MinD-RNase E interplay controls localization of polar mRNAs in E. coli.

The E. coli transcriptome at the cell's poles (polar transcriptome) is unique compared to the membrane and cytosol. Several factors have been suggested to mediate mRNA localization to the membrane, but the mechanism underlying polar localization of mRNAs remains unknown. Here, we combined a candidate system approach with proteomics to identify factors that mediate mRNAs localization to the cell poles. We identified the pole-to-pole oscillating protein MinD as an essential factor regulating polar mRNA localization, although it is not able to bind RNA directly. We demonstrate that RNase E, previously shown to interact with MinD, is required for proper localization of polar mRNAs. Using in silico modeling followed by experimental validation, the membrane-binding site in RNase E was found to mediate binding to MinD. Intriguingly, not only does MinD affect RNase E interaction with the membrane, but it also affects its mode of action and dynamics. Polar accumulation of RNase E in ΔminCDE cells resulted in destabilization and depletion of mRNAs from poles. Finally, we show that mislocalization of polar mRNAs may prevent polar localization of their protein products. Taken together, our findings show that the interplay between MinD and RNase E determines the composition of the polar transcriptome, thus assigning previously unknown roles for both proteins.

Systematic discovery of protein interaction interfaces using AlphaFold and experimental validation.

Structural resolution of protein interactions enables mechanistic and functional studies as well as interpretation of disease variants. However, structural data is still missing for most protein interactions because we lack computational and experimental tools at scale. This is particularly true for interactions mediated by short linear motifs occurring in disordered regions of proteins. We find that AlphaFold-Multimer predicts with high sensitivity but limited specificity structures of domain-motif interactions when using small protein fragments as input. Sensitivity decreased substantially when using long protein fragments or full length proteins. We delineated a protein fragmentation strategy particularly suited for the prediction of domain-motif interfaces and applied it to interactions between human proteins associated with neurodevelopmental disorders. This enabled the prediction of highly confident and likely disease-related novel interfaces, which we further experimentally corroborated for FBXO23-STX1B, STX1B-VAMP2, ESRRG-PSMC5, PEX3-PEX19, PEX3-PEX16, and SNRPB-GIGYF1 providing novel molecular insights for diverse biological processes. Our work highlights exciting perspectives, but also reveals clear limitations and the need for future developments to maximize the power of Alphafold-Multimer for interface predictions.

Structural study of UFL1-UFC1 interaction uncovers the role of UFL1 N-terminal helix in ufmylation.

Ufmylation plays a crucial role in various cellular processes including DNA damage response, protein translation, and ER homeostasis. To date, little is known about how the enzymes responsible for ufmylation coordinate their action. Here, we study the details of UFL1 (E3) activity, its binding to UFC1 (E2), and its relation to UBA5 (E1), using a combination of structural modeling, X-ray crystallography, NMR, and biochemical assays. Guided by Alphafold2 models, we generate an active UFL1 fusion construct that includes its partner DDRGK1 and solve the crystal structure of this critical interaction. This fusion construct also unveiled the importance of the UFL1 N-terminal helix for binding to UFC1. The binding site suggested by our UFL1-UFC1 model reveals a conserved interface, and competition between UFL1 and UBA5 for binding to UFC1. This competition changes in the favor of UFL1 following UFM1 charging of UFC1. Altogether, our study reveals a novel, terminal helix-mediated regulatory mechanism, which coordinates the cascade of E1-E2-E3-mediated transfer of UFM1 to its substrate and provides new leads to target this modification.

Matching protein surface structural patches for high-resolution blind peptide docking.

Peptide docking can be perceived as a subproblem of protein­protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled. Traditional peptide-centered approaches use information about peptide sequences to generate representative conformer ensembles, which can then be rigid-body docked to the receptor. Alternatively, one may look at this problem from the viewpoint of the receptor, namely, that the protein surface defines the peptide-bound conformation. Here, we present PatchMAN (Patch-Motif AligNments), a global peptide-docking approach that uses structural motifs to map the receptor surface with backbone scaffolds extracted from protein structures. On a nonredundant set of protein­peptide complexes, starting from free receptor structures, PatchMAN successfully models and identifies near-native peptide­protein complexes in 58%/84% within 2.5 Å/5 Å interface backbone RMSD, with corresponding sampling in 81%/100% of the cases, outperforming other approaches. PatchMAN leverages the observation that structural units of peptides with their binding pocket can be found not only within interfaces, but also within monomers. We show that the bound peptide conformation is sampled based on the structural context of the receptor only, without taking into account any sequence information. Beyond peptide docking, this approach opens exciting new avenues to study principles of peptide­protein association, and to the design of new peptide binders. PatchMAN is available as a server at https://furmanlab.cs.huji.ac.il/patchman/.

