Papers by Faidon Brotzakis
AlphaFold Prediction of Structural Ensembles of Disordered Proteins
Deep learning methods of predicting protein structures have reached an accuracy comparable to tha... more 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.
ABSTRACTIn recent years, major advances in cryo-electron microscopy (cryo-EM) have enabled the ro... more ABSTRACTIn 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 v...
The Journal of chemical physics, Jan 21, 2018
Molecular dynamics (MD) simulations give access to equilibrium structures and dynamic properties ... more Molecular dynamics (MD) simulations give access to equilibrium structures and dynamic properties given an ergodic sampling and an accurate force-field. The force-field parameters are calibrated to reproduce properties measured by experiments or simulations. The main contribution of this paper is an approximate Bayesian framework for the calibration and uncertainty quantification of the force-field parameters, without assuming parameter uncertainty to be Gaussian. To this aim, since the likelihood function of the MD simulation models is intractable in the absence of Gaussianity assumption, we use a likelihood-free inference scheme known as approximate Bayesian computation (ABC) and propose an adaptive population Monte Carlo ABC algorithm, which is illustrated to converge faster and scales better than the previously used ABCsubsim algorithm for the calibration of the force-field of a helium system. The second contribution is the adaptation of ABC algorithms for High Performance Comput...
Correlation between the binding affinity and the conformational entropy of nanobody SARS-CoV-2 spike protein complexes
Proceedings of the National Academy of Sciences
Camelid single-domain antibodies, also known as nanobodies, can be readily isolated from naïve li... more Camelid single-domain antibodies, also known as nanobodies, can be readily isolated from naïve libraries for specific targets but often bind too weakly to their targets to be immediately useful. Laboratory-based genetic engineering methods to enhance their affinity, termed maturation, can deliver useful reagents for different areas of biology and potentially medicine. Using the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and a naïve library, we generated closely related nanobodies with micromolar to nanomolar binding affinities. By analyzing the structure–activity relationship using X-ray crystallography, cryoelectron microscopy, and biophysical methods, we observed that higher conformational entropy losses in the formation of the spike protein–nanobody complex are associated with tighter binding. To investigate this, we generated structural ensembles of the different complexes from electron microscopy maps and corr...
The presence of amyloid fibrils of alpha-synuclein is closely associated with Parkinson's dis... more The presence of amyloid fibrils of alpha-synuclein is closely associated with Parkinson's 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 alpha-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 alpha-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 alpha-synuclein aggregates.
arXiv: Statistical Mechanics, 2019
Many processes of scientific and technological interest are characterized by time scales that ren... more Many processes of scientific and technological interest are characterized by time scales that render their simulation impossible if one uses present day simulation capabilities. To overcome this challenge a variety of enhanced simulation methods has been developed. A much-used class of methods relies on the use of collective variables. The efficiency of these methods relies critically on an educated guess of the collective variables. For this reason much effort has been devoted to the construction and improvement of collective variables. Among the many methods proposed, harmonic linear discriminant analysis has proven effective. This method builds the collective coordinates solely from the knowledge of the fluctuations in the different metastable state. In this Letter we propose to improve upon the harmonic linear discriminant analysis by adding to the construction of the collective coordinates an extra bit of information, namely that of the transition state ensemble. Configurations...
Computational study of the dynamics of glassy atactic polystyrene (Master Thesis/2012)
Drug development is an increasingly active area of application of machine learning methods, due t... more Drug development is an increasingly active area of application of machine learning methods, due to the need to overcome the high attrition rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases where very few disease-modifying drugs have been approved. To address this problem, we describe a machine learning approach to identify specific inhibitors of the proliferation of α-synuclein aggregates through secondary nucleation, a process that has been implicated in Parkinson’s disease and related synucleinopathies. We use a combination of docking simulations followed by machine learning to first identify initial hit compounds and then explore the chemical space around these compounds. Our results demonstrate that this approach leads to the identification of novel chemical matter with an improved hit rate and potency over conventional similarity search approaches.
