Biology subjects

Jussupow, A.

Publications and source records attributed to Jussupow, A..

11 recordsLinked to original sources

bioRxiv2026

Buried Ion-Pairs Reshape Folding Pathways of Artificial Proteins

Ion-pairs are central charged motifs that control the biological function of several proteins. Yet, the physical principles linked to the formation of ion-pairs during protein folding remain poorly understood. Here, we show that buried ion-pairs of de novo-designed helical bundle proteins reshape the folding pathways by introducing kinetic barriers that are overcome by a hydrophobic collapse linked to packing of the protein core. By combining protein design with co-translational folding studies, nuclear magnetic resonance (NMR) spectroscopy, and molecular dynamics simulations, we show that introduction of a buried ion-pair leads to a shifted folding transition that balances desolvation and electrostatic interactions within the protein and the bulk solvent. Moreover, we find that the folding pathway is sensitive to modifications in non-polar interactions and motifs that electrostatically shield the ion-pair from its surroundings. Our combined findings provide a blueprint for understanding how functional motifs affect protein folding and how mutation of charged networks alter the stability of proteins.

biophysics↗

bioRxiv2025

Deep generative modeling of temperature-dependent structural ensembles of proteins

Deep learning has revolutionized protein structure prediction, but capturing conformational ensembles and structural variability remains an open challenge. While molecular dynamics (MD) is the foundation method for simulating biomolecular dynamics, it is computationally expensive. Recently, deep learning models trained on MD have made progress in generating structural ensembles at reduced cost. However, they remain limited in modeling atomistic details and, crucially, incorporating the effect of environmental factors. Here, we present aSAM (atomistic structural autoencoder model), a latent diffusion model trained on MD to generate heavy atom protein ensembles. Unlike most methods, aSAM models atoms in a latent space, greatly facilitating accurate sampling of side chain and backbone torsion angle distributions. Additionally, we extended aSAM into the first reported transferable generator conditioned on temperature, named aSAMt. Trained on the large and open mdCATH dataset, aSAMt captures temperature-dependent ensemble properties and demonstrates generalization beyond training temperatures. By comparing aSAMt ensembles to long MD simulations of fast folding proteins, we find that high-temperature training enhances the ability of deep generators to explore energy landscapes. Finally, we also show that our MD-based aSAMt can already capture experimentally observed thermal behavior of proteins. Our work is a step towards generalizable ensemble generation to complement physics- based approaches.

biophysics↗

bioRxiv2024

Structure Characterization of Bacterial Microcompartment Shells via X-ray Scattering and Coordinate Modeling: Evidence for adventitious capture of cytoplasmic proteins

Bacterial microcompartments (BMCs) are self-assembling, protein shell structures that are widely investigated across a broad range of biological and abiotic chemistry applications. A central challenge in BMC research is the targeted capture of enzymes during shell assembly. While crystallography and cryo-EM techniques have been successful in determining BMC shell structures, there has been only limited success in visualizing the location of BMC-captured enzyme cargo. Here, we demonstrate the opportunity to use small angle X-ray scattering (SAXS) and pair density distribution function (PDDF) measurements combined with quantitative comparison to coordinate structure models as an approach to characterize BMC shell structures in solution conditions directly relevant to biochemical function. Using this approach, we analyzed BMC shells from Haliangium ochraceum that were isolated following expression in E. coli. The analysis allowed BMC shell structures and the extent of encapsulated enzyme cargo to be identified. Notably, the results demonstrate that HO-BMC shells adventitiously capture significant amounts of cytoplasmic cargo during assembly in E. coli. Our findings highlight the utility of SAXS/PDDF analysis for evaluating BMC architectures and enzyme encapsulation, offering valuable insights for designing BMC shells as platforms for biological and abiotic catalyst capture within confined environments.

biophysics↗

bioRxiv2024

COCOMO2: A coarse-grained model for interacting folded and disordered proteins

Biomolecular interactions are essential in many biological processes, including complex formation and phase separation processes. Coarse-grained computational models are especially valuable for studying such processes via simulation. Here, we present COCOMO2, an updated residue-based coarse-grained model that extends its applicability from intrinsically disordered peptides to folded proteins. This is accomplished with the introduction of a surface exposure scaling factor, which adjusts interaction strengths based on solvent accessibility, to enable the more realistic modeling of interactions involving folded domains without additional computational costs. COCOMO2 was parameterized directly with solubility and phase separation data to improve its performance on predicting concentration-dependent phase separation for a broader range of biomolecular systems compared to the original version. COCOMO2 enables new applications including the study of condensates that involve IDPs together with folded domains and the study of complex assembly processes. COCOMO2 also provides an expanded foundation for the development of multi-scale approaches for modeling biomolecular interactions that span from residue-level to atomistic resolution. Table of Contents Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/620916v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@32d74forg.highwire.dtl.DTLVardef@1d5aa9eorg.highwire.dtl.DTLVardef@b47a5forg.highwire.dtl.DTLVardef@ce0f08_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2024

