Biology subjects

Shakhnovich, E.

Publications and source records attributed to Shakhnovich, E..

35 records · Page 2Linked to original sources

bioRxiv2022

Phosphorylation sites are evolutionary checkpoints against liquid-solid transition in protein condensates

Assemblies of multivalent RNA-binding protein FUS can exist in the functional liquid-like state as well as less dynamic and potentially toxic amyloid- and hydrogel-like states. How could then cells form liquid-like condensates while avoiding their transformation to amyloids? Here we show how post-translational phosphorylation can provide a "handle" that prevents liquid-solid transition of intracellular condensates containing FUS. Using residue-specific coarse-grained simulations, for 85 different mammalian FUS sequences, we show how the number of phosphorylation sites and their spatial arrangement affect intracluster dynamics preventing conversion to amyloids. All atom simulations further confirm that phosphorylation can effectively reduce the {beta}-sheet propensity in amyloid-prone fragments of FUS. A detailed evolutionary analysis shows that mammalian FUS PLDs are enriched in amyloid-prone stretches compared to control neutrally evolved sequences suggesting that mammalian FUS proteins evolved to self-assemble. However, in stark contrast to proteins that do not phase-separate for their function, mammalian sequences have phosphosites in close proximity to these amyloid-prone regions. These results suggest that evolution uses amyloid-prone sequences in prion-like domains to enhance phase-separation of condensate proteins while enriching phosphorylation sites in close proximity to safe-guard against liquid-solid transitions. Significance StatementIntrinsically disordered regions and prion-like domains are widely observed in proteins that get enriched in membrane-less organelles (MLOs). Mammalian Fused in Sarcoma (FUS) sequences are significantly enriched in amyloid-prone sequences suggesting that they have evolved to self-assemble. While the amyloid-prone stretches promote self-assembly of these proteins at lower threshold concentrations, these assemblies are vulnerable to aberrant liquid-solid phase transitions. Molecular simulations and bioinformatics analyses show that evolution overcomes this challenge by placing phosphosites specifically close to amyloid-prone stretches. Introduction of negatively charged residues at phosphosite locations results in fewer amyloid-prone contacts and thereby lower beta-sheet propensity. Phosphorylation can thus allow cells to utilize amyloid-prone stretches to promote biogenesis of MLOs while protecting against liquid-solid transitions.

biophysics↗

bioRxiv2021

Emergence of order in condensates composed of multi-valent, multi-domain proteins

Many RNA-binding proteins (RBPs) that assemble into membraneless organelles, have a common architecture including disordered prion-like domain (PLD) and folded RNA-binding domain (RBD). An enrichment of PLD within the condensed phase gives rise to formation, on longer time scales, amyloid-like fibrils (aging). In this study, we employ coarse-grained Langevin dynamics simulations to explore the physical basis for the structural diversity in condensed phases of multi-domain RBPs. We discovered a highly cooperative first order transition between disordered structures and an ordered phase whereby chains of PLD organize in fibrils with high nematic orientational order. An interplay between homo-domain (PLD-PLD) and hetero-domain (PLD-RBD) interactions results in variety of structures with distinct spatial architectures. Interestingly, the different structural phases also exhibit vastly different intra-cluster dynamics of proteins, with diffusion coefficients 5 (disordered structures) to 50 times (ordered structures) lower than that of the dilute phase. Cooperativity of this liquid-solid transition makes fibril formation highly malleable to mutations or post-translational modifications. Our results provide a mechanistic understanding of how multi-domain RBPs could form assemblies with distinct structural and material properties. Significance StatementAssembly of proteins and nucleic acids into dense, liquid-like pockets is associated with several key functions including stress response, gene-regulation, DNA-repair and RNA processing. Several RNA binding proteins such as FUS are known to form liquid-like condensates that progressively harden into more dynamically, solid-like structures, a phenomenon that gets accelerated by disease mutations. In this study, we discover the mechanistic origins of this transition and show that small mutational or posttranslational modifications could result in sharp disorder-order transitions that could characterize accelerated liquid-solid transition in disease mutants.

