Protein assemblies in the Arabidopsis thaliana chloroplast compartment.

Introduction: Equipped with a photosynthetic apparatus that uses the energy of solar radiation to fuel biosynthesis of organic compounds, chloroplasts are the metabolic factories of mature leaf cells. The first steps of energy conversion are catalyzed by a collection of protein complexes, which can dynamically interact with each other for optimizing metabolic efficiency under changing environmental conditions. Materials and methods: For a deeper insight into the organization of protein assemblies and their roles in chloroplast adaption to changing environmental conditions, an improved complexome profiling protocol employing a MS-cleavable cross-linker is used to stabilize labile protein assemblies during the organelle isolation procedure. Results and discussion: Changes in protein:protein interaction patterns of chloroplast proteins in response to four different light intensities are reported. High molecular mass assemblies of central chloroplast electron transfer chain components as well as the PSII repair machinery react to different light intensities. In addition, the chloroplast encoded RNA-polymerase complex was found to migrate at a molecular mass of ~8 MDa, well above its previously reported molecular mass. Complexome profiling data produced during the course of this study can be interrogated by interested readers via a web-based online resource (https://complexomemap.de/projectsinteraction-chloroplasts).

VIP1 and its close homologs confer mechanical stress tolerance in Arabidopsis leaves.

VIP1, an Arabidopsis thaliana basic leucine zipper transcription factor, and its close homologs are imported from the cytoplasm to the nucleus when cells are exposed to mechanical stress. They bind to AGCTG (G/T) and regulate mechanical stress responses in roots. However, their role in leaves is unclear. To clarify this, mutant lines (QM1 and QM2) that lack the functions of VIP1 and its close homologs (bZIP29, bZIP30 and PosF21) were generated. Brushing more severely damaged QM1 and QM2 leaves than wild-type leaves. Genes regulating stress responses and cell wall properties were downregulated in brushed QM2 leaves and upregulated in brushed VIP1-GFP-overexpressing (VIP1-GFPox) leaves compared to wild-type leaves in a transcriptome analysis. The VIP1-binding sequence AGCTG (G/T) was enriched in the promoters of genes downregulated in brushed QM2 leaves compared to wild-type leaves and in those upregulated in brushed VIP1-GFPox leaves. Calmodulin-binding transcription activators (CAMTAs) are known regulators of mechanical stress responses, and the CAMTA-binding sequence CGCGT was enriched in the promoters of genes upregulated in the brushed QM2 leaves and in those downregulated in the brushed VIP1-GFPox leaves. These findings suggest that VIP1 and its homologs upregulate genes via AGCTG (G/T) and influence CAMTA-dependent gene expression to enhance mechanical stress tolerance in leaves.

Discovering new mode-of-action pesticide leads inhibiting protein-protein interactions: example targeting plant O-acetylserine sulfhydrylase.

BACKGROUND: The widespread evolution of pesticide resistance poses a significant challenge to current agriculture, necessitating the discovery of molecules with new modes of action. Despite extensive efforts, no major molecules with new modes of action have been commercialized for decades. Most pesticides function by binding to specific pockets on target enzymes, enabling a single target site mutation to confer resistance. An alternative approach is the disruption of protein-protein interactions (PPI), which require complementary mutations on both interacting partners for resistance to occur. Thus, our aim is the discovery and design of small-molecule inhibitors that target the interface of the PPI complex of O-acetylserine sulfhydrylase (OASS) and serine acetyltransferase (SAT), key obligatory interacting plant enzymes involved in the biosynthesis of the amino acid cysteine. RESULTS: By employing in silico filtering techniques on a virtual library of 30 million small molecules, we identified initial hits capable of binding OASS and interfering with its interaction with a peptide derived from SAT with a half-maximal inhibitory concentration (IC50) of 34 µm. Subsequently, we conducted molecular chemical optimizations, generating an early lead molecule (PJ4) with an IC50 value of 4 µm. PJ4 successfully inhibited the germination of Arabidopsis thaliana seedlings and inhibited clover growth in a pre-emergence application at an effective concentration of 4.6 kg ha-1. CONCLUSION: These new compounds described herein can serve as promising leads for further optimization as herbicides with a new mode-of-action. This technology can be used for discovering new modes of action chemicals inhibiting all pest groups. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Navigating the human-monkeypox virus interactome: HuPoxNET atlas reveals functional insights.

