Biophysical characterization of high-confidence, small human proteins.

Significant efforts have been made to characterize the biophysical properties of proteins. Small proteins have received less attention because their annotation has historically been less reliable. However, recent improvements in sequencing, proteomics, and bioinformatics techniques have led to the high-confidence annotation of small open reading frames (smORFs) that encode for functional proteins, producing smORF-encoded proteins (SEPs). SEPs have been found to perform critical functions in several species, including humans. While significant efforts have been made to annotate SEPs, less attention has been given to the biophysical properties of these proteins. We characterized the distributions of predicted and curated biophysical properties, including sequence composition, structure, localization, function, and disease association of a conservative list of previously identified human SEPs. We found significant differences between SEPs and both larger proteins and control sets. Additionally, we provide an example of how our characterization of biophysical properties can contribute to distinguishing protein-coding smORFs from non-coding ones in otherwise ambiguous cases.

Biophysical characterization of high-confidence, small human proteins.

Significant efforts have been made to characterize the biophysical properties of proteins. Small proteins have received less attention because their annotation has historically been less reliable. However, recent improvements in sequencing, proteomics, and bioinformatics techniques have led to the high-confidence annotation of small open reading frames (smORFs) that encode for functional proteins, producing smORF-encoded proteins (SEPs). SEPs have been found to perform critical functions in several species, including humans. While significant efforts have been made to annotate SEPs, less attention has been given to the biophysical properties of these proteins. We characterized the distributions of predicted and curated biophysical properties, including sequence composition, structure, localization, function, and disease association of a conservative list of previously identified human SEPs. We found significant differences between SEPs and both larger proteins and control sets. Additionally, we provide an example of how our characterization of biophysical properties can contribute to distinguishing protein-coding smORFs from non-coding ones in otherwise ambiguous cases.

Coiled-coil domains are sufficient to drive liquid-liquid phase separation in protein models.

Liquid-liquid phase separation (LLPS) is thought to be a main driving force in the formation of membraneless organelles. Examples of such organelles include the centrosome, central spindle, and stress granules. Recently, it has been shown that coiled-coil (CC) proteins, such as the centrosomal proteins pericentrin, spd-5, and centrosomin, might be capable of LLPS. CC domains have physical features that could make them the drivers of LLPS, but it is unknown if they play a direct role in the process. We developed a coarse-grained simulation framework for investigating the LLPS propensity of CC proteins, in which interactions that support LLPS arise solely from CC domains. We show, using this framework, that the physical features of CC domains are sufficient to drive LLPS of proteins. The framework is specifically designed to investigate how the number of CC domains, as well as the multimerization state of CC domains, can affect LLPS. We show that small model proteins with as few as two CC domains can phase separate. Increasing the number of CC domains up to four per protein can somewhat increase LLPS propensity. We demonstrate that trimer-forming and tetramer-forming CC domains have a dramatically higher LLPS propensity than dimer-forming coils, which shows that multimerization state has a greater effect on LLPS than the number of CC domains per protein. These data support the hypothesis of CC domains as drivers of protein LLPS, and have implications in future studies to identify the LLPS-driving regions of centrosomal and central spindle proteins.

Programmable de novo designed coiled coil-mediated phase separation in mammalian cells.

Membraneless liquid compartments based on phase-separating biopolymers have been observed in diverse cell types and attributed to weak multivalent interactions predominantly based on intrinsically disordered domains. The design of liquid-liquid phase separated (LLPS) condensates based on de novo designed tunable modules that interact in a well-understood, controllable manner could improve our understanding of this phenomenon and enable the introduction of new features. Here we report the construction of CC-LLPS in mammalian cells, based on designed coiled-coil (CC) dimer-forming modules, where the stability of CC pairs, their number, linkers, and sequential arrangement govern the transition between diffuse, liquid and immobile condensates and are corroborated by coarse-grained molecular simulations. Through modular design, we achieve multiple coexisting condensates, chemical regulation of LLPS, condensate fusion, formation from either one or two polypeptide components or LLPS regulation by a third polypeptide chain. These findings provide further insights into the principles underlying LLPS formation and a design platform for controlling biological processes.

ParSe 2.0: A web tool to identify drivers of protein phase separation at the proteome level.