biophysics↗

bioRxiv2021

A systems-guided approach to discover the intracellular target of a novel evolution-drug lead

Elucidating intracellular drug targets has been a difficult problem. While machine learning analysis of omics data has been a promising approach, going from large-scale trends to specific targets remains a challenge. Here, we developed a systems-guided hierarchic workflow that utilizes metabolic and structural analysis to narrow in on specific targets suggested by statistical and machine learning analysis of metabolomics data. Utilizing a novel multi-valent DHFR-targeting antibiotic compound, CD15-3, as a case study, we first measured global metabolomics and applied statistics and machine learning to locate broad areas of metabolic perturbation under antibiotic stress. We then tested the ability of suggested compounds to rescue growth and performed metabolic modelling to identify pathways whose inhibition was consistent with growth rescue patterns. Next, we utilized protein structural similarity to further prioritize candidate drug targets within these pathways. Overexpression and in vitro activity assays of a top candidate target, HPPK (folK), showed complete recovery from drug induced growth inhibition and with microscopy. As interest in white-box machine learning methods continues to grow, this study demonstrates how established machine learning methods can be combined with mechanistic analyses to improve the resolution of drug target finding workflows.

systems biology↗

bioRxiv2021

Effect of RNA on morphology and dynamics of membraneless organelles.

Membraneless organelles (MLOs) are spatiotemporally regulated structures that concentrate multi-valent proteins or RNA, often in response to stress. The proteins enriched within MLOs are often classified as high-valency "scaffolds" or low valency "clients", with the former being associated with a phase-separation promoting role. In this study, we employ a minimal model for P-body components, with a defined protein-protein interaction network, to study their phase-separation at biologically realistic low protein concentrations. Without RNA multivalent proteins can assemble into solid-like clusters only in the regime of high concentration and stable interactions. RNA molecules promote cluster formation in an RNA-length dependent manner, even in the regime of weak interactions and low protein volume fraction. Our simulations reveal that long RNA chains act as super-scaffolds that stabilize large RNA-protein clusters by recruiting low-valency proteins within them while also ensuring functional "liquid-like" turnover of components. Our results suggest that RNA-mediated phase separation could be a plausible mechanism for spatiotemporally regulated phase-separation in the cell.

biophysics↗

bioRxiv2020

Development of antibacterial compounds that block evolutionary pathways to resistance

Antibiotic resistance is a worldwide challenge. A potential approach to block resistance is to simultaneously inhibit WT and known escape variants of the target bacterial protein. Here we applied an integrated computational and experimental approach to discover compounds that inhibit both WT and trimethoprim (TMP) resistant mutants of E. coli dihydrofolate reductase (DHFR). We identified a novel compound (CD15-3) that inhibits WT DHFR and its TMP resistant variants L28R, P21L and A26T with IC50 50-75 {micro}M against WT and TMP-resistant strains. Resistance to CD15-3 was dramatically delayed compared to TMP in in vitro evolution. Whole genome sequencing of CD15-3 resistant strains showed no mutations in the target folA locus. Rather, gene duplication of several efflux pumps gave rise to weak (about twofold increase in IC50) resistance against CD15-3. Altogether, our results demonstrate the promise of strategy to develop evolution drugs - compounds which block evolutionary escape routes in pathogens.

microbiology↗

bioRxiv2020

DBFOLD: An efficient algorithm for computing folding pathways of complex proteins

