Biology subjects

Erman, B.

Publications and source records attributed to Erman, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Flexibility Drives Information Flow in Proteins: Fluctuation Potential Gradients Dictate Directional Entropy Transfer

Allosteric communication in biomacromolecules is fundamentally governed by thermal fluctuation gradients, yet standard Gaussian Network Models (GNMs) treat atomic contacts as uniform, binary couplings without differentiating core constraints from solvent-exposed surface flexibility. Here, we present an analytical matrix framework that incorporates continuous distance-dependent weighting into the Kirchhoff matrix L. This formulation captures the steep steric constraints of hydrophobic core packing versus peripheral surface loops while strictly recovering the classic unweighted GNM as a high-temperature limit (T [->]{infty}). Using Schur complements of partitioned joint covariance matrices, we show that conditional fluctuation variances and higher-order entropy-transfer terms reduce analytically to exact ratios of submatrix determinants (covariance minors), eliminating the need for fitting parameters or molecular dynamics trajectories. Applied to KRAS (PDB: 6GOD), this framework constructs an integrated directional entropy-transfer asymmetry map. Order-1 minors (h(i) = Kii) establish a single-node fluctuation potential gradient, while order-2 minors (Rij) define pairwise channel bandwidths. Higher-order minors show multi-body spatial coupling: order-4 minors identify rigid core residues such as Phe156 as strategic interlobe relay hubs linking Lobe 1 and Lobe 2, and an order-3 triad cooperation index demonstrates that signal transmission from Switch II (Gln61) to Gly60 and Phe156 converges on a single, mechanically integrated allosteric sector. By deriving directional information flow directly from experimental atomic displacement parameters, this approach establishes a rigorous, computationally efficient framework for mapping allosteric networks across structural ensembles.

bioinformatics↗

bioRxiv2026

The Geometry of Allostery: A Laplacian Minor Hierarchy for Many-Body Protein Communication

Quantifying how cooperative, many-body relationships drive allostery in protein networks remains a major challenge. To address this, we develop the Laplacian minor hierarchy, a mathematical framework that characterizes the geometric invariants of a protein network. Lower-order minors yield standard metrics including the partition function and effective distances, whereas higher-order minors define novel topological measures: cooperation indices, each bounded between zero and one, that characterize pathway correlations at increasing levels of complexity, the third-order minor determines whether allosteric pathways are correlated or uncorrelated, and the fourth-order minor quantifies how distinct pathways communicate through intermediary residues. We apply this framework to analyze the evolutionary adaptation of the PSD95pdz3 domain from Class I to Class II ligand specificity via mutations G330T and H372A. The cooperation index demonstrates a distinct evolutionary hierarchy: the G330T mutation establishes distributed pathway couplings that the H372A mutation subsequently exploits, whereas H372A alone produces minimal global changes. Furthermore, the fourth-order analysis identifies His317 as a critical intermediary node bridging the class-switching (330-372) and class-bridging (330-400) allosteric pathways. These results demonstrate that allosteric dependencies emerge only when mutations accumulate in specific combinations, with a hierarchical organization of pathways structured around position 330 and intermediary nodes His317 and Phe400. Rather than predicting allosteric mechanisms, this framework provides a mechanistic explanation for why and how allostery emerges during protein evolution.

bioinformatics↗

bioRxiv2026

Spanning-Tree Thermostatistics of Protein Allostery: An Exact Kirchhoff Framework with Application to Oncogenic KRAS

This study introduces a statistical mechanical framework for allosteric communication in proteins based on the spanning-tree ensemble of residue contact networks. By representing C protein backbones as weighted graphs, we identify each spanning tree as a topological microstate. The canonical partition function is evaluated analytically via the determinant of the reduced weighted Kirchhoff (Laplacian) matrix, allowing for the derivation of global thermodynamic functions (including Helmholtz free energy, internal energy, entropy, and heat capacity) without stochastic sampling. Allosteric channels between specific residue pairs are defined as sub-ensembles containing unique simple paths. Using the Burton-Pemantle theorem and the Moore-Penrose pseudoinverse of the graph Laplacian, we compute path probabilities and channel-specific thermodynamics. This methodology enables a decomposition of channel heat capacity into energetic and topological components and quantifies residue-level allosteric importance through fractional contributions to the channel partition function. The framework was applied to the G12D mutation in KRAS, comparing wild-type (PDB: 6GOD) and mutant (PDB: 6GOF) structures. Results show that while global thermodynamic properties remain highly conserved across the tight structural superposition, channel-level analysis shows a substantial internal redistribution of allosteric importance among intermediate residues, highlighted by the primary 12-61 signaling axis and distal routes (including shifts in residues such as Q61 and F156). Operating on C backbone geometry, these topological shifts provide predictive hypotheses for subsequent molecular dynamics and experimental testing. Overall, this approach offers a rigorous, parameter-robust framework for understanding how point mutations perturb distal signaling networks.

