African Swine Fever Virus Protein-Protein Interaction Prediction.

The African swine fever virus (ASFV) is an often deadly disease in swine and poses a threat to swine livestock and swine producers. With its complex genome containing more than 150 coding regions, developing effective vaccines for this virus remains a challenge due to a lack of basic knowledge about viral protein function and protein-protein interactions between viral proteins and between viral and host proteins. In this work, we identified ASFV-ASFV protein-protein interactions (PPIs) using artificial intelligence-powered protein structure prediction tools. We benchmarked our PPI identification workflow on the Vaccinia virus, a widely studied nucleocytoplasmic large DNA virus, and found that it could identify gold-standard PPIs that have been validated in vitro in a genome-wide computational screening. We applied this workflow to more than 18,000 pairwise combinations of ASFV proteins and were able to identify seventeen novel PPIs, many of which have corroborating experimental or bioinformatic evidence for their protein-protein interactions, further validating their relevance. Two protein-protein interactions, I267L and I8L, I267L__I8L, and B175L and DP79L, B175L__DP79L, are novel PPIs involving viral proteins known to modulate host immune response.

Structural Analysis of the Drosophila melanogaster GSTome.

Glutathione transferase (GST) is a superfamily of ubiquitous enzymes, multigenic in numerous organisms and which generally present homodimeric structures. GSTs are involved in numerous biological functions such as chemical detoxification as well as chemoperception in mammals and insects. GSTs catalyze the conjugation of their cofactor, reduced glutathione (GSH), to xenobiotic electrophilic centers. To achieve this catalytic function, GSTs are comprised of a ligand binding site and a GSH binding site per subunit, which is very specific and highly conserved; the hydrophobic substrate binding site enables the binding of diverse substrates. In this work, we focus our interest in a model organism, the fruit fly Drosophila melanogaster (D. mel), which comprises 42 GST sequences distributed in six classes and composing its GSTome. The goal of this study is to describe the complete structural GSTome of D. mel to determine how changes in the amino acid sequence modify the structural characteristics of GST, particularly in the GSH binding sites and in the dimerization interface. First, we predicted the 3D atomic structures of each GST using the AlphaFold (AF) program and compared them with X-ray crystallography structures, when they exist. We also characterized and compared their global and local folds. Second, we used multiple sequence alignment coupled with AF-predicted structures to characterize the relationship between the conservation of amino acids in the sequence and their structural features. Finally, we applied normal mode analysis to estimate thermal B-factors of all GST structures of D. mel. Particularly, we extracted flexibility profiles of GST and identify key residues and motifs that are systematically involved in the ligand binding/dimerization processes and thus playing a crucial role in the catalytic function. This methodology will be extended to guide the in silico design of synthetic GST with new/optimal catalytic properties for detoxification applications.

Novel Insights into Stereoselective Reproductive Toxicity Induced by Mefentrifluconazole in Earthworms (Eisenia fetida): First Report of Estrogenic Effects.

Widespread use of the new chiral triazole fungicide mefentrifluconazole (MFZ) poses a threat to soil organisms. Although triazole fungicides have been reported to induce reproductive disorders in vertebrates, significant research gaps remain regarding their impact on the reproductive health of soil invertebrates. Here, reproduction-related toxicity end points were explored in earthworms (Eisenia fetida) after exposure for 28 d to soil containing 4 mg/kg racemic MFZ, R-(-)-MFZ, and S-(+)-MFZ. The S-(+)-MFZ treatment resulted in a more pronounced reduction in the number of cocoons and juveniles compared to R-(-)-MFZ treatment, and the expression of annetocin gene was significantly downregulated following exposure to both enantiomers. This reproductive toxicity has been attributed to the disruption of ovarian steroidogenesis at the transcriptional level. Further studies revealed that MFZ enantiomers were able to activate the estrogen receptor (ER). Indirect evidence for this estrogenic effect is provided by the introduction of 17ß-estradiol, which also induces reproductive disorders through ER activation.

A random mutagenesis screen enriched for missense mutations in bacterial effector proteins.

