Biology subjects

Nissley, D. A.

Publications and source records attributed to Nissley, D. A..

11 recordsLinked to original sources

bioRxiv2026

Protein Stability, Turnover Kinetics, and Abundance Constrain the Scaling of Protein Interaction Networks

The propensity of proteins to form oligomers is ultimately dictated by their structural configuration(s). Proteins that persist in a discrete conformational state may form a limited number of specific interactions while those that sample a broader structural ensemble may instead associate with a wider array of partners. These intrinsic tendencies potentially constrain the way proteins navigate wider interaction networks. In this work, we aggregated and surveyed a wide variety of biophysical, biochemical, and cellular descriptors of the S. cerevisiae proteome to identify biases in the connectivity of its protein-protein interaction network. Using mass spectrometry-based interactome measurements and various protein stability estimates, we find that a disproportionate number of abundant, yet unstable binding proteins act as network hubs. Moreover, we show that these features alone can be used to discriminate between hubs and non-hub proteins with high accuracy (AUROC = 0.898). Interestingly, we find that half-lives of hub proteins depend on whether or not they reside within static complexes and/ or whether they interact with molecular chaperones. Finally, we note that the observed connectivity biases associated with abundant, unstable proteins only pertain to network hubs, but not to the bottlenecks that connect them. Together, our findings reveal how the conformational stability of a protein may constrain its context within protein-protein interaction networks.

systems biology↗

bioRxiv2026

ANYI: The ANnotated Yeast Interactome

Although several existing protein-protein interaction (PPI) databases provide yeast PPI data, none unify large-scale network topology information with detailed biophysical, proteostasis, and regulatory annotations in a single protein-centric framework. To address this gap, we developed the ANnotated Yeast Interactome (ANYI), an open, integrated resource that combines experimental yeast PPIs with sixteen feature annotation types, including protein abundance, half-life, disorder content, post-translational modifications, conformational stability, chaperone interactions, sequence, and structure. ANYI integrates 3,927 proteins with 155 annotation features, forming a unified matrix that enables systematic cross-layer analyses. Available via GitHub and Docker Hub with an interactive network browser for broad accessibility, ANYI provides both experienced and beginner computational scientists with tools to investigate the yeast interactome. For example, users can directly test whether highly connected hub proteins exhibit distinct stability, disorder, or proteostasis signatures relative to peripheral nodes. AVAILABILITY AND IMPLEMENTATIONThe code used to assemble ANYI is available on GitHub at https://github.com/NCEMS/energetic-origins-of-PPI-connectivity and the database itself and interactive browser tool are available on Docker Hub as dannissleypsu/anyi-browser:v1.0.2.

bioinformatics↗

bioRxiv2026

Native entanglement misfolding contributes to age-associated structural changes across the Saccharomyces cerevisiae proteome

Aging at the subcellular level involves the simultaneous decline in the cells ability to maintain protein homeostasis and rise in misfolded proteins through a positive feedback loop. Here, we test if a widespread class of protein misfolding could contribute to proteome aging by examining if statistical associations exist between age-related changes in protein structure, measured by limited proteolysis mass spectrometry data of the aging Saccharomyces cerevisiae proteome, with structural annotations and molecular simulations. We find that globular proteins that are likely to exhibit entanglement misfolding are 121% more likely to exhibit age-related structural changes, and these changes are 59% more likely to be localized to natively entangled regions. Proteins containing native entanglements are seven-fold more likely to misfold, according to simulations, and populate long-lived, near-native misfolded states. Thus, the age-related structural changes in yeast proteins can be explained in part by the accumulation of misfolded proteins involving entanglements.

biochemistry↗

bioRxiv2024

Computational design of developable therapeutic antibodies: efficient traversal of binder landscapes and rescue of escape mutations

