ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Robust prediction of relative binding energies for protein-protein complex mutations using free energy perturbation calculations.

Computational free energy-based methods have the potential to significantly improve throughput and decrease costs of protein design efforts. Such methods must reach a high level of reliability, accuracy, and automation to be effectively deployed in practical industrial settings in a way that impacts protein design projects. Here, we present a benchmark study for the calculation of relative changes in protein-protein binding affinity for single point mutations across a variety of systems from the literature, using free energy perturbation (FEP+) calculations. We describe a method for robust treatment of alternate protonation states for titratable amino acids, which yields improved correlation with and reduced error compared to experimental binding free energies. Following careful analysis of the largest outlier cases in our dataset, we assess limitations of the default FEP+ protocols and introduce an automated script which identifies probable outlier cases that may require additional scrutiny and calculates an empirical correction for a subset of charge-related outliers. Through a series of three additional case study systems, we discuss how protein FEP+ can be applied to real-world protein design projects, and suggest areas of further study.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Astrocyte morphogenesis requires self-recognition.

Self-recognition is a fundamental cellular process across evolution and forms the basis of neuronal self-avoidance1-4. Clustered protocadherins (Pcdh), comprising a large family of isoform-specific homophilic recognition molecules, play a pivotal role in neuronal self-avoidance required for mammalian brain development5-7. The probabilistic expression of different Pcdh isoforms confers unique identities upon neurons and forms the basis for neuronal processes to discriminate between self and non-self5,6,8. Whether this self-recognition mechanism exists in astrocytes, the other predominant cell type of the brain, remains unknown. Here, we report that a specific isoform in the Pcdhγ cluster, γC3, is highly enriched in human and murine astrocytes. Through genetic manipulation, we demonstrate that γC3 acts autonomously to regulate astrocyte morphogenesis in the mouse visual cortex. To determine if γC3 proteins act by promoting recognition between processes of the same astrocyte, we generated pairs of γC3 chimeric proteins capable of heterophilic binding to each other, but incapable of homophilic binding. Co-expressing complementary heterophilic binding isoform pairs in the same γC3 null astrocyte restored normal morphology. By contrast, chimeric γC3 proteins individually expressed in single γC3 null mutant astrocytes did not. These data establish that self-recognition is essential for astrocyte development in the mammalian brain and that, by contrast to neuronal self-recognition, a single Pcdh isoform is both necessary and sufficient for this process.

ZEPPI: proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence co-evolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. ZEPPI can be implemented on a proteome-wide scale as evidenced by calculations on millions of structural models of dimeric complexes in the E. coli and human interactomes found in the PrePPI database. A number of examples that illustrate how these tools can yield novel functional hypotheses are provided.

Push-pull mechanics of E-cadherin ectodomains in biomimetic adhesions.

E-cadherin plays a central role in cell-cell adhesion. The ectodomains of wild-type cadherins form a crystalline-like two-dimensional lattice in cell-cell interfaces mediated by both trans (apposed cell) and cis (same cell) interactions. In addition to these extracellular forces, adhesive strength is further regulated by cytosolic phenomena involving α and ß catenin-mediated interactions between cadherin and the actin cytoskeleton. Cell-cell adhesion can be further strengthened under tension through mechanisms that have not been definitively characterized in molecular detail. Here we quantitatively determine the role of the cadherin ectodomain in mechanosensing. To this end, we devise an E-cadherin-coated emulsion system, in which droplet surface tension is balanced by protein binding strength to give rise to stable areas of adhesion. To reach the honeycomb/cohesive limit, an initial emulsion compression by centrifugation facilitates E-cadherin trans binding, whereas a high protein surface concentration enables the cis-enhanced stabilization of the interface. We observe an abrupt concentration dependence on recruitment into adhesions of constant crystalline density, reminiscent of a first-order phase transition. Removing the lateral cis interaction with a "cis mutant" shifts this transition to higher surface densities leading to denser, yet weaker adhesions. In both proteins, the stabilization of progressively larger areas of deformation is consistent with single-molecule experiments that show a force-dependent lifetime enhancement in the cadherin ectodomain, which may be attributed to the "X-dimer" bond.

Free Energy Perturbation Calculations of Mutation Effects on SARS-CoV-2 RBD::ACE2 Binding Affinity.