biophysics↗

bioRxiv2026

Molecular Thermodynamics of KRAS Activation

The GTPase KRAS executes a conformational switch between a GTP-bound active state and a GDP-bound inactive state, a process central to oncogenic signalling. However, the structural basis of this switching at the level of residue-contact organization remains incompletely characterized by traditional binary structural models. Here, we present a statistical-mechanical generalization of the Gaussian Network Model (GNM) by constructing spanning-tree partition functions for residue-contact graphs using the weighted Kirchhoff Laplacian in conjunction with the Matrix-Tree Theorem. Within this framework, the standard GNM is recovered in the high-temperature limit, whereas the present formulation enables a continuous Boltzmann-weighted ensemble analysis. We compute the network free energy F, mean contact energy[E] , heat capacity Cv, and thermodynamic entropy S across an effective temperature sweep that maps the combinatorial diversity of the contact network, thereby probing the topological landscape rather than structural melting. Differential analysis reveals that KRAS activation reflects a systematic entropy-enthalpy compensation mechanism: the active state incurs a systematic energetic penalty ({Delta}[E] > 0) that is offset by a marked gain in conformational entropy ({Delta}S > 0), with a free-energy crossover occurring at kT {approx} 2.41 [A]. Edge marginal inclusion probabilities, obtained via effective-resistance theory, identify Switch I (residues 25-40) as the primary allosteric locus of nucleotide-driven network reorganization. This approach provides a thermodynamically grounded perspective on KRAS allostery, quantitatively demonstrating how network architecture enables functional versatility through entropy-driven conformational flexibility. Entropy-driven KRAS activation revealed by spanning-tree thermodynamicsSpanning-tree partition functions built from weighted residue-contact graphs show that KRAS activation involves entropy-enthalpy compensation. The GDP-bound network (left) is dominated by a small number of low-energy spanning trees, whereas the GTP-bound network (right) accesses a larger ensemble with greater topological diversity. The active state pays an energetic cost ({Delta}[E] > 0) offset by a conformational entropy gain ({Delta}S > 0), with Switch I (residues 25-40, dashed box) emerging as the primary site of nucleotide-driven network reorganisation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=118 SRC="FIGDIR/small/707891v2_ufig1.gif" ALT="Figure 1"> View larger version (12K): org.highwire.dtl.DTLVardef@e4fa0borg.highwire.dtl.DTLVardef@dbf90corg.highwire.dtl.DTLVardef@1bb8f94org.highwire.dtl.DTLVardef@cf46a4_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Intrinsic Error Correction in Protein Allostery: Quantifying Noise Suppression via Spanning Tree Statistics

Allosteric regulation in proteins arises from collective dynamics distributed over networks of residue contacts, but how multiple communication pathways contribute to signal transmission and noise suppression remains unclear. Here we develop a spanning-tree-based framework to quantify allosteric communication as an ensemble of interacting pathways in protein contact networks. We introduce a dynamic distance measure linking local perturbations of residue interactions to global changes in network entropy, establishing a local-to-global scaling between local dynamics and global sensitivity. Using spanning-tree calculus, we derive exact probabilities for all simple paths connecting pre-specified functional residue pairs. This enables a comparison between an approximate description based on uniform path usage and a topology-aware description in which path probabilities are determined by the Burton-Pemantle theorem and reflect network dependencies. From these path ensembles, we define corresponding signal-to-noise ratios and quantify how pathway multiplicity and statistical weighting shape noise suppression. Applied to KRAS and to 20 additional allosteric proteins spanning diverse functional classes, the analysis shows large variability in path usage, entropy reduction, and signal-to-noise enhancement, while consistently demonstrating that topology-aware weighting concentrates signal transmission onto dominant short pathways. This suggests that the robustness of allosteric signaling is a fundamental emergent property of protein contact topology. These results provide a quantitative framework linking protein structure, dynamics, and information flow, and show that robustness in allosteric communication, manifested as noise suppression through pathway redundancy, can be interpreted as an intrinsic error-correction or noise averaging mechanism arising from network topology.

