Biology subjects

Honig, B.

Publications and source records attributed to Honig, B..

18 recordsLinked to original sources

bioRxiv2026

A functional investigation of antibody Fc-FcRn variant binding guided by in silico free energy perturbation methods

Accurate calculation of energy changes upon mutation is a key requirement for the effective use of computational methods in protein design. In this study, we applied free energy perturbation (FEP) calculations to predict the effects of mutations on the binding free energy between the immunoglobulin subtype G (IgG) antibody fragment-crystallizable (Fc) region and the neonatal Fc receptor (FcRn), an interaction that is primarily responsible for antibody half-life. We assembled an extensive experimental dataset of Fc-FcRn binding affinities for wild-type (wt) and mutant complexes, including values from literature and from newly measured results. Starting from a crystal structure of the M252Y/S254T/T256E ("YTE") Fc variant bound to FcRn, we prepared all-atom models of human IgG1-subtype wt and YTE variant Fc-FcRn complexes, adding explicit hydrogens and assigning protonation states for key ionizable residues. Initial results using standard FEP protocols to compute relative binding free energies were promising but exhibited multiple outliers. By accounting for coupling effects for FEP mutations near key histidine residues, we improved the results for several outliers, suggesting such coupling as an important approach for pH-sensitive systems. Further, upon determining new crystal structures of four Fc variants at multiple pH values, we observed subtle conformational changes in unbound Fc; by accounting for these conformational changes in FEP calculations, we additionally improved agreement with experiment. The detailed structural and energetic analyses of the Fc-FcRn system we present here thus provide an accurate energy-calculation framework to enable rational in silico design of novel Fc variants. SignificanceThe ability to determine changes in binding affinity upon mutation is critical to structure-based protein design. In this study, we demonstrate a successful computational approach using free energy perturbation (FEP) calculations on the antibody Fc-FcRn complex, a medically relevant system with implications for both therapeutic and prophylactic antibody use. Our successful calculation of accurate binding energies across a wide range of cases speaks to the power of the FEP methodology in navigating the free energy landscapes of dynamic molecular complexes. Furthermore, we show that accurate Fc-FcRn affinity calculations required careful consideration of conformational flexibility between bound and unbound states, contributing to our functional understanding of a system that will be important for future rational antibody-design efforts. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/721095v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@f754d8org.highwire.dtl.DTLVardef@1e366beorg.highwire.dtl.DTLVardef@6e67caorg.highwire.dtl.DTLVardef@602a14_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Branch-specific axon pruning induced by Dpr4/DIP-{Theta} transneuronal interactions

Neuronal remodeling is a conserved, late developmental mechanism to refine neural circuits. Although remodeling typically occurs with remarkable spatiotemporal precision, its underlying molecular mechanisms remain poorly understood. In the Drosophila mushroom body (MB) circuit, {gamma}-Kenyon cells ({gamma}-KCs) undergo stereotyped remodeling during metamorphosis, in which they prune their larval vertical and medial axonal branches and subsequently regrow a medial, adult-specific branch. Our previous transcriptional profiling of developing {gamma}-KCs revealed dynamic expression of Defective proboscis extension response (Dpr) proteins and their binding partners, Dpr-interacting proteins (DIPs), members of the Immunoglobulin (Ig) superfamily. Despite their established roles in neurodevelopment, how Dpr/DIPs function - given their lack of intracellular domains - remains unclear. Here, we show that overexpression of Dpr4 in developing {gamma}-KCs cell-autonomously inhibits axon pruning. Strikingly, this effect is branch-specific: the vertical axonal branch fails to prune, while the medial branch prunes normally. To our knowledge, this represents the first demonstration of branch-specific control of pruning in this system. Moreover, the adult medial branch regrows normally, indicating that pruning and regrowth are independently regulated at the level of individual branches. We demonstrate that this unique branch-specificity arises from trans-neuronal interactions between Dpr4 in {gamma}-KCs and DIP-{theta} in dopaminergic neurons that selectively innervate the vertical larval MB lobe. Furthermore, our findings suggest that this phenotype relies on an Ig2 domain of a Dpr family member, implying the involvement of a third binding partner. Leveraging this robust overexpression phenotype to probe downstream mechanisms, we find that loss of the transmembrane adhesion protein N-Cadherin suppresses the Dpr4-induced pruning defect. Together, our findings highlight the local impact of Dpr/DIP-mediated trans-neuronal interactions on the spatial regulation of remodeling, and provide genetic evidence implicating N-Cadherin as a potential downstream mediator of Dpr/DIP function within a developing neural circuit.

neuroscience↗

bioRxiv2025

Integrating structural homology with deep learning to achieve highly accurate protein-protein interface prediction for the human interactome

