CLoNe: automated clustering based on local density neighborhoods for application to biomolecular structural ensembles

Streamlining NMR Chemical Shift Predictions for Intrinsically Disordered Proteins: Design of Ensembles with Dimensionality Reduction and Clustering

By merging advanced dimensionality reduction (DR) and clustering algorithm (CA) techniques, our study advances the sampling procedure for predicting NMR chemical shifts (CS) in intrinsically disordered proteins (IDPs), making a significant leap forward in the field of protein analysis/modeling. We enhance NMR CS sampling by generating clustered ensembles that accurately reflect the different properties and phenomena encapsulated by the IDP trajectories. This investigation critically assessed different rapid CS predictors, both neural network (e.g., Sparta+ and ShiftX2) and database-driven (ProCS-15), and highlighted the need for more advanced quantum calculations and the subsequent need for more tractable-sized conformational ensembles. Although neural network CS predictors outperformed ProCS-15 for all atoms, all tools showed poor agreement with HN CSs, and the neural network CS predictors were unable to capture the influence of phosphorylated residues, highly relevant for IDPs. This study also addressed the limitations of using direct clustering with collective variables, such as the widespread implementation of the GROMOS algorithm. Clustered ensembles (CEs) produced by this algorithm showed poor performance with chemical shifts compared to sequential ensembles (SEs) of similar size. Instead, we implement a multiscale DR and CA approach and explore the challenges and limitations of applying these algorithms to obtain more robust and tractable CEs. The novel feature of this investigation is the use of solvent-accessible surface area (SASA) as one of the fingerprints for DR alongside previously investigated α carbon distance/angles or ϕ/ψ dihedral angles. The ensembles produced with SASA tSNE DR produced CEs better aligned with the experimental CS of between 0.17 and 0.36 r2 (0.18-0.26 ppm) depending on the system and replicate. Furthermore, this technique produced CEs with better agreement than traditional SEs in 85.7% of all ensemble sizes. This study investigates the quality of ensembles produced based on different input features, comparing latent spaces produced by linear vs nonlinear DR techniques and a novel integrated silhouette score scanning protocol for tSNE DR.

Synthesis of new non-natural L-glycosidic flavonoid derivatives and their evaluation as inhibitors of Trypanosoma cruzi ecto-nucleoside triphosphate diphosphohydrolase 1 (TcNTPDase1)

Trypanosoma cruzi is the pathogen of Chagas disease, a neglected tropical disease that affects more than 6 million people worldwide. There are no vaccines to prevent infection, and the therapeutic arsenal is very minimal and toxic. The unique E-NTPDase of T. cruzi (TcNTPDase1) plays essential roles in adhesion and infection and is a virulence factor. Quercetin is a flavonoid with antimicrobial, antiviral, and antitumor activities. Its potential as a partial inhibitor of NTPDases has also been demonstrated. In this work, we synthesized the non-natural L-glycoside derivatives of quercetin and evaluated them as inhibitors of recombinant TcNTPDase1 (rTcNTPDase1). These compounds, and quercetin and miquelianin, a natural quercetin derivative, were also tested. Compound 16 showed the most significant inhibitory effect (94%). Quercetin, miquelianin, and compound 14 showed inhibition close to 50%. We thoroughly investigated the inhibitory effect of 16. Our data suggested a competitive inhibition with a Ki of 8.39 μM (± 0.90). To better understand the interaction of compound 16 and rTcNTPDase1, we performed molecular dynamics simulations of the enzyme and docking analyses with the compounds. Our predictions show that compound 16 binds to the enzyme's catalytic site and interacts with important residues for NTPDase activity. As an inhibitor of a critical T. cruzi enzyme, (16) could be helpful as a starting point in the developing of a future treatment for Chagas disease. Furthermore, the discovery of (16) as an inhibitor of TcNTPDase1 may open new avenues in the study and development of new inhibitors of E-NTPDases.

Differential Effects of Post-translational Modifications on the Membrane Interaction of Huntingtin Protein

Huntington's disease is a neurodegenerative disorder caused by an expanded polyglutamine stretch near the N-terminus of the huntingtin (HTT) protein, rendering the protein more prone to aggregate. The first 17 residues in HTT (Nt17) interact with lipid membranes and harbor multiple post-translational modifications (PTMs) that can modulate HTT conformation and aggregation. In this study, we used a combination of biophysical studies and molecular simulations to investigate the effect of PTMs on the helicity of Nt17 in the presence of various lipid membranes. We demonstrate that anionic lipids such as PI4P, PI(4,5)P2, and GM1 significantly enhance the helical structure of unmodified Nt17. This effect is attenuated by single acetylation events at K6, K9, or K15, whereas tri-acetylation at these sites abolishes Nt17-membrane interaction. Similarly, single phosphorylation at S13 and S16 decreased but did not abolish the POPG and PIP2-induced helicity, while dual phosphorylation at these sites markedly diminished Nt17 helicity, regardless of lipid composition. The helicity of Nt17 with phosphorylation at T3 is insensitive to the membrane environment. Oxidation at M8 variably affects membrane-induced helicity, highlighting a lipid-dependent modulation of the Nt17 structure. Altogether, our findings reveal differential effects of PTMs and crosstalks between PTMs on membrane interaction and conformation of HTT. Intriguingly, the effects of phosphorylation at T3 or single acetylation at K6, K9, and K15 on Nt17 conformation in the presence of certain membranes do not mirror that observed in the absence of membranes. Our studies provide novel insights into the complex relationship between Nt17 structure, PTMs, and membrane binding.

PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces

Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their  binding  interfaces remains a challenge. In this study, we present a geometric transformer that acts directly on atomic coordinates labeled only with element names. The resulting model-the Protein Structure Transformer, PeSTo-surpasses the current state of the art in predicting protein-protein interfaces and can also predict and differentiate between interfaces involving nucleic acids, lipids, ions, and small molecules with high confidence. Its low computational cost enables processing high volumes of structural data, such as molecular dynamics ensembles allowing for the discovery of interfaces that remain otherwise inconspicuous in static experimentally solved structures. Moreover, the growing foldome provided by de novo structural predictions can be easily analyzed, providing new opportunities to uncover unexplored biology.

Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories

Determining the optimal number and identity of structural clusters from an ensemble of molecular configurations continues to be a challenge. Recent structural clustering methods have focused on the use of internal coordinates due to the innate rotational and translational invariance of these features. The vast number of possible internal coordinates necessitates a feature space supervision step to make clustering tractable but yields a protocol that can be system type-specific. Particle positions offer an appealing alternative to internal coordinates but suffer from a lack of rotational and translational invariance, as well as a perceived insensitivity to regions of structural dissimilarity. Here, we present a method, denoted shape-GMM, that overcomes the shortcomings of particle positions using a weighted maximum likelihood alignment procedure. This alignment strategy is then built into an expectation maximization Gaussian mixture model (GMM) procedure to capture metastable states in the free-energy landscape. The resulting algorithm distinguishes between a variety of different structures, including those indistinguishable by root-mean-square displacement and pairwise distances, as demonstrated on several model systems. Shape-GMM results on an extensive simulation of the fast-folding HP35 Nle/Nle mutant protein support a four-state folding/unfolding mechanism, which is consistent with previous experimental results and provides kinetic details comparable to previous state-of-the art clustering approaches, as measured by the VAMP-2 score. Currently, training of shape-GMMs is recommended for systems (or subsystems) that can be represented by ≲200 particles and ≲100k configurations to estimate high-dimensional covariance matrices and balance computational expense. Once a shape-GMM is trained, it can be used to predict the cluster identities of millions of configurations.

RCDPeaks: memory-efficient density peaks clustering of long molecular dynamics

MOTIVATION

Density Peaks is a widely spread clustering algorithm that has been previously applied to Molecular Dynamics (MD) simulations. Its conception of cluster centers as elements displaying both a high density of neighbors and a large distance to other elements of high density, particularly fits the nature of a geometrical converged MD simulation. Despite its theoretical convenience, implementations of Density Peaks carry a quadratic memory complexity that only permits the analysis of relatively short trajectories.

RESULTS

Here, we describe DP+, an exact novel implementation of Density Peaks that drastically reduces the RAM consumption in comparison to the scarcely available alternatives designed for MD. Based on DP+, we developed RCDPeaks, a refined variant of the original Density Peaks algorithm. Through the use of DP+, RCDPeaks was able to cluster a one-million frames trajectory using less than 4.5 GB of RAM, a task that would have taken more than 2 TB and about 3× more time with the fastest and less memory-hunger alternative currently available. Other key features of RCDPeaks include the automatic selection of parameters, the screening of center candidates and the geometrical refining of returned clusters.

AVAILABILITY AND IMPLEMENTATION

The source code and documentation of RCDPeaks are free and publicly available on GitHub (https://github.com/LQCT/RCDPeaks.git).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CATBOSS: Cluster Analysis of Trajectories Based on Segment Splitting

Molecular dynamics (MD) simulations are an exceedingly and increasingly potent tool for molecular behavior prediction and analysis. However, the enormous wealth of data generated by these simulations can be difficult to process and render in a human-readable fashion. Cluster analysis is a commonly used way to partition data into structurally distinct states. We present a method that improves on the state of the art by taking advantage of the temporal information of MD trajectories to enable more accurate clustering at a lower memory cost. To date, cluster analysis of MD simulations has generally treated simulation snapshots as a mere collection of independent data points and attempted to separate them into different clusters based on structural similarity. This new method, cluster analysis of trajectories based on segment splitting (CATBOSS), applies density-peak-based clustering to classify trajectory segments learned by change detection. Applying the method to a synthetic toy model as well as four real-life data sets–trajectories of MD simulations of alanine dipeptide and valine dipeptide as well as two fast-folding proteins–we find CATBOSS to be robust and highly performant, yielding natural-looking cluster boundaries and greatly improving clustering resolution. As the classification of points into segments emphasizes density gaps in the data by grouping them close to the state means, CATBOSS applied to the valine dipeptide system is even able to account for a degree of freedom deliberately omitted from the input data set. We also demonstrate the potential utility of CATBOSS in distinguishing metastable states from transition segments as well as promising application to cases where there is little or no advance knowledge of intrinsic coordinates, making for a highly versatile analysis tool.