bioinformatics↗

bioRxiv2025

Mapping the Peptide Interaction Fingerprint of the Behcet's disease associated HLA-B*51

The strongest genetic risk factor for Behcets disease, a relapsing inflammatory disorder marked by recurrent mucocutaneous ulcers and uveitis, is an allele of the class I major histocompatibility complex (MHC-I) molecule, which presents intracellular peptides to CD8+ T cells. The molecular mechanisms linking the peptide preferences of this allele (HLA-B*51:01) to dysregulated immunity remain unclear, limiting efforts to design peptide-based modulators of antigen presentation. Here, we define HLA-B*51:01s peptide selection rules by mapping the "interaction fingerprint" of 36 self-peptides using long-duration all-atom MD simulations. These uncovered a conserved hydrophobic-polar blueprint that is tuned by peptide length. High-speed atomic force microscopy rate in silico pulling experiments suggest a three-tier hierarchy of mechanical resilience: 9-mers resist the highest forces, 8-mers exhibit intermediate resistance, and 10/11-mers rupture most easily. Our comprehensive analysis provides an atomistic framework for understanding the molecular mechanisms underlying HLA-B*51:01 pathobiology and offers quantitative parameters to guide the design of therapeutic peptides or small molecules to modulate antigen presentation in Behcets disease. STATEMENT OF SIGNIFICANCEBehcets disease is strongly linked to HLA-B*51:01, a molecule that displays protein fragments to killer T cells, yet how this allele selects its peptides is poorly understood. Here, we combine all-atom equilibrium molecular dynamics simulations with steered MD pulling simulations that mimic high-speed atomic force microscopy experiments to map how 36 self-peptides of different lengths engage the HLA-B*51:01 groove. We uncover a conserved hydrophobic-polar interaction blueprint and show that 9-mers form the mechanically most resilient complexes, whereas shorter or longer peptides detach more easily under force. This length-tuned interaction "fingerprint" provides an atomistic framework for understanding HLA-B*51:01-driven immune dysregulation and guides the rational design of peptide-based or small-molecule modulators for Behcets disease.

biophysics↗

bioRxiv2025

Maximum Entropy in the Gaussian Network Model: A Thermodynamic Reference State for Protein Dynamics

Allosteric communication in proteins relies on network connectivity patterns that channel conformational signals between distant sites. We introduce a unified mathematical framework based on three complementary measures of network organization derived from a single quantity. The first, the dynamic distance Rij, quantifies the mean-squared relative fluctuation between residue pairs. From this foundation, we derive two further metrics: the edge centrality, which identifies contacts critical for global connectivity by measuring their recurrence across all possible communication pathways, and the entropy sensitivity, which quantifies how perturbations to specific interactions alter system-wide flexibility. The mathematical structure shows that both topological centrality and thermodynamic sensitivity are linear functions of the dynamic distance. This derived unification demonstrates that residue pairs with high dynamic dissimilarity simultaneously function as flexible bottlenecks essential for allosteric communication. Applied to the oncoprotein KRAS, all three measures converge to identify the same residue pairs, corresponding to experimentally known allosteric sites. This convergence provides a unified graph-theoretical explanation for their functional importance. Analysis of the G12D mutation and adagrasib binding shows how local perturbations rewire global communication pathways, highlighting specific residue pairs that gain or lose importance as network bottlenecks.

bioinformatics↗

bioRxiv2025

Extending the Gaussian Network Model: Integrating Local, Allosteric, and Structural Factors for Improved Residue-Residue Correlation Analysis

The Gaussian Network Model (GNM) has been successful in explaining protein dynamics by modeling proteins as elastic networks of alpha carbons connected by harmonic springs. However, its uniform interaction assumption and neglect of higher-order correlations limit its accuracy in predicting experimental B-factors and residue cross-correlations critical for understanding allostery and information transfer. This study introduces an information-theoretic enhancement to the GNM, incorporating mutual information-based corrections to the Kirchhoff matrix to account for multi-body interactions and contextual residue dynamics. By iteratively optimizing B-factor predictions and applying a Monte Carlo-driven maximum entropy approach to refine covariances, our method achieves significant improvements, reducing RMSDs between predicted and experimental B-factors by 26-46% across eight representative proteins. The model contextualizes residue assignments based on local density, secondary structure, solvent exposure, and allosteric roles, showing complex dynamic patterns beyond simple neighbor counts. Enhanced predictions of mutual information and entropy transfer in proteins like KRAS highlight improved capture of allosteric communication pathways. This evolvable framework, capable of incorporating additional effects and utilizing contextual residue assignments, enables precise studies of mutation effects on protein dynamics, with improved cross-correlation predictions potentially increasing accuracy in drug design and function prediction.