Monkeypox virus, a close relative of variola virus, has significantly increased the incidence of monkeypox disease in humans, with several clinical symptoms. The sporadic spread of the disease outbreaks has resulted in the need for a comprehensive understanding of the molecular mechanisms underlying disease infection and potential therapeutic targets. Protein-protein interactions play a crucial role in various cellular processes and regulate different immune signals during virus infection. Computational algorithms have gained high significance in the prediction of potential protein interaction pairs. Here, we developed a comprehensive database called HuPoxNET (https://kaabil.net/hupoxnet/) using the state-of-the-art MERN stack technology. The database leverages two sequence-based computational models to predict strain-specific protein-protein interactions between human and monkeypox virus proteins. Furthermore, various protein annotations of the human and viral proteins such as gene ontology, KEGG pathways, subcellular localization, protein domains, and novel drug targets identified from our study are also available on the database. HuPoxNET is a user-friendly platform for the scientific community to gain more insights into the monkeypox disease infection and aid in the development of therapeutic drugs against the disease.

Adaptation of turnip mosaic virus to Arabidopsis thaliana involves rewiring of VPg-host proteome interactions.

The outcome of a viral infection depends on a complex interplay between the host physiology and the virus, mediated through numerous protein-protein interactions. In a previous study, we used high-throughput yeast two-hybrid (HT-Y2H) to identify proteins in Arabidopsis thaliana that bind to the proteins encoded by the turnip mosaic virus (TuMV) genome. Furthermore, after experimental evolution of TuMV lineages in plants with mutations in defense-related or proviral genes, most mutations observed in the evolved viruses affected the VPg cistron. Among these mutations, D113G was a convergent mutation selected in many lineages across different plant genotypes, including cpr5-2 with constitutive expression of systemic acquired resistance. In contrast, mutation R118H specifically emerged in the jin1 mutant with affected jasmonate signaling. Using the HT-Y2H system, we analyzed the impact of these two mutations on VPg's interaction with plant proteins. Interestingly, both mutations severely compromised the interaction of VPg with the translation initiation factor eIF(iso)4E, a crucial interactor for potyvirus infection. Moreover, mutation D113G, but not R118H, adversely affected the interaction with RHD1, a zinc-finger homeodomain transcription factor involved in regulating DNA demethylation. Our results suggest that RHD1 enhances plant tolerance to TuMV infection. We also discuss our findings in a broad virus evolution context.

INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.

An overwhelming majority of protein-protein interaction (PPI) studies are conducted in a select few model organisms largely due to constraints in time and cost of the associated 'wet lab' experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method that incorporates orthology data using a new 'quintuplet' neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intraspecies and cross-species tasks using strict evaluation datasets. We show that INTREPPPID's orthologous locality loss increases performance because of the biological relevance of the orthologue data and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community.

Establishment of an RNA-based transient expression system in the green alga Chlamydomonas reinhardtii.

