Structure Prediction Programs Research Articles

Abstract 3559: A validated bioinformatics tool-set for predicting TCR specificity

Abstract Background: The T-cell receptor (TCR) is a transmembrane receptor on T cells, which is responsible for recognizing any foreign antigen derived peptide, presented on infected or abnormal cells by the MHC-I complex. Predicting TCR specificity computationally is a long-standing problem. Many methods leverage large libraries of TCR sequences and cognate epitopes as input for machine learning and predict specificity from sequence alone. Most methods generally do not perform very well, especially in the case of unseen peptides, i.e. for cases where peptides were not part of the input library. Methods: Here we describe three independent but interacting in-silico modules for TCR discovery. 1) The first module predicts specificity from deep phenotypic T cell profiles. We produce these profiles using a high-throughput pipeline, which combines multiplexed CyTOF with subsequent VDJ CITE-Seq. The generated multiomics data is aggregated into a database called TCAPS, which is the basis for machine learning models. These models can predict T cell specificity from the deep phenotype alone, i.e. without TCR sequence information. The resolution however is limited to the target class and hence the actual epitope has to be determined through subsequent deorphanization. This deorphanization step is augmented by the other two modules also described here. 2) The second module enables ultrafast sequence similarity search, which is used to find similar TCRs with known epitope specificity 3) The third module ranks TCR candidates for prioritisation for in-vitro validation. This is achieved by running the Alphafold-based structure prediction program TCRDock. Results: 1) We demonstrate how TCR specificity can be correctly predicted from multiomics deep T-cell profiles and specifically highlight two in-vitro validated predictions, for which the corresponding epitope/antigen were not part of the input panel. 2) Using a recently published TCR clustering benchmark TCRScapes, we demonstrate the superior performance of our ultrafast sequence similarity search. 3) We furthermore showcase TCRdock-directed experiments on TCRs specific for SF3B1mut-induced splice variants. TCRdock correctly ranked TCR candidates, including artificial combinations, whose functionality we prove through in-vitro validation. Conclusions: We describe three modules for TCR discovery and demonstrate their utility by benchmarking and in-vitro validation of multiple predictions, including two examples against unseen epitopes/antigens. Citation Format: Andreas Wilm, Florian Schmidt, Melissa Wirawan, Manisha Cooray, Kan Xing Wu, Katja Fink, Dan MacLeod. A validated bioinformatics tool-set for predicting TCR specificity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3559.

Putative purine nucleoside interacting residues in the malaria parasite purine uptake transporter PfENT1 are critical for transporter function.

Malaria remains a major public health threat for billions of people worldwide. Infection with obligate intracellular, unicellular parasites from the genus Plasmodium causes malaria. Plasmodium falciparum causes the deadliest form of human malaria. Plasmodium parasites are purine auxotrophic. They rely on purine import from the host red blood cell cytoplasm via equilibrative nucleoside transporters to supply substrates to the purine salvage pathway. We previously developed a high throughput screening assay to identify inhibitors of the P. falciparum Equilibrative Nucleoside Transporter Type 1 (PfENT1). Screening a small molecule library identified PfENT1 inhibitors that blocked proliferation of P. falciparum parasites in in vitro culture. The goal of the current work was to validate a high-resolution model of PfENT1 predicted by the AlphaFold protein structure prediction program. We superimposed the predicted PfENT1 structure on the human homologue structure, hENT1, and developed a structure-based sequence alignment. We mutated the residues in PfENT1 aligned with and flanking the residues in hENT1 that interact with the purine analog, nitrobenzylthioinosine (NBMPR). Mutation of the PfENT1 residues Q135, D287, and R291 that are predicted to form hydrogen bonds to purine nucleosides eliminated purine and pyrimidine transport function in various yeast-based growth and radiolabeled substrate uptake assays. Mutation of two flanking residues, W53 and S290, also resulted in inactive protein. Mutation of L50 that forms hydrophobic interactions with the purine nucleobase reduced transport function. Based on our results the AlphaFold predicted structure for PfENT1 may be useful in guiding medicinal chemistry efforts to improve the potency of our PfENT1 inhibitors.

Partial Atomic Model of the Tailed Lactococcal Phage TP901-1 as Predicted by AlphaFold2: Revelations and Limitations.

