Biology subjects

Hacisuleyman, A.

Publications and source records attributed to Hacisuleyman, A..

18 recordsLinked to original sources

bioRxiv2026

Heimdall SNAREs reveal an ancestral architecture of eukaryotic SNARE complexes

SNARE proteins are central to eukaryotic cell biology, where they drive membrane fusion by assembling into four-helix bundles that bring opposing membranes into close proximity. Their ubiquitous role in intracellular membrane trafficking raises fundamental questions about their evolutionary origin and the emergence of eukaryotic cellular complexity. Here, we characterize SNARE-like proteins from Heimdallarchaeota, a lineage within the Asgard archaea that is closely related to eukaryotes. We show that a Heimdall SNARE forms a stable homotetrameric four-helix bundle in solution and determine its structure by X-ray crystallography. Strikingly, the Heimdall complex closely resembles eukaryotic SNARE complexes in its overall architecture and core organization, despite being composed of four identical subunits. Molecular dynamics simulations further reveal conserved physical features of the bundle alongside distinctive properties of its central 0-layer. These findings demonstrate that the characteristic four-helix architecture of SNAREs predates the emergence of eukaryotic cells and provide structural insight into the evolution of the membrane fusion machinery.

biochemistry↗

bioRxiv2026

An alphabet of allostery: a transferable transfer entropy contact patterns predicts allosteric-site character and conformational rewiring

Allostery is increasingly understood as the propagation of dynamic information through a protein, yet the computational descriptors of that flow are computed one structure at a time and carry no transferable, sequence-level prior. Here we build an alphabet of allostery: a dictionary of local contact words, short sequence windows anchored by three-to four-residue spatial cliques, each carrying a distribution of net Gaussian network model transfer entropy scores pooled over a non-redundant set of Protein Data Bank structures. The alphabet consists of 131,611,766 unique words drawn from 212,860,934 clique observations. Projecting any proteins sequence and structure onto this dictionary yields a per-residue allosteric track, net TE, source, sink, and switch channels, with no system-specific fitting. Validating against the Allosteric Database, annotated allosteric-site residues behave as transfer entropy sinks (information receivers): the sink channel discriminates sites from the rest of the protein with pooled ROC-AUC = 0.543 over 646,629 residues (permutation z = 19.1), an effect small in magnitude but overwhelmingly significant and robust to word-frequency leakage. The directional channels are mechanistically informative in a two-state experiment on nine canonical allosteric proteins: source residues predict the largest apo[->]holo conformational rewiring (meta-analytic Spearman {rho} = +0.106, positive in 7/9 proteins) while sink residues mark the most conformationally stable positions ({rho} = -0.105, 8/9). Sinks thus mark where allosteric signal is received and sources mark where it drives motion. Finally, we mine the most context-variable words into a compact, hydrophobic-enriched switch vocabulary that we propose as a design dictionary for engineering allosteric mechanisms.

bioinformatics↗

bioRxiv2026

Detecting the Information Flow in Proteins by Hodge Decomposition

Allosteric communication in proteins is commonly quantified as a directed or undirected coupling between residues, but such descriptors mix distinct modes of signalling into a single pattern. Here we treat the net transfer entropy flux from a dynamic Gaussian network(dGNM) model as an edge flow on the residue contact graph and apply the combinatorial Hodge decomposition, which dissects the flow orthogonally into a gradient (global source-to-sink hierarchy), a curl (local three-clique circulation) and a harmonic (cavity-scale circulation) component. Applied to the wild-type KRAS and ten oncogenic KRAS variants spanning the principal GTPase-cycle mechanism classes, partial-hydrolysis position-12 (G12D, G12C, G12S), GAP-occluding position-12 (G12V, G12R), catalytic switch II (Q61R, Q61H), fast-cycling (G13D, A146T) and a combined steric and catalytic double mutant (G12D/Q61H), on a side-chain-centroid contact network, the decomposition shows that the transfer entropy flux is overwhelmingly hierarchical: the gradient term carries 97.5-98.4% of the flux in every variant (permutation p = 0.002), and the recovered scalar potential is strongly anti-correlated with each residues net outgoing transfer entropy (Spearman {rho} {approx} -0.92 to -0.95). The hierarchy is conserved in magnitude but relocated by mutations: the dominant information sources move from the C-terminal 5/hypervariable region in wild type into the nucleotide-processing core, the switch I/II machinery and the 4/distal lobe in a way that tracks the GTPase-cycle mechanism of the substitution, while the sinks remain fixed. The method provides a parameter-free, residue-level readout of how mutations of different mechanism reposition the source of allosteric signalling in KRAS.

