EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Abstract

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein–ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

WARIO: Weighted families of contact maps to characterize conformational ensembles of (highly-)flexible proteins

“…The clustering method performed on the contact function matrix (1) is based on the combination of a dimensionality reduction technique with an efficient clustering algorithm, similarly to state-of-the-art approaches [30,31]. Here, we opt for UMAP [20] to Ąrst embed the data (1) into a 10-dimensional space.…”

“…In distance-based methods, structural data are featured by Euclidean distances between residue pairs. This metric combined with dimensionality reduction and a clustering algorithm has been previously used to characterize Ćexible proteins [30,31]. Concretely, we implemented the UMAP + HDBSCAN pipeline on the structural data featured with pairwise Euclidean distances between all C β atoms (C α for glycines) to characterize the CHCHD4 MD ensemble.…”

“…Besides, their incorporation into clustering algorithms has shown remarkable empirical efficiency [25Ű29]. This idea has been successfully exploited for analyzing ensembles of conformations produced by MD simulations in recent studies [30,31]. However, in these works, conformations were usually featured by descriptors such as atom coordinates or backbone torsion angles, and compared using root-mean-square deviation (RMSD) [32,33], whose suitability to compare unfolded conformations is questionable.…”

Protein ensemble modeling and analysis with MMMx

“…Therefore, D c 1 c 2 can be interpreted as a distance in an abstract Euclidean conformer space whose dimension is not known a priori. The D c 1 c 2 can be arranged in a distance matrix D. Clustering of conformers based on D is similar in spirit to clustering based on other pairwise Euclidean distances proposed earlier (Alston et al, 2023;Conev et al, 2023). Distance geometry methods allow embedding D in the conformer space and thus representing the set of conformers in this abstract space.…”

“…Here, we introduce a single‐valued similarity measure that abstracts from the ensemble width. The ensemble analysis module of MMMx complements existing toolkits such as PENSA (Vögele et al, 2022 ), ProDy (Zhang et al, 2021 ), or EnGens (Conev et al, 2023 ), which aim to summarize the information from ensemble structures or molecular dynamics (MD) trajectories into simpler descriptors of the conformational landscape. For example, the interpretation of ensemble models of IDPs and IDRs requires the detection of weak deviations from a polymeric random coil (Alston et al, 2023 ; Ritsch et al, 2021 ).…”