The retinoid X receptor has a critical role in synthetic rexinoid-induced increase in cellular all-trans-retinoic acid.

Rexinoids are agonists of nuclear rexinoid X receptors (RXR) that heterodimerize with other nuclear receptors to regulate gene transcription. A number of selective RXR agonists have been developed for clinical use but their application has been hampered by the unwanted side effects associated with the use of rexinoids and a limited understanding of their mechanisms of action across different cell types. Our previous studies showed that treatment of organotypic human epidermis with the low toxicity UAB30 and UAB110 rexinoids resulted in increased steady-state levels of all-trans-retinoic acid (ATRA), the obligatory ligand of the RXR-RAR heterodimers. Here, we investigated the molecular mechanism underlying the increase in ATRA levels using a dominant negative RXRα that lacks the activation function 2 (AF-2) domain. The results demonstrated that overexpression of dnRXRα in human organotypic epidermis markedly reduced signaling by resident ATRA, suggesting the existence of endogenous RXR ligand, diminished the biological effects of UAB30 and UAB110 on epidermis morphology and gene expression, and nearly abolished the rexinoid-induced increase in ATRA levels. Global transcriptome analysis of dnRXRα-rafts in comparison to empty vector-transduced rafts showed that over 95% of the differentially expressed genes in rexinoid-treated rafts constitute direct or indirect ATRA-regulated genes. Thus, the biological effects of UAB30 and UAB110 are mediated through the AF-2 domain of RXRα with minimal side effects in human epidermis. As ATRA levels are known to be reduced in certain epithelial pathologies, treatment with UAB30 and UAB110 may represent a promising therapy for normalizing the endogenous ATRA concentration and signaling in epithelial tissues.

Improving furfural tolerance in a xylose-fermenting yeast Spathaspora passalidarum CMUWF1-2 via adaptive laboratory evolution.

BACKGROUND: Spathaspora passalidarum is a yeast with the highly effective capability of fermenting several monosaccharides in lignocellulosic hydrolysates, especially xylose. However, this yeast was shown to be sensitive to furfural released during pretreatment and hydrolysis processes of lignocellulose biomass. We aimed to improve furfural tolerance in a previously isolated S. passalidarum CMUWF1-2, which presented thermotolerance and no detectable glucose repression, via adaptive laboratory evolution (ALE). RESULTS: An adapted strain, AF2.5, was obtained from 17 sequential transfers of CMUWF1-2 in YPD broth with gradually increasing furfural concentration. Strain AF2.5 could tolerate higher concentrations of furfural, ethanol and 5-hydroxymethyl furfuraldehyde (HMF) compared with CMUWF1-2 while maintaining the ability to utilize glucose and other sugars simultaneously. Notably, the lag phase of AF2.5 was 2 times shorter than that of CMUWF1-2 in the presence of 2.0 g/l furfural, which allowed the highest ethanol titers to be reached in a shorter period. To investigate more in-depth effects of furfural, intracellular reactive oxygen species (ROS) accumulation was observed and, in the presence of 2.0 g/l furfural, AF2.5 exhibited 3.41 times less ROS accumulation than CMUWF1-2 consistent with the result from nuclear chromatins diffusion, which the cells number of AF2.5 with diffuse chromatins was also 1.41 and 1.24 times less than CMUWF1-2 at 24 and 36 h, respectively. CONCLUSIONS: An enhanced furfural tolerant strain of S. passalidarum was achieved via ALE techniques, which shows faster and higher ethanol productivity than that of the wild type. Not only furfural tolerance but also ethanol and HMF tolerances were improved.

A structural basis for the activation of peroxisome proliferator-activated receptor gamma (PPARγ) by perfluorooctanoic acid (PFOA).

