Biology subjects

Shakhnovich, E.

Publications and source records attributed to Shakhnovich, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Linker stretching optimizes bonding entropy in sticker-spacer condensates

Phase separating biopolymers are often described by the sticker-spacer model, which accounts for non-attractive linkers that connect discrete attractive moieties. "Associative polymer" theories describe these systems as two coexisting fluids of stickers and spacers, respectively. In such theories the stickers and spacers are decoupled in the sense that sticker binding does not affect spacer conformations or vice versa. Informed by coarse-grained simulations, we develop a phenomenological mean-field theory to determine the microscopic coupling between the sticker and spacer degrees of freedom. Accounting for linkers connecting adjacent spacers introduces free energy contributions due to conformational entropy of the spacer and the binding combinatorics of the stickers. These contributions have opposite dependencies on spacer length because longer spacers allow a greater volume to search for binding partners but incur a greater entropy cost upon bond formation. The length dependent terms from these contributions cancel leaving a length-independent elastic term arising from the fact that a small amount of spacer stretching lowers the free energy by increasing the number of possible binding partners. The cancellation of spacer length from the theory justifies the decoupling approximation and supports mean-field treatments of heterogeneous spacers.

biophysics↗

bioRxiv2026

Direct estimation of genotype fitness from time series

Heterogeneous adapting populations, whether in laboratory evolution experiments or global-scale pandemics, experience complex evolutionary dynamics due to the interplay of selection, mutation, and stochasticity. Inference of individual genotypes fitnesses therefore becomes difficult, especially when many lineages are competing and data are noisy. Existing fitness inference methods tend to rely on assumptions on the fitness landscapes maximum order of epistasis, or they require complicated iterative optimization algorithms to converge on fitness estimates. Here, we show that fitness landscapes can be computed from time series data, without any restrictions on epistatic order or iterative optimization, using a simple, closed-form mathematical expression that is easily implemented with standard matrix operations used commonly in linear algebra. We demonstrate successful fitness inference from noisy in silico evolutionary dynamics from four different noisy microscopic processes, including Wright-Fisher, Moran, ProSeD (serial dilution), and barcoded passage simulations. Then, we illustrate the broad applicability of the equation to five experimental time series datasets, including barcoded yeast evolution experiments, murine norovirus-1 serial passage experiments, and SARS-CoV-2 global genomic prevalence data. Our formula successfully infers fitnesses for even for rare genotypes several orders of magnitude less prevalent than top lineages, works with both laboratory evolution and epidemiological data, and can be implemented in most modern scientific programming languages.

genetics↗

bioRxiv2026

Co-targeting Metabolic Neighbours Constraints Bacterial Adaptive Evolution

Antibiotic resistance often arises through genetic changes that reduce drug susceptibility, with mutation representing one of the primary mechanisms. Here, we report a dual targeting strategy that restricts these escape routes by simultaneously inhibiting two metabolic neighbours in folate biosynthesis pathway. To establish a general framework for limiting adaptive escape in bacteria, we screened the folate pathway to identify enzymes with shared conserved structural features. This analysis identified dihydroneopterin aldolase (folB) and dihydroneopterin triphosphate 2'-epimerase (folX) as optimal dual targets due to their shared pocket architecture and their roles early in the folate pathway. We then screened a large chemical space of FDA-approved small molecules to identify candidates predicted to bind both folB and folX, followed by experimental validation. We identified Arbutin showing the strongest growth inhibition in E. coli and emerging as the potent dual-target inhibitor, validated by metabolic rescue assays that confirmed direct inhibition of both folB and folX. Experimental evolution further revealed that populations exposed to Arbutin exhibit constrained adaptive trajectories and minimal increases in tolerated drug concentration, consistent with the evolutionary bottleneck imposed by dual-enzyme inhibition. Together, these results establish a framework for developing evolution-resistant antibacterials by targeting structurally convergent enzymes within metabolic pathways.

