The Critical Assessment of Structure Prediction (CASP) brings together a diverse group of scientists, from deep learning experts to NMR specialists, all aimed at developing accurate prediction algorithms that can effectively characterize the structural aspects of biomolecules relevant to their functions. Engagement within the CASP community has traditionally been limited to the prediction season and the conference, with limited discourse in the 1.5 years between CASP seasons. CASP special interest groups (SIGs) were established in 2023 to encourage continuous dialogue within the community. The online seminar series has drawn global participation from across disciplines and career stages. This has facilitated cross-disciplinary discussions fostering collaborations. The archives of these seminars have become a vital learning tool for newcomers to the field, lowering the barrier to entry.

Disordered regions are an important functional feature of many multidomain proteins. A prime example is proteins in membraneless organelles, which contain folded domains that engage in specific interactions and disordered low-complexity (LC) domains that mediate liquid-liquid phase separation. Studying these complex architectures remains challenging due to their conformational variability. Native mass spectrometry (nMS) is routinely employed to analyze conformations and interactions of folded or disordered proteins; however, its ability to analyze proteins with disordered LC domains has not been investigated. Here, we analyze the ionization and conformational states of designed model proteins that recapitulate key features of proteins found in membraneless organelles. Our results show that charge state distributions (CSDs) in nMS reflect partial disorder regardless of the protein sequence, providing insights into their conformational plasticity and interactions. By applying the same CSD analysis to a spider silk protein fragment, we find that interactions between folded domains that trigger silk assembly simultaneously induce conformational changes in the LC domains. Lastly, using intact nucleosomes, we demonstrate that CSDs are a good predictor for the disorder content of complex native assemblies. We conclude that nMS reliably informs about the conformational landscape of proteins with LC domains, which is crucial for understanding protein condensates in cellular environments.

Cross-linking mass spectrometry (XL-MS) allows characterizing protein-protein interactions (PPIs) in native biological systems by capturing cross-links between different proteins (inter-links). However, inter-link identification remains challenging, requiring dedicated data filtering schemes and thorough error control. Here, we benchmark existing data filtering schemes combined with error rate estimation strategies utilizing concatenated target-decoy protein sequence databases. These workflows show shortcomings either in sensitivity (many false negatives) or specificity (many false positives). To ameliorate the limited sensitivity without compromising specificity, we develop an alternative target-decoy search strategy using fused target-decoy databases. Furthermore, we devise a different data filtering scheme that takes the inter-link context of the XL-MS dataset into account. Combining both approaches maintains low error rates and minimizes false negatives, as we show by mathematical simulations, analysis of experimental ground-truth data, and application to various biological datasets. In human cells, inter-link identifications increase by 75% and we confirm their structural accuracy through proteome-wide comparisons to AlphaFold2-derived models. Taken together, target-decoy fusion and context-sensitive data filtering deepen and fine-tune XL-MS-based interactomics.

In this issue of Molecular Cell, Schmid and Walter present “Predictomes,”1 a machine-learning-based platform that utilizes AlphaFold-Multimer (AF-M) to identify high-confidence protein-protein interactions (PPIs). Their SPOC classifier is better than existing methods at separating true and false interactions.

A crucial event in sexual reproduction is when haploid sperm and egg fuse to form a new diploid organism at fertilization. In mammals, direct interaction between egg JUNO and sperm IZUMO1 mediates gamete membrane adhesion, yet their role in fusion remains enigmatic. We used AlphaFold to predict the structure of other extracellular proteins essential for fertilization to determine if they could form a complex that may mediate fusion. We first identified TMEM81, whose gene is expressed by mouse and human spermatids, as a protein having structural homologies with both IZUMO1 and another sperm molecule essential for gamete fusion, SPACA6. Using a set of proteins known to be important for fertilization and TMEM81, we then systematically searched for predicted binary interactions using an unguided approach and identified a pentameric complex involving sperm IZUMO1, SPACA6, TMEM81 and egg JUNO, CD9. This complex is structurally consistent with both the expected topology on opposing gamete membranes and the location of predicted N-glycans not modeled by AlphaFold-Multimer, suggesting that its components could organize into a synapse-like assembly at the point of fusion. Finally, the structural modeling approach described here could be more generally useful to gain insights into transient protein complexes difficult to detect experimentally.

Motivation

Understanding metal–protein interaction can provide structural and functional insights into cellular processes. As the number of protein sequences increases, developing fast yet precise computational approaches to predict and annotate metal-binding sites becomes imperative. Quick and resource-efficient pre-trained protein language model (pLM) embeddings have successfully predicted binding sites from protein sequences despite not using structural or evolutionary features (multiple sequence alignments). Using residue-level embeddings from the pLMs, we have developed a sequence-based method (M-Ionic) to identify metal-binding proteins and predict residues involved in metal binding.

