specific force field (RSFF) and the high sampling efficiency of high-temperature molecular dynamics (high-T MD). Herein, we propose a protocol that combines high-T MD with RSFF2C and a recently developed reweighting method based on probability densities for accurate structure prediction of proline-containing CPs. Our method successfully predicted 19 out of 23 CPs with the backbone rmsd < 1.0 Å compared to X-ray structures. Furthermore, we performed high-T MD and density reweighting on the sunflower trypsin inhibitor (SFTI-1)/trypsin complex to demonstrate its applicability in studying CP-complexes containing cis-prolines. Our results show that the conformation of SFTI-1 in aqueous solution is consistent with its bound conformation, potentially facilitating its binding.

An mRNA vaccine for pancreatic cancer designed by applying in silico immunoinformatics and reverse vaccinology approaches

Pancreatic ductal adenocarcinoma is the most prevalent pancreatic cancer, which is considered a significant global health concern. Chemotherapy and surgery are the mainstays of current pancreatic cancer treatments; however, a few cases are suitable for surgery, and most of the cases will experience recurrent episodes. Compared to DNA or peptide vaccines, mRNA vaccines for pancreatic cancer have more promise because of their delivery, enhanced immune responses, and lower proneness to mutation. We constructed an mRNA vaccine by analyzing S100 family proteins, which are all major activators of receptors for advanced glycation end products. We applied immunoinformatic approaches, including physicochemical properties analysis, structural prediction and validation, molecular docking study, in silico cloning, and immune simulations. The designed mRNA vaccine was estimated to have a molecular weight of 165023.50 Da and was highly soluble (grand average of hydropathicity of -0.440). In the structural assessment, the vaccine seemed to be a well-stable and functioning protein (Z score of -8.94). Also, the docking analysis suggested that the vaccine had a high affinity for TLR-2 and TLR-4 receptors. Additionally, the molecular mechanics with generalized Born and surface area solvation analysis of the "Vaccine-TLR-2" (-141.07 kcal/mol) and "Vaccine-TLR-4" (-271.72 kcal/mol) complexes also suggests a strong binding affinity for the receptors. Codon optimization also provided a high expression level with a GC content of 47.04% and a codon adaptation index score 1.0. The appearance of memory B-cells and T-cells was also observed over a while, with an increased level of helper T-cells and immunoglobulins (IgM and IgG). Moreover, the minimum free energy of the mRNA vaccine was predicted at -1760.00 kcal/mol, indicating the stability of the vaccine following its entry, transcription, and expression. This hypothetical vaccine offers a groundbreaking tool for future research and therapeutic development of pancreatic cancer.

Engineering lentivirus envelope VSV-G for liver targeted delivery of IDOL-shRNA to ameliorate hypercholesterolemia and atherosclerosis

Lentiviral vectors (LVs) have been widely used as a tool for gene therapies. However, tissue-selective transduction after systemic delivery remains a challenge. Inducible degrader of low-density lipoprotein receptor is an attractive target for treating hypercholesterolemia. Here, a liver-targeted LV, CS8-LV-shIDOL, is developed by incorporating a hepatocyte-targeted peptide derived from circumsporozoite protein (CSP) into the lentivirus envelope for liver-targeted delivery of IDOL-shRNA (short hairpin RNA) to alleviate hypercholesterolemia. Tail-vein injection of CS8-LV-shIDOL results in extremely high accumulation in liver and nearly undetectable levels in other organs in mice. In addition, it shows superior therapeutic efficacy in lowering serum low-density lipoprotein cholesterol (LDL-C) and reducing atherosclerotic lesions over unmodified LV-shIDOL in hyperlipidemic mice. Mechanically, the envelope-engineered CS8-LV-shIDOL can enter liver cells via low-density lipoprotein receptor-related protein (LRP). Thus, this study provides a novel approach for liver-targeted delivery of IDOL-shRNA to treat hypercholesterolemia by using an envelope-engineered LV, and this delivery system has great potential for liver-targeted transgene therapy.

Construction of an aerolysin-based multi-epitope vaccine against Aeromonas hydrophila: an in silico machine learning and artificial intelligence-supported approach

Aeromonas hydrophila, a gram-negative coccobacillus bacterium, can cause various infections in humans, including septic arthritis, diarrhea (traveler's diarrhea), gastroenteritis, skin and wound infections, meningitis, fulminating septicemia, enterocolitis, peritonitis, and endocarditis. It frequently occurs in aquatic environments and readily contacts humans, leading to high infection rates. This bacterium has exhibited resistance to numerous commercial antibiotics, and no vaccine has yet been developed. Aiming to combat the alarmingly high infection rate, this study utilizes in silico techniques to design a multi-epitope vaccine (MEV) candidate against this bacterium based on its aerolysin toxin, which is the most toxic and highly conserved virulence factor among the Aeromonas species. After retrieval, aerolysin was processed for B-cell and T-cell epitope mapping. Once filtered for toxicity, antigenicity, allergenicity, and solubility, the chosen epitopes were combined with an adjuvant and specific linkers to create a vaccine construct. These linkers and the adjuvant enhance the MEV's ability to elicit robust immune responses. Analyses of the predicted and improved vaccine structure revealed that 75.5%, 19.8%, and 1.3% of its amino acids occupy the most favored, additional allowed, and generously allowed regions, respectively, while its ERRAT score reached nearly 70%. Docking simulations showed the MEV exhibiting the highest interaction and binding energies (-1,023.4 kcal/mol, -923.2 kcal/mol, and -988.3 kcal/mol) with TLR-4, MHC-I, and MHC-II receptors. Further molecular dynamics simulations demonstrated the docked complexes' remarkable stability and maximum interactions, i.e., uniform RMSD, fluctuated RMSF, and lowest binding net energy. In silico models also predict the vaccine will stimulate a variety of immunological pathways following administration. These analyses suggest the vaccine's efficacy in inducing robust immune responses against A. hydrophila. With high solubility and no predicted allergic responses or toxicity, it appears safe for administration in both healthy and A. hydrophila-infected individuals.

Sulfated disaccharide protects membrane and DNA damages from arginine-rich dipeptide repeats in ALS

Hexanucleotide repeat expansion in C9ORF72 (C9) is the most prevalent mutation among amyotrophic lateral sclerosis (ALS) patients. The patients carry over ~30 to hundreds or thousands of repeats translated to dipeptide repeats (DPRs) where poly-glycine-arginine (GR) and poly-proline-arginine (PR) are most toxic. The structure-function relationship is still unknown. Here, we examined the minimal neurotoxic repeat number of poly-GR and found that extension of the repeat number led to a loose helical structure disrupting plasma and nuclear membrane. Poly-GR/PR bound to nucleotides and interfered with transcription. We screened and identified a sulfated disaccharide that bound to poly-GR/PR and rescued poly-GR/PR-induced toxicity in neuroblastoma and C9-ALS-iPSC-derived motor neurons. The compound rescued the shortened life span and defective locomotion in poly-GR/PR expressing Drosophila model and improved motor behavior in poly-GR-injected mouse model. Overall, our results reveal structural and toxicity mechanisms for poly-GR/PR and facilitate therapeutic development for C9-ALS.

