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Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate‐based approach to systematically model novel protein assemblies. Here, we use a combination of in‐cell crosslinking mass spectrometry and co‐fractionation mass spectrometry (CoFrac‐MS) to identify protein–protein interactions in the model Gram‐positive bacterium Bacillus subtilis . We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the Subti Wiki database with AlphaFold‐Multimer and, after controlling for the false‐positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein–protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria. Synopsis An integrative approach using crosslinking mass spectrometry (MS), co‐fractionation MS and Alphafold‐Multimer discovers novel protein complexes and their topologies in the model gram‐positive bacterium Bacillus subtillis. Crosslinking mass spectrometry and co‐fractionation mass spectrometry identify protein interactions from intact cells. AlphaFold‐Multimer confidently predicts the structure of dimeric complexes which can be validated with crosslinks. The binding site of YneR on the E1 subunit of the pyruvate dehydrogenase complex identifies it as an inhibitor of pyruvate dehydrogenase activity, PdhI. The approach can assign structure, function, and interactors of uncharacterized proteins in whole cells without requiring genetic manipulation. Graphical Abstract An integrative approach using crosslinking mass spectrometry (MS), co‐fractionation MS, and AlphaFold‐Multimer discovers novel protein complexes and their topologies in the model Gram‐positive bacterium Bacillus subtillis .

Cytokinins (CK) are one of the most important classes of phytohormones that regulate a wide range of processes in plants. A CK receptor, a sensor hybrid histidine kinase, was discovered more than 20 years ago, but the structural basis for its signaling is still a challenge for plant biologists. To date, only two fragments of the CK receptor structure, the sensory module and the receiver domain, were experimentally resolved. Some other regions were built up by molecular modeling based on structures of proteins homologous to CK receptors. However, in the long term, these data have proven insufficient for solving the structure of the full-sized CK receptor. The functional unit of CK receptor is the receptor dimer. In this article, a molecular structure of the dimeric form of the full-length CK receptor based on AlphaFold Multimer and ColabFold modeling is presented for the first time. Structural changes of the receptor upon interacting with phosphotransfer protein are visualized. According to mathematical simulation and available data, both types of dimeric receptor complexes with hormones, either half- or fully liganded, appear to be active in triggering signals. In addition, the prospects of using this and similar models to address remaining fundamental problems of CK signaling were outlined.

Serine integrases are phage- (or mobile element-) encoded enzymes that catalyse site-specific recombination reactions between a short DNA sequence on the phage genome ( attP ) and a corresponding host genome sequence ( attB ), thereby integrating the phage DNA into the host genome. Each integrase has its unique pair of attP and attB sites, a feature that allows them to be used as orthogonal tools for genome modification applications. In the presence of a second protein, the Recombination Directionality Factor (RDF), integrase catalyses the reverse excisive reaction, generating new recombination sites, attR and attL . In addition to promoting attR x attL reaction, the RDF inhibits attP x attB recombination. This feature makes the directionality of integrase reactions programmable, allowing them to be useful for building synthetic biology devices. In this report, we describe the degree of orthogonality of both integrative and excisive reactions for three related integrases (ϕC31, ϕBT1, and TG1) and their RDFs. Among these, TG1 integrase is the most active, showing near complete recombination in both attP x attB and attR x attL reactions, and the most directional in the presence of its RDF. Our findings show that there is varying orthogonality among these three integrases – RDF pairs. ϕC31 integrase was the least selective, with all three RDFs activating it for attR x attL recombination. Similarly, ϕC31 RDF was the least effective among the three RDFs in promoting the excisive activities of the integrases, including its cognate ϕC31 integrase. ϕBT1 and TG1 RDFs were noticeably more effective than ϕC31 RDF at inhibiting attP x attB recombination by their respective integrases, making them more suitable for building reversible genetic switches. AlphaFold-Multimer predicts very similar structural interactions between each cognate integrase – RDF pair. The binding surface on the RDF is much more conserved than the binding surface on the integrase, an indication that specificity is determined more by the integrase than the RDF. Overall, the observed weak integrase/RDF orthogonality across the three enzymes emphasizes the need for identifying and characterizing more integrase – RDF pairs. Additionally, the ability of a particular integrase’s preferred reaction direction to be controlled to varying degrees by non-cognate RDFs provides a path to tunable, non-binary genetic switches.