To remodel their hosts and escape immune defenses, many pathogens rely on large arsenals of proteins (effectors) that are delivered to the host cell using dedicated translocation machinery. Effectors hold significant insight into the biology of both the pathogens that encode them and the host pathways that they manipulate. One of the most powerful systems biology tools for studying effectors is the model organism, Saccharomyces cerevisiae. For many pathogens, the heterologous expression of effectors in yeast is growth inhibitory at a frequency much higher than housekeeping genes, an observation ascribed to targeting conserved eukaryotic proteins. Abrogation of yeast growth inhibition has been used to identify bacterial suppressors of effector activity, host targets, and functional residues and domains within effector proteins. We present here a yeast-based method for enriching for informative, in-frame, missense mutations in a pool of random effector mutants. We benchmark this approach against three effectors from Legionella pneumophila, an intracellular bacterial pathogen that injects a staggering >330 effectors into the host cell. For each protein, we show how in silico protein modeling (AlphaFold2) and missense-directed mutagenesis can be combined to reveal important structural features within effectors. We identify known active site residues within the metalloprotease RavK, the putative active site in SdbB, and previously unidentified functional motifs within the C-terminal domain of SdbA. We show that this domain has structural similarity with glycosyltransferases and exhibits in vitro activity consistent with this predicted function.

Everything AlphaFold tells us about protein knots.

Recent advances in Machine Learning methods in structural biology opened up new perspectives for protein analysis. Utilizing these methods allows us to go beyond the limitations of empirical research, and take advantage of the vast amount of generated data. We use a complete set of potentially knotted protein models identified in all high-quality predictions from the AlphaFold Database to search for any common trends that describe them. We show that the vast majority of knotted proteins have 31 knot and that the presence of knots is preferred in neither Bacteria, Eukaryota, or Archaea domains. On the contrary, the percentage of knotted proteins in any given proteome is around 0.4%, regardless of the taxonomical group. We also verified that the organism's living conditions do not impact the number of knotted proteins in its proteome, as previously expected. We did not encounter an organism without a single knotted protein. What is more, we found four universally present families of knotted proteins in Bacteria, consisting of SAM synthase, and TrmD, TrmH, and RsmE methyltransferases.

Structures prediction and replica exchange molecular dynamics simulations of α-synuclein: A case study for intrinsically disordered proteins.

In recent years, a variety of three-dimensional structure prediction tools, including AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold, have been employed in the investigation of intrinsically disordered proteins. However, a comprehensive validation of these tools specifically for intrinsically disordered proteins has yet to be conducted. In this study, we utilize AlphaFold2, AlphaFold3, I-TASSER, C-I-TASSER, Phyre2, ESMFold, and RoseTTAFold to predict the structure of a model intrinsically disordered α-synuclein protein. Additionally, extensive replica exchange molecular dynamics simulations of the intrinsically disordered protein are conducted. The resulting structures from both structure prediction tools and replica exchange molecular dynamics simulations are analyzed for radius of gyration, secondary and tertiary structure properties, as well as Cα and Hα chemical shift values. A comparison of the obtained results with experimental data reveals that replica exchange molecular dynamics simulations provide results in excellent agreement with experimental observations. However, none of the structure prediction tools utilized in this study can fully capture the structural characteristics of the model intrinsically disordered protein. This study shows that a cluster of ensembles are required for intrinsically disordered proteins. Artificial-intelligence based structure prediction tools such as AlphaFold3 and C-I-TASSER could benefit from stochastic sampling or Monte Carlo simulations for generating an ensemble of structures for intrinsically disordered proteins.

δ-Conotoxin Structure Prediction and Analysis through Large-Scale Comparative and Deep Learning Modeling Approaches.

The δ-conotoxins, a class of peptides produced in the venom of cone snails, are of interest due to their ability to inhibit the inactivation of voltage-gated sodium channels causing paralysis and other neurological responses, but difficulties in their isolation and synthesis have made structural characterization challenging. Taking advantage of recent breakthroughs in computational algorithms for structure prediction that have made modeling especially useful when experimental data is sparse, this work uses both the deep-learning-based algorithm AlphaFold and comparative modeling method RosettaCM to model and analyze 18 previously uncharacterized δ-conotoxins derived from piscivorous, vermivorous, and molluscivorous cone snails. The models provide useful insights into the structural aspects of these peptides and suggest features likely to be significant in influencing their binding and different pharmacological activities against their targets, with implications for drug development. Additionally, the described protocol provides a roadmap for the modeling of similar disulfide-rich peptides by these complementary methods.

Using site-directed mutagenesis to further the understanding of insulin receptor-insulin like growth factor-1 receptor heterodimer structure.