The strength of binding between human angiotensin converting enzyme 2 (ACE2) and the receptor binding domain (RBD) of viral spike protein plays a role in the transmissibility of the SARS-CoV-2 virus. In this study we focus on a subset of RBD mutations that have been frequently observed in infected individuals and probe binding affinity changes to ACE2 using surface plasmon resonance (SPR) measurements and free energy perturbation (FEP) calculations. Our SPR results are largely in accord with previous studies but discrepancies do arise due to differences in experimental methods and to protocol differences even when a single method is used. Overall, we find that FEP performance is superior to that of other computational approaches examined as determined by agreement with experiment and, in particular, by its ability to identify stabilizing mutations. Moreover, the calculations successfully predict the observed cooperative stabilization of binding by the Q498R N501Y double mutant present in Omicron variants and offer a physical explanation for the underlying mechanism. Overall, our results suggest that despite the significant computational cost, FEP calculations may offer an effective strategy to understand the effects of interfacial mutations on protein-protein binding affinities and, hence, in a variety of practical applications such as the optimization of neutralizing antibodies.

Specific regulation of BACH1 by the hotspot mutant p53R175H reveals a distinct gain-of-function mechanism.

Although the gain of function (GOF) of p53 mutants is well recognized, it remains unclear whether different p53 mutants share the same cofactors to induce GOFs. In a proteomic screen, we identified BACH1 as a cellular factor that recognizes the p53 DNA-binding domain depending on its mutation status. BACH1 strongly interacts with p53R175H but fails to effectively bind wild-type p53 or other hotspot mutants in vivo for functional regulation. Notably, p53R175H acts as a repressor for ferroptosis by abrogating BACH1-mediated downregulation of SLC7A11 to enhance tumor growth; conversely, p53R175H promotes BACH1-dependent tumor metastasis by upregulating expression of pro-metastatic targets. Mechanistically, p53R175H-mediated bidirectional regulation of BACH1 function is dependent on its ability to recruit the histone demethylase LSD2 to target promoters and differentially modulate transcription. These data demonstrate that BACH1 acts as a unique partner for p53R175H in executing its specific GOFs and suggest that different p53 mutants induce their GOFs through distinct mechanisms.

PrePPI: A structure informed proteome-wide database of protein-protein interactions.

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features ( https://honiglab.c2b2.columbia.edu/PrePPI ). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome.

PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions.

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.

PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.

We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

On the formation of ordered protein assemblies in cell-cell interfaces.

Crystal structures of many cell-cell adhesion receptors reveal the formation of linear "molecular zippers" comprising an ordered one-dimensional array of proteins that form both intercellular (trans) and intracellular (cis) interactions. The clustered protocadherins (cPcdhs) provide an exemplar of this phenomenon and use it as a basis of barcoding of vertebrate neurons. Here, we report both Metropolis and kinetic Monte Carlo simulations of cPcdh zipper formation using simplified models of cPcdhs that nevertheless capture essential features of their three-dimensional structure. The simulations reveal that the formation of long zippers is an implicit feature of cPcdh structure and is driven by their cis and trans interactions that have been quantitatively characterized in previous work. Moreover, in agreement with cryo-electron tomography studies, the zippers are found to organize into two-dimensional arrays even in the absence of attractive interactions between individual zippers. Our results suggest that the formation of ordered two-dimensional arrays of linear zippers of adhesion proteins is a common feature of cell-cell interfaces. From the perspective of simulations, they demonstrate the importance of a realistic depiction of adhesion protein structure and interactions if important biological phenomena are to be properly captured.

Affinity requirements for control of synaptic targeting and neuronal cell survival by heterophilic IgSF cell adhesion molecules.

Neurons in the developing brain express many different cell adhesion molecules (CAMs) on their surfaces. CAM-binding affinities can vary by more than 200-fold, but the significance of these variations is unknown. Interactions between the immunoglobulin superfamily CAM DIP-α and its binding partners, Dpr10 and Dpr6, control synaptic targeting and survival of Drosophila optic lobe neurons. We design mutations that systematically change interaction affinity and analyze function in vivo. Reducing affinity causes loss-of-function phenotypes whose severity scales with the magnitude of the change. Synaptic targeting is more sensitive to affinity reduction than is cell survival. Increasing affinity rescues neurons that would normally be culled by apoptosis. By manipulating CAM expression together with affinity, we show that the key parameter controlling circuit assembly is surface avidity, which is the strength of adherence between cell surfaces. We conclude that CAM binding affinities and expression levels are finely tuned for function during development.

How clustered protocadherin binding specificity is tuned for neuronal self-/nonself-recognition.

The stochastic expression of fewer than 60 clustered protocadherin (cPcdh) isoforms provides diverse identities to individual vertebrate neurons and a molecular basis for self-/nonself-discrimination. cPcdhs form chains mediated by alternating cis and trans interactions between apposed membranes, which has been suggested to signal self-recognition. Such a mechanism requires that cPcdh cis dimers form promiscuously to generate diverse recognition units, and that trans interactions have precise specificity so that isoform mismatches terminate chain growth. However, the extent to which cPcdh interactions fulfill these requirements has not been definitively demonstrated. Here, we report biophysical experiments showing that cPcdh cis interactions are promiscuous, but with preferences favoring formation of heterologous cis dimers. Trans homophilic interactions are remarkably precise, with no evidence for heterophilic interactions between different isoforms. A new C-type cPcdh crystal structure and mutagenesis data help to explain these observations. Overall, the interaction characteristics we report for cPcdhs help explain their function in neuronal self-/nonself-discrimination.

