Biology subjects

Lotthammer, J. M.

Publications and source records attributed to Lotthammer, J. M..

12 recordsLinked to original sources

bioRxiv2025

Accurate predictions of conformational ensembles of disordered proteins with STARLING

Intrinsically disordered proteins and regions (collectively IDRs) are found across all kingdoms of life and play critical roles in virtually every eukaryotic cellular process. In contrast to folded proteins, IDRs lack a stable 3D structure and are instead described in terms of a conformational ensemble, a collection of energetically accessible interconverting structures. This unique structural plasticity facilitates diverse molecular recognition and function; thus, a convenient way to view IDRs is through their ensembles. Here, we combine advances in physics-based force fields for IDPs with the power of modern multi-scale generative modeling to develop STARLING, an approach for the rapid and accurate prediction of IDR ensembles directly from sequence. STARLING enables ensembles of hundreds of conformers to be generated in seconds and works on GPUs and CPUs. This, in turn, dramatically lowers the barrier to the computational interrogation of IDR function through the lens of emergent biophysical properties complementing bioinformatic protein sequence analysis. We evaluate STARLINGs accuracy against extant experimental data and offer a series of vignettes illustrating how STARLING can enable rapid hypothesis generation for IDR function and aid the interpretation of experimental data.

biophysics↗

bioRxiv2024

Disentangling folding from energetic traps in simulations of disordered proteins

Protein conformational heterogeneity plays an essential role in a myriad of different biological processes. Extensive conformational heterogeneity is especially characteristic of intrinsically disordered proteins and protein regions (collectively IDRs), which lack a well-defined three-dimensional structure and instead rapidly exchange between a diverse ensemble of configurations. An emerging paradigm recognizes that the conformational biases encoded in IDR ensembles can play a central role in their biological function, necessitating understanding these sequence-ensemble relations. All-atom simulations have provided critical insight into our modern understanding of the solution behavior of IDRs. However, effectively exploring the accessible conformational space associated with large, heterogeneous ensembles is challenging. In particular, identifying poorly sampled or energetically trapped regions of disordered proteins in simulations often relies on qualitative assessment based on visual inspection of simulations and/or analysis data. These approaches, while convenient, run the risk of masking poorly-sampled simulations. In this work, we present an algorithm for quantifying per-residue local conformational heterogeneity in protein simulations. Our work builds on prior work and compares the similarity between backbone dihedral angle distributions generated from molecular simulations in a limiting polymer model and across independent all-atom simulations. In this regime, the polymer model serves as a statistical reference model for extensive conformational heterogeneity in a real chain. Quantitative comparisons of probability vectors generated from these simulations reveal the extent of conformational sampling in a simulation, enabling us to distinguish between situations in which protein regions are well-sampled, poorly-sampled, or folded. To demonstrate the effectiveness of this approach, we apply our algorithm to several toy, synthetic, and biological systems. Accurately assessing local conformational sampling in simulations of IDRs will help better quantify new enhanced sampling methods, ensure force field comparisons are equivalent, and provide confidence that conclusions drawn from simulations are robust.

biophysics↗

bioRxiv2024

Metapredict enables accurate disorder prediction across the Tree of Life

Intrinsically disordered proteins and protein regions (collectively IDRs) are critical in numerous cellular processes. To understand how IDRs facilitate function, we need tools to accurately and rapidly identify them from sequence. While many methods for disorder prediction exist, we are currently limited by throughput and accuracy for evolutionary scale analyses. To bridge this gap, we developed metapredict V3, an updated version of our disorder predictor that enables evolutionary-scale disorder prediction. Metapredict V3 enables proteome-scale prediction with state-of-the-art accuracy in seconds and was developed with a focus on usability. It is distributed as a web server, Python software package, command-line interface, and Google Colab notebook. Here, we leverage the accuracy and throughput of metapredict V3 to predict disorder for over 20,000 proteomes to evaluate the prevalence of disorder across the kingdoms of life.

bioinformatics↗

bioRxiv2024

Protein surface chemistry encodes an adaptive resistance to desiccation

Cellular desiccation - the loss of nearly all water from the cell - is a recurring stress in an increasing number of ecosystems that can drive protein unfolding and aggregation. For cells to survive, at least some of the proteome must resume function upon rehydration. Which proteins tolerate desiccation, and the molecular determinants that underlie this tolerance, are largely unknown. Here, we apply quantitative and structural proteomic mass spectrometry to show that certain proteins possess an innate capacity to tolerate rehydration following extreme water loss. Structural analysis points to protein surface chemistry as a key determinant for desiccation tolerance, which we test by showing that rational surface mutants can convert a desiccation sensitive protein into a tolerant one. Desiccation tolerance also has strong overlap with cellular function, with highly tolerant proteins responsible for production of small molecule building blocks, and intolerant proteins involved in energy-consuming processes such as ribosome biogenesis. As a result, the rehydrated proteome is preferentially enriched with metabolite and small molecule producers and depleted of some of the cells heaviest consumers. We propose this functional bias enables cells to kickstart their metabolism and promote cell survival following desiccation and rehydration. TeaserProteins can resist extreme dryness by tuning the amino acids on their surfaces.

