Biology subjects

Atilgan, A. R.

Publications and source records attributed to Atilgan, A. R..

13 recordsLinked to original sources

bioRxiv2026

Temporal Hydrogen-Bond Network Analysis Reveals Substrate-Directed Connectivity in Dihydrofolate Reductase

Hydrogen-bond networks are central to protein function, but most network analyses rely on static representations that neglect how interactions evolve in time. Here, we introduce a framework that combines instantaneous and temporal graph analysis of hydrogen-bond networks derived from molecular dynamics trajectories to quantify ligand-directed hydrogen-bond connectivity. We apply the method to E. coli dihydrofolate reductase (DHFR) and its L28R mutant, computing shortest hydrogen-bond paths from all residues to the substrate dihydrofolate (DHF). The instantaneous analysis reveals that DHF-directed connectivity is organized through a sparse set of preferred routes, with D27 and T113 acting as prominent hubs in the wild-type enzyme. Temporal analysis highlights residues that preferentially support time-ordered DHF-directed connectivity. Comparison with L28R shows that the mutation preserves the main substrate-contacting architecture and the overall communication scaffold but redistributes pathway usage, persistence, and temporal convergence. The network-derived hotspots partially overlap with independent coevolution signals, most strongly in the K109-I115 region, while overlap with cryptic-site predictors is more limited. This pattern indicates that the hydrogen-bond network captures evolutionarily supported communication regions in DHFR that are not fully recovered by static structural approaches. The framework is broadly applicable to ligand-binding proteins and provides a route to identify persistent, delayed, and mutation-sensitive signaling pathways directly from time-ordered simulation data.

biophysics↗

bioRxiv2026

Functional Calmodulin States are Selected from an Electrostatically Tuned Free Energy Landscape

Calmodulin (CaM) is a versatile calcium-binding protein whose structural flexibility enables regulation of diverse cellular processes. Capturing its full conformational landscape remains challenging due to high energy barriers between states. Here we employ well-tempered metadynamics simulations using key collective variables to explore CaM conformations under calcium-bound and calcium-free states at physiological and low salt concentrations. We identify four principal conformations that shift in population depending on calcium binding and ionic strength. Calcium binding favors compact states, while low salt conditions flatten the energy landscape, facilitating transitions, but also causing kinetic trapping due to salt-bridge interactions. Comparison with experimental CaM-protein complexes reveals that target binding stabilizes extended conformations distinct from minima accessible to free CaM. These findings elucidate how calcium and ionic environment orchestrate CaMs conformational dynamics, enhancing understanding of its functional adaptability in cellular calcium signaling.

biophysics↗

bioRxiv2025

A thermodynamic cycle to predict the competitive inhibition outcomes of an evolving enzyme

Understanding competitive inhibition at the molecular level is essential for unraveling the dynamics of enzyme-inhibitor interactions and predicting the evolutionary outcomes of resistance mutations. In this study, we present a framework linking competitive inhibition to alchemical free energy perturbation (FEP) calculations, focusing on E. coli dihydrofolate reductase (DHFR) and its inhibition by trimethoprim (TMP). Using thermodynamic cycles, we relate experimentally measured binding constants (Ki and Km) to free energy differences associated with wild-type and mutant forms of DHFR with a mean error of 0.9 kcal/mol, providing insights into the molecular underpinnings of TMP resistance. Our findings highlight the importance of local conformational dynamics in competitive inhibition. Mutations in DHFR affect substrate and inhibitor binding affinities differently, influencing the fitness landscape under selective pressure from TMP. Our FEP simulations reveal that resistance mutations stabilize inhibitor-bound or substrate-bound states through specific structural and/or dynamical effects. The interplay of these effects showcases significant epistasis in certain cases. The ability to separately assess substrate and inhibitor binding provides valuable insights, allowing for a more precise interpretation of mutation effects and epistatic interactions. Furthermore, we identify key challenges in FEP simulations, including convergence issues arising from charge-changing mutations and long-range allosteric effects. By integrating computational and experimental data, we provide an effective approach for predicting the functional impact of resistance mutations and their contributions to evolutionary fitness landscapes. These insights pave the way for constructing robust mutational scanning protocols and designing more effective therapeutic strategies against resistant bacterial strains.

biophysics↗

bioRxiv2025

Multiply Perturbed Response to Disclose Allosteric Control of Conformational Change: Application to Fluorescent Biosensor Design