2B4: A potential target in Staphylococcus aureus associated allergic inflammation.

Staphylococcus aureus (SA) and its exotoxins activate eosinophils (Eos) and mast cells (MCs) via CD48, a GPI-anchored receptor belonging to the signaling lymphocytes activation molecules (SLAM) family. 2B4 (CD244), an immuno-regulatory transmembrane receptor also belonging to the SLAM family, is the high-affinity ligand for CD48. 2B4 is expressed on several leukocytes including NK cells, T cells, basophils, monocytes, dendritic cells (DCs), and Eos. In the Eos and MCs crosstalk carried out by physical and soluble interactions (named the 'allergic effector unit', AEU), 2B4-CD48 binding plays a central role. As CD48 and 2B4 share some structural characteristics and SA colonization accompanies most of the allergic diseases, we hypothesized that SA exotoxins (e.g. Staphylococcus enterotoxin B, SEB) can also bind and activate 2B4 and thereby possibly further aggravate inflammation. To check our hypothesis, we used in vitro, in silico, and in vivo methods. By enzyme-linked immunosorbent assay (ELISA), flow cytometry (FC), fluorescence microscopy, and microscale thermophoresis, we have shown that SEB can bind specifically to 2B4. By Eos short- and long-term activation assays, we confirmed the functionality of the SEB-2B4 interaction. Using computational modeling, we identified possible SEB-binding sites on human and mouse 2B4. Finally, in vivo, in an SEB-induced peritonitis model, 2B4-KO mice showed a significant reduction of inflammatory features compared with WT mice. Altogether, the results of this study confirm that 2B4 is an important receptor in SEB-mediated inflammation, and therefore a role is suggested for 2B4 in SA associated inflammatory conditions.

Unbiased phenotype and genotype matching maximizes gene discovery and diagnostic yield.

PURPOSE: Widespread application of next-generation sequencing, combined with data exchange platforms, has provided molecular diagnoses for countless families. To maximize diagnostic yield, we implemented an unbiased semi-automated genematching algorithm based on genotype and phenotype matching. METHODS: Rare homozygous variants identified in 2 or more affected individuals, but not in healthy individuals, were extracted from our local database of ∼12,000 exomes. Phenotype similarity scores (PSS), based on human phenotype ontology terms, were assigned to each pair of individuals matched at the genotype level using HPOsim. RESULTS: 33,792 genotype-matched pairs were discovered, representing variants in 7567 unique genes. There was an enrichment of PSS ≥0.1 among pathogenic/likely pathogenic variant-level pairs (94.3% in pathogenic/likely pathogenic variant-level matches vs 34.75% in all matches). We highlighted founder or region-specific variants as an internal positive control and proceeded to identify candidate disease genes. Variant-level matches were particularly helpful in cases involving inframe indels and splice region variants beyond the canonical splice sites, which may otherwise have been disregarded, allowing for detection of candidate disease genes, such as KAT2A, RPAIN, and LAMP3. CONCLUSION: Semi-automated genotype matching combined with PSS is a powerful tool to resolve variants of uncertain significance and to identify candidate disease genes.

Discriminating physiological from non-physiological interfaces in structures of protein complexes: A community-wide study.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94, respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines recalled the physiological dimers with significantly higher accuracy than the non-physiological set, lending support to the reliability of our benchmark dataset annotations. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

Protein language models can capture protein quaternary state.