We have developed an algorithm, ParSe, which accurately identifies from the primary sequence those protein regions likely to exhibit physiological phase separation behavior. Originally, ParSe was designed to test the hypothesis that, for flexible proteins, phase separation potential is correlated to hydrodynamic size. While our results were consistent with that idea, we also found that many different descriptors could successfully differentiate between three classes of protein regions: folded, intrinsically disordered, and phase-separating intrinsically disordered. Consequently, numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. Built from that finding, ParSe 2.0 uses an optimal set of property scales to predict domain-level organization and compute a sequence-based prediction of phase separation potential. The algorithm is fast enough to scan the whole of the human proteome in minutes on a single computer and is equally or more accurate than other published predictors in identifying proteins and regions within proteins that drive phase separation. Here, we describe a web application for ParSe 2.0 that may be accessed through a browser by visiting https://stevewhitten.github.io/Parse_v2_FASTA to quickly identify phase-separating proteins within large sequence sets, or by visiting https://stevewhitten.github.io/Parse_v2_web to evaluate individual protein sequences.

Coiled-coil domains are sufficient to drive liquid-liquid phase separation of proteins in molecular models.

Liquid-liquid phase separation (LLPS) is thought to be a main driving force in the formation of membraneless organelles. Examples of such organelles include the centrosome, central spindle, and stress granules. Recently, it has been shown that coiled-coil (CC) proteins, such as the centrosomal proteins pericentrin, spd-5, and centrosomin, might be capable of LLPS. CC domains have physical features that could make them the drivers of LLPS, but it is unknown if they play a direct role in the process. We developed a coarse-grained simulation framework for investigating the LLPS propensity of CC proteins, in which interactions which support LLPS arise solely from CC domains. We show, using this framework, that the physical features of CC domains are sufficient to drive LLPS of proteins. The framework is specifically designed to investigate how the number of CC domains, as well as multimerization state of CC domains, can affect LLPS. We show that small model proteins with as few as two CC domains can phase separate. Increasing the number of CC domains up to four per protein can somewhat increase LLPS propensity. We demonstrate that trimer-forming and tetramer-forming CC domains have a dramatically higher LLPS propensity than dimer-forming coils, which shows that multimerization state has a greater effect on LLPS than the number of CC domains per protein. These data support the hypothesis of CC domains as drivers of protein LLPS, and has implications in future studies to identify the LLPS-driving regions of centrosomal and central spindle proteins.

Intrinsically disordered regions that drive phase separation form a robustly distinct protein class.

Protein phase separation is thought to be a primary driving force for the formation of membrane-less organelles, which control a wide range of biological functions from stress response to ribosome biogenesis. Among phase-separating (PS) proteins, many have intrinsically disordered regions (IDRs) that are needed for phase separation to occur. Accurate identification of IDRs that drive phase separation is important for testing the underlying mechanisms of phase separation, identifying biological processes that rely on phase separation, and designing sequences that modulate phase separation. To identify IDRs that drive phase separation, we first curated datasets of folded, ID, and PS ID sequences. We then used these sequence sets to examine how broadly existing amino acid property scales can be used to distinguish between the three classes of protein regions. We found that there are robust property differences between the classes and, consequently, that numerous combinations of amino acid property scales can be used to make robust predictions of protein phase separation. This result indicates that multiple, redundant mechanisms contribute to the formation of phase-separated droplets from IDRs. The top-performing scales were used to further optimize our previously developed predictor of PS IDRs, ParSe. We then modified ParSe to account for interactions between amino acids and obtained reasonable predictive power for mutations that have been designed to test the role of amino acid interactions in driving protein phase separation. Collectively, our findings provide further insight into the classification of IDRs and the elements involved in protein phase separation.

Examining Cultural Structures and Functions in Biology.

Scientific culture and structure organize biological sciences in many ways. We make choices concerning the systems and questions we study. Our research then amplifies these choices into factors that influence the directions of future research by shaping our hypotheses, data analyses, interpretation, publication venues, and dissemination via other methods. But our choices are shaped by more than objective curiosity-we are influenced by cultural paradigms reinforced by societal upbringing and scientific indoctrination during training. This extends to the systems and data that we consider to be ethically obtainable or available for study, and who is considered qualified to do research, ask questions, and communicate about research. It is also influenced by the profitability of concepts like open-access-a system designed to improve equity, but which enacts gatekeeping in unintended but foreseeable ways. Creating truly integrative biology programs will require more than intentionally developing departments or institutes that allow overlapping expertise in two or more subfields of biology. Interdisciplinary work requires the expertise of large and diverse teams of scientists working together-this is impossible without an authentic commitment to addressing, not denying, racism when practiced by individuals, institutions, and cultural aspects of academic science. We have identified starting points for remedying how our field has discouraged and caused harm, but we acknowledge there is a long path forward. This path must be paved with field-wide solutions and institutional buy-in: our solutions must match the scale of the problem. Together, we can integrate-not reintegrate-the nuances of biology into our field.