Bacteria are engaged in a constant battle against preying viruses, called bacteriophages (or phages). These remarkable nano-machines pack and store their genomes in a capsid and inject it into the cytoplasm of their bacterial prey following specific adhesion to the host cell surface. Tailed phages possessing dsDNA genomes are the most abundant phages in the bacterial virosphere, particularly those with long, non-contractile tails. All tailed phages possess a nano-device at their tail tip that specifically recognizes and adheres to a suitable host cell surface receptor, being proteinaceous and/or saccharidic. Adhesion devices of tailed phages infecting Gram-positive bacteria are highly diverse and, for the majority, remain poorly understood. Their long, flexible, multi-domain-encompassing tail limits experimental approaches to determine their complete structure. We have previously shown that the recently developed protein structure prediction program AlphaFold2 can overcome this limitation by predicting the structures of phage adhesion devices with confidence. Here, we extend this approach and employ AlphaFold2 to determine the structure of a complete phage, the lactococcal P335 phage TP901-1. Herein we report the structures of its capsid and neck, its extended tail, and the complete adhesion device, the baseplate, which was previously partially determined using X-ray crystallography.

Comprehensive analysis of protein-protein interactions (PPIs) with structure prediction program for breast cancer determination

Breast cancer is a prevalent disease that primarily affects women, with significant implications for public health. Early detection improves survival rates, and technological advancements can enhance early diagnosis. Protein structure prediction methods can provide valuable insights into the structure of proteins involved in breast cancer, including human epidermal growth factor receptor-2 (HER2). HER2 is a known protein receptor that plays a critical role in breast cancer development and is a target for therapy. Predicting the binding affinity between HER2 and potential ligands can help identify novel treatment options. This study aimed to predict the structure of HER2 using I-TASSER and determine potential ligands using empirical graph neural network-based scoring functions. Molecular docking simulations were performed to evaluate the binding conformation and stability of the HER2-ligand complexes. The results showed the potential ligands identified by I-TASSER: AEE, UUU, and Mg2+. Afterward, their binding affinity with HER2 was assessed, yielding AEE as the best binding with the lowest vina score.

AlphaFold Accurately Predicts the Structure of Ribosomally Synthesized and Post-Translationally Modified Peptide Biosynthetic Enzymes.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a growing class of natural products biosynthesized from a genetically encoded precursor peptide. The enzymes that install the post-translational modifications on these peptides have the potential to be useful catalysts in the production of natural-product-like compounds and can install non-proteogenic amino acids in peptides and proteins. However, engineering these enzymes has been somewhat limited, due in part to limited structural information on enzymes in the same families that nonetheless exhibit different substrate selectivities. Despite AlphaFold2's superior performance in single-chain protein structure prediction, its multimer version lacks accuracy and requires high-end GPUs, which are not typically available to most research groups. Additionally, the default parameters of AlphaFold2 may not be optimal for predicting complex structures like RiPP biosynthetic enzymes, due to their dynamic binding and substrate-modifying mechanisms. This study assessed the efficacy of the structure prediction program ColabFold (a variant of AlphaFold2) in modeling RiPP biosynthetic enzymes in both monomeric and dimeric forms. After extensive benchmarking, it was found that there were no statistically significant differences in the accuracy of the predicted structures, regardless of the various possible prediction parameters that were examined, and that with the default parameters, ColabFold was able to produce accurate models. We then generated additional structural predictions for select RiPP biosynthetic enzymes from multiple protein families and biosynthetic pathways. Our findings can serve as a reference for future enzyme engineering complemented by AlphaFold-related tools.

Search of a genomic sequence database for potential novel blood group antigens: Investigation into why some amino acid substitutions are not immunogenic.