biophysics↗

bioRxiv2024

Dynamic Coupling and Entropy Changes in KRAS G12D Mutation: Insights into Molecular Flexibility, Allostery and Function

The oncogenic G12D mutation in KRAS is a major driver of cancer progression, yet the complete mechanism by which this mutation alters protein dynamics and function remains incompletely understood. Here, we investigate how the G12D mutation alters KRASs conformational landscape and residue-residue interactions using molecular dynamics simulations coupled with entropy calculations and mutual information (MI) analysis. We demonstrate that the mutation increases local entropy at key functional residues (D12, Y32, G60, and Q61), and introduces new peaks to the Ramachandran angles, disrupting the precise structural alignment necessary for GTP hydrolysis. Notably, while individual residue entropy increases, joint entropy analysis shows a complex reorganization pattern. MI analysis identifies enhanced dynamic coupling between distant residues, suggesting that the mutation establishes new long-range interactions that stabilize the active state. These findings show how G12D mutation redefines KRASs dynamic network, leading to persistent activation through enhanced residue coupling rather than mere local disruption. Our results suggest novel therapeutic strategies focused on modulating protein dynamics rather than targeting specific binding sites, potentially offering new approaches to combat KRAS-driven cancers. HighlightsO_LIThe G12D mutation increases entropy and mutual information between residue pairs, resulting in more correlated motions due to enhanced flexibility. C_LIO_LICharacteristic features of local and long-range allostery in wild-type (WT) and G12D KRAS are determined using structural proximity and mutual information analysis. C_LIO_LIThe dynamics of KRAS interactions with its natural ligand atoms and the coordination of bridging water molecules provide insights into the changes induced by the G12D mutation. C_LI

bioinformatics↗

bioRxiv2024

Fluctuation-Driven Synergy, Redundancy, Signal to Noise Ratio and Error Correction in Protein Allostery

This study explores the relationship between residue fluctuations and molecular communication in proteins, emphasizing the role of these dynamics in allosteric regulation. We employ computational tools including the Gaussian Network Model, mutual information, and interaction information, to analyze how stochastic interactions among residues contribute to functional interactions while also introducing noise. Our approach is based on the postulate that residues experience continuous stochastic bombardment from impulses generated by their neighbors, forming a complex network characterized by small-world scaling topology. By mapping these interactions through the Kirchhoff matrix framework, we demonstrate how conserved correlations enhance signaling pathways and provide stability against noise-like fluctuations. Notably, we highlight the importance of selecting relevant eigenvalues to optimize the signal-to-noise ratio in our analyses, a topic that has yet to be thoroughly investigated in the context of residue fluctuations. This work underscores the significance of viewing proteins as adaptive information processing systems, and emphasizes the fundamental mechanisms of biological information processing. The basic idea of this paper is the following: Given two interacting residues on an allosteric path, what are the contributions of the remaining residues on this interaction. This naturally leads to the concept of synergy, redundancy and noise in proteins, which we analyze in detail for three proteins CheY, Tyrosine Phosphatase and {beta}-Lactoglobulin.

bioinformatics↗

bioRxiv2024

Role of Mutual Information Profile Shifts in Assessing the Pathogenicity of Mutations on Protein Functions: The case of Pyrin Mutations

This paper presents a novel method to assess the pathogenicity of Pyrin protein mutations by using mutual information (MI) as a measure to quantify the correlation between residue motions or fluctuations and associated changes affecting the phenotype. The concept of MI profile shift is presented to quantify changes in MI upon mutation, revealing insights into residue-residue interactions at critical positions. We apply this method to the Pyrin protein variants, which are associated with an autosomal recessively inherited disease called familial Mediterranean fever (FMF) since the available tools do not help predict the pathogenicity of the most penetrant variants. We demonstrate the utility of MI profile shifts in assessing the effects of mutations on protein stability, function, and disease phenotype. The importance of MI shifts, for the pyrin example the negative shifts, as indicators of severe functional effects is emphasized, along with exploring potential compensatory mechanisms indicated by positive MI shifts, which are otherwise random and inconsequential. The paper also discusses challenges in relating MI profile changes to disease severity and advocates for comprehensive analysis considering genetic, environmental, and stochastic factors. Overall, this study provides insights into the molecular mechanisms underlying the pathogenesis of FMF and offers a framework for identifying potential therapeutic targets based on MI profile changes induced by mutations.

