Biology subjects

Verkhivker, G.

Publications and source records attributed to Verkhivker, G..

At least 19 recordsLinked to original sources

bioRxiv2026

Frustration Quenching and Network Topology of the Energy Landscape as Primary Determinants of Protein-Ligand Binding Pose Prediction by Deep Learning Models

Deep learning co-folding and docking models accurately predict ligand binding poses at orthosteric sites yet systematically underperform at allosteric pockets. Here, we demonstrate that this accuracy gap reflects a fundamental biophysical property of local energy landscape topology specifically, the magnitude of frustration quenching upon ligand binding rather than an intrinsic algorithmic defect. By evaluating leading co-folding and docking paradigms across curated benchmarks and large-scale uncurated datasets, we show that AI prediction success strongly tracks frustration quenching which the degree to which ligand binding relieves local energetic strain to form a steep energy funnel. Topologically, frustration-weighted residue interaction networks reveal that orthosteric pockets act as high-betweenness communication hubs embedded within contiguous, minimally frustrated pathways. In contrast, allosteric sites reside in low-betweenness peripheral regions with fragmented frustration profiles, presenting flatter, entropy-dominated landscapes that challenge structure prediction algorithms. Comparing generative diffusion with single-shot regression demonstrates that multi-pose sampling capacity is required to cross energy barriers and discover the frustration funnel, whereas joint receptor-ligand coordinate co-adaptation governs local optimization within the funnel. Collectively, this study reframes the allosteric "blind spot" as a quantitative property of energy landscape architecture rather than a defect of any specific predictive framework. We demonstrate that frustration quenching magnitude and structural network centrality serve as physical descriptors that define the intrinsic limits of AI performance, reframing AI accuracy from a benchmark metric into a physics-grounded quantitative reporter of macromolecular binding landscape architecture.

biophysics↗

bioRxiv2026

Dynamics-aware geometric learning predicts disease-associated molecular perturbations

Missense mutations and post-translational modifications (PTMs) are major molecular perturbations that reshape protein function but are traditionally studied independently. Current computational approaches largely rely on sequence conservation or static structural features, limiting our understanding of how perturbations alter intrinsic protein dynamics. We present DynGeo-Pheno, a unified geometric deep learning framework that integrates protein language model representations with anisotropic network model-derived dynamics to jointly capture evolutionary, structural, and biophysical information. DynGeo-Pheno predicts disease-associated phosphosites and pathogenic missense mutations with high accuracy on independent test datasets. Ablation analyses indicate that protein dynamics provide complementary information beyond sequence evolution and structural topology for pathogenicity prediction. Beyond predictive performance, DynGeo-Pheno reveals that disease-associated perturbations preferentially localize to functional structural regions, including ligand-binding pockets and PPI interfaces. Mechanistically, phosphosites and missense mutations appear to exhibit distinct yet convergent dynamic signatures. Phosphosites preferentially occur in flexible regulatory regions, whereas pathogenic mutations are enriched in ordered structural elements. Despite these differences, both perturbation types display enhanced long-range coupling, increased perturbation responsiveness, and elevated mechanical stability, indicating that pathogenic residues preferentially occupy mechanically constrained and allosteric regulatory sites. This study provides compelling evidence that intrinsic protein dynamics is an important complementary determinant of pathogenicity and establishes a unified framework for interpretable AI predictions and mechanistic understanding of how genetic and regulatory perturbations may shape protein function.

biophysics↗

bioRxiv2026

Localized Rigidification and Allosteric Modulation Mechanisms of SARS-CoV-2 Spike Neutralization by Class 3 and Class 4 Antibodies at Atomic Resolution: An Integrated Computational Study of Binding, Dynamics, and Allostery

The relentless evolution of SARS-CoV-2 and the emergence of highly antibody-evasive variants underscore the need to decipher the molecular principles that govern antibody neutralization breadth and resilience. In this study, we employ an integrated computational framework combining structural analysis, conformational dynamics, mutational scanning, binding energetics, and allosteric network modeling to dissect the mechanistic signatures of class 3 and class 4 antibodies targeting the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein. Through comprehensive analysis of antibody-RBD complexes including individual antibodies (COV2-3835, COV2-3891, COV2-3906) and synergistic dual-antibody pairs we uncover a fundamental mechanistic dichotomy that distinguishes these two antibody classes and explains their differential patterns of neutralization potency, breadth, and resilience to viral escape. Our analysis reveals that class 3 antibodies achieve neutralization with mechanical perturbation strictly confined to the binding interface. In contrast, class 4 antibodies employ a long-range allosteric destabilization mechanism, anchoring to a structurally rigid hydrophobic core and establishing a mechanical conduit through the {beta}-sheet core that transmits conformational changes. Mutational scanning and rigorous energetic analysis reveal fundamentally different vulnerability landscapes: class 4 epitopes are defined by an immutable hydrophobic core that is exquisitely sensitive to mutation yet evolutionarily constrained across sarbecoviruses, explaining their ultra-broad binding and limited escape potential. Class 3 epitopes exhibit a plastic periphery with a conserved anchor and variable sensitivity in peripheral regions, creating multiple escape pathways. These predictions show excellent agreement with experimental deep mutational scanning data, validating our computational approach and establishing a quantitative framework for predicting immune escape. Allosteric network analysis identifies the {beta}-sheet core as the critical communication conduit for class 4 antibodies, with specific residues serving as essential hubs that connect the hydrophobic core to the RBM loop. The convergence of high communication centrality with extreme perturbation sensitivity at these positions establishes them as the most critical allosteric hotspots, essential for function and resistant to mutation. The proposed multi-pronged computational framework provides a generalizable approach for understanding antibody neutralization mechanisms and predicting immune escape across diverse viral targets, with implications for the rational design of next-generation antibody therapeutics that balance potency, breadth, and resilience.