Polypeptide blood group antigens are typically identified through investigation of the antibodies they induce. Human genome sequence databases are a new tool to identify AA substitutions that potentially create blood group antigens. The Erythrogene genomic sequence database was searched for missense mutations not known to be blood group antigens in the extracellular domains of selected RBC proteins in European populations. Any mutations found with prevalence of 1%-90% and not known to have induced antibodies in transfusion practice were analyzed using protein structural analysis and epitope prediction programs to determine why they apparently lack immunogenicity. Thirteen missense mutations not known to create blood group antigens were identified in the extracellular domains of Kell, BCAM, and RhD proteins, but not in RhCE, Urea Transporter 1 (Kidd), Atypical Chemokine Receptor 1 (Duffy), glycophorin A or glycophorin B. While 11 of the 13 mutations had low prevalence (<1%), a Kell Ser726Pro substitution and a BCAM Val196Ile substitution had predicted phenotype prevalences of 43.2% and 5.7%, respectively. Ser726Pro had multiple properties of a linear B-cell epitope, but possible suboptimal protein location for B-cell receptor binding and limited T-cell epitope possibilities. Val196Ile was not predicted to be in a linear B-cell epitope. Multiple potential new blood group antigens of low prevalence were identified. Whether they are antigenic remains to be determined. Two higher prevalence variants in Kell and BCAM are unlikely antigens, otherwise their antibodies presumably would already have been identified. Possible reasons for their poor immunogenicity were identified.

Structures of antibody/MHC-I complexes reveal deficiencies in structure prediction programs

Abstract The remarkable success of structural prediction programs such as AlphaFold2 and RosettaFold have revolutionized the contemporary approach to protein structure determination. However, intrinsically disordered proteins (IDPs) and protein/protein complexes provide a greater challenge to such algorithms. In particular, there has been limited success in the structural prediction of antibody/protein antigen complexes and TCR/MHC complexes, in part because the database of such structures is limited. Here, we present several high-resolution X-ray structures of anti-MHC/MHC-I complexes consistent with previous exon-shuffling and epitope mapping data that contradict computational predictions. Specifically, two mAb previously mapped to the alpha3 domain of H2-D(d) or H2-L(d)/D(b) respectively are shown to bind in distinct orientations, either of which may sterically block CD8 binding. In addition, a peptide dependent, but peptide non-specific anti-H2-D(d) mAb binds to loops of the alpha2 domain. These results substantiate the need to determine experimental structures of antibody/protein complexes as we proceed to develop avenues for the de novo design of specific antibodies. Supported by the Intramural Research Program of the NIAID, NIH Supported by the Intramural Research Program of the NIAID, NIH

Dataset of theoretical multinary perovskite oxides

Perovskite oxides (ternary chemical formula ABO3) are a diverse class of materials with applications including heterogeneous catalysis, solid-oxide fuel cells, thermochemical conversion, and oxygen transport membranes. However, their multicomponent (chemical formula {A}_{x}{A}_{1-x}^{text{'}}{B}_{y}{B}_{1-y}^{text{'}}{O}_{3}) chemical space is underexplored due to the immense number of possible compositions. To expand the number of computed {A}_{x}{A}_{1-x}^{{prime} }{B}_{y}{B}_{1-y}^{{prime} }{O}_{3} compounds we report a dataset of 66,516 theoretical multinary oxides, 59,708 of which are perovskites. First, 69,407 {A}_{0.5}{A}_{0.5}^{{prime} }{B}_{0.5}{B}_{0.5}^{{prime} }{O}_{3} compositions were generated in the a−b+a− Glazer tilting mode using the computationally-inexpensive Structure Prediction and Diagnostic Software (SPuDS) program. Next, we optimized these structures with density functional theory (DFT) using parameters compatible with the Materials Project (MP) database. Our dataset contains these optimized structures and their formation (ΔHf) and decomposition enthalpies (ΔHd) computed relative to MP tabulated elemental references and competing phases, respectively. This dataset can be mined, used to train machine learning models, and rapidly and systematically expanded by optimizing more SPuDS-generated {A}_{0.5}{A}_{0.5}^{{prime} }{B}_{0.5}{B}_{0.5}^{{prime} }{O}_{3} perovskite structures using MP-compatible DFT calculations.

Chaperoning of specific tau structure by immunophilin FKBP12 regulates the neuronal resilience to extracellular stress.

Alzheimer's disease and related tauopathies are characterized by the pathogenic misfolding and aggregation of the microtubule-associated protein tau. Understanding how endogenous chaperones modulate tau misfolding could guide future therapies. Here, we show that the immunophilin FKBP12, the 12-kDa FK506-binding protein (also known as FKBP prolyl isomerase 1A), regulates the neuronal resilience by chaperoning a specific structure in monomeric tau. Using a combination of mouse and cell experiments, in vitro aggregation experiments, nuclear magnetic resonance-based structural analysis of monomeric tau, site-specific phosphorylation and mutation, as well as structure-based analysis using the neural network-based structure prediction program AlphaFold, we define the molecular factors that govern the binding of FKBP12 to tau and its influence on tau-induced neurotoxicity. We further demonstrate that tyrosine phosphorylation of tau blocks the binding of FKBP12 to two highly specific structural motifs in tau. Our data together with previous results demonstrating FKBP12/tau colocalization in neurons and neurofibrillary tangles support a critical role of FKBP12 in regulating tau pathology.