Chlamydomonas reinhardtii, a unicellular green alga, is a prominent model for green biotechnology and for studying organelles' function and biogenesis, such as chloroplasts and cilia. However, the stable expression of foreign genes from the nuclear genome in C. reinhardtii faces several limitations, including low expression levels and significant differences between clones due to genome position effects, epigenetic silencing, and time-consuming procedures. We developed a robust transient expression system in C. reinhardtii to overcome these limitations. We demonstrated efficient entry of in vitro-transcribed mRNA into wall-less cells and enzymatically dewalled wild-type cells via electroporation. The endogenous or exogenous elements can facilitate efficient transient expression of mRNA in C. reinhardtii, including the 5' UTR of PsaD and the well-characterized Kozak sequence derived from the Chromochloris zofingiensis. In the optimized system, mRNA expression was detectable in 120 h with a peak around 4 h after transformation. Fluorescently tagged proteins were successfully transiently expressed, enabling organelle labeling and real-time determination of protein sub-cellular localization. Remarkably, transiently expressed IFT46 compensated for the ift46-1 mutant phenotype, indicating the correct protein folding and function of IFT46 within the cells. Additionally, we demonstrated the feasibility of our system for studying protein-protein interactions in living cells using bimolecular fluorescence complementation. In summary, the established transient expression system provides a powerful tool for investigating protein localization, function, and interactions in C. reinhardtii within a relatively short timeframe, which will significantly facilitate the study of gene function, genome structure, and green biomanufacturing in C. reinhardtii and potentially in other algae.

Malivhu: A Comprehensive Bioinformatics Resource for Filtering SARS and MERS Virus Proteins by Their Classification, Family and Species, and Prediction of Their Interactions Against Human Proteins.

COVID 19 pandemic is still ongoing, having taken more than 6 million human lives with it, and it seems that the world will have to learn how to live with the virus around. In consequence, there is a need to develop different treatments against it, not only with vaccines, but also new medicines. To do this, human-virus protein-protein interactions (PPIs) play a key part in drug-target discovery, but finding them experimentally can be either costly or sometimes unreliable. Therefore, computational methods arose as a powerful alternative to predict these interactions, reducing costs and helping researchers confirm only certain interactions instead of trying all possible combinations in the laboratory. Malivhu is a tool that predicts human-virus PPIs through a 4-phase process using machine learning models, where phase 1 filters ssRNA(+) class virus proteins, phase 2 filters Coronaviridae family proteins and phase 3 filters severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) species proteins, and phase 4 predicts human-SARS-CoV/SARS-CoV-2/MERS protein-protein interactions. The performance of the models was measured with Matthews correlation coefficient, F1-score, specificity, sensitivity, and accuracy scores, getting accuracies of 99.07%, 99.83%, and 100% for the first 3 phases, respectively, and 94.24% for human-SARS-CoV PPI, 94.50% for human-SARS-CoV-2 PPI, and 95.45% for human-MERS PPI on independent testing. All the prediction models developed for each of the 4 phases were implemented as web server which is freely available at https://kaabil.net/malivhu/.

A Comparative Analysis of SARS-CoV-2 Variants of Concern (VOC) Spike Proteins Interacting with hACE2 Enzyme.

In late 2019, the emergence of a novel coronavirus led to its identification as SARS-CoV-2, precipitating the onset of the COVID-19 pandemic. Many experimental and computational studies were performed on SARS-CoV-2 to understand its behavior and patterns. In this research, Molecular Dynamic (MD) simulation is utilized to compare the behaviors of SARS-CoV-2 and its Variants of Concern (VOC)-Alpha, Beta, Gamma, Delta, and Omicron-with the hACE2 protein. Protein structures from the Protein Data Bank (PDB) were aligned and trimmed for consistency using Chimera, focusing on the receptor-binding domain (RBD) responsible for ACE2 interaction. MD simulations were performed using Visual Molecular Dynamics (VMD) and Nanoscale Molecular Dynamics (NAMD2), and salt bridges and hydrogen bond data were extracted from the results of these simulations. The data extracted from the last 5 ns of the 10 ns simulations were visualized, providing insights into the comparative stability of each variant's interaction with ACE2. Moreover, electrostatics and hydrophobic protein surfaces were calculated, visualized, and analyzed. Our comprehensive computational results are helpful for drug discovery and future vaccine designs as they provide information regarding the vital amino acids in protein-protein interactions (PPIs). Our analysis reveals that the Original and Omicron variants are the two most structurally similar proteins. The Gamma variant forms the strongest interaction with hACE2 through hydrogen bonds, while Alpha and Delta form the most stable salt bridges; the Omicron is dominated by positive potential in the binding site, which makes it easy to attract the hACE2 receptor; meanwhile, the Original, Beta, Delta, and Omicron variants show varying levels of interaction stability through both hydrogen bonds and salt bridges, indicating that targeted therapeutic agents can disrupt these critical interactions to prevent SARS-CoV-2 infection.