Developing therapeutic antibodies is a challenging endeavour, often requiring large-scale screening to produce initial binders, that still often require optimisation for developability. We present a computational pipeline for the discovery and design of therapeutic antibody candidates, which incorporates physics- and AI-based methods for the generation, assessment, and validation of developable candidate antibodies against diverse epitopes, via efficient few-shot experimental screens. We demonstrate that these orthogonal methods can lead to promising designs. We evaluated our approach by experimentally testing a small number of candidates against multiple SARS-CoV-2 variants in three different tasks: (i) traversing sequence landscapes of binders, we identify highly sequence dissimilar antibodies that retain binding to the Wuhan strain, (ii) rescuing binding from escape mutations, we show up to 54% of designs gain binding affinity to a new subvariant and (iii) improving developability characteristics of antibodies while retaining binding properties. These results together demonstrate an end-to-end antibody design pipeline with applicability across a wide range of antibody design tasks. We experimentally characterised binding against different antigen targets, developability profiles, and cryo-EM structures of designed antibodies. Our work demonstrates how combined AI and physics computational methods improve productivity and viability of antibody designs.

bioinformatics↗

bioRxiv2023

The molecular reach of antibodies determines their SARS-CoV-2 neutralisation potency

Key functions of antibodies, such as viral neutralisation, depend on bivalent binding but the factors that influence it remain poorly characterised. Here, we develop and employ a new bivalent model to mechanistically analyse binding between >45 patient-isolated IgG1 antibodies interacting with SARS-CoV-2 RBD surfaces. Our method reproduces the monovalent on/off-rates and enables measurements of the bivalent on-rate and the molecular reach: the maximum antigen separation that supports bivalent binding. We find large variations in these parameters across antibodies, including variations in reach (22-46 nm) that exceed the physical antibody size ([~]15 nm) due to the antigen size. The bivalent model integrates all parameters, including reach and antigen density, to predict an emergent binding potency for each antibody that matches their neutralisation potency. Indeed, antibodies with similar monovalent affinities to the same RBD-epitope but with different reaches display differences in emergent bivalent binding that match differences in their neutralisation potency. Together, our work highlights that antibodies within an isotype class binding the same antigen can display differences in molecular reach that can substantially modulate their emergent binding and functional properties. Lay SummaryAntibodies are soluble proteins that can neutralise pathogens by sticking to them. They contain two identical arms that allow them to simultaneously bind two identical antigen molecules on pathogen surfaces. Although we know that bivalent binding is important for neutralisation, we dont know how different antibodies achieve it. We developed a new model to analyse the mechanism of bivalent binding and used it to study over 45 antibodies from COVID-19 patients that bind the RBD antigen of SARS-CoV-2. Unexpectedly, we found that the molecular reach of an antibody, which is the maximum antigen separation that supports bivalent binding, varied widely between antibodies and exceeded their physical size. We show how antibody binding emerges from the interplay of multiple factors, including reach, and that this emergent binding predicts their neutralisation function. The ability to analyse and predict bivalent binding should improve our understanding and exploitation of antibodies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=155 SRC="FIGDIR/small/556503v2_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1674563org.highwire.dtl.DTLVardef@fc7feborg.highwire.dtl.DTLVardef@1325847org.highwire.dtl.DTLVardef@41ad51_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2022

Computationally profiling peptide:MHC recognition by T-cell receptors and T-cell receptor-mimetic antibodies

T-cell receptor-mimetic antibodies (TCRms) targeting disease-associated peptides presented by Major Histocompatibility Complexes (pMHCs) are set to become a major new drug modality. However, we lack a general understanding of how TCRms engage pMHC targets, which is crucial for predicting their specificity and safety. Several new structures of TCRm:pMHC complexes have become available in the past year, providing sufficient initial data for a holistic analysis of TCRms as a class of pMHC binding agents. Here, we profile the complete set of TCRm:pMHC complexes against representative TCR:pMHC complexes to quantify the TCR-likeness of their pMHC engagement. We find that intrinsic molecular differences between antibodies and TCRs lead to fundamentally different roles for their heavy/light chains and Complementarity-Determining Region loops during antigen recognition. The idiotypic properties of antibodies may increase the likelihood of TCRms engaging pMHCs with less peptide selectivity than TCRs. However, the pMHC recognition features of some TCRms, including the two TCRms currently in clinical trials, can be remarkably TCR-like. The insights gained from this study will aid in the rational design and optimisation of next-generation TCRms.

immunology↗

bioRxiv2021

Synonymous mutations can alter protein dimerization through localized interface misfolding involving self-entanglements