Proteins exhibit remarkable conformational flexibility, enabling precise functional regulation through allostery. A key application of allostery is in the design of protein-based sensors, which detect environmental changes--such as ligand binding or post-translational modifications--and convert these cues into measurable signals (e.g., fluorescence). Here, we investigate a series of ligand-binding proteins that serve as sensing domains in direct-response fluorescent biosensors, wherein ligand binding enhances fluorescence output. We employ a multiple force application approach which we term Multiply Perturbed Response (MPR) to identify "hot spot" residues that drive the conformational transition from an apo (inactive/OFF) to a holo (active/ON) state. We first present two efficient computational approaches to determine residues and forces that maximize the overlap of the observed conformational change. We then determine the overlap maximizer residues for up to five force insertion locations, and we compare them with actual insertion sites used in existing biosensors. This work enhances the utility of linear response theory-based methods in uncovering multiple functionally significant regions that trigger a known conformational change. The approach might prove useful not only in the design of biosensors, or in locating allosteric sites, but may also find applications in offering physics-based collective variables in mapping conformational transition pathways of proteins.

biophysics↗

bioRxiv2024

High throughput mutational scanning of a protein via alchemistry on a high-performance computing resource

Antibiotic resistance presents a significant challenge to public health, as bacteria can develop resistance to antibiotics through random mutations during their life cycles, making the drugs ineffective. Understanding how these mutations contribute to drug resistance at the molecular level is crucial for designing new treatment approaches. Recent advancements in molecular biology tools have made it possible to conduct comprehensive analyses of protein mutations. Computational methods for assessing molecular fitness, such as binding energies, are not as precise as experimental techniques like deep mutational scanning. Although full atomistic alchemical free energy calculations offer the necessary precision, they are seldom used to assess high throughput data as they require significantly more computational resources. We generated a computational library using deep mutational scanning for dihydrofolate reductase (DHFR), a protein commonly studied in antibiotic resistance research. Due to resource limitations, we analyzed 33 out of 159 positions, identifying 16 single amino acid replacements. Calculations were conducted for DHFR in its drug-free state and in the presence of two different inhibitors. We demonstrate the feasibility of such calculations, made possible due to the enhancements in computational resources and their optimized use.

biophysics↗

bioRxiv2024

Deciphering GB1's Single Mutational Landscape: Insights from MuMi Analysis

Mutational changes that affect the binding of the C2 fragment of Streptococcal protein G (GB1) to the Fc domain of human IgG (IgG-Fc) have been extensively studied using deep mutational scanning (DMS), and the binding affinity of all single mutations has been measured experimentally in the literature. To investigate the underlying molecular basis, we perform in-silico mutational scanning for all possible single mutations, along with 2-{micro}s-long molecular dynamics (WT-MD) of the wild-type (WT) GB1 in both unbound and IgG-Fc bound forms. We compute the hydrogen bonds between GB1 and IgG-Fc in WT-MD to identify the dominant hydrogen bonds for binding, which we then assess in conformations produced by Mutation and Minimization (MuMi) to explain the fitness landscape of GB1 and IgG-Fc binding. Furthermore, we analyze MuMi and WT-MD to investigate the dynamics of binding, focusing on the relative solvent accessibility (RSA) of residues and the probability of residues being located at the binding interface. With these analyses, we explain the interactions between GB1 and IgG-Fc and display the structural features of binding. Our findings pave the way for improved predictive accuracy in protein stability and interaction studies, which are crucial for advancements in drug design and synthetic biology.

biophysics↗

bioRxiv2024

Elucidating TolC Protein Dynamics: Structural Shifts Facilitate Efflux Mediated β-lactam Resistance