Type 2 diabetes is characterised by the disruption of insulin and insulin-like growth factor (IGF) signalling. The key hubs of these signalling cascades - the Insulin receptor (IR) and Insulin-like growth factor 1 receptor (IGF1R) - are known to form functional IR-IGF1R hybrid receptors which are insulin resistant. However, the mechanisms underpinning IR-IGF1R hybrid formation are not fully understood, hindering the ability to modulate this for future therapies targeting this receptor. To pinpoint suitable sites for intervention, computational hotspot prediction was utilised to identify promising epitopes for targeting with point mutagenesis. Specific IGF1R point mutations F450A, R391A and D555A show reduced affinity of the hybrid receptor in a BRET based donor-saturation assay, confirming hybrid formation could be modulated at this interface. These data provide the basis for rational design of more effective hybrid receptor modulators, supporting the prospect of identifying a small molecule that specifically interacts with this target.

AlphaFold-guided redesign of a plant pectin methylesterase inhibitor for broad-spectrum disease resistance.

Plant cell walls are a critical site where plants and pathogens continuously struggle for physiological dominance. Here we show that dynamic remodeling of pectin methylesterification of plant cell walls is a component of the physiological and co-evolutionary struggles between hosts and pathogens. A Phytophthora sojae secreted pectin methylesterase (PsPME1) decreases the degree of pectin methylesterification, thus synergizing with an endo-polygalacturonase (PsPG1) to weaken plant cell walls. To counter PsPME1-mediated susceptibility, a plant-derived pectin methylesterase inhibitor protein, GmPMI1, protects pectin to maintain a high methylesterification status. GmPMI1 protects plant cell walls from enzymatic degradation by inhibiting both soybean and P. sojae pectin methylesterases during infection. However, constitutive expression of GmPMI1 disrupted the tradeoff between host growth and defense responses. So, we used AlphaFold structure tools to design a modified form of GmPMI1 (GmPMI1R) which specifically targets and inhibits pectin methylesterases secreted from pathogens but not from the plants. Transient expression of GmPMI1R enhanced plant resistance to oomycetes and fungal pathogens. In summary, our work highlights biochemical modification of the cell wall as an important focal point in the physiological and co-evolutionary conflict between the hosts and microbes and serves as an important proof-of-concept for how rapid advancements in AI-driven structure-based tools can accelerate the prediction of new strategies for plant protection.

LazyAF, a pipeline for accessible medium-scale in silico prediction of protein-protein interactions.

Artificial intelligence has revolutionized the field of protein structure prediction. However, with more powerful and complex software being developed, it is accessibility and ease of use rather than capability that is quickly becoming a limiting factor to end users. LazyAF is a Google Colaboratory-based pipeline which integrates the existing ColabFold BATCH software to streamline the process of medium-scale protein-protein interaction prediction. LazyAF was used to predict the interactome of the 76 proteins encoded on the broad-host-range multi-drug resistance plasmid RK2, demonstrating the ease and accessibility the pipeline provides.

AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses.

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high-quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high-quality biological model suitable for applications such as drug design, protein interaction, or molecular dynamics studies.

C(P)XCG Proteins of Haloferax volcanii with Predicted Zinc Finger Domains: The Majority Bind Zinc, but Several Do Not.

In recent years, interest in very small proteins (µ-proteins) has increased significantly, and they were found to fulfill important functions in all prokaryotic and eukaryotic species. The halophilic archaeon Haloferax volcanii encodes about 400 µ-proteins of less than 70 amino acids, 49 of which contain at least two C(P)XCG motifs and are, thus, predicted zinc finger proteins. The determination of the NMR solution structure of HVO_2753 revealed that only one of two predicted zinc fingers actually bound zinc, while a second one was metal-free. Therefore, the aim of the current study was the homologous production of additional C(P)XCG proteins and the quantification of their zinc content. Attempts to produce 31 proteins failed, underscoring the particular difficulties of working with µ-proteins. In total, 14 proteins could be produced and purified, and the zinc content was determined. Only nine proteins complexed zinc, while five proteins were zinc-free. Three of the latter could be analyzed using ESI-MS and were found to contain another metal, most likely cobalt or nickel. Therefore, at least in haloarchaea, the variability of predicted C(P)XCG zinc finger motifs is higher than anticipated, and they can be metal-free, bind zinc, or bind another metal. Notably, AlphaFold2 cannot correctly predict whether or not the four cysteines have the tetrahedral configuration that is a prerequisite for metal binding.