Perfluorooctanoic acid (PFOA) is a widespread environmental pollutant of the perfluoroalkyl substance (PFAS) class that is extremely resistant to environmental and metabolic degradation, leading to bioaccumulation. PFOA exposure has been linked to many health effects including endocrine disruption and metabolic dysregulation, but our understanding of the molecular mechanisms resulting in these outcomes remains incomplete. One target affected by PFOA is the ligand regulated nuclear receptor peroxisome proliferator-activated receptor gamma (PPARγ) which plays a critical role in controlling metabolic homeostasis through regulating processes such as adipogenesis, glucose homeostasis, inflammation and osteogenesis. It has been previously established that PFOA activates PPARγ through binding to the PPARγ ligand binding domain (PPARγ LBD) leading to increased expression of PPARγ controlled target genes. However, the mechanism by which PFOA achieves this has remained elusive. Here, we employed a combination of X-ray crystallography and fluorescence polarization assays to provide a structural basis for PFOA mediated activation of PPARγ via binding to the PPARγ LBD. Using X-ray crystallography, the cocrystal structure of the PPARγ LBD:PFOA complex was solved. This revealed that PFOA occupies three distinct sites, two within the PPARγ LBD and one within the activation function 2 (AF2) on the protein surface. Structural comparison of PFOA binding with previously reported PPARγ:ligand complexes supports that PFOA activates PPARγ by a partial agonist mechanism at micromolar concentrations. Fluorescence polarization assays also revealed that PFOA binding to the AF2 is unlikely to occur in a cellular context and confirmed that PFOA behaves as a partial agonist in vitro, weakly recruiting a coactivator peptide to the AF2 of the PPARγ LBD. This discovery provides an advancement in understanding PFOA mediated regulation of PPARγ, giving new insight regarding regulation of PPARγ by PFAS and PFAS substitutes in general and can be applied to the design and assessment of safer PFAS.

Probiotic potential and wound-healing activity of Pediococcus pentosaceus strain AF2 isolated from Herniaria glabra L. which is traditionally used to make yogurt.

Probiotics have been a part of our lives for centuries, primarily through fermented foods. They find applications in various fields such as food, healthcare, and agriculture. Nowadays, their utilization is expanding, highlighting the importance of discovering new bacterial strains with probiotic properties suitable for diverse applications. In this study, our aim was to isolate new probiotic bacteria. Herniaria glabra L., a plant traditionally used for yogurt making in some regions and recognized in official medicine in many countries, was chosen as the source for obtaining probiotic bacteria. We conducted bacterial isolation from the plant, molecularly identified the isolated bacteria using 16S rRNA sequencing, characterized their probiotic properties, and assessed their wound-healing effects. As a result of these studies, we identified the bacterium isolated from the plant as Pediococcus pentosaceus strain AF2. We found that the strain AF2 exhibited high resistance to conditions within the gastrointestinal tract. Our reliability analysis showed that the isolate had γ-hemolytic activity and displayed sensitivity to certain tested antibiotics. At the same time, AF2 did not show gelatinase and DNase activity. We observed that the strain AF2 produced metabolites with inhibitory activity against E. coli, B. subtilis, P. vulgaris, S. typhimurium, P. aeruginosa, K. pneumoniae, E. cloacae, and Y. pseudotuberculosis. The auto-aggregation value of the strain AF2 was calculated at 73.44%. Coaggregation values against E. coli and L. monocytogenes bacteria were determined to be 56.8% and 57.38%, respectively. Finally, we tested the wound-healing effect of the strain AF2 with cell culture studies and found that the strain AF2 promoted wound healing.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor.

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2.

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

Modeling Flexible Protein Structure With AlphaFold2 and Crosslinking Mass Spectrometry.

We propose a pipeline that combines AlphaFold2 (AF2) and crosslinking mass spectrometry (XL-MS) to model the structure of proteins with multiple conformations. The pipeline consists of two main steps: ensemble generation using AF2 and conformer selection using XL-MS data. For conformer selection, we developed two scores-the monolink probability score (MP) and the crosslink probability score (XLP)-both of which are based on residue depth from the protein surface. We benchmarked MP and XLP on a large dataset of decoy protein structures and showed that our scores outperform previously developed scores. We then tested our methodology on three proteins having an open and closed conformation in the Protein Data Bank: Complement component 3 (C3), luciferase, and glutamine-binding periplasmic protein, first generating ensembles using AF2, which were then screened for the open and closed conformations using experimental XL-MS data. In five out of six cases, the most accurate model within the AF2 ensembles-or a conformation within 1 Å of this model-was identified using crosslinks, as assessed through the XLP score. In the remaining case, only the monolinks (assessed through the MP score) successfully identified the open conformation of glutamine-binding periplasmic protein, and these results were further improved by including the "occupancy" of the monolinks. This serves as a compelling proof-of-concept for the effectiveness of monolinks. In contrast, the AF2 assessment score was only able to identify the most accurate conformation in two out of six cases. Our results highlight the complementarity of AF2 with experimental methods like XL-MS, with the MP and XLP scores providing reliable metrics to assess the quality of the predicted models. The MP and XLP scoring functions mentioned above are available at https://gitlab.com/topf-lab/xlms-tools.