biophysics↗

bioRxiv2024

Direct prediction of intermolecular interactions driven by disordered regions

Intrinsically disordered regions (IDRs) are critical for a wide variety of cellular functions, many of which involve interactions with partner proteins. Molecular recognition is typically considered through the lens of sequence-specific binding events. However, a growing body of work has shown that IDRs often interact with partners in a manner that does not depend on the precise order of the amino acid order, instead driven by complementary chemical interactions leading to disordered bound-state complexes. Despite this emerging paradigm, we lack tools to describe, quantify, predict, and interpret these types of structurally heterogeneous interactions from the underlying amino acid sequences. Here, we repurpose the chemical physics developed originally for molecular simulations to develop an approach for predicting intermolecular interactions between IDRs and partner proteins. Our approach enables the direct prediction of phase diagrams, the identification of chemically-specific interaction hotspots on IDRs, and a route to develop and test mechanistic hypotheses regarding IDR function in the context of molecular recognition. We use our approach to examine a range of systems and questions to highlight its versatility and applicability.

biophysics↗

bioRxiv2024

Rhythmidia: a modern tool for circadian period analysis of filamentous fungi

Circadian rhythms are ubiquitous across the kingdoms of life and serve important roles in regulating physiology and behavior at many levels. These rhythms occur in [~]24-hour cycles and are driven by a core molecular oscillator. Circadian timekeeping enables organisms to anticipate daily changes by timing their growth and internal processes. Neurospora crassa is a model organism with a long history in circadian biology, having conserved eukaryotic clock properties and observable circadian phenotypes. A core approach for measuring circadian function in Neurospora is to follow daily oscillations in the direction of growth and spore formation along a thin glass tube (race tube). While leveraging robust phenotypic readouts is useful, interpreting the outputs of large-scale race tube experiments by hand can be time-consuming and prone to human error. To provide the field with an efficient tool for analyzing race tubes, we present Rhythmidia, a graphical user interface (GUI) tool written in Python for calculating circadian periods and growth rates of Neurospora. Rhythmidia is open source, has been benchmarked against the current state-of-the-art, and is easily accessible on GitHub.

cell biology↗

bioRxiv2023

Divergent Molecular Phenotypes in Point Mutations at the Same Residue in Beta-Myosin Heavy Chain Lead to Distinct Cardiomyopathies

In genetic cardiomyopathies, a frequently described phenomenon is how similar mutations in one protein can lead to discrete clinical phenotypes. One example is illustrated by two mutations in beta myosin heavy chain ({beta}-MHC) that are linked to hypertrophic cardiomyopathy (HCM) (Ile467Val, I467V) and left ventricular non-compaction (LVNC) (Ile467Thr, I467T). To investigate how these missense mutations lead to independent diseases, we studied the molecular effects of each mutation using recombinant human {beta}-MHC Subfragment 1 (S1) in vitro assays. Both HCM-I467V and LVNC-I467T S1 mutations exhibited similar mechanochemical functions, including unchanged ATPase and enhanced actin velocity but had distinct effects on the basal activity of myosin. HCM-I467V S1 showed no change in basal ATPase activity of myosin while LVNC-I467T reduced the basal ATPase activity by 50%. Molecular dynamics simulations reveal that I467T allosterically disrupts nucleotide binding of myosin, which may contribute to the uncoupled reduced basal activity and enhanced actin velocity observed in this mutation. These contrasting molecular effects may lead to contractile dysregulation that initiates LVNC-associated signaling pathways that progress the phenotype. Together, analysis of these mutations provides evidence that phenotypic complexity originates at the molecular level and is critical to understanding disease progression and developing therapies.

biophysics↗

bioRxiv2023

Complexes of vertebrate TMC1/2 and CIB2/3 proteins form hair-cell mechanotransduction cation channels

Calcium and integrin-binding protein 2 (CIB2) and CIB3 bind to transmembrane channel-like 1 (TMC1) and TMC2, the pore-forming subunits of the inner-ear mechano-electrical transduction (MET) apparatus. These interactions have been proposed to be functionally relevant across mechanosensory organs and vertebrate species. Here we show that both CIB2 and CIB3 can form heteromeric complexes with TMC1 and TMC2 and are integral for MET function in mouse cochlea and vestibular end organs as well as in zebrafish inner ear and lateral line. Our AlphaFold 2 models suggest that vertebrate CIB proteins can simultaneously interact with at least two cytoplasmic domains of TMC1 and TMC2 as validated using nuclear magnetic resonance spectroscopy of TMC1 fragments interacting with CIB2 and CIB3. Molecular dynamics simulations of TMC1/2 complexes with CIB2/3 predict that TMCs are structurally stabilized by CIB proteins to form cation channels. Overall, our work demonstrates that intact CIB2/3 and TMC1/2 complexes are integral to hair-cell MET function in vertebrate mechanosensory epithelia.