Atomistic simulations can provide valuable, experimentally-verifiable insights into protein folding mechanisms, but existing ab initio simulation methods are restricted to only the smallest proteins due to severe computational speed limits. The folding of larger proteins has been studied using native-centric potential functions, but such models omit the potentially crucial role of non-native interactions.Here, we present an algorithm, entitled DBFOLD, which can predict folding pathways for a wide range of proteins while accounting for the effects of non-native contacts. In addition, DBFOLD can predict the relative rates of different transitions within a proteins folding pathway. To accomplish this, rather than directly simulating folding, our method combines equilibrium Monte-Carlo simulations, which deploy enhanced sampling, with unfolding simulations at high temperatures. We show that under certain conditions, trajectories from these two types of simulations can be jointly analyzed to compute unknown folding rates from detailed balance. This requires inferring free energies from the equilibrium simulations, and extrapolating transition rates from the unfolding simulations to lower, physiologically-reasonable temperatures at which the native state is marginally stable. As a proof of principle, we show that our method can accurately predict folding pathways and Monte-Carlo rates for the well-characterized Streptococcal protein G. We then show that our method significantly reduces the amount of computation time required to compute the folding pathways of large, misfolding-prone proteins that lie beyond the reach of existing direct simulation methods. Our algorithm, which is available online, can generate detailed atomistic models of protein folding mechanisms while shedding light on the role of non-native intermediates which may crucially affect organismal fitness and are frequently implicated in disease. Author summaryMany proteins must adopt a specific structure in order to function. Computational simulations have been used to shed light on the mechanisms of protein folding, but unfortunately, realistic simulations can typically only be run for small proteins, due to severe limits in computational speed. Here, we present a method to solve this problem, whereby instead of directly simulating folding from an unfolded state, we run simulations that allow for computation of equilibrium folding free energies, alongside high temperature simulations to compute unfolding rates. From these quantities, folding rates can be computed using detailed balance. Importantly, our method can account for the effects of nonnative contacts which transiently form during folding and must be broken prior to adoption of the native state. Such contacts, which are often excluded from simple models of folding, may crucially affect real protein folding pathways and are often observed in folding intermediates implicated in disease.

biophysics↗

bioRxiv2019

Liquid-liquid microphase separation leads to formation of membraneless organelles

Proteins and nucleic acids can spontaneously self-assemble into membraneless droplet-like compartments, both in vitro and in vivo. A key component of these droplets are multi-valent proteins that possess several adhesive domains with specific interaction partners (whose number determines total valency of the protein) separated by disordered regions. Here, using multi-scale simulations we show that such proteins self-organize into micro-phase separated droplets of various sizes as opposed to the Flory-like macro-phase separated equilibrium state of homopolymers or equilibrium physical gels. We show that the micro-phase separated state is a dynamic outcome of the interplay between two competing processes: a diffusion-limited encounter between proteins, and the dynamics within small clusters that results in exhaustion of available valencies whereby all specifically interacting domains find their interacting partners within smaller clusters, leading to arrested phase separation. We first model these multi-valent chains as bead-spring polymers with multiple adhesive domains separated by semi-flexible linkers and use Langevin Dynamics (LD) to assess how key timescales depend on the molecular properties of associating polymers. Using the time-scales from LD simulations, we develop a coarse-grained kinetic model to study this phenomenon at longer times. Consistent with LD simulations, the macro-phase separated state was only observed at high concentrations and large interaction valencies. Further, in the regime where cluster sizes approach macro-phase separation, the condensed phase becomes dynamically solid-like, suggesting that it might no longer be biologically functional. Therefore, the micro-phase separated state could be a hallmark of functional droplets formed by proteins with the sticker-spacer architecture. Significance statementMembraneless organells (MO) are ubiquitous in healthy living cells, with an altered state in disease. Their formation is likened to liquid-liquid phase separation (LLPS) between MO-forming proteins. However most models of LLPS predict complete macrophase separation while in reality MOs are small droplets of various sizes, which are malleable to rapid morphological changes. Here we present a microscopic multiscale theoretical study of thermodynamics and kinetics of formation of MO. We show that MOs are long-living dynamic structures formed as a result of arrested macrophase separation. Our study provides a direct link beween the molecular properies of MO-forming proteins and the morphology and dynamics of MO paving a path to rational design and control of MO.

biophysics↗

bioRxiv2019

A case of mistaken identity: structurally similar ligand inhibits Thymidylate Kinase causing reversible filamentation of E. coli

