Biology subjects

Shakhnovich, E. I.

Publications and source records attributed to Shakhnovich, E. I..

At least 19 recordsLinked to original sources

bioRxiv2026

Chaperonin recognition of protein dynamics drives drug resistance

The emergence of drug resistance is typically driven by mutations that alter drug-target affinity, yet the role of host cellular machinery regulating these processes remains unclear. Here, we reveal that the chaperonin GroEL/S promotes drug resistance through recognition of protein dynamics. Using directed evolution of E. coli DHFR under antibiotic stress and varying GroEL/S expression, we identify a well-folded resistance variant whose fitness, despite tight inhibitor binding, is critically potentiated by GroEL/S engagement. X-ray crystallography, NMR, molecular dynamics and kinetic modeling reveal that millisecond-timescale flipping of the M20 loop generates steric accessibility and a kinetic window for chaperonin interaction that forcibly displaces tightly bound inhibitors, thereby overriding thermodynamic equilibrium of inhibitor binding to restore the active enzyme pool and preserve metabolic flux. Our findings not only reveal a novel paradigm of "dynamic recognition" where both conformational kinetics and distribution govern chaperonin recognition but also establish chaperonins as "deligandases" that actively modulate in vivo drug binding, suggesting that chaperone surveillance of the cellular proteome extends beyond quality control to govern native protein function. This mechanism defines a previously unrecognized route for the rapid development of drug resistance, with implications for understanding therapeutics of microbial infections and human malignancies.

biophysics↗

bioRxiv2025

CASPULE: A computational tool to study sticker spacer polymer condensates

Phase separated condensates are recognized as a ubiquitous mechanism of spatial organization in cell biology. Biophysical modeling of condensates provides critical insights into the dynamics and functions of these subcellular structures that are difficult to extract via experiments. Here we present an efficient computational pipeline, CASPULE (Condensate Analysis of Sticker Spacer Polymers Using the LAMMPS Engine), to simulate and analyze the biological condensates made of sticker-spacer polymers. CASPULE implements a unique force field that combines traditional Langevin dynamics with a "detailed-balance proof" protocol for single-valent bond formation between stickers. This framework allows us to study the nontrivial biophysics that emerge from singlevalent sticker interactions, coupled with the effect of separating the energetic contributions of stickers and spacers. We provide detailed documentation on how to setup the simulation environment, perform simulations and analyze the results. Through case studies, we highlight the utility and efficacy of our pipeline. Importantly, we provide statistical parameters to characterize the cluster size distribution often observed in biological systems. We envision this tool to be broadly useful in decoding the interplay of kinetics and thermodynamics underlying the formation and function of biological condensates. Code and documentation: https://caspule.github.io/caspule/ Author SummaryLiving cells contain droplet-like compartments called biomolecular condensates that organize important molecules and reactions without a surrounding membrane. Understanding how these condensates form and behave helps us learn more about healthy cellular function and diseases. Condensate forming biopolymers (proteins and nucleic acids) often contain a sticker spacer architecture that gives rise to interesting emergent properties when they assemble into a condensate. Here we report an easy-to-use computational pipeline, called CASPULE, that allows us to simulate and analyze the condensation of polymer chains. Unlike previous tools, CASPULE models both the physical motion of these molecules and specific bonding interactions between sticker segments. This user-friendly tool will enable researchers to explore the governing principles underlying the formation and function of biomolecular condensates.