neuroscience↗

bioRxiv2023

Direct prediction of intrinsically disordered protein conformational properties from sequence

Intrinsically disordered regions (IDRs) are ubiquitous across all domains of life and play a range of functional roles. While folded domains are generally well-described by a single 3D structure, IDRs exist in a collection of interconverting states known as an ensemble. This structural heterogeneity means IDRs are largely absent from the PDB, contributing to a lack of computational approaches to predict ensemble conformational properties from sequence. Here we combine rational sequence design, large-scale molecular simulations, and deep learning to develop ALBATROSS, a deep learning model for predicting IDR ensemble dimensions from sequence. ALBATROSS enables the instantaneous prediction of ensemble average properties at proteome-wide scale. ALBATROSS is lightweight, easy-to-use, and accessible as both a locally installable software package and a point-and-click style interface in the cloud. We first demonstrate the applicability of our predictors by examining the generalizability of sequence-ensemble relationships in IDRs. Then, we leverage the high-throughput nature of ALBATROSS to characterize emergent biophysical behavior of IDRs within and between proteomes. Update from previous versionO_LIThis preprint reports an updated version of the ALBATROSS network weights trained on simulations of over 42,000 sequences. C_LIO_LIIn addition, we provide new colab notebooks that enable proteome-wide IDR prediction and annotation in minutes. C_LIO_LIAll conclusions and observations made in versions 1 and 2 of this manuscript remain true and robust. C_LI

biophysics↗

bioRxiv2022

Drug specificity and affinity are encoded in the probability of cryptic pocket opening in myosin motor domains

The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least 6 of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 milliseconds of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatins binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R2=0.82). To test our ability to make blind predictions, we predicted blebbistatins binding affinity for an isoform (Myh7b) whose blebbistatin sensitivity was unknown. Encouragingly, we find good agreement between the predicted and measured IC50 (0.67 {micro}M vs. 0.36 {micro}M). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets. SignificanceDrug development requires the discovery of compounds which specifically target one member of a protein family without triggering side effects that arise from interactions with other related proteins. Myosins are a family of motor proteins that are drug targets for heart diseases, cancer, and parasitic infections. Here, we investigate why the compound blebbistatin specifically inhibits some myosins more potently than others, even though its binding site is closed in all known experimental structures. We find that the blebbistatin binding pocket opens in molecular dynamics simulations of certain myosin motors, and that the probability of opening predicts how potently blebbistatin inhibits a particular motor. Our work suggests that differences in cryptic pocket formation can be exploited to develop specific therapeutics.

biophysics↗

bioRxiv2022

Predicting the locations of cryptic pockets from single protein structures using the PocketMiner graph neural network

Cryptic pockets expand the scope of drug discovery by enabling targeting of proteins currently considered undruggable because they lack pockets in their ground state structures. However, identifying cryptic pockets is labor-intensive and slow. The ability to accurately and rapidly predict if and where cryptic pockets are likely to form from a protein structure would greatly accelerate the search for druggable pockets. Here, we present PocketMiner, a graph neural network trained to predict where pockets are likely to open in molecular dynamics simulations. Applying PocketMiner to single structures from a newly-curated dataset of 39 experimentally-confirmed cryptic pockets demonstrates that it accurately identifies cryptic pockets (ROC-AUC: 0.87) >1,000-fold faster than existing methods. We apply PocketMiner across the human proteome and show that predicted pockets open in simulations, suggesting that over half of proteins thought to lack pockets based on available structures are likely to contain cryptic pockets, vastly expanding the druggable proteome.

biophysics↗

bioRxiv2021

In Silico Electrophysiology of Inner-Ear Mechanotransduction Channel TMC1 Models

Inner-ear sensory hair cells convert mechanical stimuli from sound and head movements into electrical signals during mechanotransduction. Identification of all molecular components of the inner-ear mechanotransduction apparatus is ongoing; however, there is strong evidence that TMC1 and TMC2 are pore-forming subunits of the complex. We present molecular dynamics simulations that probe ion conduction of TMC1 models built based on two different structures of related TMEM16 proteins. Unlike most channels, the TMC1 models do not show a central pore. Instead, simulations of these models in a membrane environment at various voltages reveal a peripheral permeation pathway that is exposed to lipids and that shows cation permeation at rates comparable to those measured in hair cells. Furthermore, our analyses suggest that TMC1 gating mechanisms involve protein conformational changes and tension-induced lipid-mediated pore widening. These results provide insights into ion conduction and activation mechanisms of hair-cell mechanotransduction channels essential for hearing and balance.

biophysics↗