Experimental detection of residues critical for protein–protein interactions (PPI) is a time-consuming, costly, and labor-intensive process. Hence, high-throughput PPI-hot spot prediction methods have been developed, but they have been validated using relatively small datasets, which may compromise their predictive reliability. Here, we introduce PPI-hotspot ID , a novel method for identifying PPI-hot spots using the free protein structure, and validated it on the largest collection of experimentally confirmed PPI-hot spots to date. We explored the possibility of detecting PPI-hot spots using (i) FTMap in the PPI mode, which identifies hot spots on protein–protein interfaces from the free protein structure, and (ii) the interface residues predicted by AlphaFold-Multimer. PPI-hotspot ID yielded better performance than FTMap and SPOTONE, a webserver for predicting PPI-hot spots given the protein sequence. When combined with the AlphaFold-Multimer-predicted interface residues, PPI-hotspot ID yielded better performance than either method alone. Furthermore, we experimentally verified several PPI-hotspot ID -predicted PPI-hot spots of eukaryotic elongation factor 2. Notably, PPI-hotspot ID can reveal PPI-hot spots not obvious from complex structures, including those in indirect contact with binding partners. PPI-hotspot ID serves as a valuable tool for understanding PPI mechanisms and aiding drug design. It is available as a web server ( https://ppihotspotid.limlab.dnsalias.org/ ) and open-source code ( https://github.com/wrigjz/ppihotspotid/ ).

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons’ functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer’s disease (AD) and Parkinson’s disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington’s disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases’ advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins—a beneficial approach for drug designing.

Background The MDMX-p53 and MDM2-p53 interactions are critical regulators of the tumor-suppressive functions of p53, often disrupted in cancers through overexpression of murine double minute X (MDMX) and murine double minute 2 (MDM2). Methods In this study, we designed and computationally evaluated dual inhibitory peptides using RFdiffusion, ProteinMPNN, AlphaFold Multimer, and AlphaFold 3. Electrostatic complementarity, thermal stability, and binding affinity were assessed, followed by 300 ns molecular dynamics (MD) simulations. Results Mb2 and Mb4 (MDMX/2 binder 2 and 4) exhibited improved predicted binding affinity, enhanced electrostatic complementarity, and higher thermal stability relative to p53. Structural modeling and comparative validation confirmed reliable peptide–protein interactions. MD simulations further demonstrated stable trajectories, reduced conformational fluctuations, and persistent binding of Mb2 and Mb4 to both MDMX and MDM2. Conclusions These findings identify Mb2 and Mb4 as promising dual inhibitory peptides with potential for restoring p53 activity. This study provides a computational foundation for future experimental validation and therapeutic development.

Breakthrough achievements in protein structure prediction have occurred recently, mostly due to the advent of sophisticated machine learning methods and significant advancements in algorithmic approaches. The most recent version of the AlphaFold model, known as “AlphaFold-latest,” which expands the functionalities of the groundbreaking AlphaFold2, is the subject of this article. The goal of this novel model is to predict the three-dimensional structures of various biomolecules, such as ions, proteins, nucleic acids, small molecules, and non-standard residues. We demonstrate notable gains in precision, surpassing specialized tools across multiple domains, including protein–ligand interactions, protein–nucleic acid interactions, and antibody–antigen predictions. In conclusion, this AlphaFold framework has the ability to yield atomically-accurate structural predictions for a variety of biomolecular interactions, hence facilitating advancements in drug discovery.

Phytophthora infestans, a devastating oomycete pathogen responsible for late blight in solanaceous crops, relies on cellulose synthase (CesA) complexes for cell wall biosynthesis and virulence. Unlike plant CesAs that form homomeric trimers, oomycete CesA complexes are hypothesized to assemble as heteromeric units, yet their structural organization remains poorly defined. Here, we employed AlphaFold-Multimer and molecular docking to resolve the structural assembly of the PiCesA1–PiCesA2–PiCesA4 heterotrimer in P. infestans and identify potential ligand-binding sites for targeted inhibition. Structural modeling revealed a conserved transmembrane architecture combined with a distinctive cytosolic organization, in which N-terminal pleckstrin homology domains play a central role in heteromeric assembly. AlphaFold-Multimer consistently predicted a stable heterotrimer stabilized by cyclic interactions between pleckstrin homology domains and glycosyltransferase-A domains, forming an extensive interface network that is spatially segregated from the conserved UDP-glucose–binding catalytic core. Structure-guided docking identified potential ligands targeting pleckstrin homology–glycosyltransferase interface regions. Notably, these sites are absent or structurally divergent in plant cellulose synthases, underscoring their potential for pathogen-selective targeting. This work advances mechanistic understanding of cellulose biosynthesis in filamentous pathogens and proposes new avenues for selective disease control in agriculture.