Beta turn propensity and a model polymer scaling exponent identify intrinsically disordered phase-separating proteins.

The complex cellular milieu can spontaneously demix, or phase separate, in a process controlled in part by intrinsically disordered (ID) proteins. A protein's propensity to phase separate is thought to be driven by a preference for protein-protein over protein-solvent interactions. The hydrodynamic size of monomeric proteins, as quantified by the polymer scaling exponent (v), is driven by a similar balance. We hypothesized that mean v, as predicted by protein sequence, would be smaller for proteins with a strong propensity to phase separate. To test this hypothesis, we analyzed protein databases containing subsets of proteins that are folded, disordered, or disordered and known to spontaneously phase separate. We find that the phase-separating disordered proteins, on average, had lower calculated values of v compared with their non-phase-separating counterparts. Moreover, these proteins had a higher sequence-predicted propensity for ß-turns. Using a simple, surface area-based model, we propose a physical mechanism for this difference: transient ß-turn structures reduce the desolvation penalty of forming a protein-rich phase and increase exposure of atoms involved in π/sp2 valence electron interactions. By this mechanism, ß-turns could act as energetically favored nucleation points, which may explain the increased propensity for turns in ID regions (IDRs) utilized biologically for phase separation. Phase-separating IDRs, non-phase-separating IDRs, and folded regions could be distinguished by combining v and ß-turn propensity. Finally, we propose a new algorithm, ParSe (partition sequence), for predicting phase-separating protein regions, and which is able to accurately identify folded, disordered, and phase-separating protein regions based on the primary sequence.

Moving while you're stuck: a macroscopic demonstration of an active system inspired by binding-mediated transport in biology.

Diffusive motion is typically constrained when particles bind to the medium through which they move. However, when binding is transient and the medium is made of flexible filaments, each association or dissociation event produces a stochastic force that can overcome the medium stickiness and enable motion. This mechanism is amply used by biological systems where the act of balancing binding and displacement robustly achieves key functionalities, including bacterial locomotion or selective active filtering in cells. Here we demonstrate the feasibility of making a dynamic system with macroscopic features, in which analogous binding-mediated motion can be actively driven, precisely tuned, and conveniently studied. We find an optimal binding affinity and number of binding sites for diffusive motion, and an inverse relationship between viscosity and diffusivity.

Physics of the Nuclear Pore Complex: Theory, Modeling and Experiment.

The hallmark of eukaryotic cells is the nucleus that contains the genome, enclosed by a physical barrier known as the nuclear envelope (NE). On the one hand, this compartmentalization endows the eukaryotic cells with high regulatory complexity and flexibility. On the other hand, it poses a tremendous logistic and energetic problem of transporting millions of molecules per second across the nuclear envelope, to facilitate their biological function in all compartments of the cell. Therefore, eukaryotes have evolved a molecular "nanomachine" known as the Nuclear Pore Complex (NPC). Embedded in the nuclear envelope, NPCs control and regulate all the bi-directional transport between the cell nucleus and the cytoplasm. NPCs combine high molecular specificity of transport with high throughput and speed, and are highly robust with respect to molecular noise and structural perturbations. Remarkably, the functional mechanisms of NPC transport are highly conserved among eukaryotes, from yeast to humans, despite significant differences in the molecular components among various species. The NPC is the largest macromolecular complex in the cell. Yet, despite its significant complexity, it has become clear that its principles of operation can be largely understood based on fundamental physical concepts, as have emerged from a combination of experimental methods of molecular cell biology, biophysics, nanoscience and theoretical and computational modeling. Indeed, many aspects of NPC function can be recapitulated in artificial mimics with a drastically reduced complexity compared to biological pores. We review the current physical understanding of the NPC architecture and function, with the focus on the critical analysis of experimental studies in cells and artificial NPC mimics through the lens of theoretical and computational models. We also discuss the connections between the emerging concepts of NPC operation and other areas of biophysics and bionanotechnology.

Enhanced diffusion by reversible binding to active polymers.