Outer membrane ß-barrel structure prediction through the lens of AlphaFold2.

Most proteins found in the outer membrane of gram-negative bacteria share a common domain: the transmembrane ß-barrel. These outer membrane ß-barrels (OMBBs) occur in multiple sizes and different families with a wide range of functions evolved independently by amplification from a pool of homologous ancestral ßß-hairpins. This is part of the reason why predicting their three-dimensional (3D) structure, especially by homology modeling, is a major challenge. Recently, DeepMind's AlphaFold v2 (AF2) became the first structure prediction method to reach close-to-experimental atomic accuracy in CASP even for difficult targets. However, membrane proteins, especially OMBBs, were not abundant during their training, raising the question of how accurate the predictions are for these families. In this study, we assessed the performance of AF2 in the prediction of OMBBs and OMBB-like folds of various topologies using an in-house-developed tool for the analysis of OMBB 3D structures, and barrOs. In agreement with previous studies on other membrane protein classes, our results indicate that AF2 predicts transmembrane ß-barrel structures at high accuracy independently of the use of templates, even for novel topologies absent from the training set. These results provide confidence on the models generated by AF2 and open the door to the structural elucidation of novel transmembrane ß-barrel topologies identified in high-throughput OMBB annotation studies or designed de novo.

A comparative study of protein structure prediction tools for challenging targets: Snake venom toxins.

Protein structure determination is a critical aspect of biological research, enabling us to understand protein function and potential applications. Recent advances in deep learning and artificial intelligence have led to the development of several protein structure prediction tools, such as AlphaFold2 and ColabFold. However, their performance has primarily been evaluated on well-characterised proteins and their ability to predict sturtctures of proteins lacking experimental structures, such as many snake venom toxins, has been less scrutinised. In this study, we evaluated three modelling tools on their prediction of over 1000 snake venom toxin structures for which no experimental structures exist. Our findings show that AlphaFold2 (AF2) performed the best across all assessed parameters. We also observed that ColabFold (CF) only scored slightly worse than AF2, while being computationally less intensive. All tools struggled with regions of intrinsic disorder, such as loops and propeptide regions, and performed well in predicting the structure of functional domains. Overall, our study highlights the importance of exercising caution when working with proteins with no experimental structures available, particularly those that are large and contain flexible regions. Nonetheless, leveraging computational structure prediction tools can provide valuable insights into the modelling of protein interactions with different targets and reveal potential binding sites, active sites, and conformational changes, as well as into the design of potential molecular binders for reagent, diagnostic, or therapeutic purposes.

Novel Substrate Prediction for the TAM Family of RTKs Using Phosphoproteomics and Structure-Based Modeling.

The TAM family of receptor tyrosine kinases is implicated in multiple distinct oncogenic signaling pathways. However, to date, there are no FDA-approved small molecule inhibitors for the TAM kinases. Inhibitor design and screening rely on tools to study the kinase activity. Our goal was to address this gap by designing a set of synthetic peptide substrates for each of the TAM family members: Tyro3, Axl, and Mer. We used an in vitro phosphoproteomics workflow to determine the substrate profile of each TAM kinase and input the identified substrates into our data processing pipeline, KINATEST-ID, producing a position-specific scoring matrix for each target kinase and generating a list of candidate synthetic peptide substrates. We synthesized and characterized a set of those substrate candidates, systematically measuring their initial phosphorylation rate with each TAM kinase by LC-MS. We also used the multimer modeling function of AlphaFold2 (AF2) to predict peptide-kinase interactions at the active site for each of the novel candidate peptide sequences against each of the TAM family kinases and observed that, remarkably, every sequence for which it predicted a putative catalytically competent interaction was also demonstrated biochemically to be a substrate for one or more of the TAM kinases. This work shows that kinase substrate design can be achieved using a combination of preference motifs and structural modeling, and it provides the first demonstration of peptide-protein interaction modeling with AF2 for predicting the likelihood of constructive catalytic interactions.