biophysics↗

bioRxiv2025

Biophysical fitness landscape design traps viral evolution

AO_SCPLOWBSTRACTC_SCPLOWEvolutionary adaptation is often visualized as a populations stochastic climb toward the top of a fitness landscape. While there exist approaches to design or synthetically evolve proteins into desired structures, there is a lack of methodology for designing, tuning, and quantitatively reshaping the fitness landscapes themselves on which protein evolution takes place. Here, we introduce foundational principles of fitness landscape design (FLD) to customize the structural peaks and valleys of biophysical fitness landscapes with quantitative accuracy, offering robust control of long-term evolutionary outcomes. Our FLD algorithms use stochastic optimization of a chemically derived biophysical fitness model to consistently discover optimal antibody ensembles which force a target protein to evolve according to a user-specified target fitness landscape. We then apply FLD to suppress the fitnesses of two SARS-CoV-2 genotype neutral networks and to discover proactive vaccines that preemptively restrict escape variant fitness trajectories before they arise. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWRapidly evolving viruses mutate to escape antibodies generated by the human immune system, leading to periodic waves of infection and death. Modern vaccine design approaches that focus on currently prevalent strains can be vulnerable to emerging escape mutations. An ideal strategy for proactive vaccine design requires not only immediate effectiveness, but also control over the viral fitness landscape to ensure optimal suppression of escape variants. Here, we introduce biophysics-based computational algorithms to discover optimal antibody ensembles that quantitatively reshape the viral fitness landscape to induce long-term suppression of fitness trajectories. These protocols, called biophysical fitness landscape design, open the door to improved pandemic preparedness via proactive vaccine, antibody, and peptide design, thinking several steps ahead of pathogen evolution.

biophysics↗

bioRxiv2024

Entropy-driven Denaturation for Sustainable Regeneration of Protein Waste

For over a century, an explanation for how concentrated ions denature proteins has proven elusive. Here, we report a novel mechanism of protein denaturation driven by entropy changes in water networks. Experiments and simulations show that ion pairs like LiBr and LiCl localize water molecules and disrupt the water networks structure, while others exert a more global effect without compromising network integrity. This disruption reduces the entropy penalty when proteins sequester water molecules during unfolding, resulting in a peculiar yet universal "inverse hydrophobic effect" that potentiates protein denaturation. Through successful isolation and systematic study of indirect solute effects, our findings offer a universal approach to salt induced protein denaturation and provide a unified framework for the decoding of the protein-water-solute nexus.

biophysics↗

bioRxiv2024

Multi-condensate state as a functional strategy to optimize the cell signaling output

The existence of multiple biomolecular condensates inside living cells is a peculiar phenomenon not compatible with the predictions of equilibrium statistical mechanics. In this work, we address the problem of multiple condensates state (MCS) from a functional perspective. We combined Langevin dynamics, reaction-diffusion simulation, and dynamical systems theory to demonstrate that MCS can indeed be a function optimization strategy. Using Arp2/3 mediated actin nucleation pathway as an example, we show that actin polymerization is maximum at an optimal number of condensates. For a fixed amount of Arp2/3, MCS produces a greater response compared to its single condensate counterpart. Our analysis reveals the functional significance of the condensate size distribution which can be mapped to the recent experimental findings. Given the spatial heterogeneity within condensates and non-linear nature of intracellular networks, we envision MCS to be a generic functional solution, so that structures of network motifs may have evolved to accommodate such configurations.

biophysics↗

bioRxiv2023

Separation of sticker-spacer energetics governs the coalescence of biomolecular condensates

Biological condensates often emerge as a multi-droplet state and never coalesce into one large droplet within the experimental timespan. Previous work revealed that the sticker-spacer architecture of biopolymers may dynamically stabilize the multi-droplet state. Here, we simulate the condensate coalescence using metadynamics approach and reveal two distinct physical mechanisms underlying the fusion of droplets. Condensates made of sticker-spacer polymers readily undergo a kinetic arrest when stickers exhibit slow exchange while fast exchanging stickers at similar levels of saturation allow merger to equilibrium states. On the other hand, condensates composed of homopolymers fuse readily until they reach a threshold density. Increase in entropy upon inter-condensate mixing of chains drives the fusion of sticker-spacer chains. We map the range of mechanisms of kinetic arrest from slow sticker exchange dynamics to density mediated in terms of energetic separation of stickers and spacers. Our predictions appear to be in qualitative agreement with recent experiments probing dynamic nature of protein-RNA condensates. Statement of significanceA key conundrum of biological condensates is the coexistence of multiple droplets, in direct variance with classical predictions of mean-field theories of polymer solutions. Our current study uncovers that the merging of sticker-spacer condensate is an entropy driven process, as opposed to the surface energy driven fusion that are observed for canonical liquid droplets. This entropy, stemming from the inter-condensate polymer exchange, makes the droplet merging process dependent on inter-sticker dissociation kinetics. Stronger inter-sticker interaction triggers a kinetic arrest, preventing the condensate merger even at a low density. Our prediction starkly correlates with recent experimental findings on protein-RNA condensates in vitro and in vivo, highlighting the biological relevance of the interplay of kinetics and thermodynamics.

