Biology subjects

Brotzakis, Z. F.

Publications and source records attributed to Brotzakis, Z. F..

At least 19 recordsLinked to original sources

bioRxiv2026

Anneal-MaxCal: kinetic ensembles from a Markov model learned along a solute-tempering ladder

A full characterisation of a molecular system requires its metastable states, their populations and their interconversion rates. Molecular Dynamics can in principle provide this characterisation but is limited by sampling and by force-field inaccuracies. Enhanced sampling addresses the first problem, but typically requires prior knowledge of reaction coordinates or metastable states and often sacrifices the kinetics, while maximum-caliber reweighting by Brotzakis, Vendruscolo and Bolhuis [Proc. Natl. Acad. Sci. U.S.A., 118(2):e2012423118, 2021] has been implemented to that matter on reweighting continuous pathways by imposing rate constants as kinetic constraints in two-state protein folding. Here, we introduce Anneal-MaxCal, which tackles both problems by imposing rate constants or time-resolved data on a Markov model learned along an annealed solute-tempering ladder spanning softer to physical interactions. MBAR assigns physical free energies to all 160 cells of a slow TICA subspace, against 79 visited at the physical coupling. The square-root approximation (SqRA), a discretised diffusion on this free-energy landscape, converts these populations into a detailed-balance rate matrix. Its prefactors, which act as a position-dependent diffusivity across each cell face, are learned from transitions at the softer couplings and extrapolated log-linearly to the physical coupling. This yields rates and a committor across the TICA space, although the physical rung contains a single barrier crossing. For alanine dipeptide, 665 ns of simulation give a relaxation time of 5430 {+/-} 996 ps against 4413 {+/-} 446 ps from 15.4 s of unbiased dynamics, whereas a constant prefactor is 2.5 times too slow. Maximum caliber then minimally perturbs this prior, with the posterior populations following from a generalised Doob transform. A synthetic time-resolved density with a tenfold slower back-transition lowers {Delta}F from 5.191 to 2.885 kBT , raises the product-basin population from 0.0055 to 0.0529 and slows relaxation 9.47-fold, leaving the reactant basin unchanged. Forward and backward rates accelerated 3 and 20-fold raise {Delta}F by 1.892 kBT and shorten relaxation from 4818 to 289 ps.

biophysics↗

bioRxiv2026

A nested shell structure coordinates enzyme communication in pyruvate oxidation

The pyruvate dehydrogenase complex (PDHc)1 links glycolysis to the Krebs cycle by catalyzing the oxidative decarboxylation of pyruvate to acetyl-CoA and CO2, a process essential for life2,3. PDHc is formed by structural proteins (E3-binding protein, E3BP)4-7, enzymatic subunits (E1, E2, E3)4,6, and mobile lipoyl domains (LDs), the latter shuttling intermediates across active sites6,8. Although numerous details regarding pyruvate oxidation steps have been elucidated9, the precise organization of the entire PDHc remains unknown due to its large size and dynamic heterogeneity. Here, we employ in silico, in vitro, and in situ methods to propose a multi-scale model of PDHc that includes approximately one million atoms and to visualize multiple conformational states. This model reveals a [~]40-50 nm nested shell structure, formed by flexible linkers that spatially coordinate the E1 and E3 enzyme complexes around the E2-E3BP core scaffold. This structure acts as a molecular sieve, selectively guiding lipoyl arms while maintaining enzyme positioning with sub-nm precision. During catalysis, the nested shell structure expands and adopts a mechanically reinforced state comparable in magnitude to viral assemblies10. Our findings provide structural context for the textbook "link reaction"11, building on decades of biochemical knowledge, are transferable to functional aspects of other -ketoacid dehydrogenase complexes, and, ultimately, expand our understanding of primary metabolism as a whole. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/724543v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@1615612org.highwire.dtl.DTLVardef@159bc8dorg.highwire.dtl.DTLVardef@69f8c1org.highwire.dtl.DTLVardef@14a83a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Structure-based Generation of a Secondary Nucleation Inhibitor in alpha-Synuclein Aggregation Using a Conditional Diffusion Model

The process of -synuclein aggregation results in the formation of amyloid fibrils, which accumulate in the brain of patients affected by Parkinsons disease. Among possible therapeutic strategies to cure this disease, one approach is based on the development of compounds capable of inhibiting -synuclein aggregation. An effective inhibition could be achieved by blocking the nucleation sites on the surface of the amyloid fibrils that are responsible for their autocatalytic proliferation. Here, we report a strategy based on deep learning to achieve this goal, which uses an E(3)-equivariant conditional diffusion model. By using this approach, we designed and tested experimentally candidate small molecules. We found that one of these small molecules acts as a potent inhibitor of secondary nucleation in -synuclein aggregation. These results provide evidence that generative diffusion models offer effective tools for drug design.