Cells are known to use reversible binding to active biopolymer networks to allow diffusive transport of particles in an otherwise impenetrable mesh. We here determine the motion of a particle that experiences random forces during binding and unbinding events while being constrained by attached polymers. Using Monte-Carlo simulations and a statistical mechanics model, we find that enhanced diffusion is possible with active polymers. However, this is possible only under optimum conditions that has to do with the relative length of the chains to that of the plate. For example, in systems where the plate is shorter than the chains, diffusion is maximum when many chains have the potential to bind but few remain bound at any one time. Interestingly, if the chains are shorter than the plate, we find that diffusion is maximized when more active chains remain transiently bound. The model provides insight into these findings by elucidating the mechanisms for binding-mediated diffusion in biology and design rules for macromolecular transport in transient synthetic polymers.

C-Terminal Tail Polyglycylation and Polyglutamylation Alter Microtubule Mechanical Properties.

Microtubules are biopolymers that perform diverse cellular functions. Microtubule behavior regulation occurs in part through post-translational modification of both the α- and ß-subunits of tubulin. One class of modifications is the heterogeneous addition of glycine and/or glutamate residues to the disordered C-terminal tails (CTTs) of tubulin. Because of their prevalence in stable, high-stress cellular structures such as cilia, we sought to determine if these modifications alter microtubules' intrinsic stiffness. Here, we describe the purification and characterization of differentially modified pools of tubulin from Tetrahymena thermophila. We found that post-translational modifications do affect microtubule stiffness but do not affect the number of protofilaments incorporated into microtubules. We measured the spin dynamics of nuclei in the CTT backbone by NMR spectroscopy to explore the mechanism of this change. Our results show that the α-tubulin CTT does not protrude out from the microtubule surface, as is commonly depicted in models, but instead interacts with the dimer's surface. This suggests that the interactions of the α-tubulin CTT with the tubulin body contributes to the stiffness of the assembled microtubule, thus providing insight into the mechanism by which polyglycylation and polyglutamylation can alter microtubule mechanical properties.

Bound-State Diffusion due to Binding to Flexible Polymers in a Selective Biofilter.

Selective biofilters are used by cells to control the transport of proteins, nucleic acids, and other macromolecules. Biological filters demonstrate both high specificity and rapid motion or high flux of proteins. In contrast, high flux comes at the expense of selectivity in many synthetic filters. Binding can lead to selective transport in systems in which the bound particle can diffuse, but the mechanisms that lead to bound diffusion remain unclear. Previous theory has proposed a molecular mechanism of bound-state mobility based only on transient binding to flexible polymers. However, this mechanism has not been directly tested in experiments. We demonstrate that bound mobility via tethered diffusion can be engineered into a synthetic gel using protein fragments derived from the nuclear pore complex. The resulting bound-state diffusion is quantitatively consistent with theory. Our results suggest that synthetic biological filters can be designed to take advantage of tethered diffusion to give rapid, selective transport.

Design principles of selective transport through biopolymer barriers.

In biological systems, polymeric materials block the movement of some macromolecules while allowing the selective passage of others. In some cases, binding enables selective transport, while in others the most inert particles appear to transit most rapidly. To study the general principles of filtering, we develop a model motivated by features of the nuclear pore complex (NPC) which are highly conserved and could potentially be applied to other biological systems. The NPC allows selective transport of proteins called transport factors, which transiently bind to disordered flexible proteins called phenylalanine-glycine-nucleoporins. While the NPC is tuned for transport factors and their cargo, we show that a single feature is sufficient for selective transport: the bound-state motion resulting from transient binding to flexible filaments. Interchain transfer without unbinding can further improve selectivity, especially for cross-linked chains. We generalize this observation to model nanoparticle transport through mucus and show that bound-state motion accelerates transport of transient nanoparticle application, even with clearance by mucus flow. Our model provides a framework to control binding-induced selective transport in biopolymeric materials.

In-Cell NMR within Budding Yeast Reveals Cytoplasmic Masking of Hydrophobic Residues of FG Repeats.

In-cell NMR spectroscopy is a powerful tool to determine the properties of proteins and nucleic acids within living cells. In-cell NMR can give site-specific measurements of interactions, modifications, and dynamics as well as their modulation by the cellular environment. In-cell NMR requires selective incorporation of heavy isotopes into a protein of interest, either through the introduction of exogenously produced protein to a cell's interior or the selective overexpression of a protein. We developed conditions to allow the use of Saccharomyces cerevisiae, which was chosen because of its genetic tractability, as a eukaryotic expression system for in-cell NMR. We demonstrate this technique using a fragment of S. cerevisiae Nsp1, an FG Nup. FG Nups are intrinsically disordered proteins containing phenylalanine (F)-glycine (G) repeats and form the selective barrier within the nuclear pore complex. Yeast FG Nups have previously been shown to be maintained in a highly dynamic state within living bacteria as measured by in-cell NMR. Interactions thought to stabilize this dynamic state are also present in the protein's native organism, although site specificity of interaction is different between the two cytosols.