bioinformatics↗

bioRxiv2024

Dynamically driven correlations in elastic net models reveal sequence of events and causality in proteins

Protein dynamics orchestrate allosteric regulation, but elucidating the sequence of events and causal relationships within these intricate processes remains challenging. We introduce the Dynamically Perturbed Gaussian Network Model (DP-GNM), a novel approach that uncovers the directionality of information flow within proteins. DP-GNM leverages time-dependent correlations to achieve two goals: identifying driver and driven residues and revealing communities of residues exhibiting synchronized dynamics. Applied to wild type and mutated structures of Cyclophilin A, DP-GNM unveils a hierarchical network of information flow, where key residues initiate conformational changes that propagate through the protein in a directed manner. This directional causality illuminates the intricate relationship between protein dynamics and allosteric regulation, providing valuable insights into protein function and potential avenues for drug design. Furthermore, DP-GNMs potential to elucidate dynamics under periodic perturbations like the circadian rhythm suggests its broad applicability in understanding complex biological processes governed by environmental cycles.

bioinformatics↗

bioRxiv2023

Fine Tuning Rigid Body Docking Results Using the Dreiding Force Field: A Computational Study of 36 Known Nanobody-Protein Complexes

This paper aims to understand the binding strategies of a nanobody-protein pair by studying known complexes. Rigid body protein-ligand docking programs produce several complexes, called decoys, which are good candidates with high scores of shape complementarity, electrostatic interactions, desolvation, buried surface area, and Lennard-Jones potentials. It is not known which decoy represents the true structure. We studied thirty-seven nanobody-protein complexes from the Single Domain Antibody Database, sd-Ab DB, http://www.sdab-db.ca/. For each structure, a large number of decoys are generated using the Fast Fourier Transform algorithm of the software ZDOCK. The decoys were ranked according to their target protein-nanobody interaction energies, calculated by using the Dreiding Force Field, with rank 1 having the lowest interaction energy. Out of thirty-six PDB structures, twenty-five true structures were predicted as rank 1. Eleven of the remaining structures required [A]ngstrom size rigid body translations of the nanobody relative to the protein to match the given PDB structure. After the translation the Dreiding interaction (DI) energies of all complexes decreased and became rank 1. In one case, rigid body rotations as well as translations of the nanobody were required for matching the crystal structure. We used a Monte Carlo algorithm that randomly translates and rotates the nanobody of a decoy and calculates the DI energy. Results show that rigid body translations and the DI energy are sufficient for determining the correct binding location and pose of ZDOCK created decoys. A survey of the sd-Ab DB showed that each nanobody makes at least one salt bridge with its partner protein, indicating that salt bridge formation is an essential strategy in nanobody-protein recognition. Based on the analysis of the thirty-six crystal structures and evidence from existing literature, we propose a set of principles that could be used in the design of nanobodies.

bioinformatics↗

bioRxiv2022

How many bits of information can be transferred between residues in a protein and how fast?

Time resolved Raman and infrared spectroscopy experiments show the basic features of information transfer between residues in proteins. Here, we present the theoretical basis of information transfer using a simple elastic net model and recently developed entropy transfer concept in proteins. Mutual information between two residues is a measure of communication in proteins which shows the maximum amount of information that may be transferred between two residues. However, it does not explain the actual amount of transfer nor the transfer rate of information between residues. For this, dynamic equations of the system are needed. We used the Schreiber theory of information transfer and the Gaussian network Model of proteins, together with the solution of the Langevin equation, to quantify allosteric information transfer. Results of the model are in perfect agreement with ultraviolet resonance Raman measurements. Analysis of the allosteric protein Human NAD-dependent isocitrate dehydrogenase shows that a multitude of paths contribute collectively to information transfer. While the peak values of information transferred are small relative to information content of residues, considering the estimated transfer rates, which are in the order of megabits per second, sustained transfer during the activity time-span of proteins may be significant.

biophysics↗

bioRxiv2021

Sibling rivalry among the ZBTB transcription factor family: homo vs. heterodimers