Synonymous mutations in messenger RNAs (mRNAs) can reduce protein-protein binding affinities by more than half despite leaving the proteins amino acid sequence unaltered. Here, we use coarse-grain simulations of protein synthesis, ejection from the ribosome, post-translational dynamics, and dimerization to understand how synonymous mutations can influence the dimerization of the two E. coli homodimers oligoribonuclease and ribonuclease T. We synthesize each protein from its wildtype, fastest- and slowest-translating synonymous mRNAs and calculate the ensemble-average interaction energy between the resulting dimers. We find, similar to experiments with other dimers, that oligoribonucleases dimerization is altered by synonymous mutations. Relative to wildtype, the dimer interaction energy becomes 4% and 10% stronger, respectively, when translated from its fastest- and slowest-translating mRNAs. Ribonuclease T dimerization, however, is insensitive to synonymous mutations. The structural and kinetic origin of these changes are misfolded states containing non-covalent lasso-entanglements, many of which structurally perturb the dimer interface, whose probability of occurrence depends on translation speed. Translation of the fast- and slow-translating mRNAs of oligoribonuclease decreases the population of these misfolded states relative to wildtype. For ribonuclease T, however, these misfolded populations are insensitive to synonymous mutations. Entanglements cause altered dimerization energies for oligoribonuclease as there is a significant association (odds ratio: 50) between non-native self-entanglements and weak-binding dimer conformations. These conclusions are independent of model resolution, as entangled structures persist in long-time-scale all-atom simulations. Thus, non-native changes in entanglement is a mechanism through which oligomer structure and function can be altered. SIGNIFICANCE STATEMENTSynonymous mutations affect a range of post-translational protein functions, including dimerization, without altering the amino acid sequence of the encoded protein. This suggests that proteins somehow retain a "memory" of their translation-elongation kinetics long after synthesis is complete. Here, we demonstrate that synonymous mutations can change the likelihood that nascent proteins misfold into self-entangled conformations. These self-entangled structures are similar to the native state but with key conformational perturbations that disrupt the dimer interface, reducing their ability to dimerize. Rearrangement of such self-entangled states to the native state is a slow process, offering a structural explanation for how translation-elongation kinetics can influence long-time-scale protein-protein binding affinities.

biophysics↗

bioRxiv2021

Current protein structure predictors do not produce meaningful folding pathways

Protein structure prediction has long been considered a gateway problem for understanding protein folding. Recent advances in deep learning have achieved unprecedented success at predicting a proteins crystal structure, but whether this achievement relates to a better modelling of the folding process remains an open question. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental folding data. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathwhay, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with parameters such as intermediate structures and the folding rate constant. These results suggest that recent advances in protein structure prediction do not yet provide an enhanced understanding of the principles underpinning protein folding.

bioinformatics↗

bioRxiv2021

Universal protein misfolding intermediates can bypass the proteostasis network and remain soluble and non-functional

Misfolded protein conformations with decreased functionality can bypass the proteostasis machinery and remain soluble in vivo. This is an unexpected phenomenon as several cellular quality control mechanisms have evolved to rid cells of misfolded proteins. Three questions, then, are: how is it structurally possible for long-lived, soluble, misfolded proteins to bypass the proteostasis machinery and processes? How widespread are these soluble, misfolded states across the proteome? And how long do they persist for? Here, we address these questions using coarse-grain molecular dynamics simulations of the synthesis, termination, and post-translational dynamics of a representative set of cytosolic E. coli proteins. We predict that half of all proteins exhibit subpopulations of misfolded conformations that are likely to bypass molecular chaperones, avoid aggregation, and not be rapidly degraded. These misfolded states may persist for months or longer for some proteins. Structurally characterizing these misfolded states, we observe they have a large amount of native structure, but also contain localized misfolded regions from non-native changes in entanglement, in which a protein segment threads through a loop formed by another portion of the protein that is not found in the native state. The surface properties of these misfolded states are native like, suggesting they may bypass the proteostasis machinery and its regulatory processes to remain soluble, while their entanglements make these states long-lived kinetic traps, as disentanglement requires unfolding of already folded portions of the protein. In terms of function, we predict that one-third of proteins have subpopulations that misfold into less-functional states that have structurally perturbed functional sites yet remain soluble. Data from limited-proteolysis mass spectrometry experiments, which interrogate the misfolded conformations populated by proteins upon unfolding and refolding, are consistent with the structural changes seen in the entangled states of glycerol-3-phosphate dehydrogenase upon misfolding. These results provide an explanation for how proteins can misfold into soluble conformations with reduced functionality that can bypass cellular quality controls, and indicate, unexpectedly, this may be a wide-spread phenomenon in proteomes. Such entanglements are observed in many native structures, suggesting the non-native entanglements we observe are plausible. More broadly, these near-native entangled structures suggest a hypothesis for how synonymous mutations can modulate downstream protein structure and function, with these mutations partitioning nascent proteins between these kinetically trapped states.

