Abstract
Crystallography and NMR system (CNS) is currently a widely used method for fragment-free ab initio protein folding from inter-residue distance or contact maps. Despite its widespread use in protein structure prediction, CNS is a decade-old macromolecular structure determination system that was originally developed for solving macromolecular geometry from experimental restraints as opposed to predictive modeling driven by interaction map data. As such, the adaptation of the CNS experimental structure determination protocol for ab initio protein folding is intrinsically anomalous that may undermine the folding accuracy of computational protein structure prediction. In this paper, we propose a new CNS-free hierarchical structure modeling method called DConStruct for folding both soluble and membrane proteins driven by distance and contact information. Rigorous experimental validation shows that DConStruct attains much better reconstruction accuracy than CNS when tested with the same input contact map at varying contact thresholds. The hierarchical modeling with iterative self-correction employed in DConStruct scales at a much higher degree of folding accuracy than CNS with the increase in contact thresholds, ultimately approaching near-optimal reconstruction accuracy at higher-thresholded contact maps. The folding accuracy of DConStruct can be further improved by exploiting distance-based hybrid interaction maps at tri-level thresholding, as demonstrated by the better performance of our method in folding free modeling targets from the 12th and 13th rounds of the Critical Assessment of techniques for protein Structure Prediction (CASP) experiments compared to popular CNS- and fragment-based approaches and energy-minimization protocols, some of which even using much finer-grained distance maps than ours. Additional large-scale benchmarking shows that DConStruct can significantly improve the folding accuracy of membrane proteins compared to a CNS-based approach. These results collectively demonstrate the feasibility of greatly improving the accuracy of ab initio protein folding by optimally exploiting the information encoded in inter-residue interaction maps beyond what is possible by CNS.
Highlights
The development of a computational method that can successfully predict the functional 3-dimensional (3D) structure of a protein molecule purely from its amino acid sequence is of central importance to structural biology [1]
This paper presents a new hierarchical structure modeling method, DConStruct, which can better exploit the information encoded in the interaction maps at multiple granularities, from binary contact maps to distance-based hybrid maps at tri-level thresholding, for improved ab initio folding
Multiple large-scale benchmarking experiments show that our proposed method can substantially improve the folding accuracy for both soluble and membrane proteins compared to state-of-the-art approaches
Summary
The development of a computational method that can successfully predict the functional 3-dimensional (3D) structure of a protein molecule purely from its amino acid sequence is of central importance to structural biology [1]. In the recent past, promising progress has been made in this endeavor mediated by reasonably accurate prediction of inter-residue distance or contact maps using sequence co-evolution coupled with deep learning [2,3,4,5,6,7], and performing data-assisted folding driven by such predicted interaction maps [8,9,10]. Even the most recent advances in protein structure prediction [8,9] are primarily due to the progress made in predicting finer-grained interaction maps, but 3D model building from the predicted fine-grained maps still routinely utilize CNS-based experimental structure determination protocol. There is a critical need to develop a fragment-free folding protocol suitable for predicted inter-residue interaction map data rather than relying on the CNS-based structure determination approach
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