A significant portion of disease-causing mutations occur at protein-protein interfaces however, the number of structurally resolved multi-protein complexes is extremely small. Here we present a computational pipeline, PIONEER2, that integrates 3D structural similarity with geometric deep learning to accurately predict protein binding partner-specific interfacial residues. We compare the performance of PIONEER2 to that of AlphaFold3 and found, using a test set of PDB structures, that their performance is quite similar. However, about 20% of AlphaFold3 predictions for protein-protein complexes in the PDB have AlphaFold3 ranking scores below 0.5, which indicates an uncertain model. For these structures, PIONEER2 outperforms AlphaFold3 at discriminating interfacial from non-interfacial residues. Further, about half of the AlphaFold3 ranking scores on high confidence protein-protein interactions (PPIs) not associated with a PDB structure are below 0.5 indicating that PIONEER2 offers superior interface prediction for a large number of PPIs for which structures are not available. We created a comprehensive 3D structurally informed interactome encompassing all 352,124 experimentally detected binary human PPIs in the current literature and made PIONEER2 interface predictions for each. We experimentally validated these predictions by generating 1,866 mutations and testing their disruptive impact on 5,010 mutation-interaction pairs. PIONEER2-predicted interfaces are found to be comparable to PDB structures in their ability to predict disruptive mutations while AlphaFold3 performance is reduced. Similarly, PIONEER2-predicted interfaces outperform AlphaFold3 in accounting for the depletion of non-deleterious common population variants and the enrichment of disease-related mutations on protein surfaces. Overall, our results suggest that PIONEER2-predicted interfaces provide a valuable tool for studying disease etiology, advancing personalized medicine and for fundamental research. We further implemented PIONEER2 as a user-friendly web server (https://pioneer2.yulab.org) platform for users to explore our 3D interactome models and conduct genome-wide functional genomics studies.

bioinformatics↗

bioRxiv2025

Combining structural modeling and deep learning to calculate the E. coli protein interactome and functional networks

We report on the integration of three methods that are computationally efficient enough to predict, on a proteome-wide scale, whether two proteins are likely to form a binary complex. The methods include PrePPI, which uses three-dimensional structure information as a basis for predictions, Topsy-Turvy which analyzes sequences using a protein language model, and ZEPPI which uses evolutionary information to evaluate protein-protein interfaces. We demonstrate how these methods can be integrated and validate the performance of the integrated method and its separate components at predicting E. coli protein-protein interactions through testing on the HINT high-quality literature-curated database of binary interactions. The integrated method identifies more high-confidence (FPR [≤] 0.001) interactions ([~]20K) than any of the component methods. The AF3Complex algorithm was used to predict the structures of 400 protein-protein interactions, and 78% of the integrated method predictions resulted in models deemed accurate by the AF3Complex evaluation score. Notably, essentially all AF3Complex models have at least partially overlapping interfaces with PrePPI models of the complexes. Finally, we clustered the high-confidence E. coli interactome and obtained 385 subnetworks which have high functional coherence defined by enrichment of Gene Ontology Biological Process terms, thus, illustrating that our methods which contain no explicit functional information provide biologically meaningful protein interactions. Biological insights derived from the subnetworks, including the annotation of proteins of unknown function, are discussed in detail. Overall, independent validations support the accuracy of the comprehensive E. coli interactome we have presented.

systems biology↗

bioRxiv2025

Targeting Monoallelic CREBBP/EP300 Mutations in Germinal Center-Derived B-Cell Lymphoma with a First-in-Class Histone Acetyltransferase Activator

Germinal center B-cell lymphomas frequently exhibit monoallelic loss-of-function mutations in the histone acetyltransferases CREBBP and p300, which contribute to lymphoma development, disrupt normal germinal center biology, and promote immune evasion. This study evaluates YF2, a histone acetyltransferase activator that increases CREBBP/p300 activity, as a potential therapeutic approach for B-cell lymphoma. YF2 binds the bromodomain of CREBBP/p300, increasing their auto-acetylation and enzymatic activity. It also induces cytotoxicity in B-cell lymphoma cell lines, with a stronger response in those carrying CREBBP/EP300 mutations. Additionally, YF2 increases the acetylation of key CREBBP/p300 substrates, including H3K27, p53, and BCL6, leading to enhanced apoptosis and altered B-cell diTerentiation. YF2 is well tolerated in vivo and extends survival in both cell line- and patient-derived xenograft mouse models. Moreover, YF2 exposure upregulates antigen-presentation markers and reshapes the immune microenvironment, strengthening responses to immune checkpoint blockade. Taken together, these findings support pharmacologic activation of CREBBP/p300 with YF2 as a compelling therapeutic strategy for B-cell lymphoma. SignificanceYF2 stimulates CREBBP/p300 activity to normalize B-cell diTerentiation and enhance immune recognition, oTering a potential approach to limit lymphoma progression and strengthen immunotherapy responses.