BACKGROUND: Determining a protein's quaternary state, i.e. the number of monomers in a functional unit, is a critical step in protein characterization. Many proteins form multimers for their activity, and over 50% are estimated to naturally form homomultimers. Experimental quaternary state determination can be challenging and require extensive work. To complement these efforts, a number of computational tools have been developed for quaternary state prediction, often utilizing experimentally validated structural information. Recently, dramatic advances have been made in the field of deep learning for predicting protein structure and other characteristics. Protein language models, such as ESM-2, that apply computational natural-language models to proteins successfully capture secondary structure, protein cell localization and other characteristics, from a single sequence. Here we hypothesize that information about the protein quaternary state may be contained within protein sequences as well, allowing us to benefit from these novel approaches in the context of quaternary state prediction. RESULTS: We generated ESM-2 embeddings for a large dataset of proteins with quaternary state labels from the curated QSbio dataset. We trained a model for quaternary state classification and assessed it on a non-overlapping set of distinct folds (ECOD family level). Our model, named QUEEN (QUaternary state prediction using dEEp learNing), performs worse than approaches that include information from solved crystal structures. However, it successfully learned to distinguish multimers from monomers, and predicts the specific quaternary state with moderate success, better than simple sequence similarity-based annotation transfer. Our results demonstrate that complex, quaternary state related information is included in such embeddings. CONCLUSIONS: QUEEN is the first to investigate the power of embeddings for the prediction of the quaternary state of proteins. As such, it lays out strengths as well as limitations of a sequence-based protein language model approach, compared to structure-based approaches. Since it does not require any structural information and is fast, we anticipate that it will be of wide use both for in-depth investigation of specific systems, as well as for studies of large sets of protein sequences. A simple colab implementation is available at: https://colab. RESEARCH: google.com/github/Furman-Lab/QUEEN/blob/main/QUEEN_prediction_notebook.ipynb .

Elucidation of Short Linear Motif-Based Interactions of the FERM Domains of Ezrin, Radixin, Moesin, and Merlin.

The ERM (ezrin, radixin, and moesin) family of proteins and the related protein merlin participate in scaffolding and signaling events at the cell cortex. The proteins share an N-terminal FERM [band four-point-one (4.1) ERM] domain composed of three subdomains (F1, F2, and F3) with binding sites for short linear peptide motifs. By screening the FERM domains of the ERMs and merlin against a phage library that displays peptides representing the intrinsically disordered regions of the human proteome, we identified a large number of novel ligands. We determined the affinities for the ERM and merlin FERM domains interacting with 18 peptides and validated interactions with full-length proteins through pull-down experiments. The majority of the peptides contained an apparent Yx[FILV] motif; others show alternative motifs. We defined distinct binding sites for two types of similar but distinct binding motifs (YxV and FYDF) using a combination of Rosetta FlexPepDock computational peptide docking protocols and mutational analysis. We provide a detailed molecular understanding of how the two types of peptides with distinct motifs bind to different sites on the moesin FERM phosphotyrosine binding-like subdomain and uncover interdependencies between the different types of ligands. The study expands the motif-based interactomes of the ERMs and merlin and suggests that the FERM domain acts as a switchable interaction hub.

Structural insights into the role of the WW2 domain on tandem WW-PPxY motif interactions of oxidoreductase WWOX.

Class I WW domains are present in many proteins of various functions and mediate protein interactions by binding to short linear PPxY motifs. Tandem WW domains often bind peptides with multiple PPxY motifs, but the interplay of WW-peptide interactions is not always intuitive. The WW domain-containing oxidoreductase (WWOX) harbors two WW domains: an unstable WW1 capable of PPxY binding and stable WW2 that cannot bind PPxY. The WW2 domain has been suggested to act as a WW1 domain chaperone, but the underlying mechanism of its chaperone activity remains to be revealed. Here, we combined NMR, isothermal calorimetry, and structural modeling to elucidate the roles of both WW domains in WWOX binding to its PPxY-containing substrate ErbB4. Using NMR, we identified an interaction surface between these two domains that supports a WWOX conformation compatible with peptide substrate binding. Isothermal calorimetry and NMR measurements also indicated that while binding affinity to a single PPxY motif is marginally increased in the presence of WW2, affinity to a dual-motif peptide increases 10-fold. Furthermore, we found WW2 can directly bind double-motif peptides using its canonical binding site. Finally, differential binding of peptides in mutagenesis experiments was consistent with a parallel N- to C-terminal PPxY tandem motif orientation in binding to the WW1-WW2 tandem domain, validating structural models of the interaction. Taken together, our results reveal the complex nature of tandem WW-domain organization and substrate binding, highlighting the contribution of WWOX WW2 to both protein stability and target binding.

Who Binds Better? Let Alphafold2 Decide!

Deep learning is revolutionizing structural biology to an unprecedented extent. Spearheaded by DeepMind's Alphafold2, structural models of high quality can be generated, and are now available for most known proteins and many protein interactions. The next challenge will be to leverage this rich structural corpus to learn about binding: which protein can contact which partner(s), and at what affinity? In a recent study, Chang and Perez have presented an elegant approach towards this challenging goal for interactions that involve a short peptide binding to its receptor. The basic idea is straightforward: given a receptor that binds to two peptides, if the receptor sequence is presented with both peptides together at the same time, AlphaFold2 should model the tighter binding peptide into the binding site, while excluding the second. A simple idea that works!