The relationship between sequence variation and phenotype is poorly understood. Here we use metabolomic analysis to elucidate the molecular mechanism underlying the filamentous phenotype of E. coli strains that carry destabilizing mutations in Dihydrofolate Reductase (DHFR). We find that partial loss of DHFR activity causes reversible filamentation despite SOS response indicative of DNA damage, in contrast to thymineless death (TLD) achieved by complete inhibition of DHFR activity by high concentrations of antibiotic Trimethoprim. This phenotype is triggered by a disproportionate drop in intracellular dTTP, which could not be explained by drop in dTMP based on the Michaelis-Menten like in vitro activity curve of Thymidylate Kinase (Tmk), a downstream enzyme that phosphorylates dTMP to dTDP. Instead, we show that a highly cooperative (Hill coefficient 2.5) in vivo activity of Tmk is the cause of suboptimal dTTP levels. dTMP supplementation rescues filamentation and restores in vivo Tmk kinetics to Michaelis-Menten. Overall, this study highlights the important role of cellular environment in sculpting enzymatic kinetics with system level implications for bacterial phenotype.

microbiology↗

bioRxiv2019

Co-translational folding allows misfolding-prone proteins to circumvent deep kinetic traps

Many large proteins suffer from slow or inefficient folding in vitro. Here, we provide evidence that this problem can be alleviated in vivo if proteins start folding co-translationally. Using an all-atom simulation-based algorithm, we compute the folding properties of various large protein domains as a function of nascent chain length, and find that for certain proteins, there exists a narrow window of lengths that confers both thermodynamic stability and fast folding kinetics. Beyond these lengths, folding is drastically slowed by non-native interactions involving C-terminal residues. Thus, co-translational folding is predicted to be beneficial because it allows proteins to take advantage of this optimal window of lengths and thus avoid kinetic traps. Interestingly, many of these proteins sequences contain conserved rare codons that may slow down synthesis at this optimal window, suggesting that synthesis rates may be evolutionarily tuned to optimize folding. Using kinetic modelling, we show that under certain conditions, such a slowdown indeed improves co-translational folding efficiency by giving these nascent chains more time to fold. In contrast, other proteins are predicted not to benefit from co-translational folding due to a lack of significant non-native interactions, and indeed these proteins sequences lack conserved C-terminal rare codons. Together, these results shed light on the factors that promote proper protein folding in the cell, and how biomolecular self-assembly may be optimized evolutionarily.\n\nSignificance StatementMany proteins must adopt a specific structure in order to perform their functions, and failure to do so has been linked to disease. Although small proteins often fold rapidly and spontaneously to their native conformations, larger proteins are less likely to fold correctly due to the myriad incorrect arrangements they can adopt. Here, we show that this problem can be alleviated if proteins start folding while they are being translated, namely, built one amino acid at a time on the ribosome. This process of co-translational folding biases certain proteins away from misfolded states that tend to hinder spontaneous refolding. Signatures of unusually slow translation suggest that some of these proteins have evolved to fold co-translationally.

biophysics↗

bioRxiv2019

Conformational catalysis of cataract-associated aggregation by interacting intermediates in a human eye lens crystallin

Human {gamma}D-crystallin (H{gamma}D) is an abundant and highly stable two-domain protein in the core region of the eye lens. Destabilizing mutations and post-translational modifications in this protein are linked to onset of aggregation that causes cataract disease (lens turbidity). Wild-type H{gamma}D greatly accelerates aggregation of the cataract-related W42Q variant, without itself aggregating. The mechanism of this \"inverse prion\" catalysis of aggregation remained unknown. Here we provide evidence that an early unfolding intermediate with an opened domain interface enables transient dimerization of the C-terminal domains of wild-type and mutant, or mutant and mutant, H{gamma}D molecules, which deprives the mutants N-terminal domain of intramolecular stabilization by the native domain interface and thus accelerates its misfolding to a distinct, aggregation-prone intermediate. A detailed kinetic model predicts universal power-law scaling relationships for lag time and rate of the resulting aggregation, which are in excellent agreement with the data. The mechanism reported here, which we term interface stealing, can be generalized to explain how common domain-domain interactions can have surprising consequences, such as conformational catalysis of unfolding, in multidomain proteins.\n\nSignificanceMost known proteins in nature consist of multiple domains. Interactions between domains may lead to unexpected folding and misfolding phenomena. This study of human {gamma}D-crystallin, a two-domain protein in the eye lens, revealed one such surprise: conformational catalysis of misfolding via intermolecular domain interface \"stealing.\" An intermolecular interface between the more stable domains outcompetes the native intramolecular domain interface. Loss of the native interface in turn promotes misfolding and subsequent aggregation, especially in cataract-related {gamma}D-crystallin variants. This phenomenon is likely a contributing factor in the development of cataract disease, the leading worldwide cause of blindness. However, interface stealing likely occurs in many proteins composed of two or more interacting domains.