Exploring Intrinsic Disorder in Human Synucleins and Associated Proteins.

In this work, we explored the intrinsic disorder status of the three members of the synuclein family of proteins-α-, ß-, and γ-synucleins-and showed that although all three human synucleins are highly disordered, the highest levels of disorder are observed in γ-synuclein. Our analysis of the peculiarities of the amino acid sequences and modeled 3D structures of the human synuclein family members revealed that the pathological mutations A30P, E46K, H50Q, A53T, and A53E associated with the early onset of Parkinson's disease caused some increase in the local disorder propensity of human α-synuclein. A comparative sequence-based analysis of the synuclein proteins from various evolutionary distant species and evaluation of their levels of intrinsic disorder using a set of commonly used bioinformatics tools revealed that, irrespective of their origin, all members of the synuclein family analyzed in this study were predicted to be highly disordered proteins, indicating that their intrinsically disordered nature represents an evolutionary conserved and therefore functionally important feature. A detailed functional disorder analysis of the proteins in the interactomes of the human synuclein family members utilizing a set of commonly used disorder analysis tools showed that the human α-synuclein interactome has relatively higher levels of intrinsic disorder as compared with the interactomes of human ß- and γ- synucleins and revealed that, relative to the ß- and γ-synuclein interactomes, α-synuclein interactors are involved in a much broader spectrum of highly diversified functional pathways. Although proteins interacting with three human synucleins were characterized by highly diversified functionalities, this analysis also revealed that the interactors of three human synucleins were involved in three common functional pathways, such as the synaptic vesicle cycle, serotonergic synapse, and retrograde endocannabinoid signaling. Taken together, these observations highlight the critical importance of the intrinsic disorder of human synucleins and their interactors in various neuronal processes.

GRB2: A dynamic adaptor protein orchestrating cellular signaling in health and disease.

GRB2, or Growth Factor Receptor-Bound Protein 2, is a pivotal adaptor protein in intracellular signal transduction pathways, particularly within receptor tyrosine kinase (RTK) signaling cascades. Its crystal structure reveals a modular architecture comprising a single Src homology 2 (SH2) domain flanked by two Src homology 3 (SH3) domains, facilitating dynamic interactions critical for cellular signaling. While SH2 domains recognize phosphorylated tyrosines, SH3 domains bind proline-rich sequences, enabling GRB2 to engage with various downstream effectors. Folding and binding studies of GRB2 in its full-length form and isolated domains highlight a complex interplay between its protein-protein interaction domains on the folding energy landscape and in driving its function. Being at the crosslink of many key molecular pathways in the cell, GRB2 possesses a role in cancer pathogenesis, particularly in mediating the Ras-mitogen activated protein kinase (MAPK) pathway. Thus, pharmacological targeting of GRB2 domains is a promising field in cancer therapy, with efforts focused on disrupting protein-protein interactions. However, the dynamic interplay driving GRB2 function suggests the presence of allosteric sites at the interface between domains that could be targeted to modulate the binding properties of its constituent domains. We propose that the analysis of GRB2 proteins from other species may provide additional insights to make the allosteric pharmacological targeting of GRB2 a more feasible strategy.

Neutrophil-specific interactome of ARHGAP25 reveals novel partners and regulatory insights.