bioinformatics↗

bioRxiv2025

Rapid and Interpretable Protein Contact Map Prediction Using a Pattern-Matching Strategy

Protein sequence determines the structure, function, and dynamics of a protein. In recent years, enormous progress has been made in translating sequence information into structural information using machine learning approaches. However, because of the underlying methodology, it is an immense computational challenge to extract this information from the ever-increasing number of sequences. In the present study, we show that it is possible to create two-dimensional contact maps from sequences, for which only a few exemplary structures are available on a laptop without the need for GPUs or high-performance computing clusters. This is achieved by using a pattern matching approach. The resulting contact maps largely reflect the interactions in the three-dimensional structures. The validity of our method was tested on the 25 protein domains, with abundant structural data, achieving correlations of 0.73-0.94 between predicted and experimental contact maps. To demonstrate broader applicability, we further validated our approach on 7,599 poorly annotated sequences using homologous structural templates, achieving a mean F1-score of 0.609 {+/-} 0.095 and mean accuracy of 0.954 {+/-} 0.036 when compared against high-confidence AlphaFold structures. These results demonstrate that our pattern matching approach maintains robust performance even when relying on a small number of structural templates.

bioinformatics↗

bioRxiv2025

De novo design of light-regulated dynamic proteins using deep learning

Recent advances in deep learning have enabled accurate design of static protein structures, but the de novo design of protein functions controlled by programmable, intramolecular conformational changes remains an unsolved challenge. Here, we present a general deep learning-guided framework for designing dynamic, multi-domain proteins allosterically regulated by light. By integrating photoresponsive domains into de novo scaffolds, we engineered conformational switches that exhibit precise, reversible structural transitions upon illumination. Structural, spectroscopic, and functional analyses validated our designs and demonstrated precise spatio-temporal optogenetic control of diverse cellular processes, including subcellular localization, intercellular signaling, and population-level behaviors. This work establishes a broadly applicable strategy for encoding long-range allosteric control through designed intramolecular motions, and opens new avenues for programming dynamic protein functions from first principles, with implications for basic research, synthetic biology, and therapeutic development.

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

Unveiling Conserved Allosteric Hot Spots in Protein Domains from Sequences

The amino acid sequence determines the structure, function, and dynamics of a protein. In recent years, enormous progress has been made in translating sequence information into 3D structural information using artificial intelligence. However, because of the underlying methodology, it is an immense computational challenge to extract this information from the ever-increasing number of sequences. In the present study, we show that it is possible to create 2D contact maps from sequences, for which only a few exemplary structures are available on a laptop without the need for GPUs. This is achieved by using a pattern-matching approach. The resulting contact maps largely reflect the interactions in the 3D structures and contain information about its function and dynamics. This approach was used to explore the evolutionarily conserved allosteric mechanisms and identify the source- sink (driver-driven) relationships by using an established method that combines Schreibers concept of entropy transfer with a simple Gaussian network model. The validity of our method was tested on the DHFR, PDZ, SH3, and S100 domains, with our predictions consistently aligning with the experimental findings.

bioinformatics↗

bioRxiv2024

A look beyond the QR code of SNARE proteins

Soluble N-ethylmaleimide-sensitive factor Attachment protein REceptor (SNARE) proteins catalyze the fusion process of vesicles with target membranes in eukaryotic cells. To do this, they assemble in a zipper-like fashion into stable complexes between the membranes. Structural studies have shown that the complexes consist of four different helices, which we subdivide into Qa-, Qb-, Qc-, and R-helix on the basis of their sequence signatures. Using a combination of biochemistry, modeling and molecular dynamics, we investigated how the four different types are arranged in a complex. We found that there is a matching pattern in the core of the complex that dictates the position of the four fundamental SNARE types in the bundle, resulting in a QabcR complex. In the cell, several different cognate QabcR-SNARE complexes catalyze the different transport steps between the compartments of the endomembrane system. Each of these cognate QabcR complexes is compiled from a repertoire of about 20 SNARE subtypes. Our studies show that exchange within the four types is largely tolerated structurally, although some non-cognate exchanges lead to structural imbalances. This suggests that SNARE complexes have evolved for a catalytic mechanism, a mechanism that leaves little scope for selectivity beyond the QabcR rule.