Efflux-mediated {beta}-lactam resistance represents a significant public health challenge, limiting the efficacy of various {beta}-lactam antibiotics against numerous clinically relevant pathogenic bacteria. Structural and functional analyses have revealed that the efflux protein TolC in several Gram-negative bacteria serves as a conduit for antibiotics, bacteriocins, and phages, affecting bacterial susceptibility and virulence. In this study, we conducted a comprehensive examination of the efflux of {beta}-lactam drugs mediated by TolC, employing extensive experimental and computational analyses. Our computational investigations into the molecular dynamics of drug-free TolC revealed critical unidirectional movements of the trimeric TolC and identified residues significantly involved in TolC opening. To corroborate these findings, we performed a whole-gene-saturation mutagenesis assay, systematically mutating each residue of TolC to 19 other amino acids and measuring the fitness effects of these mutations under {beta}-lactam-induced selection. The {beta}-lactams oxacillin, piperacillin, and carbenicillin were selected for this study because they are effluxed by the AcrAB-TolC complex with varying efficiencies. This approach clarified the similarities and differences in the efflux processes of the three {beta}-lactam antibiotics through the trimeric TolC. Further analysis of TolCs efflux mechanism for these {beta}-lactam antibiotics via steered molecular dynamics simulations revealed the existence of general and drug-specific mechanisms employed by TolC. We identified key positions at the periplasmic entry of TolC whose altered dynamics influence long-range efflux motions as allosteric modulators. Our findings provide valuable insights into the structural dynamics of TolC, establishing a foundation for understanding the key mechanisms behind multidrug resistance and principles for designing new antibiotics and antibiotic derivatives capable of circumventing the bacterial efflux mechanism.

biophysics↗

bioRxiv2023

A Stochastic Programming Approach to the Antibiotics Time Machine Problem

Antibiotics Time Machine is an important problem to understand antibiotic resistance and how it can be reversed. Mathematically, it can be modelled as follows: Consider a set of genotypes, each of which contain a set of mutated and unmutated genes. Suppose that a set of growth rate measurements of each genotype under a set of antibiotics are given. The transition probabilities of a realization of a Markov chain associated with each arc under each antibiotic are computable via a predefined function given the growth rate realizations. The aim is to maximize the expected probability of reaching to the genotype with all unmutated genes given the initial genotype in a predetermined number of transitions, considering the following two sources of uncertainties: i) the randomness in growth rates, ii) the randomness in transition probabilities, which are functions of growth rates. We develop stochastic mixed-integer linear programming and dynamic programming approaches to solve static and dynamic versions of the Antibiotics Time Machine Problem under the aforementioned uncertainties. We adapt a Sample Average Approximation approach that exploits the special structure of the problem and provide accurate solutions that perform very well in an out-of-sample analysis.

evolutionary biology↗

bioRxiv2020

Dynamic Community Composition Unravels Allosteric Communication in PDZ3

The third domain of PSD-95 (PDZ3) is a model for investigating allosteric communication in protein and ligand interactions. While motifs contributing to its binding specificity have been scrutinized, a conformational dynamical basis is yet to be established. Despite the miniscule structural changes due to point mutants, the observed significant binding affinity differences have previously been assessed with a focus on two -helices located at the binding groove (2) and the C-terminus (3). Here, we employ a new computational approach to develop a generalized view on the molecular basis of PDZ3 binding selectivity and interaction communication for a set of point mutants of the protein (G330T, H372A, G330T-H372A) and its ligand (CRIPT named L1 and its T-2F variant L2) along with the wild type (WT). To analyze the dynamical aspects hidden in the conformations that are produced by molecular dynamics simulations, we utilize variations in community composition calculated based on the betweenness centrality measure from graph theory. We find that the highly charged N-terminus which is located far from the ligand has the propensity to share the same community with the ligand in the biologically functional complexes, indicating a distal segment might mediate the binding dynamics. N- and C-termini of PDZ3 share communities, and 3 acts as a hub for the whole protein by sustaining the communication with all structural segments, albeit being a trait not unique to the functional complexes. Moreover, 2 which lines the binding cavity frequently parts communities with the ligand and is not a controller of the binding but is rather a slave to the overall dynamics coordinated by the N-terminus. Thus, ligand binding fate in PDZ3 is traced to the population of community compositions extracted from dynamics despite the lack of significant conformational changes.

biophysics↗

bioRxiv2020

Could network structures generated with simple rules imposed on a cubic lattice reproduce the structural descriptors of globular proteins?