Identification of autosomal recessive novel genes and retinal phenotypes in members of the solute carrier (SLC) superfamily.

PURPOSE: This study aimed to investigate the clinical and genetic aspects of solute carrier (SLC) genes in inherited retinal diseases (IRDs). METHODS: Exome sequencing data were filtered to identify pathogenic variants in SLC genes. Analysis of transcript and protein expression was performed on fibroblast cell lines and retinal sections. RESULTS: Comprehensive analysis of 433 SLC genes in 913 exome sequencing IRD samples revealed homozygous pathogenic variants in 6 SLC genes, including 2 candidate novel genes, which were 2 variants in SLC66A1, causing autosomal recessive retinitis pigmentosa (ARRP), and a variant in SLC39A12, causing autosomal recessive mild widespread retinal degeneration with marked macular involvement. In addition, we present 4 families with ARRP and homozygous null variants in SLC37A3 that were previously suggested to cause retinitis pigmentosa, 2 of which cause exon skipping. The recently reported SLC4A7- c.2007dup variant was found in 2 patients with ARRP resulting in the absence of protein. Finally, variants in SLC24A1 were found in 4 individuals with either ARRP or congenital stationary night blindness. CONCLUSION: We report on SLC66A1 and SLC39A12 as candidate novel IRD genes, establish SLC37A3 pathogenicity, and provide further evidence of SLC4A7 as IRD genes. We extend the phenotypic spectrum of SLC24A1 and suggest that its ARRP phenotype may be more common than previously reported.

A Versatile Synthetic Affinity Probe Reveals Inhibitory Synapse Ultrastructure and Brain Connectivity.

Visualization of inhibitory synapses requires protocol tailoring for different sample types and imaging techniques, and usually relies on genetic manipulation or the use of antibodies that underperform in tissue immunofluorescence. Starting from an endogenous ligand of gephyrin, a universal marker of the inhibitory synapse, we developed a short peptidic binder and dimerized it, significantly increasing affinity and selectivity. We further tailored fluorophores to the binder, yielding "Sylite"-a probe with outstanding signal-to-background ratio that outperforms antibodies in tissue staining with rapid and efficient penetration, mitigation of staining artifacts, and simplified handling. In super-resolution microscopy Sylite precisely localizes the inhibitory synapse and enables nanoscale measurements. Sylite profiles inhibitory inputs and synapse sizes of excitatory and inhibitory neurons in the midbrain and combined with complimentary tracing techniques reveals the synaptic connectivity.

Multi-system neurological disorder associated with a CRYAB variant.

We report a multiplex family with extended multisystem neurological phenotype associated with a CRYAB variant. Two affected siblings were evaluated with whole exome sequencing, muscle biopsy, laser microdissection, and mass spectrometry-based proteomic analysis. Both patients and their mother manifested a combination of early-onset cataracts, cardiomyopathy, cerebellar ataxia, optic atrophy, cognitive impairment, and myopathy. Whole exome sequencing identified a heterozygous c.458C>T variant mapped to the C-terminal extension domain of the Alpha-crystallin B chain, disrupting its function as a molecular chaperone and its ability to suppress protein aggregation. In accordance with the molecular findings, muscle biopsies revealed subsarcolemmal deposits that appeared dark with H&E and trichrome staining were negative for the other routine histochemical staining and for amyloid with the Congo-red stain. Electron microscopy demonstrated that the deposits were composed of numerous parallel fibrils. Laser microdissection and mass spectrometry-based proteomic analysis revealed that the inclusions are almost exclusively composed of crystallized chaperones/heat shock proteins. Moreover,  a structural model suggests that Ser153 could be involved in monomer stabilization, dimer association, and possible binding of partner proteins. We propose that our report potentially expands the complex phenotypic spectrum of alpha B-crystallinopathies with possible effect of a CRYAB variant on the central nervous system.

Better together: Elements of successful scientific software development in a distributed collaborative community.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Modeling beta-sheet peptide-protein interactions: Rosetta FlexPepDock in CAPRI rounds 38-45.