biochemistry↗

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↗

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

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↗

bioRxiv2020

Fluctuation, correlation and perturbation-response behavior of nature-made and artificial nanobodies

Nanobodies, like other antibodies bind their targets through complementarity determining regions (CDRs). Improving nanobody-antigen binding affinities by introducing mutations in these CDRs is critical for biotechnological applications. However, any mutation is expected to introduce changes in the behavior of the protein, such as fluctuations of residues, correlation of fluctuations of residue pairs, response of a residue to perturbation of another. Most importantly, the nanoscale dynamics of the protein may change. In these respects, the problem is similar to the general problem of dynamic allostery, a perturbation at one site affecting the response at another site. Using the Gaussian Network Model of proteins, we show that CDR mutations indeed modify the fluctuation profile and dynamics of the nanobody. Effects are not confined to CDR regions but extend throughout the full structure. We introduce a computational scheme where fluctuations of a residue are perturbed by a force and response amplitude and response time of the remaining residues are determined. Response to a perturbation of a residue shows a synchronous and an asynchronous component. The model is used to quantify the effects of mutation on protein dynamics: highly perturbable residues and highly responsive residues of the nanobody are determined. Residues whose perturbation has no effect on protein behavior may also be determined with the present model. Three known nanobodies produced by nature are used as an illustrative example and their various modifications which we generated by CDR residue mutations are analyzed.

biophysics↗

bioRxiv2019

ModiBodies: A computational method for modifying nanobodies to improve their antigen binding affinity and specificity

Nanobodies are special derivatives of antibodies, which consist of only a single chain. Their hydrophilic side prevents them from having the solubility and aggregation problems of conventional antibodies, and they retain the similar size and affinity of the binding area to the antigen. Nanobodies have become of considerable interest for next-generation biotechnological tools for antigen recognition. They can be easily engineered due to their high stability and compact size. They have three complementarity determining regions, CDRs, which are enlarged to provide a similar binding surface to that of regular antibodies. The binding residues are more exposed to the environment. One common strategy to improve protein solubility is to replace hydrophobic residues with hydrophilic ones on the binding surface which contributes to both stability and solubility of nanobodies.[1] Here, we propose an algorithm that uses the 3D structures of protein-nanobody complexes as the initial structures and by successive mutations in the CDR domains to find optimum binding amino acids for hypervariable residues of CDRs to increase the binding affinity and nanobody selectivity. We used the MDM4-VH9 complex, (PDB id 2VYR), fructose-bisphosphate aldolase from Trypanosoma congolense, (PDB id 5O0W), and human lysozyme, (PDB id 4I0C). as benchmark studies and identified similar amino acid patterns in hypervariable residues of CDRs with experimentally optimized ones. According to this method, better binding nanobodies can be generated by using this algorithm in a short time. We suggest that this method can complement existing immune and synthetic library-based methods, without the need of experiments or large libraries.

bioengineering↗

bioRxiv2019

How many bits of information can be transferred within a protein and how fast?

Amount and rate of information that may be transferred from one residue to another in a protein is quantified using the transfer entropy concept of information theory. Information transfer from one residue to the second is defined as the decrease in the uncertainty in the second residue due to coupling with past values of the first. Three types of information transfer between pairs of residues are defined: transfer between residues that are (i) close in both space and along the primary protein chain, (ii) close in space but distant along the chain, and (iii) distant in space and along the chain may be distinguished. The widely studied third PDZ domain from the synaptic protein PSD-95 is used as an example. The three types of transfer show that residues close in space and chain transfer the largest amount of information. Transfer along the primary chain is also significant. Predictions of the model show that significant amount of transfer may also take place between spatially distant pairs of residues. The latter forms the basis of dynamic allostery in proteins. The role of information transfer between distant pairs in relation to coevolution has been controversial, some works assigning it to near neighbor residue pairs only and others supporting long range coupling. The present paper shows that significant amount of information may be transferred between distant pairs in PSD-95.Transfer rates of the order of gigabytes per second are predicted by the present theory. Information transfer between a specific set of residue pairs exhibit strong directionality, or causality, an observation that may be of use in protein engineering and drug design.