A direct way to spot structural features that are universally shared among proteins is to find proper analogues from simpler condensed matter systems. In most cases, sphere-packing arguments provide a straightforward route for structural comparison, as they successfully characterize a wide array of materials such as close packed crystals, dense liquids, and structural glasses. In the current study, the feasibility of creating ensembles of artificial structures that can automatically reproduce a large number of geometrical and topological descriptors of globular proteins is investigated. Towards this aim, a simple cubic (SC) arrangement is shown to provide the best background lattice after a careful analysis of the residue packing trends from 210 proteins. It is shown that a minimalistic set of ground rules imposed on this lattice is sufficient to generate structures that can mimic real proteins. In the proposed method, 210 such structures are generated by randomly removing residues (beads) from clusters that have a SC lattice arrangement until a predetermined residue concentration is achieved. All generated structures are checked for residue connectivity such that a path exists between any two residues. Two additional sets are prepared from the initial structures via random relaxation and a reverse Monte Carlo simulated annealing (RMC-SA) algorithm, which targets the average radial distribution function (RDF) of 210 globular proteins. The initial and relaxed structures are compared to real proteins via RDF, bond orientational order parameters, and several descriptors of network topology. Based on these features, results indicate that the structures generated with 40% occupancy via the proposed method closely resemble real residue networks. The broad correspondence established this way indicates a non-superficial link between the residue networks and the defect laden cubic crystalline order. The presented approach of identifying a minimalistic set of operations performed on a target lattice such that each resulting cluster possess structural characteristics largely indistinguishable from that of a coarse-grained globular protein opens up new venues in structural characterization, native state recognition, and rational design of proteins.

biophysics↗

bioRxiv2020

N-terminus of the third PDZ Domain of PSD-95 Orchestrates Allosteric Communication for Selective Ligand Binding

PDZ domains constitute common models to study single domain allostery without significant structural changes. The third PDZ domain of PSD-95 (PDZ3) is known to have selective structural features that confer unique modulatory roles to this unit. In this model system two residues, H372 directly connected to the binding site and G330 holding an off-binding-site position, were designated to assess the effect of mutations on binding selectivity. It has been observed that the H372A and G330T/H372A mutations change ligand preferences from class I (T/S amino acid at position -2 of the ligand) to class II (hydrophobic amino acid at the same position). Alternatively, the G330T single mutation leads to the recognition of both ligand classes. We have performed a series of molecular dynamics (MD) simulations for wild-type, H372A, G330T single mutants and a double mutant of PDZ3 in the absence and presence of both types of ligands. With the combination of free energy difference calculations and a detailed analysis of MD trajectories, class switching and class bridging behavior of PDZ3 mutants, as well as their effects on ligand selection and binding affinities are explained. We show that the dynamics of the charged N-terminus plays a fundamental role in determining the binding preferences in PDZ3 by altering the electrostatic energy. These findings are corroborated by simulations on N-termini truncated versions of these systems. The dynamical allostery orchestrated by the N-terminus offers a fresh perspective to the study of communication pathways in proteins.

biophysics↗

bioRxiv2018

High-order epistasis in catalytic power of dihydrofolate reductase gives rise to a rugged fitness landscape in the presence of trimethoprim selection

Evolutionary fitness landscapes of certain antibiotic target enzymes have been comprehensively mapped showing strong high order epistasis between mutations, but understanding these effects at the biochemical and molecular levels remained open. Here, we carried out an extensive experimental and computational study to quantitatively understand the evolutionary dynamics of Escherichia coli dihydrofolate reductase (DHFR) enzyme in the presence of trimethoprim induced selection. Biochemical and structural characterization of resistance-conferring mutations targeting a total of ten residues spanning the substrate binding pocket of DHFR revealed distinct resistance mechanisms. Next, we experimentally measured biochemical parameters (Km, Ki, and kcat) for a mutant library carrying all possible combinations of six resistance-conferring DHFR mutations and quantified epistatic interactions between them. We found that the epistasis between DHFR mutations is high-order for catalytic power of DHFR (kcat and Km), but less prevalent for trimethoprim affinity (Ki). Taken together our data provide a concrete illustration of how epistatic coupling at the level of biochemical parameters can give rise to complex fitness landscapes, and suggest new strategies for developing mutant specific inhibitors.

evolutionary biology↗

bioRxiv2017

MODE-TASK: Large-scale protein motion tools

SummaryMODE-TASK, a novel software suite, comprises Principle Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using molecular dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive.\n\nAvailability and ImplementationMODE-TASK has been open-sourced, and is available for download from https://github.com/RUBi-ZA/MODE-TASK, implemented in Python and C++.\n\nSupplementary informationDocumentation available at http://mode-task.readthedocs.io.

bioinformatics↗