cancer biology↗

bioRxiv2024

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. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/590325v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1c5e607org.highwire.dtl.DTLVardef@1810ee5org.highwire.dtl.DTLVardef@1f8f1acorg.highwire.dtl.DTLVardef@c28053_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIReliable calculation of relative binding free energy changes for most protein mutations to within [~]1 kcal/mol. C_LIO_LIAutomated Protein FEP+ Groups treatment of alternate protonation states for titratable residues. C_LIO_LIApplication of FEP+ methodology to "real-world" protein design projects. C_LI

biophysics↗

bioRxiv2024

Cis inhibition of co-expressed DIPs and Dprs shapes neural development

In Drosophila, two interacting adhesion protein families, Dprs and DIPs, coordinate the assembly of neural networks. While intercellular DIP/Dpr interactions have been well characterized, DIPs and Dprs are often co-expressed within the same cells, raising the question as to whether they also interact in cis. We show, in cultured cells and in vivo, that DIP- and DIP-{delta} can interact in cis with their ligands, Dpr6/10 and Dpr12, respectively. When co-expressed in cis with their cognate partners, these Dprs regulate the extent of trans binding, presumably through competitive cis interactions. We demonstrate the neurodevelopmental effects of cis inhibition in fly motor neurons and in the mushroom body. We further show that a long disordered region of DIP- at the C-terminus is required for cis but not trans interactions, likely because it alleviates geometric constraints on cis binding. Thus, the balance between cis and trans interactions plays a role in controlling neural development.

biochemistry↗

bioRxiv2023

ZIPPI: proteome-scale sequence-based evaluation of protein-protein interaction models

Predicting protein-protein interactions (PPI) is a challenging problem of central importance in fundamental biology. With the increasing number of available PPI prediction methods and databases, an effective evaluation model would be extremely valuable. Here we introduce ZIPPI (Z-score for Information about Protein-Protein Interfaces), which evaluates structural models of a complex based on sequence co-evolution and conservation involving residues that are in contact in the interface. The interface Z-score (ZIPPI score) is calculated by comparing metrics for interface contacts to metrics obtained from randomly chosen surface residues. Since contacting residues are defined by the structural model, this obviates the need of accounting for indirect interactions with methods such as Direct Coupling Analysis. Although ZIPPI relies on species-paired multiple sequence alignments, its focus on contacting interfacial residues and the avoidance of direct coupling methods makes it computationally efficient. The performance of ZIPPI is evaluated through applications to experimentally determined complexes from the Protein Data Bank (PDB) and to decoys from the Critical Assessment of PRedicted Interactions (CAPRI) experiment. We demonstrate how ZIPPI can be implemented on a genome-wide scale by calculating scores for millions of structural models of protein-protein interactions in the E. coli interactome as predicted by PrePPI. Many PrePPI predictions filtered by ZIPPI score are novel. In all, this proteome-scale method shows promising feasibility for applications to the full human protein interactome, which is not yet accessible to deep learning methods.

systems biology↗

bioRxiv2023

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. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/530276v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@b66c15org.highwire.dtl.DTLVardef@71d817org.highwire.dtl.DTLVardef@21d984org.highwire.dtl.DTLVardef@4f6f55_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

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.

systems biology↗

bioRxiv2022

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 nearly 7 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 in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability 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. A likelihood ratio for the binding of C to Q is obtained from naive Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.

systems biology↗

bioRxiv2022

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 in practical applications such as the optimization of neutralizing antibodies.

biophysics↗

bioRxiv2021

How clustered protocadherin binding specificity is tuned for neuronal self/non-self-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/non-self- 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/non-self-discrimination.

biophysics↗

bioRxiv2021

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, and CAM interactions are essential for the determination of synaptic connectivity patterns. CAM binding affinities can vary by more than 200-fold, but the significance of affinity differences among CAMs is unknown. Here we provide a systematic characterization of the in vivo consequences of altering CAM affinity. Interactions between DIP- and its binding partners Dpr6 and Dpr10 control synaptic targeting and cell survival for Drosophila optic lobe neurons. We generated mutations that change DIP-::Dpr10 binding affinity and introduced these into the endogenous loci. We show that cell survival and synaptic targeting have different affinity requirements, and that there is a threshold affinity required for targeting. Reducing affinity causes graded loss-of-function phenotypes, while increasing affinity rescues cells that would normally die. Affinity reduction can be compensated for by increasing gene copy number.

neuroscience↗

bioRxiv2020

A sweep of earth's virome reveals host-guided viral protein structural mimicry; with implications for human disease