ARHGAP25, a crucial molecule in immunological processes, serves as a Rac-specific GTPase-activating protein. Its role in cell migration and phagocyte functions, affecting the outcome of complex immunological diseases such as rheumatoid arthritis, renders it a promising target for drug research. Despite its importance, our knowledge of its intracellular interactions is still limited. This study employed proteomic analysis of glutathione S-transferase (GST)-tag pulldowns and co-immunoprecipitation from neutrophilic granulocyte cell lysate, revealing 76 candidates for potential physical interactions that complement ARHGAP25's known profile. Notably, four small GTPases (RAC2, RHOG, ARF4, and RAB27A) exhibited high affinity for ARHGAP25. The ARHGAP25-RAC2 and ARHGAP25-RHOG interactions appeared to be affected by the activation state of the small GTPases, suggesting a GTP-GDP cycle-dependent interaction. In silico dimer prediction pinpointed ARHGAP25's GAP domain as a credible binding interface, suggesting its suitability for GTP hydrolysis. Additionally, a list of Fc receptor-related kinases, phosphatases, and three of the 14-3-3 members were identified as potential partners, with in silico predictions highlighting eight binding sites, presenting novel insight on a potential regulatory mechanism for ARHGAP25.

Targeting Grb2 SH3 Domains with Affimer Proteins Provides Novel Insights into Ras Signalling Modulation.

Src homology 3 (SH3) domains play a critical role in mediating protein-protein interactions (PPIs) involved in cell proliferation, migration, and the cytoskeleton. Despite their abundance in the human proteome, the functions and molecular interactions of many SH3 domains remain unknown, and this is in part due to the lack of SH3-domain-specific reagents available for their study. Affimer proteins have been developed as affinity reagents targeting a diverse range of targets, including those involved in PPIs. In this study, Affimer proteins were isolated against both the N- and C-terminal SH3 domains (NSH3 and CSH3) of growth-factor-receptor-bound protein 2 (Grb2), an adapter protein that provides a critical link between cell surface receptors and Ras signalling pathways. Targeting the CSH3 alone for the inhibition of PPIs appeared sufficient for curtailing Ras signalling in mammalian cell lines stimulated with human epidermal growth factor (EGF), which conflicts with the notion that the predominant interactions with Ras activating Son of sevenless (SOS) occur via the NSH3 domain. This result supports a model in which allosteric mechanisms involved in Grb2-SOS1 interaction modulate Ras activation.

Unveiling the double-edged sword: SOD1 trimers possess tissue-selective toxicity and bind septin-7 in motor neuron-like cells.

Misfolded species of superoxide dismutase 1 (SOD1) are associated with increased death in amyotrophic lateral sclerosis (ALS) models compared to insoluble protein aggregates. The mechanism by which structurally independent SOD1 trimers cause cellular toxicity is unknown but may drive disease pathology. Here, we uncovered the SOD1 trimer interactome-a map of potential tissue-selective protein-binding partners in the brain, spinal cord, and skeletal muscle. We identified binding partners and key pathways associated with SOD1 trimers and found that trimers may affect normal cellular functions such as dendritic spine morphogenesis and synaptic function in the central nervous system and cellular metabolism in skeletal muscle. We discovered SOD1 trimer-selective enrichment of genes. We performed detailed computational and biochemical characterization of SOD1 trimer protein binding for septin-7. Our investigation highlights key proteins and pathways within distinct tissues, revealing a plausible intersection of genetic and pathophysiological mechanisms in ALS through interactions involving SOD1 trimers.

A conserved role for AKT in the replication of emerging flaviviruses in vertebrates and vectors.

