Biology subjects

Bitran, A.

Publications and source records attributed to Bitran, A..

6 recordsLinked to original sources

bioRxiv2026

A detailed molecular picture of protein folding during active translation

All proteins can begin to fold on the ribosome, and many rely on co-translational folding to attain their native conformation. This process is not accounted for using structure prediction algorithms such as AlphaFold and its molecular details remain largely unknown. Here, we develop a hydrogen-deuterium pulse-labeling approach which reveals nascent polypeptide folding at a high level of structural detail and its kinetic coupling with translation. Two proteins exhibit hierarchical folding of structures smaller than a domain during translation. A third protein, however, does not have time to conformationally equilibrate on the ribosome, instead becoming kinetically trapped. This subsequently biases post-translational folding to avoid an aggregation-prone intermediate populated during refolding from denaturant. Our results reveal diverse strategies that promote robust protein folding during non-equilibrium translation.

biophysics↗

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↗

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↗

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↗

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

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↗