biophysics↗

bioRxiv2026

Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites

Allosteric regulation represents a fundamental mechanism of protein function, yet distinguishing allosteric from orthosteric protein binding sites remains a persistent computational challenge. While multimodal protein foundation models offer the potential to integrate complementary biological signals including sequence, structure, functional annotations, and conformational dynamics, their performance determinants in allosteric binding site detection remain poorly understood. We introduce a unified computational framework for profiling multimodal protein foundation models across distinct binding-site separability regimes. Rather than evaluating models solely by predictive accuracy, the framework combines systematic modality embedding ablations, encoder architecture comparisons, and variance decomposition to characterize how evolutionary, structural, functional, and dynamical information contribute to allosteric site discrimination. Using the OneProt multimodal model, we evaluate two complementary levels of multimodal integration: (a) encoder architectures that differ in the modalities incorporated during pretraining, and (b) downstream combinations of pocket, sequence, and text embeddings used for classification. To systematically probe the determinants of model performance, we benchmark these configurations across four assembled datasets of protein complexes representing a spectrum of biological complexity and a range of structural, dynamic, and evolutionary context for orthosteric and allosteric binding sites. Through comprehensive embedding ablations, encoder architecture comparisons, and variance decomposition, we demonstrate that model performance is governed primarily by intrinsic dataset properties rather than architectural complexity, with dataset identity accounting for 63.7% of explainable variance. Across all examined datasets, we identify three distinct separability regimes: a low-separability regime where current representations fail to reliably distinguish the two classes; an intermediate regime where multimodal integration substantially improves performance; and a high-separability regime where most architectures converge to near-ceiling performance. Critically, embedding contributions are regime-dependent: pocket geometry dominates when regulatory classes share structural contexts, while text and sequence embeddings become essential when evolutionary constraint distinguishes them. At the encoder level, structural and molecular dynamics encoders provide the greatest benefit in intermediate- and high-separability settings. Structure-functional analysis of correctly classified binding sites reveals that prediction success reflects the underlying biological organization of each regime. These findings establish that the success of multimodal foundation models depends critically on alignment between available modalities and the biological signatures that distinguish regulatory classes in each dataset.

bioinformatics↗

bioRxiv2026

Frustration Landscapes of Broadly Neutralizing SARS-CoV-2 Spike Antibodies Targeting Conserved Epitopes Reveal Energetic Logic of Escape-Proof and Escape-Prone Mechanisms

The continued evolution of SARS-CoV-2 has enabled escape from most monoclonal antibodies, yet a subset of broadly neutralizing antibodies targeting three newly identified super-conserved RBD epitopes--SCORE-A, SCORE-B, and SCORE-C--retains remarkable activity against even the most recent JN.1-derived sublineages. Here we employed an integrated computational framework combining conformational dynamics, mutational scanning, MM-GBSA binding energetics, and frustration profiling to dissect the molecular mechanisms by which XGI antibodies achieve broad neutralization and resistance to immune escape. Structural analysis revealed that all three SCORE epitopes share a common architecture: a highly conserved, minimally frustrated core that provides stable anchoring, flanked by peripheral regions that accommodate antibody-specific variations. Conformational dynamics showed that SCORE-A antibodies (XGI-183) rigidify the lateral epitope while leaving the RBM partially mobile; SCORE-B antibodies (XGI-198, XGI-203) clamp the RBM apex, directly blocking ACE2; and SCORE-C antibodies (XGI-171) allosterically loosen the RBM loop, impairing receptor engagement indirectly. Mutational scanning identified a hierarchical hotspot organization where primary hotspots (e.g., K356, T500, Y380, T385) are evolutionarily constrained and minimally frustrated, while secondary hotspots (e.g., V503, Y508, S383) are neutrally frustrated and represent the principal sites of immune-driven mutations. MM-GBSA decomposition revealed that van der Waals-driven hydrophobic packing dominates binding, with electrostatic interactions providing auxiliary stabilization. Critically, frustration analysis demonstrated that immune escape hotspots reside precisely in zones of neutral frustration--"energetic playgrounds" that permit mutational exploration without destabilizing the RBD--while minimally frustrated cores are evolutionarily locked. The comparative analysis of conformational versus mutational frustration distributions revealed a unifying principle: aligned neutral frustration yields permissive, escape-prone interfaces; decoupling enables targeting of constrained cores; and convergence of minimal frustration in both distributions creates invulnerable interfaces. These findings establish that broad neutralization arises not from ultra-high-affinity anchors but from strategic energy distribution across rigid, evolutionarily informed interfaces, providing a roadmap for designing next-generation therapeutics that target the invulnerable cores of viral surface proteins.