Viruses deploy an array of genetically encoded strategies to coopt host machinery and support viral replicative cycles. Molecular mimicry, manifested by structural similarity between viral and endogenous host proteins, allow viruses to harness or disrupt cellular functions including nucleic acid metabolism and modulation of immune responses. Here, we use protein structure similarity to scan for virally encoded structure mimics across thousands of catalogued viruses and hosts spanning broad ecological niches and taxonomic range, including bacteria, plants and fungi, invertebrates and vertebrates. Our survey identified over 6,000,000 instances of structural mimicry, the vast majority of which (>70%) cannot be discerned through protein sequence. The results 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. Interrogation of proteins mimicked by human-infecting viruses points to broad diversification of cellular pathways targeted via structural mimicry, identifies biological processes that may underly autoimmune disorders, and reveals virally encoded mimics that may be leveraged to engineer synthetic metabolic circuits or may serve as targets for therapeutics. Moreover, the manner and degree to which viruses exploit molecular mimicry varies by genome size and nucleic acid type, with ssRNA viruses circumventing limitations of their small genomes by mimicking human proteins to a greater extent than their large dsDNA counterparts. Finally, we identified over 140 cellular proteins that are mimicked by CoV, providing clues about cellular processes driving the pathogenesis of the ongoing COVID-19 pandemic.

microbiology↗

bioRxiv2020

DIP/Dpr interactions and the evolutionary design of specificity in protein families

Differential binding affinities among closely related protein family members underlie many biological phenomena, including cell-cell recognition. Drosophila DIP and Dpr proteins mediate neuronal targeting in the fly through highly specific protein-protein interactions. We show here that DIPs/Dprs segregate into seven specificity subgroups defined by binding preferences between their DIP and Dpr members. We then describe a novel sequence-, structure- and energy-based computational approach, combined with experimental binding affinity measurements, to reveal how specificity is coded on the canonical DIP/Dpr interface. We show that binding specificity of DIP/Dpr subgroups is controlled by "negative constraints", which interfere with binding. To achieve specificity, each subgroup utilizes a different combination of negative constraints, which are broadly distributed and cover the majority of the protein-protein interface. We discuss the structural origins of negative constraints, and potential general implications for the evolutionary origins of binding specificity in multi-protein families.

biophysics↗

bioRxiv2019

Synaptogenic activity of the axon guidance molecule Robo2 is critical for hippocampal circuit function

The developmental transition between axon guidance and synapse formation is critical for circuit assembly but still poorly understood at the molecular level. We hypothesized that this key transition could be regulated by axon guidance cues switching their function to regulate synaptogenesis with subcellular specificity. Here, we report evidence for such a functional switch, describing a novel role for the axon guidance molecule Robo2 in excitatory synapse formation onto dendrites of CA1 pyramidal neurons (PNs) in the mouse hippocampus. Cell-autonomous deletion of Robo2 from CA1 PNs leads to a drastic reduction of the number of excitatory synapses specifically in proximal dendritic compartments. At the molecular level, we show that this novel postsynaptic function of Robo2 depends on both its canonical ligand Slit and a novel interaction with presynaptic Neurexins. Biophysical analysis reveals that Robo2 binds directly to Neurexins via its Ig4-5 domains. In vivo 2-photon Ca2+ imaging of CA1 PNs during spatial navigation in mice shows that sparse deletion of Robo2 during development drastically reduces the likelihood of place cell emergence and alters spatial coding properties of the remaining place cells. Our results identify Robo2 as a novel molecular effector linking synaptic specificity to the acquisition of spatial coding properties characterizing hippocampal circuits.

neuroscience↗

bioRxiv2018

Systematic Elucidation and Validation of OncoProtein-Centric Molecular Interaction Maps

The largely incomplete and tissue-independent nature of cancer pathways represents a key limitation to the ability to elucidate mechanistic determinants of cancer phenotypes and to predict adaptive response to targeted therapy. To address these challenges, we propose replacing canonical cancer pathways with a more accurate, comprehensive, and context-specific architecture - dubbed a Protein-Centric molecular interaction Map (PC-Map) - representing modulators, effectors, and cognate binding-partners of any oncoprotein of interest. To reconstruct these complex molecular architectures de novo, we introduce a novel OncoSig algorithm. Validation of a lung adenocarcinoma specific (LUAD) KRAS-centric PC-Map recapitulated known KRAS biology and, more critically, identified a novel repertoire of proteins eliciting synthetic lethality in KRASG12D LUAD organoid cultures. Showing the generalizable nature of the algorithm, we elucidated PC-Maps for ten recurrently mutated oncoproteins, including KRAS, in distinct tumor contexts. This revealed a highly context-specific nature of cancers regulatory and signaling architectures to an unprecedented degree of resolution.

systems biology↗