Effects of soft interactions and bound mobility on diffusion in crowded environments: a model of sticky and slippery obstacles.

Crowded environments modify the diffusion of macromolecules, generally slowing their movement and inducing transient anomalous subdiffusion. The presence of obstacles also modifies the kinetics and equilibrium behavior of tracers. While previous theoretical studies of particle diffusion have typically assumed either impenetrable obstacles or binding interactions that immobilize the particle, in many cellular contexts bound particles remain mobile. Examples include membrane proteins or lipids with some entry and diffusion within lipid domains and proteins that can enter into membraneless organelles or compartments such as the nucleolus. Using a lattice model, we studied the diffusive movement of tracer particles which bind to soft obstacles, allowing tracers and obstacles to occupy the same lattice site. For sticky obstacles, bound tracer particles are immobile, while for slippery obstacles, bound tracers can hop without penalty to adjacent obstacles. In both models, binding significantly alters tracer motion. The type and degree of motion while bound is a key determinant of the tracer mobility: slippery obstacles can allow nearly unhindered diffusion, even at high obstacle filling fraction. To mimic compartmentalization in a cell, we examined how obstacle size and a range of bound diffusion coefficients affect tracer dynamics. The behavior of the model is similar in two and three spatial dimensions. Our work has implications for protein movement and interactions within cells.

Physical determinants of bipolar mitotic spindle assembly and stability in fission yeast.

Mitotic spindles use an elegant bipolar architecture to segregate duplicated chromosomes with high fidelity. Bipolar spindles form from a monopolar initial condition; this is the most fundamental construction problem that the spindle must solve. Microtubules, motors, and cross-linkers are important for bipolarity, but the mechanisms necessary and sufficient for spindle assembly remain unknown. We describe a physical model that exhibits de novo bipolar spindle formation. We began with physical properties of fission-yeast spindle pole body size and microtubule number, kinesin-5 motors, kinesin-14 motors, and passive cross-linkers. Our model results agree quantitatively with our experiments in fission yeast, thereby establishing a minimal system with which to interrogate collective self-assembly. By varying the features of our model, we identify a set of functions essential for the generation and stability of spindle bipolarity. When kinesin-5 motors are present, their bidirectionality is essential, but spindles can form in the presence of passive cross-linkers alone. We also identify characteristic failed states of spindle assembly-the persistent monopole, X spindle, separated asters, and short spindle, which are avoided by the creation and maintenance of antiparallel microtubule overlaps. Our model can guide the identification of new, multifaceted strategies to induce mitotic catastrophes; these would constitute novel strategies for cancer chemotherapy.

Molecular Determinants of Tubulin's C-Terminal Tail Conformational Ensemble.

Tubulin is important for a wide variety of cellular processes including cell division, ciliogenesis, and intracellular trafficking. To perform these diverse functions, tubulin is regulated by post-translational modifications (PTM), primarily at the C-terminal tails of both the α- and ß-tubulin heterodimer subunits. The tubulin C-terminal tails are disordered segments that are predicted to extend from the ordered tubulin body and may regulate both intrinsic properties of microtubules and the binding of microtubule associated proteins (MAP). It is not understood how either interactions with the ordered tubulin body or PTM affect tubulin's C-terminal tails. To probe these questions, we developed a method to isotopically label tubulin for C-terminal tail structural studies by NMR. The conformational changes of the tubulin tails as a result of both proximity to the ordered tubulin body and modification by mono- and polyglycine PTM were determined. The C-terminal tails of the tubulin dimer are fully disordered and, in contrast with prior simulation predictions, exhibit a propensity for ß-sheet conformations. The C-terminal tails display significant chemical shift differences as compared to isolated peptides of the same sequence, indicating that the tubulin C-terminal tails interact with the ordered tubulin body. Although mono- and polyglycylation affect the chemical shift of adjacent residues, the conformation of the C-terminal tail appears insensitive to the length of polyglycine chains. Our studies provide important insights into how the essential disordered domains of tubulin function.