biophysics↗

bioRxiv2026

Decoding Allosteric Grammar with Explainable AI Integrating Protein Language Models and Energy Landscape Analysis: Neutral Frustration at Allosteric Binding Sites Encodes Regulatory Versatility in Protein Kinases

Allosteric regulation enables protein kinases to integrate diverse cellular signals, yet the energetic organization principles that encode spatial and evolutionary diversity of regulatory binding sites lack a complete understanding. We introduce an explainable artificial intelligence (AI) framework that uses protein language models (PLMs) not as predictive endpoints, but as diagnostic probes of biophysical organization of regulatory regions. By integrating PLM-based binding site predictions with the energy landscape-based frustration analysis, we determine that the detectability of orthosteric and allosteric binding sites reflects their energetic embedding within the protein energy landscape. Using PLM predictions as unbiased probes across a structurally and functionally diverse dataset of 453 human kinases, we observe a striking and reproducible dichotomy in predictive behavior: orthosteric ATP-binding pockets are detected with high confidence, whereas allosteric sites consistently evade robust detection. Orthosteric catalytic sites reside within minimally frustrated, optimized energetic regions that are consistently detected with high confidence. In contrast, allosteric sites are enriched in neutrally frustrated zones, producing diffuse and context-dependent predictions, revealing that the "allosteric blind spot" arises from intrinsic biophysical design rather than algorithmic limitations. Atomic-resolution analysis of ABL kinase spanning multiple conformational states and complexes bound to diverse ligands provides mechanistic validation of this principle. The myristoyl allosteric pocket in ABL remains neutrally frustrated across complexes with physiological ligands, chemically diverse modulators, from allosteric inhibitors to activators, and conformations engaged with SH2-SH3 regulatory domains. We propose that allosteric sites are encoded in persistent neutrally frustrated regions optimized for context-dependent regulatory modulation. By using explainable AI to interrogate the energetic architecture of protein kinases, this work reveals how the organization of the protein energy landscape shapes functional plasticity and algorithmic detectability of regulatory binding sites.

biophysics↗

bioRxiv2026

Decoding the Allosteric Paradox: A Dual Framework Integrating AI Cofolding Models with Landscape-Guided Interpretable AI Framework of Ligand-Protein Binding

Artificial intelligence (AI) has transformed prediction of protein structure and biomolecular interactions, yet modeling of allosteric regulation remains a persistent and unresolved challenge. We develop a dual explainable AI framework that systematically interrogates AI Co-Folding models AlphaFold3, Protenix, Boltz-2, Chai-1, and DynamicBind on rigorously stratified datasets of orthosteric and allosteric ligand-protein complexes. While all AI models excel in accurate modeling of orthosteric ligand binding, a universal and architecture-independent collapse emerges in prediction of allosteric complexes. The biophysical logic for this dichotomy is unveiled through physics-based lens of the energy landscape theory and local frustration analysis. Orthosteric binding creates dominant energetic funnels via ligand-induced minimal frustration quenching, while allosteric sites preserve neutral frustration landscapes in both apo and holo protein states. The findings show that conformational heterogeneity and evolutionary plasticity encoded in allosteric binding landscapes may conceal the recurrent recognition patterns AI models are trained to detect. By linking prediction outcomes to frustration landscapes, this study recasts AI shortcomings in allosteric ligand binding as diagnostic indicators of fundamental biophysical constraints, establishing a physics-informed framework that turns the allosteric blind spot into mechanistic insight for next-generation landscape-aware predictive tools.

biophysics↗

bioRxiv2026

From Latent Manifolds to Functional Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Targeted Kinase Inhibitor Design