The BTB domain is an oligomerization domain found in over 200 proteins encoded in the human genome. In the family of BTB domain and Zinc Finger-containing (ZBTB) transcription factors, 49 members share the same protein architecture. The N-terminal BTB domain is structurally conserved among the family members and serves as the dimerization site while the C-terminal zinc finger motifs mediate DNA binding. The available BTB domain structures from this family reveal a natural inclination for homodimerization. In this study we investigated the potential for heterodimer formation in the cellular environment. We selected five BTB homodimers and four heterodimer structures. We performed in vitro binding assays with fluorescent protein-BTB domain fusions to assess dimer formation. We tested the binding of several BTB pairs, and we were able to confirm the heterodimeric physical interaction between the BTB domains of PATZ1 and PATZ2, previously reported only in an interactome mapping experiment. We also found this pair to be co-expressed in several immune system cell types. Finally, we used the available structures of BTB domain dimers and newly constructed models in extended molecular dynamics simulations (500 ns) to understand the energetic determinants of homo and heterodimer formation. We conclude that heterodimer formation, although frequently described as less preferred than homodimers, is a possible mechanism to increase the combinatorial specificity of this transcription factor family.

molecular biology↗

bioRxiv2021

Gaussian Network Model Revisited: Effects of Mutation and Ligand Binding on Protein Behavior

The coarse-grained Gaussian Network model, GNM, considers only the alpha carbons of the folded protein. Therefore it is not directly applicable to the study of mutation or ligand binding problems where atomic detail is required. This shortcoming is improved by including the local effect of heavy atoms in the neighborhood of each alpha carbon into the Kirchoff Adjacency Matrix. The presence of other atoms in the coordination shell of each alpha carbon diminishes the magnitude of fluctuations of that alpha carbon. But more importantly, it changes the graph-like connectivity structure, i.e., the Kirchoff Adjacency Matrix of the whole system which introduces amino acid specific and position specific information into the classical coarse-grained GNM which was originally modelled in analogy with phantom network theory of rubber elasticity. With this modification, it is now possible to make predictions on the effects of mutation and ligand binding on residue fluctuations and their pair-correlations. We refer to the new model as all-atom GNM. Using examples from published data we show that the all-atom GNM applied to in silico mutated proteins and to their laboratory mutated structures gives similar results. Thus, loss and gain of correlations, which may be related to loss and gain of function, may be studied by using simple in silico mutations only.

bioinformatics↗

bioRxiv2021

Binding mechanism of neutralizing Nanobodies targeting SARS-CoV-2 Spike Glycoprotein

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters human cells upon binding of its spike (S) glycoproteins to ACE2 receptors. Several nanobodies neutralize SARS-CoV-2 infection by binding to the receptor-binding domain (RBD) of S protein, but the underlying mechanism is not well understood. Here, we identified an extended network of pairwise interactions between RBD and nanobodies H11-H4, H11-D4, and Ty1 by performing all-atom molecular dynamics (MD) simulations. Simulations of the nanobody-RBD-ACE2 complex revealed that H11-H4 more strongly binds to RBD without overlapping with ACE2 and triggers dissociation of ACE2 due to electrostatic repulsion. In comparison, Ty1 binding results in dissociation of ACE2 from RBD due to an overlap with the ACE2 binding site, whereas H11-D4 binding does not trigger ACE2 dissociation. Mutations in SARS-CoV-2 501Y.V1 and 501.V2 variants resulted in a negligible effect on RBD-ACE2 binding. However, the 501.V2 variant weakened H11-H4 and H11-D4 binding while strengthening Ty1 binding to RBD. Our simulations indicate that all three nanobodies can neutralize 501Y.V1 while Ty1 is more effective against the 501.V2 variant.

immunology↗

bioRxiv2020

Synchronous and Asynchronous Response in Dynamically Perturbed Proteins

We developed a Dynamic Gaussian Network Model to study perturbation and response in proteins. The model is based on the solution of the Langevin equation in the presence of noise and perturbation. A residue is perturbed periodically with a given frequency and the response of other residues is determined in terms of a storage and loss modulus of the protein. The amount of work lost upon periodic perturbation and the residues that contribute significantly to the lost work is determined. The model shows that perturbation introduces new dynamic correlations into the system with time delayed synchronous and asynchronous components. Residues whose perturbation induces large correlations in the protein and those that do not lead to correlations may be identified. The model is used to investigate the dynamic modulation of nanobodies. Despite its simplicity, the model explains several features of perturbation and response such as the role of loops and linkers in perturbation, dispersion of work of perturbation, and information transfer through preexisting pathways, all shown to be important factors in allostery.

biophysics↗