The European Physical Journal B, 2021
The maximum caliber approach implements the maximum entropy principle for trajectories by maximiz... more The maximum caliber approach implements the maximum entropy principle for trajectories by maximizing a path entropy under external constraints. The maximum caliber approach can be applied to a diverse set of equilibrium and non-equilibrium problems concerning the properties of trajectories connecting different states of a system. In this review, we recapitulate the basic concepts of the maximum entropy principle and of its maximum caliber implementation for path ensembles, and review recent applications of this approach. In particular, we describe how we recently used this approach to introduce a framework, called here the continuum path ensemble maximum caliber (CoPE-MaxCal) method, to impose kinetic constraints in molecular simulations, for instance to include experimental information about transition rates. Such incorporation of dynamical information can ameliorate inaccuracies of empirical force fields, and lead to improved mechanistic insights. We conclude by offering an outloo...
Chemical Science, 2021
A structural ensemble derived from cryo-electron microscopy reveals a cryptic pocket site in inte... more A structural ensemble derived from cryo-electron microscopy reveals a cryptic pocket site in intermediate states along the opening pathway of the SARS-CoV-2 spike protein.
Proceedings of the National Academy of Sciences, 2020
From the point of view of statistical mechanics, a full characterization of a molecular system re... more From the point of view of statistical mechanics, a full characterization of a molecular system requires an accurate determination of its possible states, their populations, and the respective interconversion rates. Toward this goal, well-established methods increase the accuracy of molecular dynamics simulations by incorporating experimental information about states using structural restraints and about populations using thermodynamics restraints. However, it is still unclear how to include experimental knowledge about interconversion rates. Here, we introduce a method of imposing known rate constants as constraints in molecular dynamics simulations, which is based on a combination of the maximum-entropy and maximum-caliber principles. Starting from an existing ensemble of trajectories, obtained from either molecular dynamics or enhanced trajectory sampling, this method provides a minimally perturbed path distribution consistent with the kinetic constraints, as well as modified free...
Tau is a microtubule-associated protein that regulates the stability of microtubules. The affinit... more 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 va...
The Journal of Chemical Physics, 2019
Transition path sampling is a powerful technique for investigating rare transitions, especially w... more Transition path sampling is a powerful technique for investigating rare transitions, especially when the mechanism is unknown and one does not have access to the reaction coordinate. Straightforward application of transition path sampling does not directly provide the free energy landscape nor the kinetics. This drawback has motivated the development of path sampling extensions able to simultaneously access both kinetics and thermodynamics, such as transition interface sampling, and the reweighted path ensemble. However, performing transition interface sampling is more involved than standard two-state transition path sampling and still requires (some) insight into the reaction to define interfaces. While packages that can efficiently compute path ensembles for transition interface sampling are now available, it would be useful to directly compute the free energy from a single standard transition path sampling simulation. To achieve this, we present here an approximate method, denoted virtual interface exchange transition path sampling, that makes use of the rejected pathways in a form of waste recycling. The method yields an approximate reweighted path ensemble that allows an immediate view of the free energy landscape from a standard single transition path sampling simulation, as well as enables a committor analysis.
The Journal of Chemical Physics, 2018
Many processes of scientific importance are characterized by time scales that extend far beyond t... more Many processes of scientific importance are characterized by time scales that extend far beyond the reach of standard simulation techniques. To circumvent this impediment, a plethora of enhanced sampling methods has been developed. One important class of such methods relies on the application of a bias that is a function of a set of collective variables specially designed for the problem under consideration. The design of good collective variables can be challenging and thereby constitutes the main bottle neck in the application of these methods. To address this problem, recently we have introduced Harmonic Linear Discriminant Analysis, a method to systematically construct collective variables as linear combinations of a set of descriptors. The method uses input information that can be gathered in short unbiased molecular dynamics simulations in which the system is trapped in the metastable states. Here, to scale up our examination of the method's efficiency, we applied it to the folding of chignolin in water. Interestingly, already before any biased simulations were run, the constructed one-dimensional collective variable revealed much of the physics that underlies the folding process. In addition, using it in metadynamics, we were able to run simulations in which the system goes from the folded state to the unfolded one and back, where to get fully converged results, we combined metadynamics with parallel tempering. Finally, we examined how the collective variable performs when different sets of descriptors are used in its construction.