Synaptogenic activity of the axon guidance molecule Robo2 underlies hippocampal circuit function.

Synaptic connectivity within adult circuits exhibits a remarkable degree of cellular and subcellular specificity. We report that the axon guidance receptor Robo2 plays a role in establishing synaptic specificity in hippocampal CA1. In vivo, Robo2 is present and required postsynaptically in CA1 pyramidal neurons (PNs) for the formation of excitatory (E) but not inhibitory (I) synapses, specifically in proximal but not distal dendritic compartments. In vitro approaches show that the synaptogenic activity of Robo2 involves a trans-synaptic interaction with presynaptic Neurexins, as well as binding to its canonical extracellular ligand Slit. In vivo 2-photon Ca2+ imaging of CA1 PNs during spatial navigation in awake behaving mice shows that preventing Robo2-dependent excitatory synapse formation cell autonomously during development alters place cell properties of adult CA1 PNs. Our results identify a trans-synaptic complex linking the establishment of synaptic specificity to circuit function.

Sorting of cadherin-catenin-associated proteins into individual clusters.

The cytoplasmic tails of classical cadherins form a multiprotein cadherin-catenin complex (CCC) that constitutes the major structural unit of adherens junctions (AJs). The CCC in AJs forms junctional clusters, "E clusters," driven by cis and trans interactions in the cadherin ectodomain and stabilized by α-catenin-actin interactions. Additional proteins are known to bind to the cytoplasmic region of the CCC. Here, we analyze how these CCC-associated proteins (CAPs) integrate into cadherin clusters and how they affect the clustering process. Using a cross-linking approach coupled with mass spectrometry, we found that the majority of CAPs, including the force-sensing protein vinculin, interact with CCCs outside of AJs. Accordingly, structural modeling shows that there is not enough space for CAPs the size of vinculin to integrate into E clusters. Using two CAPs, scribble and erbin, as examples, we provide evidence that these proteins form separate clusters, which we term "C clusters." As proof of principle, we show, by using cadherin ectodomain monoclonal antibodies (mAbs), that mAb-bound E-cadherin forms separate clusters that undergo trans interactions. Taken together, our data suggest that, in addition to its role in cell-cell adhesion, CAP-driven CCC clustering serves to organize cytoplasmic proteins into distinct domains that may synchronize signaling networks of neighboring cells within tissues.

Dimerization of Cadherin-11 involves multi-site coupled unfolding and strand swapping.

Cadherin extracellular domain 1 (EC1) mediates homophilic dimerization in adherens junctions. Conserved Trp2 and Trp4 residues in type II cadherins anchor the EC1 A strand intermolecularly in strand-swapped dimers. Herein, NMR spectroscopy is used to elucidate the roles of Trp2 and Trp4 in Cadherin-11 dimerization. The monomeric state, with the A strand and Trp side chains packed intramolecularly, is in equilibrium with sparsely populated partially and fully A-strand-exposed states, in which Trp2 (and Trp4, respectively) side-chain packing is disrupted. Exchange kinetics between the major state and the partially (fully) A-strand-exposed state is slow-intermediate (intermediate-fast). A separate very fast process exchanges ordered and random-coil BC-loop conformations with populations dependent on A-strand exposure and dimerization status. In addition, very slow processes connect the folded A-strand-exposed conformation to partially unfolded states, which may represent additional domain-swapping intermediates. The dimerization mechanism of type II cadherins is revealed as coupled folding and strand swapping.

Integrating 3D structural information into systems biology.

Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus-host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.

A Sweep of Earth's Virome Reveals Host-Guided Viral Protein Structural Mimicry and Points to Determinants of Human Disease.

Viruses deploy genetically encoded strategies to coopt host machinery and support viral replicative cycles. Here, we use protein structure similarity to scan for molecular mimicry, manifested by structural similarity between viral and endogenous host proteins, across thousands of cataloged viruses and hosts spanning broad ecological niches and taxonomic range, including bacteria, plants and fungi, invertebrates, and vertebrates. This survey identified over 6,000,000 instances of structural mimicry; more than 70% of viral mimics cannot be discerned through protein sequence alone. We demonstrate that the manner and degree to which viruses exploit molecular mimicry varies by genome size and nucleic acid type and identify 158 human proteins that are mimicked by coronaviruses, providing clues about cellular processes driving pathogenesis. Our observations point to molecular mimicry as a pervasive strategy employed by viruses and indicate that the protein structure space used by a given virus is dictated by the host proteome. A record of this paper's transparent peer review process is included in the Supplemental Information.