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Many large proteins suffer from slow or inefficient folding in vitro. It has long been known that this problem can be alleviated in vivo if proteins start folding cotranslationally. However, the molecular mechanisms underlying this improvement have not been well established. To address this question, we use an all-atom simulation-based algorithm to compute the folding properties of various large protein domains as a function of nascent chain length. We 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 nonnative interactions involving C-terminal residues. Thus, cotranslational 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 modeling, we show that under certain conditions, such a slowdown indeed improves cotranslational folding efficiency by giving these nascent chains more time to fold. In contrast, other proteins are predicted not to benefit from cotranslational folding due to a lack of significant nonnative 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.

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 protein’s 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. 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.

Self-organization in the cell relies on the rapid and specific binding of molecules to their cognate targets. Correct bindings must be stable enough to promote the desired function even in the crowded and fluctuating cellular environment. In systems with many nearly matched targets, rapid and stringent formation of stable products is challenging. Mechanisms that overcome this challenge have been previously proposed, including separating the process into multiple stages; however, how particular in vivo systems overcome the challenge remains unclear. Here we consider a kinetic system, inspired by homology dependent pairing between double stranded DNA in bacteria. By considering a simplified tractable model, we identify different homology testing stages that naturally occur in the system. In particular, we first model dsDNA molecules as short rigid rods containing periodically spaced binding sites. The interaction begins when the centers of two rods collide at a random angle. For most collision angles, the interaction energy is weak because only a few binding sites near the collision point contribute significantly to the binding energy. We show that most incorrect pairings are rapidly rejected at this stage. In rare cases, the two rods enter a second stage by rotating into parallel alignment. While rotation increases the stability of matched and nearly matched pairings, subsequent rotational fluctuations reduce kinetic trapping. Finally, in vivo chromosome are much longer than the persistence length of dsDNA, so we extended the model to include multiple parallel collisions between long dsDNA molecules, and find that those additional interactions can greatly accelerate the searching.

Proteins, the molecules that perform the majority of tasks required to sustain life at the molecular level, must generally fold into a specific structure in order to perform their molecular functions. Despite decades of research, we do not fully understand how proteins fold up into their correct structures, starting off as a linear chain of amino acids, while avoiding incorrect misfolded structures linked to diseases such as Alzheimer's, Parkinson's, and various forms of cancer. It was previously believed that most proteins can autonomously fold into their native structures, driven by the physical and chemical interactions between a protein's constituent amino acids. But growing evidence suggests that, for a large number of proteins, these interactions instead cause the chain to misfold into nonnative structures, thus necessitating the assistance of additional cellular mechanisms to ensure the correct native state is attained. For instance, in the cell many proteins can start to fold as they are being synthesized on the ribosome. Previous studies have demonstrated that this process, known as co-translational folding, can significantly improve the native folding efficiency for many proteins that cannot efficiently fold autonomously.Furthermore, recent bioinformatics studies have shown that, in many organisms, co-translational folding tends to begin at nascent chain lengths associated with evolutionarily conserved, slowly translating codons, suggesting that it is widely beneficial to slow down synthesis and give proteins time to fold co-translationally. But the precise molecular mechanisms through which co-translational folding helps proteins efficiently reach their native state and avoid detrimental misfolded states, remains poorly understood, largely owing to immense technical difficulties in studying this highly dynamic process. Such an understanding is necessary if we are to rationally manipulate protein quality control mechanisms in the cell to alleviate misfolding diseases.The goal of this dissertation is to develop and apply novel interdisciplinary pipeline, combining theory, atomistic simulation, and in vitro experiments to elucidate, at the molecular level, why certain proteins which face difficulty folding autonomously can reach their native states much more efficiently via co-translational folding. In Chapter 1, we present a novel algorithm, known as DBFOLD, that uses atomistic Monte-Carlo simulation, machine-learning based analysis, and statistical physics theory to predict detailed folding pathways and rates for large proteins while accounting for the possibility of non-native misfolding--a crucial feature omitted from many existing atomistic simulation algorithms for the sake of computational feasibility. In Chapter 2, we apply the DBFOLD algorithm to predict the co-translational folding mechanismsof certain E. coli proteins with conserved clusters of slow codons and to explain why these proteins benefit from folding co-translationally. We find that, for these proteins, there is a narrow window of intermediate translation lengths at which native-like folding is both thermodynamically favorable and kinetically fast. But beyond these lengths, folding kinetics slow down by orders of magnitude due to deep nonnative traps stabilized by newly-synthesized C-terminal residues. Thus, co-translational folding is predicted to help these proteins circumvent deep kinetic traps and rapidly reach their native state--strategically-evolved slow codons at these lengths can give the nascent chain additional time to take advantage of these optimal folding windows.A key advantage of our atomistic simulations is that they generate highly, specific, experimentally testable predictions. In Chapter 3, we test these predictions as they apply to E. coli MarR, one of the proteins predicted to circumvent deep folding traps via co-translational folding. Using in vitro refolding and mutagenesis experiments, we confirm the existence of these trapped states and preliminarily show that the simulations can accurately predict their structure and underlying molecular interactions. Our experiments thus lend support to our atomistic model for the MarR folding landscape, and indirectly support the predicted mechanism by which co-translational folding may allow folding traps to be circumvented. The work also sheds light on evolutionary tradeoffs that MarR faces between various biophysical properties under selection.Finally in Chapter 4, we apply our combined computational/experimental methodology to investigate the folding mechanism of the receptor binding domain (RBD) of the SARS-CoV-2--the virus behind the Covid-19 pandemic--with the ultimate goal of linking biophysical folding properties to viral fitness and pathology. We find that the RBD can only refold reversibly if its disulfides are kept intact during denaturation, whereas their disruption leads to spontaneous misfolding into a molten-globule like nonnative state which is highly aggregation-prone. But our simulations predict that the RBD can solve this problem by folding co-translationally during secretion in to the endoplasmic reticulum--this process is predicted to increase the odds that the correct disulfides form and ultimately lock the protein into its native state, thus minimizing nonnative misfolding.Together, these results present and validate a novel interdisciplinary pipeline that significantly advances our detailed molecular understanding of co-translational protein folding in the cell--a process long known to be beneficial albeit through poorly understood mechanisms. We expect that future work will continue probing the detailed molecular models generated here, along with their crucial evolutionary and biomedical implications.

All proteins can begin to fold on the ribosome, and many proteins critically rely on co- translational folding to attain their native conformation. The molecular details underlying this crucial process, however, remain largely unknown and are not accounted for by structure predictions such as AlphaFold. To probe high-resolution folding during active translation, we develop a novel application of hydrogen-deuterium pulse labeling. We show that two proteins sequentially adopt stable structure during elongation, while a third protein only has time to loosely fold during active elongation. This loose folding kinetically traps the N-terminus and alters the post-translational folding pathway, allowing it to circumvent an aggregation-prone intermediate. These results highlight the crucial non-equilibrium coupling between translation and folding and reveal diverse strategies to promote robust co-translational folding.

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.

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.Competing Interest StatementThe authors have declared no competing interest.

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.

Abstract 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 protein’s 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 summary Many 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.

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.