Tpgen: a language model for stable protein design with a specific topology structure

BACKGROUND

Natural proteins occupy a small portion of the protein sequence space, whereas artificial proteins can explore a wider range of possibilities within the sequence space. However, specific requirements may not be met when generating sequences blindly. Research indicates that small proteins have notable advantages, including high stability, accurate resolution prediction, and facile specificity modification.

RESULTS

This study involves the construction of a neural network model named TopoProGenerator(TPGen) using a transformer decoder. The model is trained with sequences consisting of a maximum of 65 amino acids. The training process of TopoProGenerator incorporates reinforcement learning and adversarial learning, for fine-tuning. Additionally, it encompasses a stability predictive model trained with a dataset comprising over 200,000 sequences. The results demonstrate that TopoProGenerator is capable of designing stable small protein sequences with specified topology structures.

CONCLUSION

TPGen has the ability to generate protein sequences that fold into the specified topology, and the pretraining and fine-tuning methods proposed in this study can serve as a framework for designing various types of proteins.

Design of a novel multi-epitopes vaccine against Escherichia fergusonii: a pan-proteome based in- silico approach

Escherichia fergusonii a gram-negative rod-shaped bacterium in the Enterobacteriaceae family, infect humans, causing serious illnesses such as urinary tract infection, cystitis, biliary tract infection, pneumonia, meningitis, hemolytic uremic syndrome, and death. Initially treatable with penicillin, antibiotic misuse led to evolving resistance, including resistance to colistin, a last-resort drug. With no licensed vaccine, the study aimed to design a multi-epitope vaccine against E. fergusonii. The study started with the retrieval of the complete proteome of all known strains and proceeded to filter the surface exposed virulent proteins. Seventeen virulent proteins (4 extracellular, 4 outer membranes, 9 periplasmic) with desirable physicochemical properties were identified from the complete proteome of known strains. Further, these proteins were processed for B-cell and T-cell epitope mapping. Obtained epitopes were evaluated for antigenicity, allergenicity, solubility, MHC-binding, and toxicity and the filtered epitopes were fused by specific linkers and an adjuvant into a vaccine construct. Structure of the vaccine candidate was predicted and refined resulting in 78.1% amino acids in allowed regions and VERIFY3D score of 81%. Vaccine construct was docked with TLR-4, MHC-I, and MHC-II, showing binding energies of -1040.8 kcal/mol, -871.4 kcal/mol, and -1154.6 kcal/mol and maximum interactions. Further, molecular dynamic simulation of the docked complexes was carried out resulting in a significant stable nature of the docked complexes (high B-factor and deformability values, lower Eigen and high variance values) in terms of intermolecular binding conformation and interactions. The vaccine was also reported to stimulate a variety of immunological pathways after administration. In short, the designed vaccine revealed promising predictions about its immune protective potential against E. fergusonii infections however experimental validation is needed to validate the results.

AI-based AlphaFold2 significantly expands the structural space of the autophagy pathway

ABBREVIATIONS

AF2: AlphaFold2; AF2-Mult: AlphaFold2 multimer; ATG: autophagy-related; CTD: C-terminal domain; ECTD: extreme C-terminal domain; FR: flexible region; MD: molecular dynamics; NTD: N-terminal domain; pLDDT: predicted local distance difference test; UBL: ubiquitin-like.

Role of environmental specificity in CASP results

BACKGROUND

Recently, significant progress has been made in the field of protein structure prediction by the application of artificial intelligence techniques, as shown by the results of the CASP13 and CASP14 (Critical Assessment of Structure Prediction) competition. However, the question of the mechanism behind the protein folding process itself remains unanswered. Correctly predicting the structure also does not solve the problem of, for example, amyloid proteins, where a polypeptide chain with an unaltered sequence adopts a different 3D structure.

RESULTS

This work was an attempt at explaining the structural variation by considering the contribution of the environment to protein structuring. The application of the fuzzy oil drop (FOD) model to assess the validity of the selected models provided in the CASP13, CASP14 and CASP15 projects reveals the need for an environmental factor to determine the 3D structure of proteins. Consideration of the external force field in the form of polar water (Fuzzy Oil Drop) and a version modified by the presence of the hydrophobic compounds, FOD-M (FOD-Modified) reveals that the protein folding process is environmentally dependent. An analysis of selected models from the CASP competitions indicates the need for structure prediction as dependent on the consideration of the protein folding environment.

CONCLUSIONS

The conditions governed by the environment direct the protein folding process occurring in a certain environment. Therefore, the variation of the external force field should be taken into account in the models used in protein structure prediction.

Bioengineering of air-filled protein nanoparticles by genetic and chemical functionalization

BACKGROUND

Various bacteria and archaea, including halophilic archaeon Halobacterium sp. NRC-1 produce gas vesicle nanoparticles (GVNPs), a unique class of stable, air-filled intracellular proteinaceous nanostructures. GVNPs are an attractive tool for biotechnological applications due to their readily production, purification, and unique physical properties. GVNPs are spindle- or cylinder-shaped, typically with a length of 100 nm to 1.5 μm and a width of 30-250 nm. Multiple monomeric subunits of GvpA and GvpC proteins form the GVNP shell, and several additional proteins are required as minor structural or assembly proteins. The haloarchaeal genetic system has been successfully used to produce and bioengineer GVNPs by fusing several foreign proteins with GvpC and has shown various applications, such as biocatalysis, diagnostics, bioimaging, drug delivery, and vaccine development.

RESULTS

We demonstrated that native GvpC can be removed in a low salt buffer during the GVNP purification, leaving the GvpA-based GVNP's shell intact and stable under physiological conditions. Here, we report a genetic engineering and chemical modification approach for functionalizing the major GVNP protein, GvpA. This novel approach is based on combinatorial cysteine mutagenesis within GvpA and genetic expansion of the N-terminal and C-terminal regions. Consequently, we generated GvpA single, double, and triple cysteine variant libraries and investigated the impact of mutations on the structure and physical shape of the GVNPs formed. We used a thiol-maleimide chemistry strategy to introduce the biotechnological relevant activity by maleimide-activated streptavidin-biotin and maleimide-activated SpyTag003-SpyCatcher003 mediated functionalization of GVNPs.