biophysics↗

bioRxiv2023

Biophysical principles predict fitness of SARS-CoV-2 variants

SARS-CoV-2 employs its spike proteins receptor binding domain (RBD) to enter host cells. The RBD is constantly subjected to immune responses, while requiring efficient binding to host cell receptors for successful infection. However, our understanding of how RBDs biophysical properties contribute to SARS-CoV-2s epidemiological fitness remains largely incomplete. Through a comprehensive approach, comprising large-scale sequence analysis of SARS-CoV-2 variants and the discovery of a fitness function based on binding thermodynamics, we unravel the relationship between the biophysical properties of RBD variants and their contribution to viral fitness. We developed a biophysical model that uses statistical mechanics to map the molecular phenotype space, characterized by binding constants of RBD to ACE2, LY-CoV016, LY-CoV555, REGN10987, and S309, onto a epistatic fitness landscape. We validate our findings through experimentally measured and machine learning (ML) estimated binding affinities, coupled with infectivity data derived from population-level sequencing. Our analysis reveals that this model effectively predicts the fitness of novel RBD variants and can account for the epistatic interactions among mutations, including explaining the later reversal of Q493R. Our study sheds light on the impact of specific mutations on viral fitness and delivers a tool for predicting the future epidemiological trajectory of previously unseen or emerging low frequency variants. These insights offer not only greater understanding of viral evolution but also potentially aid in guiding public health decisions in the battle against COVID-19 and future pandemics. Significance StatementThis research presents a biophysical model that maps the molecular properties of SARS-CoV-2s receptor binding domain into an epistatic fitness landscape. By linking the binding affinities of the virus to its epidemic fitness, we offer a powerful tool for understanding and predicting the emergence and success of new viral variants. Our model, validated with real-world data and informed by theoretical insights, provides a foundation for interpreting the evolutionary trajectory of past pandemics and predicting those of the future. The adaptability of this biophysical model extends to the key proteins of other viruses as well, signifying its potential in guiding public health interventions, and advancing our understanding of viral evolution.

biophysics↗

bioRxiv2023

Epistasis meets pleiotropy in shaping biophysical protein subspaces associated with antimicrobial resistance

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis--describing the nonlinear interaction between mutations and their phenotypic consequences--manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into "subspaces" corresponding to a set of kinetic and thermodynamic traits [(kcat, KM, Ki, and Tm (melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, and Chlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

systems biology↗

bioRxiv2022

Systematic conformation-to-phenotype mapping via limited deep-sequencing of proteins

Non-native conformations drive protein misfolding diseases, complicate bioengineering efforts, and fuel molecular evolution. No current experimental technique is well-suited for elucidating them and their phenotypic effects. Especially intractable are the transient conformations populated by intrinsically disordered proteins. We describe an approach to systematically discover, stabilize, and purify native and non-native conformations, generated in vitro or in vivo, and directly link conformations to molecular, organismal, or evolutionary phenotypes. This approach involves high-throughput disulfide scanning (HTDS) of the entire protein. To reveal which disulfides trap which chromatographically resolvable conformers, we devised a deep-sequencing method for double-Cys variant libraries of proteins that precisely and simultaneously locates both Cys residues within each polypeptide. HTDS of the abundant E. coli periplasmic chaperone HdeA revealed distinct classes of disordered hydrophobic conformers with variable cytotoxicity depending on where the backbone was cross-linked. HTDS can bridge conformational and phenotypic landscapes for many proteins that function in disulfide-permissive environments.