A Perspective on the Prospective Use of AI in Protein Structure Prediction.

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

Roxburgh was right: Aponogeton microphyllus and Aponogeton undulatus are distinct species.

Aponogeton microphyllus, previously placed under the synonymy of A. undulatus, is recognized here as a distinct species based on morphology, chromosome number, and molecular phylogenetics (nuclear ribosomal internal transcribed (ITS) spacer region). Observations on the type and live specimens revealed morphological differences between the two species. Aponogeton microphyllus flowered regularly and set seeds. Aponogeton undulatus flowered rarely, did not set seeds, but showed formation of young plantlets on the inflorescence axis. Similarly, different chromosome numbers were recorded in Aponogeton microphyllus and the two forms of A. undulatus, viz., AF1 and AF2, which occur in distinct populations. Aponogeton microphyllus exhibited polysomaty with root-tip cells showing 2n=40, 42, and 44 chromosomes. The two forms of A. undulatus, AF1 and AF2, showed 2n=84 and 86 chromosomes, respectively. Based on the ITS data, both species occupied two separate clades. Plastid trnK intron region indicated a close relationship between both species. Our study suggests the need for comprehensive phylogenetic analyses of A. undulatus across its distribution range based on more advanced techniques such as highthroughput sequencing data to understand the A. undulatus species complex and to detect natural hybrids of this species.

Channel Formation in Cry Toxins: An Alphafold-2 Perspective.

Bacillus thuringiensis (Bt) strains produce pore-forming toxins (PFTs) that attack insect pests. Information for pre-pore and pore structures of some of these Bt toxins is available. However, for the three-domain (I-III) crystal (Cry) toxins, the most used Bt toxins in pest control, this crucial information is still missing. In these Cry toxins, biochemical data have shown that 7-helix domain I is involved in insertion in membranes, oligomerization and formation of a channel lined mainly by helix α4, whereas helices α1 to α3 seem to have a dynamic role during insertion. In the case of Cry1Aa, toxic against Manduca sexta larvae, a tetrameric oligomer seems to precede membrane insertion. Given the experimental difficulty in the elucidation of the membrane insertion steps, we used Alphafold-2 (AF2) to shed light on possible oligomeric structural intermediates in the membrane insertion of this toxin. AF2 very accurately (<1 Å RMSD) predicted the crystal monomeric and trimeric structures of Cry1Aa and Cry4Ba. The prediction of a tetramer of Cry1Aa, but not Cry4Ba, produced an 'extended model' where domain I helices α3 and α2b form a continuous helix and where hydrophobic helices α1 and α2 cluster at the tip of the bundle. We hypothesize that this represents an intermediate that binds the membrane and precedes α4/α5 hairpin insertion, together with helices α6 and α7. Another Cry1Aa tetrameric model was predicted after deleting helices α1 to α3, where domain I produced a central cavity consistent with an ion channel, lined by polar and charged residues in helix α4. We propose that this second model corresponds to the 'membrane-inserted' structure. AF2 also predicted larger α4/α5 hairpin n-mers (14 ≤n ≤ 17) with high confidence, which formed even larger (~5 nm) pores. The plausibility of these models is discussed in the context of available experimental data and current paradigms.

How accurately can one predict drug binding modes using AlphaFold models?

Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.

How AlphaFold2 shaped the structural coverage of the human transmembrane proteome.

AlphaFold2 (AF2) provides a 3D structure for every known or predicted protein, opening up new prospects for virtually every field in structural biology. However, working with transmembrane protein molecules pose a notorious challenge for scientists, resulting in a limited number of experimentally determined structures. Consequently, algorithms trained on this finite training set also face difficulties. To address this issue, we recently launched the TmAlphaFold database, where predicted AlphaFold2 structures are embedded into the membrane plane and a quality assessment (plausibility of the membrane-embedded structure) is provided for each prediction using geometrical evaluation. In this paper, we analyze how AF2 has improved the structural coverage of membrane proteins compared to earlier years when only experimental structures were available, and high-throughput structure prediction was greatly limited. We also evaluate how AF2 can be used to search for (distant) homologs in highly diverse protein families. By combining quality assessment and homology search, we can pinpoint protein families where AF2 accuracy is still limited, and experimental structure determination would be desirable.