biophysics↗

bioRxiv2026

Evolutionary dynamics under phenotypic uncertainty

AO_SCPLOWBSTRACTC_SCPLOWClassical population genetics has largely relied on the same stochastic differential equations (SDEs) for over 60 years to describe evolutionary dynamics. However, these SDEs ignore the fact that phenotype heterogeneity and noise are ubiquitous in biological systems from bacteria to cancers. Here, we develop Probabilistic Phenotype Genetics (ProP Gen) theory as a mathematical framework for evolutionary dynamics under phenotypic uncertainty. Our newly introduced class of SDEs show that, remarkably, phenotypic uncertainty causes many central tenets of classical population genetics to break down, such as invariance of evolutionary dynamics to global shifts in absolute fitness at fixed population size. We revisit the long-studied problem of valley crossing in rugged fitness landscapes, discovering that lower-probability, high-fitness "phenotypic bridges" can substantially accelerate fitness valley crossing even at low mutation rates. We show our theory also explains a paradoxical concept we call "phenotypic buoying" whereby low-fitness phenotypes can exist at surprisingly high frequencies when carried by a high-fitness phenotype which acts as a source. ProP Gen theory uncovers complex phase diagrams of simultaneous coexistence between genotype-phenotype pairs due to phenotypic buoying, which we derive analytically exactly and verify numerically. Notably, the standard Wright-Fisher branching process is unsuitable for probabilistic phenotype numerics because it cannot correctly incorporate phenotypic uncertainty. Thus, we develop a more general, experimentally-inspired discrete-time simulation algorithm, Probabilistic Serial Dilution (ProSeD), which allows for overlapping generations, phenotypic noise, and stochastic phenotype switching. Finally, we show that the new diffusion limit of population genetics recapitulates the experimentally observed resuscitation and partitioning dynamics of bacterial "persister" strains. ProP Gen theory offers promise for describing and predicting the evolutionary dynamics of cancers, which have been empirically observed to exploit phenotypic uncertainty to evade treatment.

evolutionary biology↗

bioRxiv2025

Biophysically Grounded Deep Learning Improves Protein-Protein ΔΔG Prediction

Accurate {Delta}{Delta}G prediction requires integrating machine learning with biophysical insight. Existing approaches typically prioritize one while neglecting the other. We introduce an encoder-agnostic framework that injects interpretable biophysical priors into residue-level deep learning representations via cross-embedding attention. ProtBFF consistently improves performance under homology-based-clustering evaluation and enables general-purpose encoders to surpass state-of-the-art specialized models or larger models. Our results show that integrating simple, mechanistic priors into pretrained representations yields more trust-worthy predictors, offering a practical solution for broader protein engineering applications. Codegithub.com/Jfeldman34/ProtBFF

bioinformatics↗

bioRxiv2025

Constrained Evolutionary Funnels Shape Viral Immune Escape

Understanding how viral proteins adapt under immune pressure while preserving viability is crucial for anticipating antibody-resistant variants. We present a probabilistic framework that predicts viral escape trajectories and shows that immune evasion is channeled into a small set of viable "escape funnels" within the vast mutational space. These escape funnels arise from the combined constraints of protein viability and antibody escape, modeled using a generative model trained on homologs and deep mutational scanning data. We derive a mean-field approximation of evolutionary path ensembles, enabling us to quantify both the fitness and entropy of escape routes. Applied to SARS-CoV-2 receptor binding domain, our framework reveals convergent evolution patterns, predicts mutation sites in variants of concern, and explains differences in antibody-cocktail effectiveness. In particular, cocktails with de-correlated escape profiles slow viral adaptation by forcing longer, higher-cost escape paths. SIGNIFICANCEViruses evolve to evade our immune defenses, but with constraints. Like navigating a minefield, each step toward immune escape comes at the potential cost of structural stability and functionality. We show that despite the vast mutational space, immune escape is funneled into a small set of predictable pathways. Using a statistical-physics model grounded in antibody experiments and SARS-CoV-2 epidemiology data, we identify these escape funnels--enabling therapies designed to block them before they are ever used.

bioinformatics↗

bioRxiv2025

Phase diagrams for biophysical fitness landscape design

Populations evolve toward the top of fitness landscapes in the way that physical particles experience forces toward the bottom of a potential energy landscape. We recently introduced fitness landscape design (FLD) as a means of customizing the shape of a proteins biophysical fitness landscape by using antibody sequences and concentrations as tunable control parameters. Until now, previous evidence for the feasibility of FLD has been numerical and simulated. Here, we derive an explicit analytical theory for the FLD phase diagram, providing tight bounds on the designability frontiers describing the extent to which fitnesses of different protein sequences can be tuned independently of each other. We then leverage a recently published experimental dataset of binding affinities between over 62,000 antibody variants and each of three glycoprotein antigens to construct experimental FLD phase diagrams, which show close agreement with theory. These results support the feasibility of engineering quantitatively programmable fitness landscapes for laboratory protein evolution experiments.

biophysics↗

bioRxiv2025

Complete Enzyme Clustering Enhances Coenzyme Q Biosynthesisvia Substrate Channeling