biochemistry↗

bioRxiv2021

Subpopulations of soluble, misfolded proteins commonly bypass chaperones: How it happens at the molecular level

Subpopulations of soluble, misfolded proteins can bypass chaperones within cells. The scope of this phenomenon and the lifetimes of these states have not been experimentally quantified, and how such misfolding happens at the molecular level is poorly understood. We address the first issue through a meta-analysis of the experimental literature. We find that in all quantitative protein refolding-function studies, there is always a subpopulation of soluble but misfolded and less-functional protein that does not fold in the presence of one or more chaperones. This subpopulation ranges from 8% to 50% of the soluble protein molecules in solution. Fitting the experimental time traces to a kinetic model, we find these chaperone-bypassing misfolded states take months or longer to fold and function in the presence of different chaperones. We next addressed how, at the molecular level, some misfolded proteins can evade chaperones by simulating six different proteins interacting with E. colis GroEL and HtpG chaperones when those proteins are in folded, unfolded, or long-lived, soluble, misfolded states. We observe that both chaperones strongly bind the unfolded state and weakly bind the folded and misfolded states to a similar degree. Thus, these chaperones cannot distinguish between the folded and long-lived misfolded states of these proteins. A structural analysis reveals the misfolded states are highly similar to the native state - having a similar size, amount of exposed hydrophobic surface area, and level of tertiary structure formation. These results demonstrate that in vitro it is common for appreciable subpopulations of proteins to remain misfolded, soluble, and evade the refolding action of chaperones for very long times. Further, these results suggest that this happens because these misfolded subpopulations are near-native and therefore interact with chaperones to a similar extent as properly folded proteins. More broadly, these results indicate a mechanism in which long-time scale changes in protein structure and function can persist in cells because some proteins non-native states can bypass components of the proteostasis machinery. TEASERNear-native, misfolded protein conformations explain why some soluble proteins fail to refold in the presence of chaperones.

biophysics↗

bioRxiv2018

Domain topology, stability, and translation speed determine mechanical force generation on the ribosome

The concomitant folding of a nascent protein domain with its synthesis can generate mechanical forces that act on the ribosome and alter translation speed. Such changes in speed can affect the structure and function of the newly synthesized protein as well as cellular phenotype. The domain properties that govern force generation have yet to be identified and understood, and the influence of translation speed is unknown as all reported measurements have been carried out on arrested ribosomes. Here, using coarse-grained molecular simulations and statistical mechanical modeling of protein synthesis, we demonstrate that force generation is determined by a domains stability and topology, as well as translation speed. The statistical mechanical models we create predict how force profiles depend on these properties. These results indicate that force measurements on arrested ribosomes will not always accurately reflect what happens in a cell, especially for slow-folding domains, and suggest the possibility that certain domain properties may be enriched or depleted across the structural proteome of organisms through evolutionary selection pressures to modulate protein synthesis speed and post-translational protein behavior.\n\nSignificance StatementMechanochemistry, the influence of molecular-scale mechanical forces on chemical processes, can occur on actively translating ribosomes through the force-generating actions of motor proteins and the co-translational folding of domains. Such forces are transmitted to the ribosomes catalytic core and alter rates of protein synthesis; representing a form of mechanical allosteric communication. These changes in translation-elongation kinetics are biologically important because they can influence protein structure, function, and localization within a cell. Many fundamental questions are unresolved concerning the properties of protein domains that determine mechanical force generation, the effect of translation speed on this force, and exactly how, at the molecular level, force is generated. In this study we answer these questions using cutting-edge molecular simulations and statistical mechanical modeling.

biophysics↗