Peptide-protein docking is challenging due to the considerable conformational freedom of the peptide. CAPRI rounds 38-45 included two peptide-protein interactions, both characterized by a peptide forming an additional beta strand of a beta sheet in the receptor. Using the Rosetta FlexPepDock peptide docking protocol we generated top-performing, high-accuracy models for targets 134 and 135, involving an interaction between a peptide derived from L-MAG with DLC8. In addition, we were able to generate the only medium-accuracy models for a particularly challenging target, T121. In contrast to the classical peptide-mediated interaction, in which receptor side chains contact both peptide backbone and side chains, beta-sheet complementation involves a major contribution to binding by hydrogen bonds between main chain atoms. To establish how binding affinity and specificity are established in this special class of peptide-protein interactions, we extracted PeptiDBeta, a benchmark of solved structures of different protein domains that are bound by peptides via beta-sheet complementation, and tested our protocol for global peptide-docking PIPER-FlexPepDock on this dataset. We find that the beta-strand part of the peptide is sufficient to generate approximate and even high resolution models of many interactions, but inclusion of adjacent motif residues often provides additional information necessary to achieve high resolution model quality.

ClusPro in rounds 38 to 45 of CAPRI: Toward combining template-based methods with free docking.

Targets in the protein docking experiment CAPRI (Critical Assessment of Predicted Interactions) generally present new challenges and contribute to new developments in methodology. In rounds 38 to 45 of CAPRI, most targets could be effectively predicted using template-based methods. However, the server ClusPro required structures rather than sequences as input, and hence we had to generate and dock homology models. The available templates also provided distance restraints that were directly used as input to the server. We show here that such an approach has some advantages. Free docking with template-based restraints using ClusPro reproduced some interfaces suggested by weak or ambiguous templates while not reproducing others, resulting in correct server predicted models. More recently we developed the fully automated ClusPro TBM server that performs template-based modeling and thus can use sequences rather than structures of component proteins as input. The performance of the server, freely available for noncommercial use at https://tbm.cluspro.org, is demonstrated by predicting the protein-protein targets of rounds 38 to 45 of CAPRI.

Phosphorylation of the WWOX Protein Regulates Its Interaction with p73.

We describe a molecular characterization of the interaction between the cancer-related proteins WWOX and p73. This interaction is mediated by the first of two WW domains (WW1) of WWOX and a PPXY-motif-containing region in p73. While phosphorylation of Tyr33 of WWOX and association with p73 are known to affect apoptotic activity, the quantitative effect of phosphorylation on this specific interaction is determined here for the first time. Using ITC and fluorescence anisotropy, we measured the binding affinity between WWOX domains and a p73 derived peptide, and showed that this interaction is regulated by Tyr phosphorylation of WW1. Chemical synthesis of the phosphorylated domains of WWOX revealed that the binding affinity of WWOX to p73 is decreased when WWOX is phosphorylated. This result suggests a fine-tuning of binding affinity in a differential, ligand-specific manner: the decrease in binding affinity of WWOX to p73 can free both partners to form new interactions.

A unique PRDM13-associated variant in a Georgian Jewish family with probable North Carolina macular dystrophy and the possible contribution of a unique CFH variant.

Purpose: North Carolina macular dystrophy (NCMD) is an autosomal dominant maculopathy that is considered a non-progressive developmental disorder with variable expressivity. Our study aimed to clinically and genetically characterize macular dystrophy in a family (MOL1154) consisting of six affected subjects with a highly variable maculopathy phenotype in which no correlation between age and severity exists. Methods: Clinical characterization included visual acuity testing and electroretinography. Genetic analysis included Sanger sequencing and whole exome sequencing (WES). Results: WES analysis performed on DNA samples from two individuals revealed a heterozygous deletion of six nucleotides [c.2247_2252del; p.(Leu750_Lys751del)] in the CFH gene. Co-segregation analysis revealed that five of the six NCMD affected subjects carried this deletion, while one individual who had a relatively mild phenotype compatible with dry age-related macular degeneration (AMD) did not carry it. We subsequently analyzed the upstream region of PRDM13 that has previously been reported to be associated with NCMD and identified a unique heterozygous transversion (chr6:100040974A>C) located within the previously described suspected control region in all six affected individuals. This transversion is likely to cause NCMD. Conclusions: NCMD has a wide spectrum of clinical phenotypes that can overlap with AMD, making it challenging to correctly diagnose affected individuals and family members. The DNA sequence variant we found in the CFH gene of some of the affected family members may suggest some role as a modifier gene. However, this variant still does not explain the huge phenotypic variability of NCMD and needs to be studied in other and larger populations.