biophysics↗

bioRxiv2019

Evolutionary dynamics determines adaptation to inactivation of an essential gene.

Genetic inactivation of essential genes creates an evolutionary scenario distinct from escape from drug inhibition, but the mechanisms of microbe adaptations in such cases remain unknown. Here we inactivate E. coli dihydrofolate reductase (DHFR) by introducing D27G,N,F chromosomal mutations in a key catalytic residue with subsequent adaptation by serial dilutions. The partial reversal G27->C occurred in three evolutionary trajectories. Conversely, in one trajectory for D27G and in all trajectories for D27F,N strains adapted to grow at very low supplement folAmix concentrations but did not escape entirely from supplement auxotrophy. Major global shifts in metabolome and proteome occurred upon DHFR inactivation, which were partially reversed in adapted strains. Loss of function mutations in two genes, thyA and deoB, ensured adaptation to low folAmix by rerouting the 2-Deoxy-D-ribose-phosphate metabolism from glycolysis towards synthesis of dTMP. Multiple evolutionary pathways of adaptation to low folAmix converge to highly accessible yet suboptimal fitness peak.

evolutionary biology↗

bioRxiv2018

Mutation rate variability as a driving force in adaptive evolution

Mutation rate is a key determinant of the pace as well as outcome of evolution, and variability in this rate has been shown in different scenarios to play a key role in evolutionary adaptation and resistance evolution under stress. Here we investigate the dynamics of resistance fixation in a bacterial population with variable mutation rates and show that evolutionary outcomes are most sensitive to mutation rate variations when the population is subject to environmental and demographic conditions that suppress the evolutionary advantage of high-fitness subpopulations. By directly mapping a molecular-level biophysical fitness function to the system-level dynamics of the population we show that both low and very high, but not intermediate, levels of stress result in a disproportionate effect of hypermutation on resistance fixation and that traditional definitions of the selection coefficient are insufficient to account for this effect. We demonstrate how this behavior is directly tied to the extent of genetic hitchhiking in the system, the propagation of high-mutation rate cells through association with high-fitness mutations. Our results indicate a substantial role for mutation rate flexibility in the evolution of antibiotic resistance under conditions that present a weak advantage over wildtype to resistant cells.

evolutionary biology↗

bioRxiv2018

Growth tradeoffs produce complex microbial communities without direct interactions

The relationship between the dynamics of a community and its constituent pairwise interactions is a fundamental problem in ecology. Higher-order ecological effects beyond pairwise interactions may be key to complex ecosystems, but mechanisms to produce these effects remain poorly understood. Here we show that higher-order effects can arise from variation in multiple microbial growth traits, such as lag times and growth rates, on a single limiting resource with no other interactions. These effects produce a range of ecological phenomena: an unlimited number of strains can exhibit multi stability and neutral coexistence, potentially with a single keystone strain; strains that coexist in pairs do not coexist all together; and the champion of all pairwise competitions may not dominate in a mixed community. Since variation in multiple growth traits is ubiquitous in microbial populations due to pleiotropy and non-genetic variation, our results indicate these higher-order effects may also be widespread, especially in laboratory ecology and evolution experiments.

ecology↗

bioRxiv2017

ProteomeVis: a web app for exploration of protein properties from structure to sequence evolution across organisms’ proteomes