biophysics↗

bioRxiv2016

Entropy Transfer between Residue Pairs Shows that Allostery is an Intrinsic Propertyof Proteins: Quantifying Allosteric Communication in Ubiquitin

AbstractIt has recently been proposed by Gunasakaran et al. that allostery may be an intrinsic property of all proteins. Here, we apply Schreibers transfer entropy formulation to the non-allosteric protein Ubiquitin and show that there are indeed systematic pathways of entropy and information transfer between residues that correlate well with the activities of the protein. We use 600 nanosecond molecular dynamics trajectories for Ubiquitin and its complex with human polymerase iota and evaluate entropy transfer between all pairs of residues of Ubiquitin and quantify the binding susceptibility changes upon complex formation. Calculations show that specific residues act as entropy reservoirs in Ubiquitin and others as entropy sinks. Using the plausible conjecture that extracting entropy from a residue makes it more susceptible for interaction with a partner, we explain the ternary complex formation of Ubiquitin in terms of entropy transfer. Finally, we show that time delayed correlation of fluctuations of two interacting residues possesses an intrinsic causality that tells which residue controls the interaction and which one is controlled. Our work shows that time delayed correlations, entropy transfer and causality are the required new concepts for explaining allosteric communication in proteins.\n\nAuthor SummaryAllosteric communication is essential for the function of proteins. Recent work shows that allostery results from dynamic processes in the protein associated with atomic fluctuations leading to entropic interactions that involve ensemble of pathways rather than discrete two state transitions. Based on this new picture of allostery, it was proposed that allostery may indeed be an intrinsic property of all proteins. In order to test this hypothesis, we derive the computational tools for quantifying allosteric communication, and explain allostery in terms of entropy transfer, a new concept based on information theory. We use long molecular dynamics simulations of proteins from which we calculate the transfer of entropy between pairs of residues. Results of simulations show that certain residues act as entropy sources while others as entropy sinks. Evaluation of time delayed correlations shows the presence of causality of interactions that allow us to differentiate between residues that are drivers in allosteric activity and those that are driven. Identification of driver-driven relations is important for drug design. Using the example of Ubiquitin, a protein that is not known to be allosteric, we identify paths of information transfer that control its binding to diverse partners in the Ubiquitin-Proteasome System. We conclude that allosteric communication resulting from entropy transfer between residues is an intrinsic property of all proteins.

biophysics↗

bioRxiv2016

Causality, Transfer Entropy and Allosteric Communication Landscapes inProteins with Harmonic Interactions

A fast and approximate method of generating allosteric communication landscapes is presented by using Schreiber's entropy transfer concept in combination with the Gaussian Network Model of proteins. Predictions of the model and the allosteric communication landscapes generated show that information transfer in proteins does not necessarily take place along a single path, but through an ensemble of pathways. The model emphasizes that knowledge of entropy only is not sufficient for determining allosteric communication and additional information based on time delayed correlations has to be introduced, which leads to the presence of causality in proteins. The model provides a simple tool for mapping entropy sink-source relations into pairs of residues. Residues that should be manipulated to control protein activity may be determined with this approach. This should be of great importance for allosteric drug design and for understanding the effects of mutations on protein function. The model is applied to determine allosteric communication in two proteins, Ubiquitin and Pyruvate Kinase. Predictions are in agreement with detailed molecular dynamics simulations and experimental evidence.\n\nSignificanceProteins perform their function by an exchange of information within themselves and with their environments through correlated fluctuations of their atoms. Fluctuations of one atom may drive the fluctuations of another. Information transmitted in this way leads to allosteric communication which is described as the process in which action at one site of the protein is transmitted to another site at which the protein performs its activity. Disruption of allosteric communication by mutation for example leads to disease. The present paper incorporates information theoretic concepts into the well known Gaussian Network Model of proteins and allows for rapid characterization of allosteric communication landscapes for normal functioning as well as malfunctioning proteins.

biophysics↗