The design of selective kinase inhibitors remains a formidable challenge due to the high structural conservation of the ATP-binding site across the kinome. While modern generative AI has enabled rapid exploration of chemical space, many advanced models operate as black boxes, obscuring the chemical rationale behind design choices and limiting interpretability. To explore these bottlenecks, we present a modular, generative framework for de novo design of SRC kinase inhibitors, integrating ChemVAE-based latent space modeling, a chemically interpretable Kinase Inhibition Likelihood (KIL) scoring function, Bayesian optimization, and cluster-guided local neighborhood sampling. The results demonstrate that kinase inhibitors spontaneously organize into a coherent, low-dimensional manifold in latent space, with SRC acting as a structural "hub" that enables rational scaffold transformation. Our local neighborhood sampling-based approach successfully converts inhibitors from other kinase families (notably LCK) into novel SRC-like chemotypes, with LCK-derived molecules accounting for [~]40% of high-similarity outputs. Critically, we expose a fundamental representation gap: despite aromatic ring count being a top KIL feature, SMILES-based generation systematically fails to access multi-ring pharmacophores characteristic of clinical kinase inhibitors. This limitation cannot be overcome by scoring refinement alone, demanding topology-aware representations. Our framework also demonstrates that unbiased exploration paired with cluster-guided sampling outperforms active-biased optimization, which traps search in narrow local optima. By exposing representational gaps and showcasing scaffold-aware navigation of latent space, this study argues for hybrid systems that combine the diagnostic transparency of interpretable machine learning frameworks with the generative power of modern architectures.

bioinformatics↗

bioRxiv2026

Protein Language Models and Structure-Based Machine Learning for Prediction of Allosteric Binding Sites in Protein Kinases: An Explainable AI Framework Grounded in Energy Landscape-Encoded Frustration

Reliable identification of allosteric binding sites remains a major bottleneck in structure-based drug discovery, particularly in protein kinase families where such sites are often structurally cryptic, evolutionarily non-conserved, and sparsely populated. In this work, we present a systematic analysis of binding site prediction across a rigorously curated dataset of human kinase-ligand complexes, encompassing 453 kinases and spanning five inhibitor classes: Type I, Type I.5, and Type II (orthosteric ATP-competitive) and Type III/IV (non-ATP allosteric) modulators. We employed the pretrained protein language model (PLM) ESM2-650M model that was fine-tuned for prediction of protein-ligand binding sites by replacing the original masked language modeling head with a token-level classification head that acts as a projection layer that maps the high-dimensional latent representation of each residue to a scalar probability score for a given protein residue to be part of the binding site. We employed this fine-tuned sequence-based PLM and structure-based detection approach P2Rank for identification of orthosteric and allosteric binding sites in protein kinases. Our analysis reveals a stark performance divergence: while both methods achieve high precision-recall (AUPR = 0.64-0.76) on orthosteric sites, PLM performance collapses on allosteric sites (AUPR = 0.06), despite retaining moderate ranking ability (AUROC = 0.70). This deficit persists even after strict control for sequence similarity, structural redundancy, and extreme class imbalance (allosteric residues constitute <3% of the kinase domain). To mechanistically interpret this discrepancy, we integrate large-scale local frustration analysis, a physics-based framework derived from energy landscape theory that quantifies the energetic stability of residue-residue interactions under mutational and conformational perturbations. We find that, although the global frustration landscape is conserved across kinase states dominated by neutral frustration (55-75% of residues), local binding sites exhibit fundamentally distinct mutational constraints. Orthosteric pockets are enriched in minimally frustrated residues, whereas allosteric sites are characterized by neutral mutational frustration, indicating evolutionary permissiveness and sequence degeneracy. This study reframes the performance of AI approaches in predicting protein binding sites as a reflection of functional design that can be rationalized through lens of the landscape-encoded protein frustration as an explainable AI framework.

biophysics↗

bioRxiv2025

Neutral Frustration Landscape Architecture of SARS-CoV-2 Spike -Antibody Interfaces Shapes Immune Evasion Mechanisms for Ultrapotent Neutralizing Antibodies and Determines Pathways of Viral Adaptation: Insights from Integrative Computational Approach

The relentless evolution of SARS-CoV-2 underscores the urgent need to decipher the molecular principles that enable certain antibodies to maintain exceptional breadth and resilience against immune escape. In this study, we employ a multi-pronged computational framework integrating structural analysis, conformational dynamics, mutational scanning, MM-GBSA binding energetics, and conformational and mutational frustration profiling to dissect the mechanisms of ultrapotent neutralization by a cohort of broadly reactive Class 1 antibodies (BD55-1205, 19-77, ZCP4C9, ZCP3B4) and the Class 4/1 antibody ADG20. We reveal a unifying biophysical architecture: these antibodies bind via rigid, pre-configured interfaces that distribute binding energy across extensive epitopes through numerous suboptimal yet synergistic interactions, predominantly with backbone atoms and conserved side chains. This distributed redundancy enables tolerance to mutations at key sites like F456L or A475V without catastrophic loss of affinity. Mutational scanning identifies a hierarchical hotspot organization where primary hotspots (e.g., H505, Y501, Y489, Y421) which overlap with ACE2-contact residues and incur high fitness costs upon mutation are buffered by secondary hotspots (e.g., F456, L455) that are more permissive to variation. MM-GBSA energy decomposition confirms that van der Waals-driven hydrophobic packing dominates binding, with primary hotspots contributing disproportionately to affinity, while electrostatic networks provide auxiliary stabilization that mitigates mutational effects. Critically, both conformational and mutational frustration analyses demonstrate that immune escape hotspots reside in neutral-frustration landscape that permit mutational exploration without destabilizing the RBD, explaining the repeated emergence of convergent mutations across lineages. Our results establish that broad neutralization arises not from ultra-high-affinity anchors, but rather from strategic energy distribution across rigid, evolutionarily informed interfaces. By linking distributed binding, neutral frustration landscapes, and viral fitness constraints, this framework provides a predictive blueprint for designing next-generation therapeutics and vaccines capable of withstanding viral evolution.