biophysics↗

bioRxiv2021

Switching an active site helix in dihydrofolate reductase reveals limits to sub-domain modularity

To what degree are individual structural elements within proteins modular such that similar structures from unrelated proteins can be interchanged? We study sub-domain modularity by creating 20 chimeras of an enzyme, E. coli dihydrofolate reductase (DHFR), in which a catalytically important, 10-residue -helical sequence is replaced by -helical sequences from a diverse set of proteins. The chimeras stably fold but have a range of diminished thermal stabilities and catalytic activities. Evolutionary coupling analysis indicates that the residues of this -helix are under selection pressure to maintain catalytic activity in DHFR. We performed molecular dynamics simulations using replica exchange with solute-tempering. Chimeras with low catalytic activity exhibit non-helical conformations that block the binding site and disrupt the positioning of the catalytically essential residue D27. Simulation observables and in vitro measurements of thermal stability and substrate binding affinity are strongly correlated. Several E. coli strains with chromosomally integrated chimeric DHFRs can grow, with growth rates that follow predictions from a kinetic flux model that depends on the intracellular abundance and catalytic activity of DHFR. Our findings show that although -helices are not universally substitutable, the molecular and fitness effects of modular segments can be predicted by the biophysical compatibility of the replacement segment. Statement of Significance-helices are ubiquitous components of protein structure that exhibit a degree of independent folding behavior, making them plausible structural modules within proteins. Here, we assess the effects of switching the sequence of an -helix in an essential enzyme for -helical sequences from evolutionarily unrelated proteins. The resultant chimeric proteins can still fold but enzymatic activity, stability, and cellular growth rates are negatively affected. Computational investigations reveal how residues in an -helix have been shaped by selection pressure to maintain catalytic activity and a specific, helical conformation of the protein. More broadly, we illustrate how molecular and fitness effects of switching protein segments depend on the protein and cellular context.

biophysics↗

bioRxiv2020

A native chemical chaperone in the human eye lens

Cataract is one of the most prevalent protein aggregation disorders and still the most common cause of vision loss worldwide. The metabolically quiescent core region of the human lens lacks cellular or protein turnover; it has therefore evolved remarkable mechanisms to resist light-scattering protein aggregation for a lifetime. We now report that one such mechanism involves an unusually abundant lens metabolite, myo-inositol, suppressing aggregation of lens crystallins. We quantified aggregation suppression using our previously well-characterized in vitro aggregation assays of oxidation-mimicking human {gamma}D-crystallin variants and investigated myo-inositols molecular mechanism of action using solution NMR, negative-stain TEM, differential scanning fluorometry, thermal scanning Raman spectroscopy, turbidimetry in redox buffers, and free thiol quantitation. Unlike many known chemical chaperones, myo-inositols primary target was neither the native nor the unfolded state of the protein, nor the final aggregated state, but rather the rate-limiting bimolecular step on the aggregation pathway. Given recent metabolomic evidence that it is severely depleted in human cataractous lenses compared to age-matched controls, we suggest that maintaining or restoring healthy levels of myo-inositol in the lens may be a simple, safe, and globally accessible strategy to prevent or delay lens opacification due to age-onset cataract.