One third of all emerging infectious diseases are vector-borne, with no licensed antiviral therapies available against any vector-borne viruses. Zika virus and Usutu virus are two emerging flaviviruses transmitted primarily by mosquitoes. These viruses modulate different host pathways, including the PI3K/AKT/mTOR pathway. Here, we report the effect on ZIKV and USUV replication of two AKT inhibitors, Miransertib (ARQ-092, allosteric inhibitor) and Capivasertib (AZD5363, competitive inhibitor) in different mammalian and mosquito cell lines. Miransertib showed a stronger inhibitory effect against ZIKV and USUV than Capivasertib in mammalian cells, while Capivasertib showed a stronger effect in mosquito cells. These findings indicate that AKT plays a conserved role in flavivirus infection, in both the vertebrate host and invertebrate vector. Nevertheless, the specific function of AKT may vary depending on the host species. These findings indicate that AKT may be playing a conserved role in flavivirus infection in both, the vertebrate host and the invertebrate vector. However, the specific function of AKT may vary depending on the host species. A better understanding of virus-host interactions is therefore required to develop new treatments to prevent human disease and new approaches to control transmission by insect vectors.

Bebtelovimab-bound SARS-CoV-2 RBD mutants: resistance profiling and validation with escape mutations, clinical results, and viral genome sequences.

The dynamic evolution of SARS-CoV-2 variants necessitates ongoing advancements in therapeutic strategies. Despite the promise of monoclonal antibody (mAb) therapies like bebtelovimab, concerns persist regarding resistance mutations, particularly single-to-multipoint mutations in the receptor-binding domain (RBD). Our study addresses this by employing interface-guided computational protein design to predict potential bebtelovimab-resistance mutations. Through extensive physicochemical analysis, mutational preferences, precision-recall metrics, protein-protein docking, and energetic analyses, combined with all-atom, and coarse-grained molecular dynamics (MD) simulations, we elucidated the structural-dynamics-binding features of the bebtelovimab-RBD complexes. Identification of susceptible RBD residues under positive selection pressure, coupled with validation against bebtelovimab-escape mutations, clinically reported resistance mutations, and viral genomic sequences enhances the translational significance of our findings and contributes to a better understanding of the resistance mechanisms of SARS-CoV-2.

Heat shock protein (Hsp27)-ceramide synthase (Cers1) protein-protein interactions provide a new avenue for unexplored anti-cancer mechanism and therapy.

Hsp27 is a member of the small heat-shock proteins (sHSPs) - the known cellular line of defence against abnormal protein folding behaviors. Nevertheless, its upregulation is linked to a variety of pathological disorders, including several types of cancers. The ceramide synthases (CerS) mediate the synthesis of ceramide, a critical structural and signaling lipid. Functionally, downstream ceramide metabolites are implicated in the apoptosis process and their abnormal functionality has been linked to anticancer resistance. Studies showed that CerS1 are possibly inhibited by Hsp27 leading to biochemical anticancer effects in vitro. Nevertheless, the nature of such protein-protein interaction (PPI) has not been considerably investigated in molecular terms, hence, we present the first description of the dynamics CerS1-Hsp27 interaction landscapes using molecular dynamics simulations. Time-scale molecular dynamics simulation analysis indicated a system-wide conformational events of decreased stability, increased flexibility, reduced compactness, and decreased folding of CerS1. Analysis of binding energy showed a favorable interaction entailing 56 residues at the interface and a total stabilizing energy of -158 KJ/mol. The CerS1 catalytic domain experienced an opposite trend compared to the protein backbone. Yet, these residues adopted a highly compact conformation as per DCCM and DSSP analysis. Furthermore, conserved residues (SER 212, ASP 213, ALA 240, GLY 243, ASP 319) comprising the substrate shuttling machinery showed notable rigidity implying a restrained ceramide precursor access and assembly; hence, a possible inhibitory mechanism. Findings from this report would streamline a better molecular understanding of CerS1-Hsp27 interactions and decipher its potential avenue toward unexplored anti-cancer mechanisms and therapy.

Dissecting the structural and functional consequences of the evolutionary proline-glycine deletion in the wing 1 region of the forkhead domain of human FoxP1.