Controlled enzyme cargo loading in engineered bacterial microcompartment shells

Bacterial microcompartments (BMCs) are nanometer-scale organelles with a protein-based shell that serve to co-localize and encapsulate metabolic enzymes. They may provide a range of benefits to improve pathway catalysis, including substrate channeling and selective permeability. Several groups are working toward using BMC shells as a platform for enhancing engineered metabolic pathways. The microcompartment shell of Haliangium ochraceum (HO) has emerged as a versatile and modular shell system that can be expressed and assembled outside its native host and with non-native cargo. Further, the HO shell has been modified to use the engineered protein conjugation system SpyCatcher-SpyTag for non-native cargo loading. Here, we used a model enzyme, triose phosphate isomerase (Tpi), to study non-native cargo loading into four HO shell variants and begin to understand maximal shell loading levels. We also measured activity of Tpi encapsulated in the HO shell variants and found that activity was determined by the amount of cargo loaded and was not strongly impacted by the predicted permeability of the shell variant to large molecules. All shell variants tested could be used to generate active, Tpi-loaded versions, but the simplest variants assembled most robustly. We propose that the simple variant is the most promising for continued development as a metabolic engineering platform.

biochemistry↗

bioRxiv2024

Protein-Induced Membrane Strain Drives Supercomplex Formation

Mitochondrial membranes harbor the electron transport chain (ETC) that powers oxidative phosphorylation (OXPHOS) and drives the synthesis of ATP. Yet, under physiological conditions, the OXPHOS proteins operate as higher-order supercomplex (SC) assemblies, although their functional role remains poorly understood and much debated. By combining large-scale atomistic and coarse-grained molecular simulations with analysis of cryo-electron microscopic data and statistical as well as kinetic models, we show here that the formation of the mammalian I/III2 supercomplex reduces the molecular strain of inner mitochondrial membranes by altering the local membrane thickness and leading to an accumulation of both cardiolipin and quinone around specific regions of the SC. We find that the SC assembly also affects the global motion of the individual ETC proteins with possible functional consequences. On a general level, our findings suggest that molecular crowding and strain effects provide a thermodynamic driving force for the SC formation, with a possible flux enhancement in crowded biological membranes under constrained respiratory conditions. Significance StatementThe membrane-bound proteins of respiratory chains power oxidative phosphorylation (OXPHOS) and drive the synthesis of ATP. However, recent biochemical and structural data show that the OXPHOS proteins operate as higher-order supercomplex assemblies for reasons that remain elusive and much debated. Here we show that the mammalian respiratory supercomplexes reduce the molecular strain of inner mitochondrial membranes and enhance the allosteric crosstalk by altering the protein dynamics with important biochemical and physiological implications.

biophysics↗

bioRxiv2022

Effective Molecular Dynamics from Neural-Network Based Structure Prediction Models

Recent breakthroughs in neural-network based structure prediction methods, such as AlphaFold2 and RoseTTAFold, have dramatically improved the quality of computational protein structure prediction. These models also provide statistical confidence scores that can estimate uncertainties in the predicted structures, but it remains unclear to what extent these scores are related to the intrinsic conformational dynamics of proteins. Here we compare AlphaFold2 prediction scores with >60 s of explicit molecular dynamics simulations of 28 one- and two-domain proteins with varying degree of flexibility. We demonstrate a strong correlation between the statistical prediction scores and the explicit motion derived from extensive atomistic molecular dynamics simulations, and further derive an elastic network model based on the statistical scores of AlphFold2 (AF-ENM), which we benchmark in combination with coarse-grained molecular dynamics simulations. We show that our AF-ENM method reproduces the global protein dynamics with improved accuracy, providing a powerful way to derive effective molecular dynamics using neural-network based structure prediction models.

biophysics↗

bioRxiv2021

Refinement of α-synuclein ensembles against SAXS data: Comparison of force fields and methods

The inherent flexibility of intrinsically disordered proteins (IDPs) makes it difficult to interpret experimental data using structural models. On the other hand, molecular dynamics simulations of IDPs often suffer from force-field inaccuracies, and long simulations times or enhanced sampling methods are needed to obtain converged ensembles. Here, we apply metainference and Bayesian/Maximum Entropy reweighting approaches to integrate prior knowledge of the system with experimental data, while also dealing with various sources of errors and the inherent conformational heterogeneity of IDPs. We have measured new SAXS data on the protein -synuclein, and integrate this with simulations performed using different force fields. We find that if the force field gives rise to ensembles that are much more compact than what is implied by the SAXS data it is difficult to recover a reasonable ensemble. On the other hand, we show that when the simulated ensemble is reasonable, we can obtain an ensemble that is consistent with the SAXS data, but also with NMR diffusion and paramagnetic relaxation enhancement data.