CONCLUSION

The merger of these genetic and chemical functionalization approaches significantly extends these novel protein nanomaterials' bioengineering and functionalization potential to assemble catalytically active proteins, biomaterials, and vaccines onto one nanoparticle in a modular fashion.

Tissue specific expression of bacterial cellulose synthase (Bcs) genes improves cotton fiber length and strength

Fiber growing inside the cotton bolls is a highly demandable product and its quality is key to the success of the textile industry. Despite the various efforts to improve cotton fiber staple length Pakistan has to import millions of bales to sustain its industrial needs. To improve cotton fiber quality Bacterial cellulose synthase (Bcs) genes (acsA, acsB) were expressed in a local cotton variety CEMB-00. In silico studies revealed a number of conserved domains both in the cotton-derived and bacterial cellulose synthases which are essential for the cellulose synthesis. Transformation efficiency of 1.27% was achieved by using Agrobacterium shoot apex cut method of transformation. The quantitative mRNA expression analysis of the Bcs genes in transgenic cotton fiber was found to be many folds higher during secondary cell wall synthesis stage (35 DPA) than the expression during elongation phase (10 DPA). Average fiber length of the transgenic cotton plant lines S-00-07, S-00-11, S-00-16 and S-00-23 was calculated to be 13.02% higher than that of the non-transgenic control plants. Likewise, the average fiber strength was found to be 20.92% higher with an enhanced cellulose content of 22.45%. The mutated indigenous cellulose synthase genes of cotton generated through application of CRISPR/Cas9 resulted in 6.03% and 12.10% decrease in fiber length and strength respectively. Furthermore, mature cotton fibers of transgenic cotton plants were found to have increased number of twists with smooth surface as compared to non-transgenic control when analyzed under scanning electron microscope. XRD analysis of cotton fibers revealed less cellulose crystallinity index in transgenic cotton fibers as compared to control fibers due to deposition of more amorphous cellulose in transgenic fibers as a result of Bcs gene expression. This study paved the way towards unraveling the fact that Bcs genes influence cellulose synthase activity and this enzyme helps in determining the fate of cotton fiber length and strength.

Toxicity and Estrogenicity of Bisphenol TMC in Oryzias melastigma via In Vivo and In Silico Studies

Bisphenol 4-[1-(4-hydroxyphenyl)-3,3,5-trimethylcyclohexyl] phenol (BPTMC), as a substitute for bisphenol A, has been detected in environments. However, the ecotoxicological data of BPTMC are extremely scarce. Here, the lethality, developmental toxicity, locomotor behavior, and estrogenic activity of BPTMC at different concentrations (0.25-2000 μg/L) in marine medaka (Oryzias melastigma) embryos were examined. In addition, the in silico binding potentials of O. melastigma estrogen receptors (omEsrs) with BPTMC were assessed by docking study. Low-concentration BPTMC exposure (including an environmentally relevant concentration, 0.25 μg/L) resulted in stimulating effects, including hatching rate, heart rate, malformation rate, and swimming velocity. However, elevated concentrations of BPTMC led to an inflammatory response, changed heart rate and swimming velocity in the embryos and larvae. In the meantime, BPTMC (including 0.25 μg/L) altered the concentrations of estrogen receptor, vitellogenin, and endogenous 17 β-estradiol as well as the transcriptional levels of estrogen-responsive genes in the embryos or/and larvae. Furthermore, elaborate tertiary structures of omEsrs were built by ab initio modeling, and BPTMC exerted potent binding potential with three omEsrs with -47.23, -49.23, and -50.30 kJ/mol for Esr1, Esr2a, and Esr2b, respectively. This work suggests that BPTMC has potent toxicity and estrogenic effects in O. melastigma.

De novo protein fold design through sequence-independent fragment assembly simulations

De novo protein design generally consists of two steps, including structure and sequence design. Many protein design studies have focused on sequence design with scaffolds adapted from native structures in the PDB, which renders novel areas of protein structure and function space unexplored. We developed FoldDesign to create novel protein folds from specific secondary structure (SS) assignments through sequence-independent replica-exchange Monte Carlo (REMC) simulations. The method was tested on 354 non-redundant topologies, where FoldDesign consistently created stable structural folds, while recapitulating on average 87.7% of the SS elements. Meanwhile, the FoldDesign scaffolds had well-formed structures with buried residues and solvent-exposed areas closely matching their native counterparts. Despite the high fidelity to the input SS restraints and local structural characteristics of native proteins, a large portion of the designed scaffolds possessed global folds completely different from natural proteins in the PDB, highlighting the ability of FoldDesign to explore novel areas of protein fold space. Detailed data analyses revealed that the major contributions to the successful structure design lay in the optimal energy force field, which contains a balanced set of SS packing terms, and REMC simulations, which were coupled with multiple auxiliary movements to efficiently search the conformational space. Additionally, the ability to recognize and assemble uncommon super-SS geometries, rather than the unique arrangement of common SS motifs, was the key to generating novel folds. These results demonstrate a strong potential to explore both structural and functional spaces through computational design simulations that natural proteins have not reached through evolution.

In silico design of a promiscuous chimeric multi-epitope vaccine against Mycobacterium tuberculosis

Tuberculosis (TB) is a global health threat, killing approximately 1.5 million people each year. The eradication of Mycobacterium tuberculosis, the main causative agent of TB, is increasingly challenging due to the emergence of extensive drug-resistant strains. Vaccination is considered an effective way to protect the host from pathogens, but the only clinically approved TB vaccine, Bacillus Calmette-Guérin (BCG), has limited protection in adults. Multi-epitope vaccines have been found to enhance immunity to diseases by selectively combining epitopes from several candidate proteins. This study aimed to design a multi-epitope vaccine against TB using an immuno-informatics approach. Through functional enrichment, we identified eight proteins secreted by M. tuberculosis that are either required for pathogenesis, secreted into extracellular space, or both. We then analyzed the epitopes of these proteins and selected 16 helper T lymphocyte epitopes with interferon-γ inducing activity, 15 cytotoxic T lymphocyte epitopes, and 10 linear B-cell epitopes, and conjugated them with adjuvant and Pan HLA DR-binding epitope (PADRE) using appropriate linkers. Moreover, we predicted the tertiary structure of this vaccine, its potential interaction with Toll-Like Receptor-4 (TLR4), and the immune response it might elicit. The results showed that this vaccine had a strong affinity for TLR4, which could significantly stimulate CD4⁺ and CD8⁺ cells to secrete immune factors and B lymphocytes to secrete immunoglobulins, so as to obtain good humoral and cellular immunity. Overall, this multi-epitope protein was predicted to be stable, safe, highly antigenic, and highly immunogenic, which has the potential to serve as a global vaccine against TB.