The human FoxP transcription factors dimerize via three-dimensional domain swapping, a unique feature among the human Fox family, as result of evolutionary sequence adaptations in the forkhead domain. This is the case for the conserved glycine and proline residues in the wing 1 region, which are absent in FoxP proteins but present in most of the Fox family. In this work, we engineered both glycine (G) and proline-glycine (PG) insertion mutants to evaluate the deletion events in FoxP proteins in their dimerization, stability, flexibility, and DNA-binding ability. We show that the PG insertion only increases protein stability, whereas the single glycine insertion decreases the association rate and protein stability and promotes affinity to the DNA ligand.

ProBASS - a language model with sequence and structural features for predicting the effect of mutations on binding affinity.

Protein-protein interactions (PPIs) govern virtually all cellular processes. Even a single mutation within PPI can significantly influence overall protein functionality and potentially lead to various types of diseases. To date, numerous approaches have emerged for predicting the change in free energy of binding (ΔΔGbind) resulting from mutations, yet the majority of these methods lack precision. In recent years, protein language models (PLMs) have been developed and shown powerful predictive capabilities by leveraging both sequence and structural data from protein-protein complexes. Yet, PLMs have not been optimized specifically for predicting ΔΔGbind. We developed an approach to predict effects of mutations on PPI binding affinity based on two most advanced protein language models ESM2 and ESM-IF1 that incorporate PPI sequence and structural features, respectively. We used the two models to generate embeddings for each PPI mutant and subsequently fine-tuned our model by training on a large dataset of experimental ΔΔGbind values. Our model, ProBASS (Protein Binding Affinity from Structure and Sequence) achieved a correlation with experimental ΔΔGbind values of 0.83 ± 0.05 for single mutations and 0.69 ± 0.04 for double mutations when model training and testing was done on the same PDB. Moreover, ProBASS exhibited very high correlation (0.81 ± 0.02) between prediction and experiment when training and testing was performed on a dataset containing 2325 single mutations in 132 PPIs. ProBASS surpasses the state-of-the-art methods in correlation with experimental data and could be further trained as more experimental data becomes available. Our results demonstrate that the integration of extensive datasets containing ΔΔGbind values across multiple PPIs to refine the pre-trained PLMs represents a successful approach for achieving a precise and broadly applicable model for ΔΔGbind prediction, greatly facilitating future protein engineering and design studies.

Proximity Ligation Assay for the Analysis of Iron-Mediated Protein-Protein Interactions in the Nucleus.

Iron forms essential cofactors used by many nuclear enzymes involved in genome maintenance. However, unchaperoned nuclear iron may represent a threat to the surrounding genetic material as it promotes redox toxicity that may affect DNA integrity. Safely handling intracellular iron implies metal transfer and cofactor assembly processes based on protein-protein interactions. Identifying those interactions commonly occurs via high-throughput approaches using affinity purification or proximity labeling coupled with mass spectrometry analysis. However, these methods do not identify the subcellular location of the interactions. The one-on-one confirmation of proposed nuclear interactions is also challenging. Many approaches used to look at protein interactions are not tailored for looking at the nucleus because the methods used to solubilize nuclear content are harsh enough to disrupt those transient interactions. Here, we describe step-by-step the use of Proximity Ligation Assay (PLA) to analyze iron-mediated protein-protein interactions in the nucleus of cultured human cells. PLA allows the subcellular visualization of the interactions via the in situ detection of the two interacting proteins using fluorescence confocal microscopy. Briefly, cells are fixed, blocked, permeabilized, and incubated with primary antibodies directed to target proteins. Primary antibodies are recognized using PLA probes consisting of one PLUS and one MINUS oligonucleotide-labeled secondary antibody. If the two proteins are close enough (<40 nm), the PLA probes are ligated and used as the template for rolling circle amplification (RCA) with fluorescently labeled oligonucleotides that yield a signal detectable using fluorescence confocal microscopy. A fluorescently labeled membrane-specific stain (WGA) and the DNA-specific probe DAPI are used to identify cellular and nuclear boundaries, respectively. Confocal images are then analyzed using the CellProfiler software to confirm the abundance and localization of the studied protein-protein interactions.