MotivationProtein evolution spans time scales and its effects span the length of an organism. A web app named ProteomeVis is developed to provide a comprehensive view of protein evolution in the S. cerevisiae and E. coli proteomes. ProteomeVis interactively creates protein chain graphs, where edges between nodes represent structure and sequence similarities within user-defined ranges, to study the long time scale effects of protein structure evolution. The short time scale effects of protein sequence evolution is studied by sequence evolutionary rate (ER) correlation analyses with protein properties that span from the molecular to the organismal level.\n\nResultsWe demonstrate the utility and versatility of ProteomeVis by investigating the distribution of edges per node in organismal protein chain universe graphs (oPCUGs) and putative ER determinants. S. cerevisiae and E. coli oPCUGs are scale-free with scaling constants of 1.78 and 1.50, respectively. Both scaling constants can be explained by a previously reported theoretical model describing protein structure evolution (Dokholyan et al., 2002). Protein abundance most strongly correlates with ER among properties in ProteomeVis, with Spearman correlations of -0.51 (p-value<10-10) and -0.46 (p-value<10-10) for S. cerevisiae and E. coli, respectively. This result is consistent with previous reports that found protein expression to be the most important ER determinant (Zhang and Yang, 2015).\n\nAvailabilityProteomeVis is freely accessible at http://proteomevis.chem.harvard.edu/proteomevis/.\n\nContactshakhnovich@chemistry.harvard.edu\n\nSupplementary informationAvailable to download on bioRxiv.

evolutionary biology↗

bioRxiv2017

Accessibility of the Shine-Dalgarno sequence dictates N-terminal codon bias in E. coli

Despite considerable efforts, no physical mechanism has been shown to explain N-terminal codon bias in prokaryotic genomes. Using a systematic study of synonymous substitutions in two endogenous E. coli genes, we show that interactions between the coding region and the upstream Shine-Dalgarno (SD) sequence modulate the efficiency of translation initiation, affecting both intracellular mRNA and protein levels due to the inherent coupling of transcription and translation in E. coli. We further demonstrate that far-downstream mutations can also modulate mRNA levels by occluding the SD sequence through the formation of non-equilibrium secondary structures. By contrast, a non-endogenous RNA polymerase that decouples transcription and translation largely alleviates the effects of synonymous substitutions on mRNA levels. Finally, a complementary statistical analysis of the E. coli genome specifically implicates avoidance of intra-molecular base-pairing with the SD sequence. Our results provide general physical insights into the coding-level features that optimize protein expression in prokaryotes.

biophysics↗

bioRxiv2016

Exploring the mutational robustness of nucleic acidsby searching genotype neighbourhoods in sequencespace

To assess the mutational robustness of nucleic acids, many genome- and protein-level studies have been performed; in these investigations, nucleic acids are treated as genetic information carriers and transferrers. However, the molecular mechanism through which mutations alter the structural, dynamic and functional properties of nucleic acids is poorly understood. Here, we performed SELEX in silico study to investigate the fitness distribution of the nucleic acid genotype neighborhood in a sequence space for L-Arm binding aptamer. Although most mutants of the L-Arm-binding aptamer failed to retain their ligand-binding ability, two novel functional genotype neighborhoods were isolated by SELEX in silico and experimentally verified to have similar binding affinity (Kd = 69.3 M and 110.7 M) as the wild-type aptamer (Kd = 114.4 M). Based on data from the current study and previous research, mutational robustness is strongly influenced by the local base environment and ligand-binding mode, whereas bases distant from the binding pocket provide potential evolutionary pathways to approach global fitness maximum. Our work provides an example of successful application of SELEX in silico to optimize an aptamer and demonstrates the strong sensitivity of mutational robustness to the site of genetic variation.

evolutionary biology↗

bioRxiv2016

Tuning the course of evolution on the biophysical fitness landscape of an RNA virus

Predicting viral evolution remains a major challenge with profound implications for public health. Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics device, the \"Evolution Chip\", which propagates millions of independent viral sub-populations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs shape viral evolution.

evolutionary biology↗