Relaxed complex scheme and molecular dynamics simulation suggests small molecule inhibitor of human TMPRSS2 for combating COVID-19

As the coronavirus disease 19 (COVID-19) pandemic continues to pose a health and economic crisis worldwide, the quest for drugs and/or vaccines against the virus continues. The human transmembrane protease serine 2 (TMPRSS2) has attracted attention as a target for drug discovery, as inhibition of its catalytic reaction would result in the inactivation of the proteolytic cleavage of the SARS-CoV-2 S protein. As a result, the inactivation prevents viral cell entry to the host's cell. In this work, we screened and identified two potent molecules that interact and inhibit the catalytic reaction by using computational approaches. Two docking screening experiments were performed utilizing the crystal structure and holo ensemble structure obtained from molecular dynamics in bound form. There is enhancement and sensitivity of docking results to the holo ensemble as compared to the crystal structure. Compound 1 demonstrated a similar inhibition value to nafamostat by interacting with catalytic triad residues His296 and Ser441, thereby disrupting the already established hydrogen bond interaction. The stability of the ligand-TMPRSS2 complexes was studied by molecular dynamics simulation, and the binding energy was re-scored by using molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy. The obtained compounds may serve as an initial point toward the discovery of potent TMPRSS2 inhibitors upon further in vivo validation.Communicated by Ramaswamy H. Sarma.

Computational investigation of phytoalexins as potential antiviral RAP-1 and RAP-2 (Replication Associated Proteins) inhibitor for the management of cucumber mosaic virus (CMV): a molecular modeling, in silico docking and MM-GBSA study

The Replication Associated Proteins (RAP-1 and RAP-2) encoded by CMV ORF 1a and ORF 2a are required for the different stages of the viral replication cycle; being multi-functional, they are good inhibitory targets for anti-CMV compounds. As a new perspective for sustainable crop improvement, we investigated the natural plant-based antimicrobial phytoalexins for their anti-CMV potential. Here, we modeled and predicted the functional domains of RAP-1 and RAP-2, docked with a ligand library comprising 128 phytoalexins reported with broad-spectrum activity, determined their binding energies (BEs), molecular interactions, and inhibition constant (Ki), and compared with the reference plant antiviral compounds ribavirin, ningnanmycin, and benzothiadiazole (BTH). Further, the change in Gibb's free energy of binding (ΔG) and the per residue contribution of the selected top-scored ligand molecules was assessed by the prime MM-GBSA approach. Our results revealed RAP-1 as a discontinuous two-domain and RAP-2 as a multi-domain protein. The compounds glyceollidin (9.8 kcal/mol) and moracin D (7.8 kcal/mol) topped the list for RAP-1 and RAP-2 protein targets respectively and also, the lead molecules had energetically more favorable and comparative ΔG values than the top-scored plant antiviral agent ningnanmycin. The evaluation of in vitro toxicity and agrochemical-like properties showed the least toxicity of these anti-CMV compounds. Taken together, our results provide new insights in understanding the inhibitory effects of phytoalexins towards the RAP proteins and could be employed as new promising anti-CMV candidate compounds for their application in agriculture as biopesticides to combat the CMV disease incidence.Communicated by Ramaswamy H. Sarma.

Human prion protein: exploring the thermodynamic stability and structural dynamics of its pathogenic mutants

Human familial prion diseases are known to be associated with different single-point mutants of the gene coding for prion protein with a primary focus at several locations of the globular domain. We have identified 12 different single-point pathogenic mutants of human prion protein (HuPrP) with the help of extensive perturbations/mutation technique at multiple locations of HuPrP sequence related to potentiality towards conformational disorders. Among these, some of the mutants include pathogenic variants that corroborate well with the literature reported proteins while majority include some unique single-point mutants that are either not explicitly studied early or studied for variants with different residues at the specific position. Primarily, our study sheds light on the unfolding mechanism of the above mentioned mutants in depth. Besides, we could identify some mutants under investigation that demonstrates not only unfolding of the helical structures but also extension and generation of the β-sheet structures and or simultaneously have highly exposed hydrophobic surface which is assumed to be linked with the production of aggregate/fibril structures of the prion protein. Among the identified mutants, Q212E needs special attention due to its maximum exposure of hydrophobic core towards solvent and E200Q is found to be important due to its maximum extent of β-content. We are also able to identify different respective structural conformations of the proteins according to their degree of structural unfolding and those conformations can be extracted and further studied in detail. Communicated by Ramaswamy H. Sarma.

Exploration of interaction property between nonylphenol and G protein-coupled receptor 30 based on molecular simulation and biological experiments

Nonylphenol (NP), a representative of environmental hormones, can cause extensive biological effects in the human body. In this study, we first analyzed the mutual binding modes of NP and G protein coupled estrogen receptor 30 (GPR30) by molecular simulation. The 3D structure of GPR30 was successfully constructed. We found that the binding sites of NP on GPR30 are similar to that of 17β-Estradiol (E2) on GPR30. The GPR30-E2 bond complex is more stable than GPR30-NP bond complex. Next CCK-8 assay was used to detect the regulatory effect of NP on SKBR-3 cell proliferation. When NP and E2 were used alone, low concentration could promote cell proliferation, while high concentration was the opposite. The presence of E2 can promote the cell proliferation effect of NP, and inhibit the inhibitory intensity. NP could promote both the cell proliferation effect and inhibition intensity of E2. Based on our results, we conclude that the binding modes of NP and GPR30 is similar to that of E2 and GPR30. In biology, NP can play estrogen role by activating GPR30 receptor, but it can also produce cytotoxicity at higher concentration.

Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module

Prediction of the protein secondary structure is a key issue in protein science. Protein secondary structure prediction (PSSP) aims to construct a function that can map the amino acid sequence into the secondary structure so that the protein secondary structure can be obtained according to the amino acid sequence. Driven by deep learning, the prediction accuracy of the protein secondary structure has been greatly improved in recent years. To explore a new technique of PSSP, this study introduces the concept of an adversarial game into the prediction of the secondary structure, and a conditional generative adversarial network (GAN)-based prediction model is proposed. We introduce a new multiscale convolution module and an improved channel attention (ICA) module into the generator to generate the secondary structure, and then a discriminator is designed to conflict with the generator to learn the complicated features of proteins. Then, we propose a PSSP method based on the proposed multiscale convolution module and ICA module. The experimental results indicate that the conditional GAN-based protein secondary structure prediction (CGAN-PSSP) model is workable and worthy of further study because of the strong feature-learning ability of adversarial learning.