biophysics↗

bioRxiv2025

Deciphering the Mechanistic Continuum of Broadly Neutralizing Class 4 Antibodies Targeting Conserved Cryptic Epitopes of the SARS-CoV-2 Spike Protein : Operating at the Intersection of Binding, Allostery and Immune Escape

Understanding atomistic basis of multi-layer mechanisms employed by broadly reactive neutralizing antibodies of SARS-CoV-2 spike protein without directly blocking receptor engagement remains an important challenge in coronavirus immunology. Class 4 antibodies represent an intriguing case: they target a deeply conserved, cryptic epitope on the receptor-binding domain yet exhibit variable neutralization potency across subgroups F1 (CR3022, EY6A, COVA1-16), F2 (DH1047), and F3 (S2X259). In this study, we employed a multi-modal computational approach combining atomistic and coarse-grained simulations, mutational scanning of binding interfaces, binding free energy calculations, and allosteric modeling using dynamic network analysis to map the allosteric landscapes and binding hotspots of these antibodies. Through this approach, our data revealed that distal binding can influence ACE2 engagement and immune escape traits through the confluence of direct interfacial interactions and allosteric effects. We found that group F1 antibodies can operate via classic allostery by modulating flexibility in the receptor binding domain loop regions and indirectly interfering with ACE2 binding using long-range effects. Group F2 antibody DH1047 represents an intermediate mechanism, engaging residues T376, R408, V503, and Y508 hotspots which are both critical for ACE2 binding and under immune pressure. Mutational scanning and rigorous binding free energy calculations highlight the synergy between hydrophobic and electrostatic interactions, while dynamic network modeling reveals a shift toward localized communication pathways connecting the cryptic site to the ACE2 interface. Our results demonstrate how group F3 antibody S2X259 achieves efficient synergistic mechanism through confluence of direct competition with ACE2 and localized allosteric effects leading to stabilization of the spike protein at the cost of increased escape vulnerability. Dynamic network analysis identifies a shared "allosteric ring" embedded in the core of the receptor binding domain and serving a conserved structural scaffold mediating long-range signal propagation with antibody-specific extensions propagating toward the ACE2 interface. The findings of this study support a modular model of antibody-induced allostery where neutralization strategies evolve via refinement of peripheral network connections, rather than complete redesign of the epitope itself. Taken together, this study establishes a robust computational framework for atomistic understanding of mechanisms underlying neutralization activity and immune escape for class 4 antibodies which harness conformational dynamics, binding energetics, and allosteric modulation to influence viral entry. The findings highlight the modular evolution of neutralization strategies, where progressive refinement of peripheral interactions enhances potency but increases susceptibility to immune pressure.

biophysics↗

bioRxiv2025

Exploring Dynamic Modulation of Binding, Allostery and Immune Resistance in the SARS-CoV-2 Spike Complexes with Classes of Antibodies Targeting Cryptic Binding Sites: Antibody-Specific Augmentations of Conserved Allosteric Architecture Can Influence Evolution of Viral Escape

The ongoing evolution of SARS-CoV-2 variants has underscored the need to understand not only the structural basis of antibody recognition but also the dynamic and allosteric mechanisms that could underlie complexity of broad and escape-resistant neutralization. In this study, we employed a multi-scale approach integrating structural analysis, hierarchical molecular simulations, mutational scanning and network-based allosteric modeling to dissect how Class 4 antibodies (represented by S2X35, 25F9, and SA55) and Class 5 antibodies (represented by S2H97, WRAIR-2063 and WRAIR-2134) can modulate conformational behavior, binding energetics, allosteric interactions and immune escape patterns of the SARS-CoV-2 spike protein. Using hierarchical simulations of the antibody complexes with the spike protein and ensemble-based mutational scanning of binding interactions we showed that these antibodies through targeting conserved cryptic sites can exert allosteric effects that influence global conformational dynamics in the RBD functional regions. The ensemble-based mutational scanning of binding interactions revealed an excellent agreement with experimentally derived deep mutational scanning (DMS) data accurately recapitulating the known binding hotspots and escape mutations across all studied antibodies. The predicted destabilization values in functional sites are consistent with experimentally observed reductions in antibody binding affinity and immune escape profiles demonstrating that computational models can robustly reproduce and forecast mutation-induced immune escape trends. Using dynamic network modeling we characterized the antibody-induced changes in residue interaction networks and long-range interactions. The results revealed that class 4 antibodies can exhibit distinct patterns of allosteric influence despite targeting overlapping regions, while class 5 antibodies elicit consistently dense and broadly distributed allosteric networks and long-range stabilization of the RBD conformations. Dynamic network analysis identifies a conserved allosteric network core that mediates long-range interactions and incudes antibody specific allosteric extensions that connect the binding interface hotspots with allosteric hubs. This study suggests that mechanisms of binding and immune escape for classes of antibodies targeting cryptic binding sites may be determined by confluence of multiple factors including high-affinity binding and long-range allosteric effects that modulate RBD adaptability and propagation of dynamic constraints which can influence mechanisms of viral evolution and escape.