biophysics↗

bioRxiv2025

Destabilization of Helix III Initiates Early Serum Amyloid A Misfolding by Exposing Its Amyloidogenic Core

Serum amyloid A (SAA) is the principal precursor of AA amyloidosis, yet the early molecular steps triggering its pathological misfolding remain unclear. Here, we combine harmonic linear discriminant analysis (HLDA) and parallel-tempering metadynamics (PT-MetaD) to dissect the earliest conformational transitions of the disease-relevant SAA1-76 fragment. By constructing an optimized one-dimensional collective variable (sHLDA) from inter-helix contacts and helical root-mean-square deviations, we perform 4 {micro}s of enhanced sampling across 79 replicas (300-450K). Free-energy surfaces reveal a misfolding trajec-tory where helix III destabilizes first, preceding loss of helices II and I while global com-pactness persists. Solvent-accessible surface-area analysis reveals transient exposure of the aggregation-prone core (residues 42-48) within specific intermediates, implicating localized core exposure rather than wholesale unfolding as the trigger for misfolding. Temperature-dependent secondary-structure profiling confirms SAA1-76 behaves as a folded bundle with disordered loops. These findings highlight helix III stabilization and amyloidogenic segment masking as potential therapeutic strategies. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/656333v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@167fd1borg.highwire.dtl.DTLVardef@19fa62org.highwire.dtl.DTLVardef@1857066org.highwire.dtl.DTLVardef@1966fb0_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

Design of Tau Aggregation Inhibitors Using Iterative Machine Learning and a Polymorph-Specific Brain-Seeded Fibril Amplification Assay

The aggregation of tau into amyloid fibrils is associated with Alzheimers disease (AD) and related tauopathies. Since different tauopathies are characterised by the formation of distinct tau fibril morphologies, it is important to combine the search of tau aggregation inhibitors with the development of in vitro tau aggregation assays that recapitulate aggregation as it may occur in the brain. Here we address this problem by reporting an in vitro tau aggregation assay in which AD brain homogenates are used to seed the generation of first-generation tau fibrils in a polymorph-specific manner under quiescent conditions. These fibrils are then used to create amyloid seed libraries from which second-generation kinetic assays can be readily performed. Using this strategy, we illustrate an iterative machine learning method for the identification of small molecules for the polymorph-specific inhibition of the in vitro formation of tau fibrils. We further show that the small molecules selected by this procedure are potent inhibitors in a Drosophila tauopathy model.

biophysics↗

bioRxiv2025

Design of Protein Sequences with Precisely Tuned Kinetic Properties

Recent advances in computational biology have enabled solutions to the inverse folding problem - finding an amino acid sequence that folds into a target structure. An open question concerns the design of proteins that in addition to having the correct target structure also have precisely tuned kinetic properties, such as folding and unfolding rates. To address this problem, we formulate the inverse folding problem as a quest for a sequence with a target free energy landscape. To propose a procedure to address this problem, here we describe the Inverse Folding Molecular Dynamics (IF-MD) method, which combines inverse folding with enhanced sampling molecular dynamics and Bayesian optimization. IF-MD leverages ensemble averages from molecular dynamics simulations, reweighted according to a Bayesian framework, to guide the design of sequences exhibiting specific kinetic properties. We demonstrate the methodology by optisising the binding kinetics of H11, a nanobody against the SARS-CoV-2 spike receptor-binding domain (RBD), thus identifying nanobody variants with slower unbinding kinetics than H11. Mechanistic analysis reveals that this kinetic property arises from a shift towards configurations closer to the bound state and increased free energy barriers for dissociation. These findings highlight the power of IF-MD for efficiently navigating the vast sequence space to design proteins with a tailored free energy landscape.

bioinformatics↗

bioRxiv2024

Language Models for Molecular Dynamics

Molecular Dynamics (MD) simulations provide accurate descriptions of the motions of molecular systems, yet their computational demands pose significant challenges in applications in molecular biology and materials science. Given the success of deep learning methods in a wide range of fields, a timely question concerns whether these methods could be leveraged to improve the efficiency of MD simulations. To investigate this possibility, we introduce Molecular Dynamics Language Models (MDLMs), to enable the generation of MD trajectories. In the present implementation, an MDLM is trained on a short classical MD trajectory of a protein, where structural accuracy is maintained through kernel density estimations derived from extensive MD datasets. We illustrate the application of this MDLM in the case of the determination of the free energy landscape a small protein, showing that this approach makes it possible to discover conformational states undersampled in the training data. These results provide initial evidence for the use of language models for the efficient implementation of molecular dynamics.

bioinformatics↗

bioRxiv2024

AlphaFold-Metainference: Prediction of Structural Ensembles of Disordered Proteins

Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. To address this problem, we introduce the AlphaFold-Metainference method to use AlphaFold-derived distances as structural restraints in molecular dynamics simulations to construct structural ensembles of ordered and disordered proteins. The results obtained using AlphaFold-Metainference illustrate the possibility of making predictions of the conformational properties of disordered proteins using deep learning methods trained on the large structural databases available for folded proteins.

biophysics↗

bioRxiv2024

Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models

Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 10 compared to naive physics-based molecular docking.

bioinformatics↗

bioRxiv2024

Transient interactions between the fuzzy coat and the cross-β core of brain-derived Aβ42 filaments

A wide range of human disorders, including Alzheimers disease (AD), are characterised by the aberrant formation of amyloid fibrils. Amyloid fibrils are filamentous structures characterized by the presence of a highly-ordered cross-{beta} core. In many cases, this core structure is flanked by disordered regions, often referred to as fuzzy coat. The structural properties of fuzzy coats, and the way in which they interact with their environments, however, have not been described in full detail to date. Here, we generated the conformational ensembles of two brain-derived amyloid filaments of A{beta}42, corresponding respectively to familial and sporadic forms of AD. The approach that we used, called metadynamic electron microscopy metainference (MEMMI), enabled us to provide a characterization of the transient interactions between the fuzzy coat and the cross-{beta} core of the filaments. These calculations indicated that the familial AD filaments are less soluble than the sporadic AD filaments, and that the fuzzy coat contributes to increasing the solubility of both types of filament. In addition, by analyzing the deviations between the density maps from cryo-EM and from the MEMMI structural ensembles, we observed a slowing down in the diffusion of water and sodium ions near the surface of the filaments, offering insight into the hydration dynamics of amyloid fibrils. These results illustrate how the metainference approach can help analyse cryo-EM maps for the characterisation of the properties of amyloid fibrils.

biophysics↗

bioRxiv2023

Multiplexed Digital Characterisation of Misfolded Protein Oligomers via Solid-State Nanopores

Misfolded protein oligomers are of central importance in both the detection and treatment of Alzheimers and Parkinsons diseases. However, accurate high-throughput methods to identify and quantify oligomer populations are currently lacking. We present here a single-molecule approach for the detection of oligomeric species. The approach is based on the use of solid state nanopores and multiplexed DNA barcoding to identify and characterise oligomers from multiple samples. We study -synuclein oligomers in the presence of several small molecule inhibitors of -synuclein aggregation, as an illustration of the applicability of this method to assist the development of diagnostic and therapeutic methods for Parkinsons disease.

biophysics↗

bioRxiv2023

AlphaFold Prediction of Structural Ensembles of Disordered Proteins

Deep learning methods of predicting protein structures have reached an accuracy comparable to that of high-resolution experimental methods. It is thus possible to generate accurate models of the native states of hundreds of millions of proteins. An open question, however, concerns whether these advances can be translated to disordered proteins, which should be represented as structural ensembles because of their heterogeneous and dynamical nature. Here we show that the inter-residue distances predicted by AlphaFold for disordered proteins can be used to construct accurate structural ensembles. These results illustrate the application to disordered proteins of deep learning methods originally trained for predicting the structures of folded proteins.

biophysics↗

bioRxiv2022

Determination of the structure and dynamics of the fuzzy coat of an amyloid fibril of IAPP using cryo-electron microscopy

In recent years, major advances in cryo-electron microscopy (cryo-EM) have enabled the routine determination of complex biomolecular structures at atomic resolution. An open challenge for this approach, however, concerns large systems that exhibit continuous dynamics. To address this problem, we developed the metadynamic electron-microscopy metainference (MEMMI) method, which incorporates metadynamics, an enhanced conformational sampling approach, into the metainference method of integrative structural biology. MEMMI enables the simultaneous determination of the structure and dynamics of large heterogeneous systems by combining cryo-EM density maps with prior information through molecular dynamics, while at the same time modelling the different sources of error. To illustrate the method, we apply it to elucidate the dynamics of an amyloid fibril of the islet amyloid polypeptide (IAPP). The resulting conformational ensemble provides an accurate description of the structural variability of the disordered region of the amyloid fibril, known as fuzzy coat. The conformational ensemble also reveals that in nearly half of the structural core of this amyloid fibril the side-chains exhibit liquid-like dynamics despite the presence of the highly ordered network backbone of hydrogen bonds characteristic of the cross-{beta} structure of amyloid fibrils.

biophysics↗

bioRxiv2021

Structure-based discovery of small molecule inhibitors of the autocatalytic proliferation of alpha-synuclein aggregates