The Journal of Physical Chemistry B, 2019
Association and dissociation of proteins are fundamental processes in nature. Although simple to ... more Association and dissociation of proteins are fundamental processes in nature. Although simple to understand conceptually, the details of the underlying mechanisms and role of the solvent are poorly understood. Here, we investigate the dissociation of the hydrophilic β-lactoglobulin dimer by employing transition path sampling. Analysis of the sampled path ensembles reveals a variety of mechanisms: (1) a direct aligned dissociation (2) a hopping and rebinding transition followed by unbinding, and (3) a sliding transition before unbinding. Reaction coordinate and transition-state analysis predicts that, besides native contact and neighboring salt-bridge interactions, solvent degrees of freedom play an important role in the dissociation process. Bridging waters, hydrogen-bonded to both proteins, support contacts in the native state and nearby lying transition-state regions, whereas they exhibit faster dynamics in further lying transition-state regions, rendering the proteins more mobile and assisting in rebinding. Analysis of the structure and dynamics of the solvent molecules reveals that the dry native interface induces enhanced populations of both disordered hydration water near hydrophilic residues and tetrahedrally ordered hydration water nearby hydrophobic residues. Although not exhaustive, our sampling of rare unbiased reactive molecular dynamics trajectories enhances the understanding of protein dissociation via complex pathways including (multiple) rebinding events.
Journal of Chemical Theory and Computation, 2018
Protein conformational transitions often involve many slow degrees of freedom. Their knowledge wo... more Protein conformational transitions often involve many slow degrees of freedom. Their knowledge would give distinctive advantages since it provides chemical and mechanistic insight and accelerates the convergence of enhanced sampling techniques that rely on collective variables. In this study, we implemented a recently developed variational approach to conformational dynamics metadynamics to the conformational transition of the moderate size protein, L99A T4 Lysozyme. In order to find the slow modes of the system we combined data coming from NMR experiments as well as short MD simulations. A Metadynamics simulation based on these information reveals the presence of two intermediate states, at an affordable computational cost.
Journal of Chemical Theory and Computation, 2018
Elucidation of the ligand/protein binding interaction is of paramount relevance in pharmacology t... more 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.
Physical Chemistry Chemical Physics, 2018
The tetrahedral structure of hydration water (S) and its reorientation decay time (τ) correlates ... more The tetrahedral structure of hydration water (S) and its reorientation decay time (τ) correlates negatively for selected amino-acids in the vicinity of the ice binding site (left and right panels) of the antifreeze protein, but positively for the ice binding site central amino-acid (middle panel).
Frontiers in molecular neuroscience, 2017
Dysfunction of D-amino acid oxidase () and DAO activator ()/ genes have been linked to neuropsych... more Dysfunction of D-amino acid oxidase () and DAO activator ()/ genes have been linked to neuropsychiatric disorders. The glutamate hypothesis of schizophrenia has proposed that increased DAO activity leads to decreased D-serine, which subsequently may lead to N-methyl-D-aspartate (NMDA) receptor hypofunction. It has been shown that DAOA binds to DAO and increases its activity. However, there are also studies showing DAOA decreases DAO activity. Thus, the effect of DAOA on DAO is controversial. We aimed to understand the effect of DAOA on DAO activity in neuron-like (SH-SY5Y), astrocyte-like (1321N1) and kidney-like (HEK293) human cell lines. DAO activity was measured based on the release of hydrogen peroxide and its interaction with Amplex Red reagent. We found that DAOA increases DAO activity only in HEK293 cells, but has no effect on DAO activity in SH-SY5Y and 1321N1 cells. This might be because of different signaling pathways, or due to lower DAO and DAOA expression in SH-SY5Y and...
Phys. Chem. Chem. Phys., 2017
From right to left: three distinct growth mechanisms of a pentamer to a hexamer putative antifree... more From right to left: three distinct growth mechanisms of a pentamer to a hexamer putative antifreeze cyclic peptide nanotube.
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Papers by Faidon Brotzakis