Metabolons - transient assemblies of sequential metabolic enzymes - facilitate the reactions of multi-step metabolic pathways, yet, how they mechanistically bolster metabolic flux remains unknown. Here, we investigate the molecular determinants of metabolon formation in coenzyme Q (CoQ) biosynthesis using coarse-grained molecular dynamics simulations and biochemical experiments. We show that the COQ metabolon forms at the critical region of a phase transition, where both metabolon clustering and metabolic flux exhibit coordinated sigmoidal responses to changes in protein-protein interaction strength. These complete metabolons enable substrate channeling between sequential enzymes, leading to a crucial enhancement of CoQ production efficiency. Selectively disrupting protein-protein interactions and randomly shuffling the interaction network demonstrate that protein-proximity rather than fine structure of the metabolon clusters is imperative for substrate channeling. Grounded in both experiment and simulation, these findings provide a framework for understanding the organization and function of metabolons across diverse metabolic pathways.

biophysics↗

bioRxiv2025

Spatial Clustering of Interface Residues Enhances Few-Shot Prediction of Viral Protein Binding

AO_SCPLOWBSTRACTC_SCPLOWPredicting protein binding affinities across large combinatorial mutation spaces remains a critical challenge in molecular biology, particularly for understanding viral evolution and antibody interactions. While combinatorial mutagenesis experiments provide valuable data for training predictive models, they are typically limited due to experimental constraints. This creates a significant gap in our ability to predict the effects of more extensive mutation combinations, such as those observed in emerging SARS-CoV-2 variants. We present PROXICLUST, which strategically combines smaller combinatorial mutagenesis experiments to enable accurate predictions across larger combinatorial spaces. Our approach leverages the spatial proximity of amino acid residues to identify potential epistatic interactions, using these relationships to optimize the design of manageable-sized combinatorial experiments. By combining just two small combinatorial datasets, we achieve accurate binding affinity predictions across substantially larger mutation spaces (R2 {approx} 0.8), with performance strongly correlated with capture of high-order epistatic effects. We validated our method in five different protein-protein interaction datasets, including binding of SARS-CoV-2 receptor binding domain (RBD) to various antibodies and cellular receptors, as well as influenza RBD- antibody interactions. This work provides a practical framework for extending the predictive power of combinatorial mutagenesis beyond current experimental constraints, offering applications in viral surveillance and antibody engineering.

molecular biology↗

bioRxiv2025

Cotranslational Protein Folding Through Non-Native Structural Intermediates

Cotranslational protein folding follows a distinct pathway shaped by the vectorial emergence of the peptide and spatial constraints of the ribosome exit tunnel. Variations in translation rhythm can cause misfolding linked to disease; however, predicting cotranslational folding pathways remains challenging. Here we computationally predict and experimentally validate a vectorial hierarchy of folding resolved at the atomistic level, where early intermediates are stabilized through non-native hydrophobic interactions before rearranging into the native-like fold. Disrupting these interactions destabilizes intermediates and impairs folding. The chaperone Trigger Factor alters the cotranslational folding pathway by keeping the nascent peptide dynamic until the full domain emerges. Our results highlight an unexpected role of surface-exposed residues in protein folding on the ribosome and provide tools to improve folding prediction and protein design.

biophysics↗

bioRxiv2025

The Origin of the Ionic-strength Dependent Reentrant Behavior in Liquid-Liquid Phase Separation of Neutral IDPs

The effect of salt on coacervation of synthetic or biological polyelectrolytes and polyampholytes is well-studied. However, recent experiments showed that largely uncharged IDPs (like FUS) also undergo LLPS at physiological salt concentrations such as [Cion][~]0.15M, dissolve at higher salt concentration, and again phase separate at even higher salt concentrations such as, [Cion][~]3M. Here we use analytical theory and simulations to reveal the mechanism of these transitions. At low [Cion], the ionic solution acts as a highly correlated medium conferring long-range effective attractive interactions between spatially distant monomers. In this regime the ion concentration inside the condensate is higher than in the bulk solution. As [Cion] increases, the correlation length in the ionic plasma decreases, and the condensate dissolves. Second LLPS at high [Cion] is due to the entropy-driven crowding, and ion concentration inside the condensate is lower than in the bulk. Our study unravels a general physical mechanism of salt-dependent reentrant behavior in LLPS in uncharged IDPs.