An essential role for tungsten in the ecology and evolution of a previously uncultivated lineage of anaerobic, thermophilic Archaea

Trace metals have been an important ingredient for life throughout Earth's history. Here, we describe the genome-guided cultivation of a member of the elusive archaeal lineage Caldarchaeales (syn. Aigarchaeota), Wolframiiraptor gerlachensis, and its growth dependence on tungsten. A metagenome-assembled genome (MAG) of W. gerlachensis encodes putative tungsten membrane transport systems, as well as pathways for anaerobic oxidation of sugars probably mediated by tungsten-dependent ferredoxin oxidoreductases that are expressed during growth. Catalyzed reporter deposition-fluorescence in-situ hybridization (CARD-FISH) and nanoscale secondary ion mass spectrometry (nanoSIMS) show that W. gerlachensis preferentially assimilates xylose. Phylogenetic analyses of 78 high-quality Wolframiiraptoraceae MAGs from terrestrial and marine hydrothermal systems suggest that tungsten-associated enzymes were present in the last common ancestor of extant Wolframiiraptoraceae. Our observations imply a crucial role for tungsten-dependent metabolism in the origin and evolution of this lineage, and hint at a relic metabolic dependence on this trace metal in early anaerobic thermophiles.

How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives

It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.

Multiscale modelling of the extracellular matrix

The extracellular matrix is a complex three-dimensional network of molecules that provides cells with a complex microenvironment. The major constituents of the extracellular matrix such as collagen, elastin and associated proteins form supramolecular assemblies contributing to its physicochemical properties and organization. The structure of proteins and their supramolecular assemblies such as fibrils have been studied at the atomic level (e.g., by X-ray crystallography, Nuclear Magnetic Resonance and cryo-Electron Microscopy) or at the microscopic scale. However, many protein complexes are too large to be studied at the atomic level and too small to be studied by microscopy. Most extracellular matrix components fall into this intermediate scale, so-called the mesoscopic scale, preventing their detailed characterization. Simulation and modelling are some of the few powerful and promising approaches that can deepen our understanding of mesoscale systems. We have developed a set of modelling tools to study the self-organization of the extracellular matrix and large motion of macromolecules at the mesoscale level by taking advantage of the dynamics of articulated rigid bodies as a mean to study a larger range of motions at the cost of atomic resolution.

Structure‐conditioned amino‐acid couplings: how contact geometry affects pairwise sequence preferences

Relating a protein's sequence to its conformation is a central challenge for both structure prediction and sequence design. Statistical contact potentials, as well as their more descriptive versions that account for side‐chain orientation and other geometric descriptors, have served as simplistic but useful means of representing second‐order contributions in sequence–structure relationships. Here we ask what happens when a pairwise potential is conditioned on the fully defined geometry of interacting backbones fragments. We show that the resulting structure‐conditioned coupling energies more accurately reflect pair preferences as a function of structural contexts. These structure‐conditioned energies more reliably encode native sequence information and more highly correlate with experimentally determined coupling energies. Clustering a database of interaction motifs by structure results in ensembles of similar energies and clustering them by energy results in ensembles of similar structures. By comparing many pairs of interaction motifs and showing that structural similarity and energetic similarity go hand‐in‐hand, we provide a tangible link between modular sequence and structure elements. This link is applicable to structural modeling, and we show that scoring CASP models with structured‐conditioned energies results in substantially higher correlation with structural quality than scoring the same models with a contact potential. We conclude that structure‐conditioned coupling energies are a good way to model the impact of interaction geometry on second‐order sequence preferences.

De novo Protein Structure Prediction by Coupling Contact With Distance Profile

De novo protein structure prediction is a challenging problem that requires both an accurate energy function and an efficient conformation sampling method. In this study, a de novo structure prediction method, named CoDiFold, is proposed. In CoDiFold, contacts and distance profiles are organically combined into the Rosetta low-resolution energy function to improve the accuracy of energy function. As a result, the correlation between energy and root mean square deviation (RMSD) is improved. In addition, a population-based multi-mutation strategy is designed to balance the exploration and exploitation of conformation space sampling. The average RMSD of the models generated by the proposed protocol is decreased by 49.24 and 45.21 percent in the test set with 43 proteins compared with those of Rosetta and QUARK de novo protocols, respectively. The results also demonstrate that the structures predicted by proposed CoDiFold are comparable to the state-of-the-art methods for the 10 FM targets of CASP13. The source code and executable versions are freely available at http://github.com/iobio-zjut/CoDiFold.

In silico methods and tools for drug discovery

In the past, conventional drug discovery strategies have been successfully employed to develop new drugs, but the process from lead identification to clinical trials takes more than 12 years and costs approximately $1.8 billion USD on average. Recently, in silico approaches have been attracting considerable interest because of their potential to accelerate drug discovery in terms of time, labor, and costs. Many new drug compounds have been successfully developed using computational methods. In this review, we briefly introduce computational drug discovery strategies and outline up-to-date tools to perform the strategies as well as available knowledge bases for those who develop their own computational models. Finally, we introduce successful examples of anti-bacterial, anti-viral, and anti-cancer drug discoveries that were made using computational methods.

Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions

Sequence-based contact prediction has shown considerable promise in assisting non-homologous structure modeling, but it often requires many homologous sequences and a sufficient number of correct contacts to achieve correct folds. Here, we developed a method, C-QUARK, that integrates multiple deep-learning and coevolution-based contact-maps to guide the replica-exchange Monte Carlo fragment assembly simulations. The method was tested on 247 non-redundant proteins, where C-QUARK could fold 75% of the cases with TM-scores (template-modeling scores) ≥0.5, which was 2.6 times more than that achieved by QUARK. For the 59 cases that had either low contact accuracy or few homologous sequences, C-QUARK correctly folded 6 times more proteins than other contact-based folding methods. C-QUARK was also tested on 64 free-modeling targets from the 13th CASP (critical assessment of protein structure prediction) experiment and had an average GDT_TS (global distance test) score that was 5% higher than the best CASP predictors. These data demonstrate, in a robust manner, the progress in modeling non-homologous protein structures using low-accuracy and sparse contact-map predictions.

Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations

Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.