How AlphaFold2 Predicts Conditionally Folding Regions Annotated in an Intrinsically Disordered Protein Database, IDEAL.

AlphaFold2 (AF2) is a protein structure prediction program which provides accurate models. In addition to predicting structural domains, AF2 assigns intrinsically disordered regions (IDRs) by identifying regions with low prediction reliability (pLDDT). Some regions in IDRs undergo disorder-to-order transition upon binding the interaction partner. Here we assessed model structures of AF2 based on the annotations in IDEAL, in which segments with disorder-to-order transition have been collected as Protean Segments (ProSs). We non-redundantly selected ProSs from IDEAL and classified them based on the root mean square deviation to the corresponding region of AF2 models. Statistical analysis identified 11 structural and sequential features, possibly contributing toward the prediction of ProS structures. These features were categorized into two groups: one that contained pLDDT and the other that contained normalized radius of gyration. The typical ProS structures in the former group comprise a long α helix or a whole or part of the structural domain and those in the latter group comprise a short α helix with terminal loops.

The Active Site of the Enzyme 10-Formyl-THFDH in the Honey Bee Apis mellifera-A Key Player in Formic Acid Detoxification.

Honey bees are important managed pollinators that fulfill important ecological and economic functions. In recent decades, the obligate ectoparasite Varroa destructor severely affected the survival of honey bees, as it weakened them by different means. A common treatment against V. destructor is formic acid fumigation, which has been used for decades by beekeepers across the world. This treatment is known to be effective, but many beekeepers report adverse effects of formic acid on bees, which include damage to the brood, worker bee mortality, and queen loss. Little is known about the molecular mechanisms of formic acid detoxification in honey bees. Recently, we reported upregulation of the bee enzyme, 10-formyl-THFDH, under formic acid fumigation. Here, the active site of this enzyme is characterized by an interdisciplinary approach combining homology modeling and protein mutagenesis. In addition, the limitations of the 3D protein structure prediction program AlphaFold2 are shown in regard to docking studies. This study provides a more thorough understanding of the molecular detoxification mechanisms of formic acid in Apis mellifera.

AlphaPulldown-a python package for protein-protein interaction screens using AlphaFold-Multimer.

The artificial intelligence-based structure prediction program AlphaFold-Multimer enabled structural modelling of protein complexes with unprecedented accuracy. Increasingly, AlphaFold-Multimer is also used to discover new protein-protein interactions (PPIs). Here, we present AlphaPulldown, a Python package that streamlines PPI screens and high-throughput modelling of higher-order oligomers using AlphaFold-Multimer. It provides a convenient command-line interface, a variety of confidence scores and a graphical analysis tool. AlphaPulldown is freely available at https://www.embl-hamburg.de/AlphaPulldown. Supplementary note is available at Bioinformatics online.

Exploring Structural Diversity among Adhesion Devices Encoded by Lactococcal P335 Phages with AlphaFold2.

Bacteriophages, or phages, are the most abundant biological entities on Earth. They possess molecular nanodevices to package and store their genome, as well as to introduce it into the cytoplasm of their bacterial prey. Successful phage infection commences with specific recognition of, and adhesion to, a suitable host cell surface. Adhesion devices of siphophages infecting Gram-positive bacteria are very diverse and remain, for the majority, poorly understood. These assemblies often comprise long, flexible, and multi-domain proteins, which limit their structural analyses by experimental approaches. The protein structure prediction program AlphaFold2 is exquisitely adapted to unveil structural and functional details of such molecular machineries. Here, we present structure predictions of adhesion devices from siphophages belonging to the P335 group infecting Lactococcus spp., one of the most extensively applied lactic acid bacteria in dairy fermentations. The predictions of representative adhesion devices from types I-IV P335 phages illustrate their very diverse topology. Adhesion devices from types III and IV phages share a common topology with that of Skunavirus p2, with a receptor binding protein anchored to the virion by a distal tail protein loop. This suggests that they exhibit an activation mechanism similar to that of phage p2 prior to host binding.