Constructing networks for comparison of collagen types.

Collagens are structural proteins that are predominantly found in the extracellular matrix of multicellular animals, where they are mainly responsible for the stability and structural integrity of various tissues. All collagens contain polypeptide strands (α-chains). There are several types of collagens, some of which differ significantly in form, function, and tissue specificity. Because of their importance in clinical research, they are grouped into subdivisions, the so-called collagen families, and their sequences are often analysed. However, problems arise with highly homologous sequence segments. To increase the accuracy of collagen classification and prediction of their functions, the structure of these collagens and their expression in different tissues could result in a better focus on sequence segments of interest. Here, we analyse collagen families with different levels of conservation. As a result, clusters with high interconnectivity can be found, such as the fibrillar collagens, the COL4 network-forming collagens, and the COL9 FACITs. Furthermore, a large cluster between network-forming, FACIT, and COL28a1 α-chains is formed with COL6a3 as a major hub node. The formation of clusters also signifies, why it is important to always analyse the α-chains and why structural changes can have a wide range of effects on the body.

All-RNA-mediated targeted gene integration in mammalian cells with rationally engineered R2 retrotransposons.

All-RNA-mediated targeted gene integration methods, rendering reduced immunogenicity, effective deliverability with non-viral vehicles, and a low risk of random mutagenesis, are urgently needed for next-generation gene addition technologies. Naturally occurring R2 retrotransposons hold promise in this context due to their site-specific integration profile. Here, we systematically analyzed the biodiversity of R2 elements and screened several R2 orthologs capable of full-length gene insertion in mammalian cells. Robust R2 system gene integration efficiency was attained using combined donor RNA and protein engineering. Importantly, the all-RNA-delivered engineered R2 system showed effective integration activity, with efficiency over 60% in mouse embryos. Unbiased high-throughput sequencing demonstrated that the engineered R2 system exhibited high on-target integration specificity (99%). In conclusion, our study provides engineered R2 tools for applications based on hit-and-run targeted DNA integration and insights for further optimization of retrotransposon systems.

Utilization of an optimized AlphaFold protein model for structure-based design of a selective HDAC11 inhibitor with anti-neuroblastoma activity.

AlphaFold is an artificial intelligence approach for predicting the three-dimensional (3D) structures of proteins with atomic accuracy. One challenge that limits the use of AlphaFold models for drug discovery is the correct prediction of folding in the absence of ligands and cofactors, which compromises their direct use. We have previously described the optimization and use of the histone deacetylase 11 (HDAC11) AlphaFold model for the docking of selective inhibitors such as FT895 and SIS17. Based on the predicted binding mode of FT895 in the optimized HDAC11 AlphaFold model, a new scaffold for HDAC11 inhibitors was designed, and the resulting compounds were tested in vitro against various HDAC isoforms. Compound 5a proved to be the most active compound with an IC50 of 365 nM and was able to selectively inhibit HDAC11. Furthermore, docking of 5a showed a binding mode comparable to FT895 but could not adopt any reasonable poses in other HDAC isoforms. We further supported the docking results with molecular dynamics simulations that confirmed the predicted binding mode. 5a also showed promising activity with an EC50 of 3.6 µM on neuroblastoma cells.

Identification, characterization and classification of prokaryotic nucleoid-associated proteins.

Common throughout life is the need to compact and organize the genome. Possible mechanisms involved in this process include supercoiling, phase separation, charge neutralization, macromolecular crowding, and nucleoid-associated proteins (NAPs). NAPs are special in that they can organize the genome at multiple length scales, and thus are often considered as the architects of the genome. NAPs shape the genome by either bending DNA, wrapping DNA, bridging DNA, or forming nucleoprotein filaments on the DNA. In this mini-review, we discuss recent advancements of unique NAPs with differing architectural properties across the tree of life, including NAPs from bacteria, archaea, and viruses. To help the characterization of NAPs from the ever-increasing number of metagenomes, we recommend a set of cheap and simple in vitro biochemical assays that give unambiguous insights into the architectural properties of NAPs. Finally, we highlight and showcase the usefulness of AlphaFold in the characterization of novel NAPs.

Hidden Structural States of Proteins Revealed by Conformer Selection with AlphaFold-NMR.

