The CASP16 experiment provided the first opportunity to benchmark AlphaFold3. In contrast to AlphaFold2, AlphaFold3 can predict the structure of non-protein molecules. According to the benchmark presented by the developers, it is expected to perform slightly better than AlphaFold2 for proteins. In this study, we assess the performance of AlphaFold3 using both automatic server submissions (AF3-server) and manual predictions from the Elofsson group (Elofsson). All predictions were generated via the AlphaFold3 web server, with manual interventions applied to large targets and ligands. Compared to AlphaFold2-based methods, we found that AlphaFold3 performs slightly better for protein complexes. However, when massive sampling is applied to AlphaFold2, the difference disappears. It was also noted that, according to the official ranking from CASP, the AF3-server performs better than AlphaFold2 for easier targets, but not for harder targets. Furthermore, the performance of the AF3-server is comparable to the best methods when considering the top-ranked predictions, but slightly behind when examining the best among the five submitted models. Here, there exist targets where AF3-server, the top-ranked method, is worse than lower-ranked models, indicating that a venue for progress could be to develop better strategies for identifying the best out of the generated models. When using AF3-server to predict the stoichiometry of larger protein complexes, the accuracy is limited, especially for heteromeric targets. When analyzing the predictions including nucleic acids, it was found that, in general, the accuracy is relatively low. However, the AF3-server performance was not far behind that of the top-ranked method. In summary, AF3-server offers a user-friendly tool that provides predictions comparable to state-of-the-art methods in all categories of CASP.

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.

Virus-host protein-protein interactions (PPIs) are fundamental to viral infections, yet high-resolution identification of their structural and molecular determinants within the native context of intact infected cells has remained an unsolved challenge. Here, we provide detailed insights into the structural interactome of herpes simplex virus 1-infected human cells by combining in-cell cross-linking mass spectrometry with the selective enrichment of newly synthesized viral proteins. In productively infected cells, we obtain 739 PPIs based on 6,194 cross-links found across intracellular compartments and at the intact host endomembrane system. These structural host-virus interactome profiling (SHVIP) data resolve PPIs to the protein domain level and augment AlphaFold-based structural modeling, facilitating detailed predictions of PPI sites within structured and intrinsically disordered regions. Importantly, SHVIP captures parts of the virus-host PPI space that are elusive to traditional interaction proteomics approaches. Validation by molecular genetics confirms that these new SHVIP identifications are genuine virus-host PPIs occurring in the complex environment of intact infected cells.

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.

Motivation: Today, the prediction of structures of large protein complexes solely from their sequence information requires prior knowledge of the stoichiometry of the complex. To address this challenge, we have enhanced the Monte Carlo Tree Search algorithms in MoLPC to enable the assembly of protein complexes while simultaneously predicting their stoichiometry.

Results: In MoLPC2, we have improved the predictions by allowing sampling alternative AlphaFold predictions. Using MoLPC2, we accurately predicted the structures of 50 out of 175 nonredundant protein complexes (TM-score ≥ 0.8) without knowing the stoichiometry. MoLPC2 provides new opportunities for predicting protein complex structures without stoichiometry information.

Availability and implementation: MoLPC2 is freely available at https://github.com/hychim/molpc2. A notebook is also available from the repository for easy use.