Relationship between B-cell epitope structural properties and the immunogenicity of blood group antigens: Outlier properties of the Kell K1 antigen.

The immunogenicities of polypeptide blood group antigens vary, despite most being created by single amino acid (AA) substitutions. To study the basis of these differences, we employed an immunoinformatics approach to determine whether AA substitution sites of blood group antigens have structural features typical of B-cell epitopes and whether the extent of B-cell epitope properties is positively related to immunogenicity. Fifteen structural property prediction programs were used to determine the likelihood of β-turns, surface accessibility, flexibility, hydrophilicity, particular AA composition and AA pairs, and other B-cell epitope properties at AA substitution sites of polypeptide blood group antigens. AA substitution sites of Lua , Jka , E, c, M, Fya , C, and S were each located in regions with at least two structural features typical of B-cell epitopes. The substitution site of K, the most immunogenic non-ABO/D antigen, scored the lowest for most B-cell epitope properties and was the only one not predicted to be part of a linear B-cell epitope. The most immunogenic antigens studied (K, Jka , Lua , E) had B-cell epitope structural properties determined by the fewest programs; the least immunogenic antigens (e.g., Fya , S, C, c) had B-cell epitope properties according to the most programs. Counter to prediction, the immunogenicity of polypeptide blood group antigens was not positively related to B-cell epitope structural features present at their AA-substitution sites. Instead, it tended to be negatively related. The AA-substitution site of the most immunogenic non-ABO/D antigen, K, had the least B-cell epitope features.

Computation-Aided Design of Albumin Affibody-Inserted Antibody Fragment for the Prolonged Serum Half-Life.

Single-chain variable fragments (scFvs) have been recognized as promising agents in cancer therapy. However, short serum half-life of scFvs often limits clinical application. Fusion to albumin affibody (ABD) is an effective and convenient half-life extension strategy. Although one terminus of scFv is available for fusion of ABD, it is also frequently used for fusion of useful moieties such as small functional proteins, cytokines, or antibodies. Herein, we investigated the internal linker region for ABD fusion instead of terminal region, which was rarely explored before. We constructed two internally ABD-inserted anti-HER2 4D5scFv (4D5-ABD) variants, which have short (4D5-S-ABD) and long (4D5-L-ABD) linker length respectively. The model structures of these 4D5scFv and 4D5-ABD variants predicted using the deep learning-based protein structure prediction program (AlphaFold2) revealed high similarity to either the original 4D5scFv or the ABD structure, implying that the functionality would be retained. Designed 4D5-ABD variants were expressed in the bacterial expression system and characterized. Both 4D5-ABD variants showed anti-HER2 binding affinity comparable with 4D5scFv. Binding affinity of both 4D5-ABD variants against albumin was also comparable. In a pharmacokinetic study in mice, the 4D5-ABD variants showed a significantly prolonged half-life of 34 h, 114 times longer than that of 4D5scFv. In conclusion, we have developed a versatile scFv platform with enhanced pharmacokinetic profiles with an aid of deep learning-based structure prediction.

A structural discovery journey of streptococcal phages adhesion devices by AlphaFold2.

Successful bacteriophage infection starts with specific recognition and adhesion to the host cell surface. Adhesion devices of siphophages infecting Gram-positive bacteria are very diverse and remain, for the majority, poorly understood. These assemblies often comprise long, flexible, and multi-domain proteins, which limits their structural analyses by experimental approaches such as X-ray crystallography and electron microscopy. However, the protein structure prediction program AlphaFold2 is exquisitely adapted to unveil structural and functional details of such molecular machineries. Here, we present structure predictions of whole adhesion devices of five representative siphophages infecting Streptococcus thermophilus, one of the main lactic acid bacteria used in dairy fermentations. The predictions highlight the mosaic nature of these devices that share functional domains for which active sites and residues could be unambiguously identified. Such AlphaFold2 analyses of phage-encoded host adhesion devices should become a standard method to characterize phage-host interaction machineries and to reliably annotate phage genomes.

Protein Design: From the Aspect of Water Solubility and Stability.