biophysics↗

bioRxiv2025

Few-Shot Viral Variant Detection via Bayesian Active Learning and Biophysics

The early detection of high-fitness viral variants is critical for pandemic response, yet limited experimental resources at the onset of variant emergence hinder effective identification. To address this, we introduce an active learning framework that integrates protein language model ESM3, Gaussian process with uncertainty estimation, and a bio-physical model to predict the fitness of novel variants in a few-shot learning setting. By benchmarking on past SARS-CoV-2 data, we demonstrate that our methods accelerates the identification of high-fitness variants by up to fivefold compared to random sampling while requiring experimental characterization of fewer than 1% of possible variants. We also demonstrate that our framework benchmarked on deep mutational scans effectively identifies sites that are frequently mutated during natural viral evolution with a predictive advantage of up to two years compared to baseline strategies, particularly those enabling antibody escape while preserving ACE2 binding. Through systematic analysis of different acquisition strategies, we show that incorporating uncertainty in variant selection enables broader exploration of the sequence landscape, leading to the discovery of evolutionarily distant but potentially dangerous variants. Our results suggest that this framework could serve as an effective early warning system for identifying concerning SARS-CoV-2 variants and potentially emerging viruses with pandemic potential before they achieve widespread circulation.

bioinformatics↗

bioRxiv2024

More Structures, Less Accuracy: ESM3's Binding Prediction Paradox

This paper investigates the impact of incorporating structural information into the protein-protein interaction predictions made by ESM3, a multimodal protein language model (pLM). We utilized various structural variants as inputs and compared three widely used structure acquisition pipelines--EvoEF2, Gromacs, and Rosetta Relax--to assess their effects on ESM3s performance. Our findings reveal that the use of a consistent identical structure, regardless of whether it is relaxed or variant, consistently enhances model performance across various datasets. This improvement is striking in few-show learning. However, performance deteriorates when different relaxed mutant structures are used for each variant. Based on these results, we advise caution when integrating distinct mutant structures into ESM3 and similar models.This study highlights the critical need for careful consideration of structural inputs in protein binding affinity prediction.

molecular biology↗

bioRxiv2024

A ribosome-associating chaperone mediates GTP-driven vectorial folding of nascent eEF1A

Eukaryotic translation elongation factor 1A (eEF1A) is a highly abundant, multi-domain GTPase. Post-translational steps essential for eEF1A biogenesis are carried out by bespoke chaperones but co-translational mechanisms tailored to eEF1A folding remain unexplored. Here, we find that the N-terminal, GTP-binding domain of eEF1A is prone to co-translational misfolding and using computational approaches, yeast genetics, and microscopy analysis, we identify the conserved yet uncharacterized yeast protein Ypl225w as a chaperone dedicated to solving this problem. Proteomics and biochemical reconstitution reveal that Ypl225ws interaction with ribosomal eEF1A nascent chains depends on additional binding of Ypl225w to the UBA domain of nascent polypeptide-associated complex (NAC). Lastly, we show by orthogonal chemical genetics that Ypl225w primes eEF1A nascent chains for their subsequent binding to GTP and release from Ypl225w. Our work establishes eEF1A as a model system for chaperone-dependent co-translational folding and unveils a novel mechanism for GTP-driven folding on the ribosome.

biochemistry↗

bioRxiv2024

A comprehensive map of transient protein-protein interactions and their structural features in the E. coli 1-carbon metabolism pathway

Enzymes in a pathway often form metabolons through weak protein-protein interactions (PPI) that localize and protect labile metabolites. Due to their transient nature, the structural architecture of these enzyme assemblies has largely remained elusive, limiting our abilities to re-engineer novel metabolic pathways. Here we delineate a complete PPI map of 1225 interactions in the E. coli 1-carbon metabolism pathway using bimolecular fluorescence complementation that can capture transient interactions in vivo and show strong intra- and inter-pathway clusters within the folate and purine biosynthesis pathways. Scanning mutagenesis experiments along with AlphaFold predictions and meta-dynamics simulations reveal that most proteins use conserved "dedicated" interfaces distant from their active sites to interact with multiple partners. Diffusion-reaction simulations with shared interaction surfaces and realistic PPI networks reveal a dramatic speedup in metabolic pathway fluxes. Overall, this study sheds light on the fundamental features of metabolon biophysics and structural aspects of transient binary complexes. Significance statementEnzymes from the same metabolic pathway often form dynamic assemblies called metabolons, which channel metabolites as well as protect labile intermediates. Yet very little is known about their structural features or what makes these interactions transient. Paucity of such information has particularly affected our ability to engineer novel metabolic pathways, construct multi-scale mathematical models of cells, etc. We address this by obtaining a comprehensive map for 1225 interaction pairs in the essential 1-carbon metabolism pathway of E. coli. Using both high-throughput experiments and computation, we uncover that metabolon proteins tend to use a conserved dedicated interface to interact with their partners. These results shed light on structural and energetic aspects of PPI in metabolons at near atomic level of resolution.

