DIPEND: An Open-Source Pipeline to Generate Ensembles of Disordered Segments Using Neighbor-Dependent Backbone Preferences

Abstract

Ensemble-based structural modeling of flexible protein segments such as intrinsically disordered regions is a complex task often solved by selection of conformers from an initial pool based on their conformity to experimental data. However, the properties of the conformational pool are crucial, as the sampling of the conformational space should be sufficient and, in the optimal case, relatively uniform. In other words, the ideal sampling is both efficient and exhaustive. To achieve this, specialized tools are usually necessary, which might not be maintained in the long term, available on all platforms or flexible enough to be tweaked to individual needs. Here, we present an open-source and extendable pipeline to generate initial protein structure pools for use with selection-based tools to obtain ensemble models of flexible protein segments. Our method is implemented in Python and uses ChimeraX, Scwrl4, Gromacs and neighbor-dependent backbone distributions compiled and published previously by the Dunbrack lab. All these tools and data are publicly available and maintained. Our basic premise is that by using residue-specific, neighbor-dependent Ramachandran distributions, we can enhance the efficient exploration of the relevant region of the conformational space. We have also provided a straightforward way to bias the sampling towards specific conformations for selected residues by combining different conformational distributions. This allows the consideration of a priori known conformational preferences such as in the case of preformed structural elements. The open-source and modular nature of the pipeline allows easy adaptation for specific problems. We tested the pipeline on an intrinsically disordered segment of the protein Cd3ϵ and also a single-alpha helical (SAH) region by generating conformational pools and selecting ensembles matching experimental data using the CoNSEnsX+ server.

Description of conformational ensembles of disordered proteins by residue-local probabilities

“…While our study is based on a coarse grain analysis of the conformational space of each residue in order to obtain converged molecular conformational ensembles the probabilistic expressions can be straightforwardly extended to a fine grain analysis providing a rigorous framework to build conformational ensembles from the data included in structure-encoding coil databases using previously developed methodologies. 29,71 The remarkably fast convergence and the good accuracy of the probabilistic conformational ensembles may allow us to explore thoroughly the mechanisms of binding of IDRs, as well as the impact of mutations in their functionality. As shown in recent works, 50,72 there is still room for improvement in the accuracy of most of current force fields to represent accurately the conformational populations of IDRs.…”

The influence of random-coil chemical shifts on the assessment of structural propensities in folded proteins and IDPs

“…Chemical shis of C a are generally considered one of, if not the, most sensitive parameters to secondary structure and structural propensities and have been preferred over their counterparts in various cases. 3,37,46,52,59,[133][134][135][136][137][138][139][140] Out of the numerous RCCS predictors in Table 1, we selected those 8 which have at least some sort of local sequence correction, yield prediction for the C a atom type and could theoretically be considered appropriate for studying IDPs in aqueous solutions.…”

“…47,48 Moreover, SCSs are used in the soware such as SHIFTX, 49 NMRView 50 PESCADOR 51 and DIPEND. 52 The existence of such advanced methods indicates the importance of calculating SCS values appropriately. However, they are ambiguous quantities, where the ambiguity arises already from the denition (see eqn (1)), by the involvement of RCCSs.…”