biophysics↗

bioRxiv2025

Conformational Landscaping and Dynamic Mutational Profiling of Binding Interactions and Immune Escape for Broadly Neutralizing Class I Antibodies with SARS-CoV-2 Spike Protein: Distributed Binding Hotspot Networks Underlie Mechanism of Viral Resistance Against Existing Variants

The rapid evolution of SARS-CoV-2 has underscored the need for a detailed understanding of antibody binding mechanisms to combat immune evasion by emerging variants. In this study, we investigated the interactions between Class I neutralizing antibodies BD55-1205, BD-604, OMI-42, P5S-1H1, and P5S-2B10 and the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein using multiscale modeling which combined coarse-grained simulations and atomistic reconstruction of conformational landscapes together with mutational scanning of the binding interfaces, dynamic profiling of binding and immune escape using molecular mechanics generalized Born surface area (MM-GBSA) analysis. A central theme emerging from this work is the critical role of epitope breadth and interaction diversity in determining an antibody resilience to mutations. BD55-1205 antibody exemplifies the advantages of broad epitope coverage and distributed hotspot mechanisms. By engaging an extensive network of residues across the RBD, BD55-1205 minimizes its dependence on individual side-chain conformations, allowing it to maintain robust binding even when key residues are mutated. This adaptability is particularly evident in its tolerance to mutations at positions such as L455 and F456, which severely compromise other antibodies. The ability of BD55-1205 to sustain cumulative interactions underscores the importance of targeting diverse epitopes through multiple interaction mechanisms, a strategy that enhances resistance to immune evasion while maintaining functional integrity. In contrast, BD-604 and OMI-42, with localized binding mechanisms, are more vulnerable to escape mutations at critical positions such as L455, F456, and A475. P5S-1H1 and P5S-2B10 exhibit intermediate behavior, balancing specificity and adaptability but lacking the robustness of BD55-1205. Mutational scanning identified key residues Y421, Y489, and F456 as critical hotspots for RBD stability and antibody binding, highlighting their dual role in viral fitness and immune evasion. The computational predictions generated through mutational scanning and MM-GBSA analysis demonstrate excellent agreement with experimental data on average antibody escape scores. This study underscores the diversity of binding mechanisms employed by different antibodies and molecular basis for high affinity and excellent neutralization activity of the latest generation of antibodies.

biophysics↗

bioRxiv2025

A Conserved Architecture of Allosteric Communications and Regulatory Hotspots in the KRAS Complexes with Diverse Binding Proteins Controls Mechanisms of Effector Mimicry and Allosteric Modulation : Atomistic Revelations from Molecular Simulations and Mutational Scanning of Binding Energetics and Allostery

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS allostery remain unresolved. In this study, we employ microsecond molecular dynamics simulations, mutational scanning and binding free energy calculations together with dynamic network modeling to dissect how engineered DARPin proteins K27, K55, K13, and K1 engage KRAS through diverse molecular mechanisms ranging from effector mimicry to conformational restriction and allosteric modulation. Mutational scanning across all four DARPin systems identifies a core set of evolutionarily constrained residues that function as universal hotspots in KRAS recognition. KRAS residues I36, Y40, M67, and H95 consistently emerge as critical contributors to binding stability. Binding free energy computations show that despite similar binding modes K27 relies heavily on electrostatic contributions from major binding hotspots while K55 exploits a dense hydrophobic cluster enhancing its effector-mimetic signature. Allosteric binders K13 and K19, by contrast, stabilize a KRAS-specific pocket introducing new hinges and bottlenecks that rewire the communication architecture of KRAS without full immobilization. Network-based analysis reveals a strikingly consistent theme: despite their distinct mechanisms of recognition, all systems engage a unifying allosteric architecture that spans multiple functional motifs. This architecture is not only preserved across complexes but also mirrors the intrinsic communication framework of KRAS itself, where specific residues function as central hubs transmitting conformational changes across the protein. By integrating dynamic profiling, energetic mapping, and network-modeling our study provides a multi-scale mechanistic roadmap for targeting KRAS, revealing how engineered proteins can exploit both conserved motifs and isoform-specific features to enable precision modulation of KRAS signaling in oncogenic contexts.