Toward the solution of the protein structure prediction problem

Since Anfinsen demonstrated that the information encoded in a protein's amino acid sequence determines its structure in 1973, solving the protein structure prediction problem has been the Holy Grail of structural biology. The goal of protein structure prediction approaches is to utilize computational modeling to determine the spatial location of every atom in a protein molecule starting from only its amino acid sequence. Depending on whether homologous structures can be found in the Protein Data Bank (PDB), structure prediction methods have been historically categorized as template-based modeling (TBM) or template-free modeling (FM) approaches. Until recently, TBM has been the most reliable approach to predicting protein structures, and in the absence of reliable templates, the modeling accuracy sharply declines. Nevertheless, the results of the most recent community-wide assessment of protein structure prediction experiment (CASP14) have demonstrated that the protein structure prediction problem can be largely solved through the use of end-to-end deep machine learning techniques, where correct folds could be built for nearly all single-domain proteins without using the PDB templates. Critically, the model quality exhibited little correlation with the quality of available template structures, as well as the number of sequence homologs detected for a given target protein. Thus, the implementation of deep-learning techniques has essentially broken through the 50-year-old modeling border between TBM and FM approaches and has made the success of high-resolution structure prediction significantly less dependent on template availability in the PDB library.

Prdx1 Interacts with ASK1 upon Exposure to H2O2 and Independently of a Scaffolding Protein

Hydrogen peroxide (H₂O₂) is a key redox signaling molecule that selectively oxidizes cysteines on proteins. It can accomplish this even in the presence of highly efficient and abundant H₂O₂ scavengers, peroxiredoxins (Prdxs), as it is the Prdxs themselves that transfer oxidative equivalents to specific protein thiols on target proteins via their redox-relay functionality. The first evidence of a mammalian cytosolic Prdx-mediated redox-relay—Prdx1 with the kinase ASK1—was presented a decade ago based on the outcome of a co-immunoprecipitation experiment. A second such redox-relay—Prdx2:STAT3—soon followed, for which further studies provided insights into its specificity, organization, and mechanism. The Prdx1:ASK1 redox-relay, however, has never undergone such a characterization. Here, we combine cellular and in vitro protein–protein interaction methods to investigate the Prdx1:ASK1 interaction more thoroughly. We show that, contrary to the Prdx2:STAT3 redox-relay, Prdx1 interacts with ASK1 at elevated H₂O₂ concentrations, and that this interaction can happen independently of a scaffolding protein. We also provide evidence of a Prdx2:ASK1 interaction, and demonstrate that it requires a facilitator that, however, is not annexin A2. Our results reveal that cytosolic Prdx redox-relays can be organized in different ways and yet again highlight the differentiated roles of Prdx1 and Prdx2.

Structural characterization of cassava linamarase-linamarin enzyme complex: an integrated computational approach

Cassava linamarase is a hydrolyzing enzyme that belongs to a glycoside hydrolase family 1 (GH1). It is responsible for breaking down linamarin to toxic cyanide. The enzyme provides a defensive mechanism for plants against herbivores and has various applications in many fields. Understanding the structure of linamarase at the molecular level is a key to avail its reaction mechanism. In this study, the three-dimensional (3D) structure of linamarase was built for the first time using homology modelling and used to study its interaction with linamarin. Molecular docking calculations established the binding and orientation nature of linamarin, while molecular dynamics (MD) simulation established protein-ligand complexes' stability. Binding-free energy based on MM/PBSA was further used to rescore the docking results. An ensemble structure was found to be relatively stable compared to the modelled structure. This study sheds light on the exploration of linamarase towards understanding its reaction mechanisms.Communicated by Ramaswamy H. Sarma.

Oxidative Damage? Not a Problem! The Characterization of Humanin-like Mitochondrial Peptide in Anoxia Tolerant Freshwater Turtles

Mitochondria was long thought to be an "end function" organelle that regulated the metabolic flux and apoptosis in the cell. However, with the discovery of the mitochondrial peptide (MDP) humanin (HN/MTRNR2), the cytoprotective and pro-survival applications of MDPs have taken the forefront of therapeutic and diagnostic research. However, the regulation of humanin-like MDPs in natural model systems that can tolerate lethal environmental and cytotoxic insults remains to be investigated. Red-eared sliders are champion anaerobes that can withstand three continuous months of anoxia followed by rapid bouts of oxygen reperfusion without incurring cellular damage. Freshwater turtles employ extensive physiological and biochemical strategies to combat anoxia, with metabolic rate depression and a global enhancement of antioxidant and cytoprotective pathways being the two most important contributors. The main aim of this study was to uncover and characterize the humanin-homologue in freshwater turtles as well as investigate the differential regulation of humanin in response to short and long-term oxygen deprivation. In this study we have used de novo and homology-based protein modelling to elucidate the putative structure of humanin in red-eared sliders as well as an ELISA and western immunoblotting to confirm the protein abundance in the turtle brain and six peripheral tissues during control, 5 h, and 20 h anoxia (n = 4/group). We found that a humanin-homologue (TSE-humanin) is present in red-eared sliders and it may play a cytoprotective role against oxidative damage.

In Silico Identification of Potential Phosphorylation in the Cytoplasmic Domain of Epithelial Cell Adhesion Molecule

The epithelial cell adhesion molecule (EpCAM) is a transmembrane cell adhesion glycoprotein, which primarily contributes to stemness, proliferation, and metastasis properties of tumor cells. Regulated intramembrane proteolysis by ADAM proteases and γ-secretase cleaves EpCAM into an ∼27 kDa soluble extracellular and an ∼4 kDa cytoplasmic domain (EpICD). After the EpICD fragment is released inside the cell, the formation of a nuclear signaling complex with the FHL2 molecule is critical for exerting its regulatory role. Trop-2, a homologous protein of EpCAM, undergoes phosphorylation in its cytoplasmic domain (Trop-IC). The phosphorylation of Trop-2 is reported to be crucial for its function. This led us to ask the fundamental question if EpCAM does undergo similar post-translational modification(PTM) like its homologous protein to carry out its diverse biological function. Here, we identify a putative phosphorylation site at Tyr297 located in the cytoplasmic domain of EpCAM. Molecular dynamic simulation (MDS) of 90 ns was carried out to understand the biological/functional relevance of the putative phosphorylation. It was observed that this phosphorylation stabilizes the α-helical structure of the EpICD. Though Tyr297 does not affect the γ-secretase mediated cleavage of EpCAM, it affects the binding of EpICD to FHL2. Docking analysis revealed that phosphorylation mediated structural stability of EpICD positively impacts its binding affinity with FHL2, which was further validated using 100 ns MDS. Phosphorylated EpICD forms higher numbers of hydrogen bonds, salt bridges, and other non-bonded interactions with FHL2, leading to enhanced interactions. This in silico study reveals a potential PTM in the EpICD, providing the basis for future research in understanding the mechanism behind the diverse biological function of EpCAM.