No Effect of Continued Antiarrhythmic Drug Treatment on Top of Optimized Pulmonary Vein Isolation in Patients With Persistent Atrial Fibrillation: Results From the POWDER-AF2 Trial.

BACKGROUND: In patients with persistent atrial fibrillation (PersAF), catheter ablation aiming for pulmonary vein isolation (PVI) is associated with moderate clinical effectiveness. We investigated the benefit of continuing previously ineffective class 1C or 3 antiarrhythmic drug therapy (ADT) in the setting of a standardized PVI-only ablation strategy. METHODS: In this multicenter, randomized controlled study, patients with PersAF (≥7 days and <12 months) despite ADT were prospectively randomized 1:1 to PVI with ADT continued versus discontinued beyond the blanking period (ADT ON versus ADT OFF). Standardized catheter ablation was performed aiming for durable isolation with stable, contiguous, and optimized radio frequency applications encircling the pulmonary veins (CLOSE protocol). Clinical visits and 1-to-7-day Holter were performed at 3, 6, and 12 months. The primary end point was any documented atrial tachyarrhythmia lasting >30 seconds beyond 3 months. Prospectively defined secondary end points included repeat ablations, unscheduled arrhythmia-related visits, and quality of life among groups. RESULTS: Of 200 PersAF patients, 98 were assigned to ADT OFF and 102 to ADT ON. The longest atrial fibrillation episode qualifying for PersAF was 28 (10-90) versus 30 (11-90) days. Clinical characteristics and procedural characteristics were similar. Recurrence of atrial tachyarrhythmia was comparable in both groups (20% OFF versus 21.2% ON). No differences were observed in repeat ablations and unscheduled arrhythmia-related visits. Marked improvement in quality of life was observed in both groups. CONCLUSIONS: In patients with PersAF, there is no benefit in continuing previously ineffective ADT beyond the blanking period after catheter ablation. The high success rate of PVI-only might be explained by the high rate of durable isolation after optimized PVI and the early stage of PersAF (POWDER-AF2). REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03437356.

The Origin of Discrepancies between Predictions and Annotations in Intrinsically Disordered Proteins.

Disorder prediction methods that can discriminate between ordered and disordered regions have contributed fundamentally to our understanding of the properties and prevalence of intrinsically disordered proteins (IDPs) in proteomes as well as their functional roles. However, a recent large-scale assessment of the performance of these methods indicated that there is still room for further improvements, necessitating novel approaches to understand the strengths and weaknesses of individual methods. In this study, we compared two methods, IUPred and disorder prediction, based on the pLDDT scores derived from AlphaFold2 (AF2) models. We evaluated these methods using a dataset from the DisProt database, consisting of experimentally characterized disordered regions and subsets associated with diverse experimental methods and functions. IUPred and AF2 provided consistent predictions in 79% of cases for long disordered regions; however, for 15% of these cases, they both suggested order in disagreement with annotations. These discrepancies arose primarily due to weak experimental support, the presence of intermediate states, or context-dependent behavior, such as binding-induced transitions. Furthermore, AF2 tended to predict helical regions with high pLDDT scores within disordered segments, while IUPred had limitations in identifying linker regions. These results provide valuable insights into the inherent limitations and potential biases of disorder prediction methods.

Slc11 Synapomorphy: A Conserved 3D Framework Articulating Carrier Conformation Switch.