Results

On independent validation of recent proteins, M-Ionic reports an area under the curve (AUROC) of 0.83 (recall = 84.6%) in distinguishing metal binding from non-binding proteins compared to AUROC of 0.74 (recall = 61.8%) of the next best method. In addition to comparable performance to the state-of-the-art method for identifying metal-binding residues (Ca²⁺, Mg²⁺, Mn²⁺, Zn²⁺), M-Ionic provides binding probabilities for six additional ions (i.e. Cu²⁺, Po4³⁻⁠₄, So²⁻₄⁠, Fe²⁺, Fe³⁺, Co²⁺). We show that the pLM embedding of a single residue contains sufficient information about its neighbours to predict its binding properties.

How the self-assembly of partially disordered proteins generates functional compartments in the cytoplasm and particularly in the nucleus is poorly understood. Nucleophosmin 1 (NPM1) is an abundant nucleolar protein that forms large oligomers and undergoes liquid-liquid phase separation by binding RNA or ribosomal proteins. It provides the scaffold for ribosome assembly but also prevents protein aggregation as part of the cellular stress response. Here, we use aggregation assays and native mass spectrometry (MS) to examine the relationship between the self-assembly and chaperone activity of NPM1. We find that oligomerization of full-length NPM1 modulates its ability to retard amyloid formation in vitro. Machine learning-based structure prediction and cryo-electron microscopy reveal fuzzy interactions between the acidic disordered region and the C-terminal nucleotide-binding domain, which cross-link NPM1 pentamers into partially disordered oligomers. The addition of basic peptides results in a tighter association within the oligomers, reducing their capacity to prevent amyloid formation. Together, our findings show that NPM1 uses a grappling hook mechanism to form a network-like structure that traps aggregation-prone proteins. Nucleolar proteins and RNAs simultaneously modulate the association strength and chaperone activity, suggesting a mechanism by which nucleolar composition regulates the chaperone activity of NPM1.

Tandem Repeat Proteins (TRPs) are a class of proteins with repetitive amino acid sequences that have been studied extensively for over two decades. Different features at the level of sequence, structure, function and evolution have been attributed to them by various authors. And yet many of its salient features appear only when looking at specific subclasses of protein tandem repeats. Here, we attempt to rationalize the existing knowledge on Tandem Repeat Proteins (TRPs) by pointing out several dichotomies. The emerging picture is more nuanced than generally assumed and allows us to draw some boundaries of what is not a proper TRP. We conclude with an operational definition of a specific subset, which we have denominated STRPs (Structural Tandem Repeat Proteins), which separates a subclass of tandem repeats with distinctive features from several other less well-defined types of repeats. We believe that this definition will help researchers in the field to better characterize the biological meaning of this large yet largely understudied group of proteins.

Motivation: Protein–protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and disease states. Although numerous methods have been proposed to predict PPIs, it is still challenging for interaction prediction between unknown proteins. In this study, a novel neural network named AFTGAN was constructed to predict multi-type PPIs. Regarding feature input, ESM-1b embedding containing much biological information for proteins was added as a protein sequence feature besides amino acid co-occurrence similarity and one-hot coding. An ensemble network was also constructed based on a transformer encoder containing an AFT module (performing the weight operation on vital protein sequence feature information) and graph attention network (extracting the relational features of protein pairs) for the part of the network framework.

Results: The experimental results showed that the Micro-F1 of the AFTGAN based on three partitioning schemes (BFS, DFS and the random mode) on the SHS27K and SHS148K datasets was 0.685, 0.711 and 0.867, as well as 0.745, 0.819 and 0.920, respectively, all higher than that of other popular methods. In addition, the experimental comparisons confirmed the performance superiority of the proposed model for predicting PPIs of unknown proteins on the STRING dataset.

Availability and implementation: The source code is publicly available at https://github.com/1075793472/AFTGAN.

Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Despite near-experimental accuracy on single-chain predictions, there is still scope for improvement among multimeric predictions. Methods like AlphaFold-Multimer and FoldDock can accurately model dimers. However, how well these methods fare on larger complexes is still unclear. Further, evaluation methods of the quality of multimeric complexes are not well established.

Results: We analysed the performance of AlphaFold-Multimer on a homology-reduced dataset of homo- and heteromeric protein complexes. We highlight the differences between the pairwise and multi-interface evaluation of chains within a multimer. We describe why certain complexes perform well on one metric (e.g. TM-score) but poorly on another (e.g. DockQ). We propose a new score, Predicted DockQ version 2 (pDockQ2), to estimate the quality of each interface in a multimer. Finally, we modelled protein complexes (from CORUM) and identified two highly confident structures that do not have sequence homology to any existing structures.

Availability and implementation: All scripts, models, and data used to perform the analysis in this study are freely available at https://gitlab.com/ElofssonLab/afm-benchmark.