biophysics↗

bioRxiv2023

Enzymatic metabolon improves kinetic efficiency of reaction-limited enzyme pathways

In this work we investigate how spatial proximity of enzymes belonging to the same pathway (metabolon) affects metabolic flux. Using off-lattice Langevin Dynamics (LD) simulations in tandem with a stochastic reaction-diffusion protocol and a semi-analytical reaction-diffusion model, we systematically explored how strength of protein-protein interactions, catalytic efficiency and protein-ligand interactions affect metabolic flux through the metabolon. Formation of a metabolon leads to a greater speed up for longer pathways and especially for reaction-limited enzymes while for fully optimized diffusion-limited enzymes the effect is negligible. Notably, specific cluster architectures are not a prerequisite for enhancing reaction flux. Simulations uncover the crucial role of optimal non-specific protein-ligand interactions in enhancing catalytic efficiency of a metabolon. Our theory implies and bioinformatics analysis confirms that longer catalytic pathways are enriched in less optimal enzymes while most diffusion-limited enzymes populate shorter pathways. Our findings point towards a plausible evolutionary strategy where enzymes compensate for less-than-optimal efficiency by increasing their local concentration in the clustered state.

biophysics↗

bioRxiv2022

Allosteric communication between ligand binding domains modulates substrate inhibition in adenylate kinase

Enzymes play a vital role in life processes; they control chemical reactions and allow functional cycles to be synchronized. Many enzymes harness large-scale motions of their domains to achieve tremendous catalytic prowess and high selectivity for specific substrates. One outstanding example is provided by the three-domain enzyme adenylate kinase (AK), which catalyzes phosphotransfer between ATP to AMP. Here we study the phenomenon of substrate inhibition by AMP and its correlation with domain motions. Using single-molecule FRET spectroscopy, we show that AMP does not block access to the ATP binding site, neither by competitive binding to the ATP cognate site nor by directly closing the LID domain. Instead, inhibitory concentrations of AMP lead to a faster and more cooperative domain closure by ATP, leading in turn to an increased population of the closed state. The effect of AMP binding can be modulated through mutations throughout the structure of the enzyme, as shown by the screening of an extensive AK mutant library. Mutation of multiple conserved residues leads to increased substrate inhibition, suggesting a positive selection during evolution. Combining these insights, we developed a model that explains the complex activity of AK, particularly substrate inhibition, based on the experimentally observed opening and closing rates. Notably, the model indicates that the catalytic power is affected by the microsecond balance between the open and closed states of the enzyme. Our findings highlight the crucial role of protein motions in enzymatic activity. Significance StatementHow conformational dynamics affect the catalytic activity of enzymes remains a topic of active debate. We focus here on the domain closure dynamics of adenylate kinase (AK) and how they are affected by substrate inhibition. By screening an extensive mutant library, we show that this feature of the enzyme is well conserved in evolution. Importantly, domain closure is required in order to bring AKs substrates close together for their chemical reaction; single-molecule FRET studies directly measure the populations of the open and closed states. We find that overpopulation of the closed state can be detrimental to activity. The results allow us to develop a kinetic model that properly accounts for AK kinetics by combining conformational dynamics and biochemical steps.

biophysics↗

bioRxiv2022

Cotranslational formation of disulfides guides folding of the SARS CoV-2 receptor binding domain

Many secreted proteins contain multiple disulfide bonds. How disulfide formation is coupled to protein folding in the cell remains poorly understood at the molecular level. Here, we combine experiment and simulation to address this question as it pertains to the SARS-CoV-2 receptor binding domain (RBD). We show that, whereas RBD can refold reversibly when its disulfides are intact, their disruption causes misfolding into a nonnative molten-globule state that is highly prone to aggregation and disulfide scrambling. Thus, non-equilibrium mechanisms are needed to ensure disulfides form prior to folding in vivo. Our simulations suggest that co-translational folding may accomplish this, as native disulfide pairs are predicted to form with high probability at intermediate lengths, ultimately committing the RBD to its metastable native state and circumventing nonnative intermediates. This detailed molecular picture of the RBD folding landscape may shed light on SARS-CoV-2 pathology and molecular constraints governing SARS-CoV-2 evolution.

biophysics↗