biophysics↗

bioRxiv2020

Effect of protein structure on evolution of cotranslational folding

Cotranslational folding is expected to occur when the folding speed of the nascent chain is faster than the translation speed of the ribosome, but it is difficult to predict which proteins cotranslationally fold. Here, we simulate evolution of model proteins to investigate how native structure influences evolution of cotranslational folding. We developed a model that connects protein folding during and after translation to cellular fitness. Model proteins evolved improved folding speed and stability, with proteins adopting one of two strategies for folding quickly. Low contact order proteins evolve to fold cotranslationally. Such proteins adopt native conformations early on during the translation process, with each subsequently translated residue establishing additional native contacts. On the other hand, high contact order proteins tend not to be stable in their native conformations until the full chain is nearly extruded. We also simulated evolution of slowly translating codons, finding that slowing translation at certain positions enhances cotranslational folding. Finally, we investigated real protein structures using a previously published dataset that identified evolutionarily conserved rare codons in E. coli genes and associated such codons with cotranslational folding intermediates. We found that protein substructures preceding conserved rare codons tend to have lower contact orders, in line with our finding that lower contact order proteins are more likely to fold cotranslationally. Our work shows how evolutionary selection pressure can cause proteins with local contact topologies to evolve cotranslational folding. Statement of significanceSubstantial evidence exists for proteins folding as they are translated by the ribosome. Here we developed a biologically intuitive evolutionary model to show that avoiding premature protein degradation can be a sufficient evolutionary force to drive evolution of cotranslational folding. Furthermore, we find that whether a proteins native fold consists of more local or more nonlocal contacts affects whether cotranslational folding evolves. Proteins with local contact topologies are more likely to evolve cotranslational folding through nonsynonymous mutations that strengthen native contacts as well as through synonymous mutations that provide sufficient time for cotranslational folding intermediates to form.

biophysics↗

bioRxiv2019

Effects of single mutations on protein stability are Gaussian distributed

AbstractThe distribution of protein stability effects is known to be well-approximated by a Gaussian distribution from previous empirical fits. Starting from first-principles statistical mechanics, we more rigorously motivate this empirical observation by deriving per residue protein stability effects to be Gaussian. Our derivation requires the number of amino acids to be large, which is satisfied by the standard set of 20 amino acids found in nature. No assumption is needed on the protein length or the number of residues in close proximity in space, in contrast to previous applications of the central limit theorem to protein energetics. We support our derivation results with computational and experimental data on mutant protein stabilities across all types of protein residues. Statement of SignificanceDefining the distribution of single mutant stability effects ({Delta}{Delta}Gs) is the first step in modeling the role protein stability plays in evolution. Although empirical fits have been made to elucidate its form, a complete theoretical understanding of {Delta}{Delta}G distributions is lacking. Here, we derive how a simple Gaussian form can arise, while still including the intricacies of protein sequence and structure. We backup our derivation with previously released computational and experimental {Delta}{Delta}Gs.

biophysics↗

bioRxiv2019

Universal activation mechanism of class A GPCRs

Class A G protein-coupled receptors (GPCRs) influence virtually every aspect of human physiology. GPCR activation is an allosteric process that links agonist binding to G protein recruitment, with the hallmark outward movement of transmembrane helix 6 (TM6). However, what leads to TM6 movement and the key residue-level changes of this trigger remain less well understood. Here, by analyzing over 230 high-resolution structures of class A GPCRs, we discovered a modular, universal GPCR activation pathway that unites previous findings into a common activation mechanism, directly linking the bottom of ligand-binding pocket with G protein-coupling region. We suggest that the modular nature of the universal GPCR activation pathway allowed for the decoupling of the evolution of the ligand binding site, G protein binding region and the residues important for receptor activation. Such an architecture might have facilitated GPCRs to emerge as a highly successful family of proteins for signal transduction in nature.

bioinformatics↗

bioRxiv2018

Substrate inhibition imposes fitness penalty at high protein stability

Proteins are only moderately stable. It has long been debated whether this narrow range of stabilities is solely a result of neutral drift towards lower stability or purifying selection against excess stability is also at work -- for which no experimental evidence was found so far. Here we show that mutations outside the active site in the essential E. coli enzyme adenylate kinase result in stability-dependent increase in substrate inhibition by AMP, thereby impairing overall enzyme activity at high stability. Such inhibition caused substantial fitness defects not only in the presence of excess substrate but also under physiological conditions. In the latter case, substrate inhibition caused differential accumulation of AMP in the stationary phase for the inhibition prone mutants. Further, we show that changes in flux through Adk could accurately describe the variation in fitness effects. Taken together, these data suggest that selection against substrate inhibition and hence excess stability may have resulted in a narrow range of optimal stability observed for modern proteins.