The presence of amyloid fibrils of -synuclein is closely associated with Parkinsons disease and related synucleinopathies. It is still very challenging, however, to systematically discover small molecules that prevent the formation of these aberrant aggregates. Here, we describe a structure-based approach to identify small molecules that specifically inhibit the surface-catalyzed secondary nucleation step in the aggregation of -synuclein by binding to the surface of the amyloid fibrils. The resulting small molecules are screened using a combination of kinetic and thermodynamic assays for their ability to bind -synuclein fibrils and prevent the further generation of toxic oligomers. This study demonstrates that the combination of structure-based and kinetic-based drug discovery methods can lead to the identification of small molecules that selectively inhibit the autocatalytic proliferation of -synuclein aggregates.

biophysics↗

bioRxiv2021

A Machine Learning Approach to Identify Specific Small Molecule Inhibitors of Secondary Nucleation in alpha-Synuclein Aggregation

Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of -synuclein aggregation, a process implicated in Parkinsons disease and other synucleinopathies. Because the proliferation of -synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.

biophysics↗

bioRxiv2021

Amyloidogenic proteins in the SARS-CoV and SARS-CoV-2 proteomes

The phenomenon of protein aggregation is associated with a wide range of human diseases. Our knowledge on the aggregation behaviour of viral proteins, however, is still rather limited. Here, we investigated this behaviour in the the SARS-CoV and SARS-CoV-2 proteomes. An initial analysis using a panel of sequence-based predictors suggested the presence of multiple aggregation-prone regions in these proteomes, and revealed an enhanced aggregation propensity in some SARS-CoV-2 proteins. We then studied the in vitro aggregation of predicted aggregation-prone SARS-CoV-2 proteins, including the signal sequence peptide and fusion peptide 1 of the spike protein, a peptide from the NSP6 protein (NSP6-p), the ORF10 protein, and the NSP11 protein. Our results show that these peptides and proteins form aggregates via a nucleation-dependent mechanism. Moreover, we demonstrated that the aggregates of NSP11 are toxic to mammalian cell cultures. These findings provide evidence about the aggregation of proteins in the SARS-CoV-2 proteome. SignificanceThe aggregation of proteins is linked with human disease in a variety of ways. In the case of viral infections, one could expect that the aberrant aggregation of viral proteins may damage the host cells, and also that viral particles may trigger the misfolding and aggregation of host proteins, resulting in damage to the host organism. Here we investigate the aggregation propensity of SARS-CoV-2 proteins and show that many of them can form aggregates that are potentially cytotoxic. In perspective, these results suggest that a better understanding of the effects of viruses on the human protein homeostasis system could help future therapeutic efforts.

biophysics↗

bioRxiv2020

A Structural Ensemble of a Tau-Microtubule Complex Reveals Regulatory Tau Phosphorylation and Acetylation Mechanisms

Tau is a microtubule-associated protein that regulates the stability of microtubules. The affinity of tau for microtubules is modulated by post-translational modifications, and the dysregulation of these events has been associated with the aberrant aggregation of tau in Alzheimer's disease and related tauopathies. Here, we use the metainference cryo-electron microscopy approach to determine an ensemble of structures representing the structure and dynamics of a tau-microtubule complex comprising an extended microtubule-binding region of tau (residues 202-395). We thus identify the ground state of the complex and a series of excited states of lower populations. An analysis of the interactions in these states of structures reveals positions in the tau sequence that are important to determine the overall stability of the tau-microtubule complex. This analysis leads to the identification of positions where phosphorylation and acetylation events have destabilising effects, which we validate by using site-specific post-translationally modified tau variants obtained by chemical mutagenesis. Taken together, these results illustrate how the simultaneous determination of ground and excited states of macromolecular complexes reveals functional and regulatory mechanisms.

biophysics↗

bioRxiv2018

Accelerating the Calculation of Protein-Ligand Binding Free Energy and residence Times using Dynamically Optimized Collective Variables

Elucidation of the ligand/protein binding interaction is of paramount relevance in pharmacology to increase the success rate of drug design. To this end a number of computational methods have been proposed, however all of them suffer from limitations since the ligand binding/unbinding transitions to the molecular target involve many slow degrees of freedom that hamper a full characterization of the binding process. Being able to express this transition in simple and general slow degrees of freedom, would give a distinctive advantage, since it would require minimal knowledge of the system under study, while in turn it would elucidate its physics and accelerate the convergence speed of enhanced sampling methods relying on collective variables. In this study we pursuit this goal by combining for the first time Variation Approach to Conformational dynamics with Funnel-Metadynamics. In so doing, we predict for the benzamidine/trypsin system the ligand binding mode, and we accurately compute the absolute protein-ligand binding free energy and unbinding rate at unprecedented low computational cost. Finally, our simulation protocol reveals the energetics and structural details of the ligand binding mechanism and shows that water and binding pocket solvation/desolvation are the dominant slow degrees of freedom.

biophysics↗