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

532 Three-dimensional structure of a microbial nanomachine and its role in acne vulgaris

Treating acne with antibiotics has led to the emergence of multidrug resistant (MDR) bacteria. MDR bacterial infections incur high healthcare costs and result in higher patient mortality. New antibiotics that target specific bacteria to avoid collateral microbiota damage are needed. Antibiotics used to treat acne reduce levels of Cutibacterium acnes. Acne-associated C. acnes genomes include putative, non-ribosomal peptide synthetase (NRPS) genes. Microbes use NRPS proteins to assemble unique metabolites. Some of these metabolites are virulence factors (e.g., Escherichia coli siderophores). Inhibiting bacterial virulence factors has been proposed as a strategy to minimize antibiotic collateral damage. No C. acnes NRPS has yet been characterized. One of the putative, acne-associated NRPS C. acnes genes (PPA-RS12630) is found almost exclusively within the Propionibacterium genus, which includes C. acnes. Based on sequence homology, we hypothesize that PPA-RS12630 is an iterative NRPS with a tri-domain structure consisting of adenylation, peptidyl carrier, and condensation domains. Microbes synthesize the siderophore vicibactin using this type of NRPS. Our group used structure prediction programs to design a PPA-RS12630 E. coli expression construct. Recombinant PPA-RS12630 was expressed, purified, and used in biochemical and structural investigations. Negative-stain electron microscopy of recombinant PPA-RS12630 demonstrated a flexible, tri-domain structure consistent with an iterative NRPS. Cryogenic electron microscopy (cryoEM) data was collected on PPA-RS12630 to determine its three-dimensional structure. The high-resolution structure of this protein will provide a road map to guide the biochemical characterization of PPA-RS12630 and other related C. acnes proteins. These investigations will help us to understand the role of C. acnes in human diseases, develop precision C. acnes therapeutics, and decrease the burden of MDR bacterial infections.

Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device.

In 2021, the release of AlphaFold2 - the DeepMind’s machine-learning protein structure prediction program - revolutionized structural biology. Results of the CASP14 contest were an immense surprise as AlphaFold2 successfully predicted 3D structures of nearly all submitted protein sequences. The AlphaFold2 craze has rapidly spread the life science community since structural biologists as well as untrained biologists have now the possibility to obtain high-confidence protein structures. This revolution is opening new avenues to address challenging biological questions. Moreover, AlphaFold2 is imposing itself as an essential step of any structural biology project, and requires us to revisit our structural biology workflows. On one hand, AlphaFold2 synergizes with experimental methods including X-ray crystallography and cryo-electron microscopy. On the other hand, it is, to date, the only method enabling structural analyses of large and flexible assemblies resistant to experimental approaches. We illustrate this valuable application of AlphaFold2 with the structure prediction of the whole host adhesion device from the Lactobacillus casei bacteriophage J-1. With the ongoing improvement of AlphaFold2 algorithms and notebooks, there is no doubt that AlphaFold2-driven biological stories will increasingly be reported, which questions the future directions of experimental structural biology.

In silico design of short hairpin RNA (shRNA)Molecules for DNA pol gene of Contagious Ecthyma virus (ORFV)

Contagious ecthyma is an infectious skin disease of ruminants caused by the ORF virus (ORFV) that is a member of genus Parapoxvirus of the Poxviridae family. In addition to the significant effects on lambs and human, ORFVs have been recently shown to infect other hosts. The disease causes significant economic damages to the sheep industry, so attempts to eliminate it must be taken into.RNA interference (RNAi) is an evolutionarily conserved mechanism in which the expression of homologous target genesis is suppressed by means of double-stranded RNA molecules. Since RNAi can be considered as a therapeutic method for viral gene silencing, we tend to make the most of this capability. The present study aims to design potential shRNAs to knockdown the DNA-polymerase gene coded by ORF025.A significant number of computational methods such as clustal omega website to target alignment, BLAST-NCBI to similarity search, CLC software to secondary structure prediction, BLOCK-iTRNAi Designer and WI siRNA Selection Program and Software to design of shRNA molecules and scoring have been applied for shRNA molecules designing against the ORF025-DNA pol gene of ORFV. Then three shRNA molecules were logically designed against the ORF025-DNA pol gene. In conclusion the present study provides a strong and superior approach for achieving a validated strategy to design an antiviral shRNA molecule that meets many sequence features for efficient ORFV knockdown and treatment at the mRNA level. The efficiency of these anti ORFV shRNAs need to be tested