Computational Modeling of NLRP3 Identifies Enhanced ATP Binding and Multimerization in Cryopyrin-Associated Periodic Syndromes

Cyropyrin-associated periodic syndromes (CAPS) are clinically distinct syndromes that encompass a phenotypic spectrum yet are caused by alterations in the same gene, NLRP3. Many CAPS cases and other NLRP3-autoinflammatory diseases (NLRP3-AIDs) are directly attributed to protein-coding alterations in NLRP3 and the subsequent dysregulation of the NLRP3 inflammasome leading to IL-1β-mediated inflammatory states. Here, we used bioinformatics tools, computational modeling, and computational assessments to explore the proteomic consequences of NLRP3 mutations, which potentially drive NLRP3 inflammasome dysregulation. We analyzed 177 mutations derived from familial cold autoinflammatory syndrome (FCAS), Muckle-Wells Syndrome (MWS), and the non-hereditary chronic infantile neurologic cutaneous and articular syndrome, also known as neonatal-onset multisystem inflammatory disease (CINCA/NOMID), as well as other NLRP3-AIDs. We found an inverse relationship between clinical severity and the severity of predicted structure changes resulting from mutations in NLRP3. Bioinformatics tools and computational modeling revealed that NLRP3 mutations that are predicted to be structurally severely-disruptive localize around the ATP binding pocket and that specific proteo-structural changes to the ATP binding pocket lead to enhanced ATP binding affinity by altering hydrogen-bond and charge interactions. Furthermore, we demonstrated that NLRP3 mutations that are predicted to be structurally mildly- or moderately-disruptive affect protein-protein interactions, such as NLRP3-ASC binding and NLRP3-NLRP3 multimerization, enhancing inflammasome formation and complex stability. Taken together, we provide evidence that proteo-structural mechanisms can explain multiple mechanisms of inflammasome activation in NLRP3-AID.

The Relationship between APOL1 Structure and Function: Clinical Implications

Common variants in the APOL1 gene are associated with an increased risk of nondiabetic kidney disease in individuals of African ancestry. Mechanisms by which APOL1 variants mediate kidney disease pathogenesis are not well understood. Amino acid changes resulting from the kidney disease-associated APOL1 variants alter the three-dimensional structure and conformational dynamics of the C-terminal α-helical domain of the protein, which can rationalize the functional consequences. Understanding the three-dimensional structure of the protein, with and without the risk variants, can provide insights into the pathogenesis of kidney diseases mediated by APOL1 variants.

Prediction of B cell and T-helper cell epitopes candidates of bovine leukaemia virus (BLV) by in silico approach

The bovine leukaemia virus (BLV) is a retrovirus responsible for enzootic bovine leukaemia (EBL) disease, the most common cattle disease leading to high annual economic losses to the cattle breeding industry. Virus monitoring among the sheep and cattle herds is usually done by vaccination. Inactivated virus vaccines can partially protect the livestock from viral challenge. However, vaccinated animals are likely to be infected. So, there is an essential need for producing vaccine by other methods. Gp60 SU, encoded by Env gene, is the surface glycoprotein of BLV detected to be the major target for the host immunity against the virus. Different stages were performed to predict the potential B and T-helper cell epitopes. The general framework of the method includes retrieving the amino acid sequence of gp60 SU, conducting the sequence alignment, getting the entropy plot, retrieving the previously found epitopes, predicting the hydropathy parameters, modelling the tertiary structure of the glycoprotein, minimizing the structure energy, validating the model by Ramachandran plot, predicting the linear and discontinuous epitopes by various servers and eventually choosing the consensus immunogenic regions. Ramachandran plot scrutiny has demonstrated that the modelled prediction is accurate and suitable. By surveying overlaps of various results, 4 and 2 immunogenic regions were selected as linear and conformational epitopes respectively. Amino acids 35-53, 67-97, 288-302 and 410-421 and those of numbers 37-58 and 72-100 were the regions selected as linear and conformational epitopes respectively. The tertiary structure of the final epitope was modelled as well. A comparison of the predicted epitopes structure with that of gp60 SU envelope, illustrated that the tertiary structure of these epitopes does not change after being separated from the primary complete one. The present achievements will lead to a better interpretation of the antigen-antibody interactions against gp60 in the designing process of safe and efficient vaccines.

Octyl gallate triggers dysfunctional mitochondria leading to ROS driven membrane damage and metabolic inflexibility along with attenuated virulence in Candida albicans

Recently the high incidence of worldwide Candida infections has substantially increased. The growing problem about toxicity of antifungal drugs and multidrug resistance aggravates the need for the development of new effective strategies. Natural compounds in this context represent promising alternatives having potential to be exploited for improving human health. The present study was therefore designed to evaluate the antifungal effect of a naturally occurring phenolic, octyl gallate (OG), on Candida albicans and to investigate the underlying mechanisms involved. We demonstrated that OG at 25 μg/ml could effectively inhibit C. albicans. Mechanistic insights revealed that OG affects mitochondrial functioning as Candida cells exposed to OG did not grow on non-fermentable carbon sources. Dysfunctional mitochondria triggered generation of reactive oxygen species (ROS), which led to membrane damage mediated by lipid peroxidation. We explored that OG inhibited glucose-induced reduction in external pH and causes decrement in ergosterol levels by 45%. Furthermore, OG impedes the metabolic flexibility of C. albicans by inhibiting the glyoxylate enzyme isocitrate lyase, which was also confirmed by docking analysis. Additionally, OG affected virulence traits such as morphological transition and cell adherence. Furthermore, we depicted that OG not only prevented biofilm formation but eliminates the preformed biofilms. In vivo studies with Caenorhabditis elegans nematode model confirmed that OG could enhance the survival of C. elegans after infection with Candida. Toxicity assay using red blood cells showed only 27.5% haemolytic activity. Taken together, OG is a potent inhibitor of C. albicans that warrants further structural optimization and pharmacological investigations.

Plant Antimicrobial Peptides: State of the Art, In Silico Prediction and Perspectives in the Omics Era

Even before the perception or interaction with pathogens, plants rely on constitutively guardian molecules, often specific to tissue or stage, with further expression after contact with the pathogen. These guardians include small molecules as antimicrobial peptides (AMPs), generally cysteine-rich, functioning to prevent pathogen establishment. Some of these AMPs are shared among eukaryotes (eg, defensins and cyclotides), others are plant specific (eg, snakins), while some are specific to certain plant families (such as heveins). When compared with other organisms, plants tend to present a higher amount of AMP isoforms due to gene duplications or polyploidy, an occurrence possibly also associated with the sessile habit of plants, which prevents them from evading biotic and environmental stresses. Therefore, plants arise as a rich resource for new AMPs. As these molecules are difficult to retrieve from databases using simple sequence alignments, a description of their characteristics and in silico (bioinformatics) approaches used to retrieve them is provided, considering resources and databases available. The possibilities and applications based on tools versus database approaches are considerable and have been so far underestimated.