biophysics↗

bioRxiv2018

Accurate protein-folding transition-path statistics from a simple free-energy landscape

A central goal of protein-folding theory is to predict the stochastic dynamics of transition paths -- the rare trajectories that transit between the folded and unfolded ensembles -- using only thermodynamic information, such as a low-dimensional equilibrium free-energy landscape. However, commonly used one-dimensional landscapes typically fall short of this aim, because an empirical coordinate-dependent diffusion coefficient has to be fit to transition-path trajectory data in order to reproduce the transition-path dynamics. We show that an alternative, first-principles free-energy landscape predicts transition-path statistics that agree well with simulations and single-molecule experiments without requiring dynamical data as an input. This topological configuration model assumes that distinct, native-like substructures assemble on a timescale that is slower than native-contact formation but faster than the folding of the entire protein. Using only equilibrium simulation data to determine the free energies of these coarse-grained intermediate states, we predict a broad distribution of transition-path transit times that agrees well with the transition-path durations observed in simulations. We further show that both the distribution of finite-time displacements on a one-dimensional order parameter and the ensemble of transition-path trajectories generated by the model are consistent with the simulated transition paths. These results indicate that a landscape based on transient folding intermediates, which are often hidden by one-dimensional projections, can form the basis of a predictive model of protein-folding transition-path dynamics.

biophysics↗

bioRxiv2018

Catalysis of cataract-associated human γD crystallin aggregation via dynamic disulfide exchange

Several mutations in human {gamma}D-crystallin (H{gamma}D), a long-lived eye lens protein, cause misfolding and aggregation, leading to cataract. Surprisingly, wild-type H{gamma}D catalyzes aggregation of its cataract related W42Q variant while itself remaining soluble - the inverse of the classical prion-like scenario whereby misfolded polypeptides catalyze aggregation of natively folded ones. The search for a biochemical mechanism of catalysis of W42Q aggregation by WT has revealed that WT H{gamma}D can transfer a disulfide bond to the W42Q variant. The transferred disulfide kinetically traps an aggregation-prone intermediate made accessible by the W42Q mutation, facilitating light-scattering aggregation of the W42Q variant. The aggregating variant thus becomes a disulfide sink, removing the disulfides from solution. Such redox \"hot potato\" competitions among wild-type and mutant or modified polypeptides may be relevant for many long-lived proteins that function in oxidizing environments. In these cases aggregation may be forestalled by inhibiting disulfide flow toward damaged polypeptides.

biochemistry↗

bioRxiv2017

Evidence of evolutionary selection for co-translational folding

Recent experiments and simulations have demonstrated that proteins can fold on the ribosome. However, the extent and generality of fitness effects resulting from co-translational folding remain open questions. Here we report a genome-wide analysis that uncovers evidence of evolutionary selection for co-translational folding. We describe a robust statistical approach to identify loci within genes that are both significantly enriched in slowly translated codons and evolutionarily conserved. Surprisingly, we find that domain boundaries can explain only a small fraction of these conserved loci. Instead, we propose that regions enriched in slowly translated codons are associated with co-translational folding intermediates, which may be smaller than a single domain. We show that the intermediates predicted by a native-centric model of co-translational folding account for the majority of these loci across more than 500 E. coli proteins. By making a direct connection to protein folding, this analysis provides strong evidence that many synonymous substitutions have been selected to optimize translation rates at specific locations within genes. More generally, our results indicate that kinetics, and not just thermodynamics, can significantly alter the efficiency of self-assembly in a biological context.

biophysics↗