Transmembrane carriers of the Slc11 family catalyze proton (H+)-dependent uptake of divalent metal ions (Me2+) such as manganese and iron-vital elements coveted during infection. The Slc11 mechanism of high-affinity Me2+ cell import is selective and conserved between prokaryotic (MntH) and eukaryotic (Nramp) homologs, though processes coupling the use of the proton motive force to Me2+ uptake evolved repeatedly. Adding bacterial piracy of Nramp genes spread in distinct environmental niches suggests selective gain of function that may benefit opportunistic pathogens. To better understand Slc11 evolution, Alphafold (AF2)/Colabfold (CF) 3D predictions for bacterial sequences from sister clades of eukaryotic descent (MCb and MCg) were compared using both native and mutant templates. AF2/CF model an array of native MCb intermediates spanning the transition from outwardly open (OO) to inwardly open (IO) carriers. In silico mutagenesis targeting (i) a set of (evolutionarily coupled) sites that may define Slc11 function (putative synapomorphy) and (ii) residues from networked communities evolving during MCb transition indicates that Slc11 synapomorphy primarily instructs a Me2+-selective conformation switch which unlocks carrier inner gate and contributes to Me2+ binding site occlusion and outer gate locking. Inner gate opening apparently proceeds from interaction between transmembrane helix (h) h5, h8 and h1a. MCg1 xenologs revealed marked differences in carrier shape and plasticity, owing partly to an altered intramolecular H+ network. Yet, targeting Slc11 synapomorphy also converted MCg1 IO models to an OO state, apparently mobilizing the same residues to control gates. But MCg1 response to mutagenesis differed, with extensive divergence within this clade correlating with MCb-like modeling properties. Notably, MCg1 divergent epistasis marks the emergence of the genus Bordetella-Achromobacter. Slc11 synapomorphy localizes to the 3D areas that deviate least among MCb and MCg1 models (either IO or OO) implying that it constitutes a 3D network of residues articulating a Me2+-selective carrier conformation switch which is maintained in fast-evolving clades at the cost of divergent epistatic interactions impacting carrier shape and dynamics.

The impact of AI-based modeling on the accuracy of protein assembly prediction: Insights from CASP15.

In CASP15, 87 predictors submitted around 11 000 models on 41 assembly targets. The community demonstrated exceptional performance in overall fold and interface contact predictions, achieving an impressive success rate of 90% (compared to 31% in CASP14). This remarkable accomplishment is largely due to the incorporation of DeepMind's AF2-Multimer approach into custom-built prediction pipelines. To evaluate the added value of participating methods, we compared the community models to the baseline AF2-Multimer predictor. In over 1/3 of cases, the community models were superior to the baseline predictor. The main reasons for this improved performance were the use of custom-built multiple sequence alignments, optimized AF2-Multimer sampling, and the manual assembly of AF2-Multimer-built subcomplexes. The best three groups, in order, are Zheng, Venclovas, and Wallner. Zheng and Venclovas reached a 73.2% success rate over all (41) cases, while Wallner attained 69.4% success rate over 36 cases. Nonetheless, challenges remain in predicting structures with weak evolutionary signals, such as nanobody-antigen, antibody-antigen, and viral complexes. Expectedly, modeling large complexes also remains challenging due to their high memory compute demands. In addition to the assembly category, we assessed the accuracy of modeling interdomain interfaces in the tertiary structure prediction targets. Models on seven targets featuring 17 unique interfaces were analyzed. Best predictors achieved a 76.5% success rate, with the UM-TBM group being the leader. In the interdomain category, we observed that the predictors faced challenges, as in the case of the assembly category, when the evolutionary signal for a given domain pair was weak or the structure was large. Overall, CASP15 witnessed unprecedented improvement in interface modeling, reflecting the AI revolution seen in CASP14.

Tertiary structure assessment at CASP15.

The results of tertiary structure assessment at CASP15 are reported. For the first time, recognizing the outstanding performance of AlphaFold 2 (AF2) at CASP14, all single-chain predictions were assessed together, irrespective of whether a template was available. At CASP15, there was no single stand-out group, with most of the best-scoring groups-led by PEZYFoldings, UM-TBM, and Yang Server-employing AF2 in one way or another. Many top groups paid special attention to generating deep Multiple Sequence Alignments (MSAs) and testing variant MSAs, thereby allowing them to successfully address some of the hardest targets. Such difficult targets, as well as lacking templates, were typically proteins with few homologues. Local divergence between prediction and target correlated with localization at crystal lattice or chain interfaces, and with regions exhibiting high B-factor factors in crystal structure targets, and should not necessarily be considered as representing error in the prediction. However, analysis of exposed and buried side chain accuracy showed room for improvement even in the latter. Nevertheless, a majority of groups produced high-quality predictions for most targets, which are valuable for experimental structure determination, functional analysis, and many other tasks across biology. These include those applying methods similar to those used to generate major resources such as the AlphaFold Protein Structure Database and the ESM Metagenomic atlas: the confidence estimates of the former were also notably accurate.