biophysics↗

bioRxiv2020

Molecular recognition and dynamics of linear poly-ubiquitins: integrating coarse-grain simulations and experiments

Poly-ubiquitin chains are flexible multidomain proteins, whose conformational dynamics enable their molecular recognition by a large number of partners in multiple biological pathways. By using alternative linkage, it is possible to obtain poly-ubiquitin molecules with different dynamical properties. This flexibility is further increased by the possibility to tune the length of poly-ubiquitin chains. Characterizing the dynamics of poly-ubiquitins as a function of their length is thus relevant to understand their biology. Structural characterization of poly-ubiquitin conformational dynamics is challenging both experimentally and computationally due to increasing system size and conformational variability. Here, by developing highly efficient and accurate small-angle X-ray scattering driven Martini coarse-grain simulations, we characterize the dynamics of linear M1-linked di-, tri- and tetra-ubiquitin chains. Our data show that the behavior of the di-ubiquitin subunits is independent of the presence of additional ubiquitin modules. We propose that the conformational space sampled by linear poly-ubiquitins, in general, may follow a simple self-avoiding polymer model. These results, combined with experimental data from small angle X-ray scattering, biophysical techniques and additional simulations show that binding of NEMO, a central regulator in the NF-{kappa}B pathway, to linear poly-ubiquitin obeys a 2:1 (NEMO:poly-ubiquitin) stoichiometry in solution, even in the context of four ubiquitin units. Eventually, we show how the conformational properties of long poly-ubiquitins may modulate the binding with their partners in a length-dependent manner. SignificanceProtein conformational dynamics plays an essential role in molecular recognition mechanisms. The characterization of conformational dynamics is hampered by the conformational averaging of observable in experimental structural biology techniques and by the limitations in the accuracy of computational methods. By developing an efficient and accurate approach to combine small-angle X-ray scattering solution experiments and coarse-grain Martini simulations, we show that the conformational dynamics of linear poly-ubiquitins can be efficiently determined and to rationalize the role of poly-ubiquitin dynamic in the molecular recognition of the UBAN domain upon binding to the signaling regulator NEMO. The analysis of the conformational ensembles allows us to propose a general model of the dynamics of linear poly-ubiquitin chains where they can be described as a self-avoiding polymer with a characteristic length associated with their specific linkage.

biophysics↗

bioRxiv2018

Martini bead form factors for nucleic-acids and their application in the refinement of protein/nucleic-acid complexes against SAXS data

Small-angle X-ray scattering (SAXS) use in combination with molecular dynamics simulation is hampered by its heavy computational cost. The calculation of SAXS from atomic structures can be speed up by using a coarse grain representation of the structure. Here following the work of Niebling, et al. (J. Appl. Cryst., (2014), 47, 1190) we derived the Martini beads form factors for nucleic acids and we implemented them, together with those previously determined for proteins, in the publicly available PLUMED library. We also implemented a hybrid multi-resolution strategy to perform SAXS restrained simulations at atomic resolution by calculating on-the-fly the virtual position of the Martini beads and using them for the calculation of SAXS. The accuracy and efficiency of the method is demonstrated by refining the structure of two protein/nucleic acid complexes. Instrumental for this result is the use of metainference that allows considering and alleviating the approximations at play in our SAXS calculation.

biochemistry↗

bioRxiv2017

Structural basis for terminal loop recognition and processing of pri-miRNA-18a by hnRNP A1

Post-transcriptional mechanisms play a predominant role in the control of microRNA (miRNA) production. Recognition of the terminal loop of precursor miRNAs by RNA-binding proteins (RBPs) influences their processing; however, the mechanistic and structural basis for how levels of individual or subsets of miRNAs are regulated is mostly unexplored. We previously described a role for hnRNP A1, an RBP implicated in many aspects of RNA processing, as an auxiliary factor that promotes the Microprocessor-mediated processing of pri-mir-18a. Here, we reveal the mechanistic basis for this stimulatory role of hnRNP A1 by combining integrative structural biology with biochemical and functional assays. We demonstrate that hnRNP A1 forms a 1:1 complex with pri-mir-18a that involves binding of both RNA recognition motifs (RRMs) to cognate RNA sequence motifs in the conserved terminal loop of pri-mir-18a. Terminal loop binding induces an allosteric destabilization of base-pairing in the pri-mir-18a stem that promotes its down-stream processing. Our results highlight terminal loop RNA recognition by RNA-binding proteins as a general principle of miRNA biogenesis and regulation.

molecular biology↗