Sequence-based protein structure optimization using enhanced simulated annealing algorithm on a coarse-grained model

The understanding of protein structure is vital to determine biological function. We presented an enhanced simulated annealing (ESA) algorithm to investigate protein three-dimensional (3D) structure on a coarse-grained model. Inside the algorithm, we adjusted exploration equations to achieve good search intensity. To that end, our algorithm used (i) a multivariable disturbance operator for diversification of solution, (ii) a sign function to improve randomness of solution, and (iii) taking remainder operation performed on floating-point number to tackle out-of-range solution. By monitoring energy value throughout the simulation, the energy-optimal state can be found. The ESA algorithm was tested on artificial and real protein sequences with different lengths. The results show that our algorithm outperforms conventional simulated annealing algorithm and can compete with the reported algorithms before. Especially, our algorithm can obtain folding conformations with specific structural features. Further analysis shows that simulating trajectory of seeking the lowest energy can exhibit thermodynamic behavior of protein folding. Graphical Abstract.

An information gain-based approach for evaluating protein structure models

For three decades now, knowledge-based scoring functions that operate through the "potential of mean force" (PMF) approach have continuously proven useful for studying protein structures. Although these statistical potentials are not to be confused with their physics-based counterparts of the same name-i.e. PMFs obtained by molecular dynamics simulations-their particular success in assessing the native-like character of protein structure predictions has lead authors to consider the computed scores as approximations of the free energy. However, this physical justification is a matter of controversy since the beginning. Alternative interpretations based on Bayes' theorem have been proposed, but the misleading formalism that invokes the inverse Boltzmann law remains recurrent in the literature. In this article, we present a conceptually new method for ranking protein structure models by quality, which is (i) independent of any physics-based explanation and (ii) relevant to statistics and to a general definition of information gain. The theoretical development described in this study provides new insights into how statistical PMFs work, in comparison with our approach. To prove the concept, we have built interatomic distance-dependent scoring functions, based on the former and new equations, and compared their performance on an independent benchmark of 60,000 protein structures. The results demonstrate that our new formalism outperforms statistical PMFs in evaluating the quality of protein structural decoys. Therefore, this original type of score offers a possibility to improve the success of statistical PMFs in the various fields of structural biology where they are applied. The open-source code is available for download at https://gitlab.rpbs.univ-paris-diderot.fr/src/ig-score.

Noncanonical type 2B von Willebrand disease associated with mutations in the VWF D'D3 and D4 domains

We observed a 55-year-old Italian man who presented with mucosal and cutaneous bleeding. Results of his blood analysis showed low levels of von Willebrand factor (VWF) antigen and VWF activity (both VWF ristocetin cofactor and VWF collagen binding), mild thrombocytopenia, increased ristocetin-induced platelet aggregation, and a deficiency of high-molecular-weight multimers, all typical phenotypic hallmarks of type 2B von Willebrand disease (VWD). The analysis of the VWF gene sequence revealed heterozygous in cis mutations: (1) c.2771G>A and (2) c.6532G>T substitutions in the exons 21 and 37, respectively. The first mutation causes the substitution of an Arg residue with a Gln at position 924, in the D'D3 domain. The second mutation causes an Ala to Ser substitution at position 2178 in the D4 domain. The patient's daughter did not present the same fatherly mutations but showed only the heterozygous polymorphic c.3379C>T mutation in exon 25 of the VWF gene causing the p.P1127S substitution, inherited from her mother. The in vitro expression of the heterozygous in cis VWF mutant rVWFWT/rVWF924Q-2178S confirmed and recapitulated the ex vivo VWF findings. Molecular modeling showed that these in cis mutations stabilize a partially stretched and open conformation of the VWF monomer. Transmission electron microscopy and atomic force microscopy showed in the heterozygous recombinant form rVWFWT/rVWF924Q-2178S a stretched conformation, forming strings even under static conditions. Thus, the heterozygous in cis mutations 924Q/2178S promote conformational transitions in the VWF molecule, causing a type 2B-like VWD phenotype, despite the absence of typical mutations in the A1 domain of VWF.

Exploring the functional impact of alternative splicing on human protein isoforms using available annotation sources

In recent years, the emphasis of scientific inquiry has shifted from whole-genome analyses to an understanding of cellular responses specific to tissue, developmental stage or environmental conditions. One of the central mechanisms underlying the diversity and adaptability of the contextual responses is alternative splicing (AS). It enables a single gene to encode multiple isoforms with distinct biological functions. However, to date, the functions of the vast majority of differentially spliced protein isoforms are not known. Integration of genomic, proteomic, functional, phenotypic and contextual information is essential for supporting isoform-based modeling and analysis. Such integrative proteogenomics approaches promise to provide insights into the functions of the alternatively spliced protein isoforms and provide high-confidence hypotheses to be validated experimentally. This manuscript provides a survey of the public databases supporting isoform-based biology. It also presents an overview of the potential global impact of AS on the human canonical gene functions, molecular interactions and cellular pathways.

Construction of A Synthetic Gene Encoding the Multi-Epitope of Toxoplasma gondii and Demonstration of the Relevant Recombinant Protein Production: A Vaccine Candidate

BACKGROUND

Toxoplasma gondii is a widely-distributed parasite all over the world whose attributed severe afflicting complications in human necessitate the development of serodiagnostic tests and vaccines for it. Immunological responses to monovalent vaccines and the application of diagnostic reagents including single antigens are not optimally effective. Bioinformatics approaches were used to introduce these epitopes, predict their immunogenicity and preliminarily evaluate their potential as an effective DNA vaccine and for serodiagnostic goals.

MATERIALS AND METHODS

A 3D structure of proteins was predicted by I-TASSER server, and linear and conformational B cell and T cell epitopes were predicted using the online servers. Then, the predicted epitopes were constructed and called Toxoeb, and their expression in the prokaryotic and eukaryotic cells was demonstrated using SDS-PAGE. In the next step, Western blotting with pooled sera of mice infected with T. gondii was done.

RESULTS

The current in silico analysis revealed that the B cell epitopes with high immunogenicity for GRA4 protein were located in the residues 34-71, and 230-266, for GRA14 in 308-387, for SAG1 in 182-195, 261-278, and for GRA7 in residues 101-120, 160-176. The T cell epitopes were selected in overlapping regions with the B cell epitopes. The immunogenic region for GRA4 are in the residues 245-253, 50-58, and 40-54, for GRA14 in 307-315, 351-359, and 308- 322, for SAG1 261-269, and 259-267, and for GRA7 in the residues 103-112, and 167-175. The results of the western blotting showed that the expressed protein had immunogenicity.

CONCLUSION

Our constructed multi-epitope of T. gondii could be considered as a candidate for diagnostic and vaccination purposes.