biophysics↗

bioRxiv2025

Exploring Diverse Binding Mechanisms of Broadly Neutralizing Antibodies S309, S304, CYFN-1006 and VIR-7229 Targeting SARS-CoV-2 Spike Omicron Variants: Integrative Computational Modeling Reveals Balance of Evolutionary and Dynamic Adaptability in Shaping Molecular Determinants of Immune Escape

Evolution of SARS-CoV-2 has led to the emergence of variants with increased immune evasion capabilities, posing significant challenges to antibody-based therapeutics and vaccines. The cross-neutralization activity of antibodies against Omicron variants is governed by a complex and delicate interplay of multiple energetic factors and interaction contributions. In this study, we conducted a comprehensive analysis of the interactions between the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein and four neutralizing antibodies S309, S304, CYFN1006, and VIR-7229. Using integrative computational modeling that combined all-atom molecular dynamics (MD) simulations, mutational scanning, and MM-GBSA binding free energy calculations, we elucidated the structural, energetic, and dynamic determinants of antibody binding. Our findings reveal distinct dynamic binding mechanisms and evolutionary adaptation driving broad neutralization effect of these antibodies. We show that S309 targets conserved residues near the ACE2 interface, leveraging synergistic van der Waals and electrostatic interactions, while S304 focuses on fewer but sensitive residues, making it more susceptible to escape mutations. The analysis of CYFN-1006.1 and CYFN-1006.2 antibody binding highlights broad epitope coverage with critical anchors at T345, K440, and T346, enhancing its efficacy against variants carrying the K356T mutation which caused escape from S309 binding. Our analysis of broadly potent VIR-7229 antibody binding to XBB.1.5 and EG.5 Omicron variants emphasized a large and structurally complex epitope, demonstrating certain adaptability and compensatory effects to F456L and L455S mutations. Mutational profiling identified key residues crucial for antibody binding, including T345, P337, and R346 for S309, and T385 and K386 for S304, underscoring their roles as evolutionary "weak spots" that balance viral fitness and immune evasion. The results of this energetic analysis demonstrate a good agreement between the predicted binding hotspots and critical mutations with respect to the latest experiments on average antibody escape scores. The results of this study dissect distinct energetic mechanisms of binding and importance of targeting conserved residues and diverse epitopes to counteract viral resistance. Broad-spectrum antibodies CYFN1006 and VIR-7229 maintain efficacy across multiple variants and achieve neutralization by targeting convergent evolution hotspots while enabling tolerance to mutations in these positions through structural adaptability and compensatory interactions at the binding interface. The results of this study underscore the diversity of binding mechanisms employed by different antibodies and molecular basis for high affinity and excellent neutralization activity of the latest generation of antibodies.

biophysics↗

bioRxiv2025

Atomistic Profiling of KRAS Interactions with Monobodies and Affimer Proteins Through Ensemble-Based Mutational Scanning Unveils Conserved Residue Networks Linking Cryptic Pockets and Regulating Mechanisms of Binding, Specificity and Allostery

KRAS, a historically "undruggable" oncogenic driver, has eluded targeted therapies due to its lack of accessible binding pockets in its active state. This study investigates the conformational dynamics, binding mechanisms, and allosteric communication networks of KRAS in complexes with monobodies (12D1, 12D5) and affimer proteins (K6, K3, K69) to characterize the binding and allosteric mechanisms and hotspots of KRAS binding. Through molecular dynamics simulations, mutational scanning, binding free energy analysis and network-based analyses, we identified conserved allosteric hotspots that serve as critical nodes for long-range communication in KRAS. Key residues in {beta}-strand 4 (F78, L80, F82), -helix 3 (I93, H95, Y96), {beta}-strand 5 (V114, N116), and -helix 5 (Y157, L159, R164) consistently emerged as hotspots across diverse binding partners, forming contiguous networks linking functional regions of KRAS. Notably, {beta}-strand 4 acts as a central hub for propagating conformational changes, while the cryptic allosteric pocket centered around H95/Y96 positions targeted by clinically approved inhibitors was identified as a universal hotspot for both binding and allostery. The study also reveals the interplay between structural rigidity and functional flexibility, where stabilization of one region induces compensatory flexibility in others, reflecting KRASs adaptability to perturbations. We found that monobodies stabilize the switch II region of KRAS, disrupting coupling between switch I and II regions and leading to enhanced mobility in switch I of KRAS. Similarly, affimer K3 leverages the 3-helix as a hinge point to amplify its effects on KRAS dynamics. Mutational scanning and binding free energy analysis highlighted the energetic drivers of KRAS interactions. revealing key hotspot residues, including H95 and Y96 in the 3 helix, as major contributors to binding affinity and selectivity. Network analysis identified {beta}-strand 4 as a central hub for propagating conformational changes, linking distant functional sites. The predicted allosteric hotspots strongly aligned with experimental data, validating the robustness of the computational approach. Despite distinct binding interfaces, shared hotspots highlight a conserved allosteric infrastructure, reinforcing their universal importance in KRAS signaling. The results of this study can inform rational design of small-molecule inhibitors that mimic the effects of monobodies and affimer proteins, challenging the "undruggable" reputation of KRAS.