Recent advances in molecular modeling using deep learning can revolutionize our understanding of dynamic protein structures. NMR is particularly well-suited for determining dynamic features of biomolecular structures. The conventional process for determining biomolecular structures from experimental NMR data involves its representation as conformation-dependent restraints, followed by generation of structural models guided by these spatial restraints. Here we describe an alternative approach: generating a distribution of realistic protein conformational models using artificial intelligence-(AI-) based methods and then selecting the sets of conformers that best explain the experimental data. We applied this conformational selection approach to redetermine the solution NMR structure of the enzyme Gaussia luciferase. First, we generated a diverse set of conformer models using AlphaFold2 (AF2) with an enhanced sampling protocol. The models that best-fit NOESY and chemical shift data were then selected with a Bayesian scoring metric. The resulting models include features of both the published NMR structure and the standard AF2 model generated without enhanced sampling. This "AlphaFold-NMR" protocol also generated an alternative "open" conformational state that fits nearly as well to the overall NMR data but accounts for some NOESY data that is not consistent with first "closed" conformational state; while other NOESY data consistent with this second state are not consistent with the first conformational state. The structure of this "open" structural state differs from that of the "closed" state primarily by the position of a thumb-shaped loop between α-helices H5 and H6, revealing a cryptic surface pocket. These alternative conformational states of Gluc are supported by "double recall" analysis of NOESY data and AF2 models. Additional structural states are also indicated by backbone chemical shift data indicating partially-disordered conformations for the C-terminal segment. Considered as a multistate ensemble, these multiple states of Gluc together fit the NOESY and chemical shift data better than the "restraint-based" NMR structure and provide novel insights into its structure-dynamic-function relationships. This study demonstrates the potential of AI-based modeling with enhanced sampling to generate conformational ensembles followed by conformer selection with experimental data as an alternative to conventional restraint satisfaction protocols for protein NMR structure determination.

Mutagenesis Supports AlphaFold Prediction of How Modular Polyketide Synthase Acyl Carrier Proteins Dock With Downstream Ketosynthases.

The docking of an acyl carrier protein (ACP) domain with a downstream ketosynthase (KS) domain in each module of a polyketide synthase (PKS) helps ensure accurate biosynthesis. If the polyketide chain bound to the ACP has been properly modified by upstream processing enzymes and is compatible with gatekeeping residues in the KS tunnel, a transacylation reaction can transfer it from the 18.1-Å phosphopantetheinyl arm of the ACP to the reactive cysteine of the KS. AlphaFold-Multimer predicts a general interface for these transacylation checkpoints. Half of the solutions obtained for 50 ACP/KS pairs show the KS motif TxLGDP forming the first turn of an α-helix, as in reported structures, while half show it forming a type I ß-turn not previously observed. Solutions with the latter conformation may represent how these domains are relatively positioned during the transacylation reaction, as the entrance to the KS active site is relatively open and the phosphopantetheinylated ACP serine and the reactive KS cysteine are relatively closer-17.2 versus 20.9 Å, on average. To probe the predicted interface, 20 mutations were made to KS surface residues within the model triketide lactone synthase P1-P6-P7. The activities of these mutants are consistent with the proposed interface.

An efficient flavonoid glycosyltransferase NjUGT73B1 from Nardostachys jatamansi of alpine Himalayas discovered by structure-based protein clustering.

Tilianin and linarin, two rare glycosylated flavonoids in the aromatic endangered medicinal plant Nardostachys jatamansi (D.on)DC., play an important role in the fields of medicine, cosmetics, food and dye industries. However, there remains a lack of comprehensive understanding regarding their biosynthetic pathway. In this study, the phytochemical investigation of Nardostachys jatamansi (D.on)DC. resulted in the isolation of linarin. With help of AlphaFold2 to cluster the entire glycosyltransferase family based on predicted structure similarities, we successfully identified a flavonoid glycosyltransferase NjUGT73B1, which could efficiently catalyse the glucosylation of acacetin at 7-OH to produce tilianin, also the key precursor in the biosynthesis of linarin. Additionally, NjUGT73B1 displayed a high degree of substrate promiscuity, enabling glucosylation at 7-OH of many flavonoids. Molecular modeling and site-directed mutagenesis revealed that H19, H21, H370, F126, and F127 play the crucial roles in the glycosylation ability of NjUGT73B1. Notably, comparation with the wild NjUGT73B1, mutant H19K led to a 50% increase in the activity of producing tilianin from acacetin.