biophysics↗

bioRxiv2025

Accurate Characterization of the Allosteric Energy Landscapes, Binding Hotspots and Long-Range Communications for KRAS Complexes with Effector Proteins : Integrative Approach Using Microsecond Molecular Dynamics, Deep Mutational Scanning of Binding Energetics and Allosteric Network Modeling

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Oncogenic mutations in KRAS, including G12V, G13D, and Q61R, drive constitutive activation and hyperactivation of signaling pathways, contributing to cancer progression. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS binding and allostery by which oncogenic mutations enhance KRAS-RAF1 binding and signaling remain incompletely understood. In this study, we employ microsecond molecular dynamics simulations, Markov State Modeling, mutational scanning and binding free energy calculations together with dynamic network modeling to elucidate the effect of KRAS mutations and characterize the thermodynamic and allosteric drivers and hotspots of KRAS binding and oncogenic activation. Our simulations revealed that oncogenic mutations stabilize the open active conformation of KRAS by differentially modulating the flexibility of the switch I and switch II regions, thereby enhancing RAF1 binding affinity. The G12V mutation rigidifies both switch I and switch II, locking KRAS in a stable, active state. In contrast, the G13D mutation moderately reduces switch I flexibility while increasing switch II dynamics, restoring a balance between stability and flexibility. The Q61R mutation induces a more complex conformational landscape, characterized by the increased switch II flexibility and expansion of functional macrostates, which promotes prolonged RAF1 binding and signaling. Mutational scanning of KRAS-RAF1 complexes identified key binding affinity hotspots, including Y40, E37, D38, and D33, and together with the MM-GBSA analysis revealed the hotspots leverage synergistic electrostatic and hydrophobic binding interactions in stabilizing the KRAS-RAF1 complexes. Network-based analysis of allosteric communication identifies critical KRAS residues (e.g., L6, E37, D57, R97) that mediate long-range interactions between the KRAS core and the RAF1 binding interface. The central {beta}-sheet of KRAS emerges as a hub for transmitting conformational changes, linking distant functional sites and facilitating allosteric regulation. Strikingly, the predicted allosteric hotspots align with experimentally identified allosteric binding hotspots that define the energy landscape of KRAS allostery. This study highlights the power of integrating computational modeling with experimental data to unravel the complex dynamics of KRAS and its mutants. The identification of binding hotspots and allosteric communication routes offers new opportunities for developing targeted therapies to disrupt KRAS-RAF1 interactions and inhibit oncogenic signaling. Our results underscore the potential of computational approaches to guide the design of allosteric inhibitors and mutant-specific therapies for KRAS-driven cancers.

biophysics↗

bioRxiv2025

Decoding Mechanisms of PTEN Missense Mutations in Cancer and Autism Spectrum Disorder using Interpretable Machine Learning Approaches

Missense mutations in oncogenic proteins that are concurrently associated with neurodevelopmental disorders have garnered significant attention. Phosphatase and tensin homolog (PTEN) serves as a paradigmatic model for mapping its mutational landscape and identifying genotypic predictors of distinct phenotypic outcomes, including cancer and autism spectrum disorder (ASD). Despite extensive research into the genotype-phenotype correlations of PTEN mutations, the mechanisms underlying the dual association of specific PTEN mutations with both cancer and ASD (PTEN-cancer/ASD mutations) remain elusive. This study introduces an integrative approach that combines machine learning (ML) with structural dynamics to elucidate the molecular effects of PTEN-cancer/ASD mutations. Analysis of biophysical and network biology-based signatures reveals a complex energetic and functional landscape. Subsequently, an ML model and corresponding integrated score were developed to classify and predict PTEN-cancer/ASD mutations, underscoring the significance of protein dynamics in predicting cellular phenotypes. Further molecular dynamics simulations demonstrated that PTEN-cancer/ASD mutations induce dynamic alterations characterized by open conformational changes restricted to the P loop and coupled with inter-domain allosteric regulation. This research aims to enhance the genotypic and phenotypic understanding of PTEN-cancer/ASD mutations through an interpretable ML model integrated with structural dynamics analysis. By identifying shared mechanisms between cancer and ASD, the findings pave the way for the development of novel therapeutic strategies.

bioinformatics↗