AI Insight
This study presents two new algorithmic methods for determining protein structures from Nuclear Magnetic Resonance data when distance measurements are uncertain. The interval Angular Branch-and-Prune (iABP) and interval Torsion-angle Branch-and-Prune (iTBP) methods convert uncertain distance information into angular constraints, with iTBP additionally incorporating torsion-angle data to capture local molecular geometry. Computational experiments using Protein Data Bank structures show that both angular methods find feasible protein structures more frequently than the standard baseline method, with iTBP achieving the most accurate structural reconstructions as measured by root-mean-square deviation.
Why it matters
These improved algorithms could enhance the accuracy and efficiency of protein structure determination from NMR spectroscopy, a critical technique in structural biology and drug design. By better handling the inherent uncertainties in experimental distance measurements, these methods may enable researchers to solve protein structures that are currently difficult to determine.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract: Distance Geometry is central to protein structure determination from Nuclear Magnetic Resonance data, where distance restraints are naturally uncertain. We study the interval Discretizable Distance Geometry Problem (textit{i}DDGP) and introduce two angular Branch-and-Prune (BP) frameworks: the interval Angular Branch-and-Prune (textit{i}ABP) method and its torsion-guided extension, the interval Torsion-angle Branch-and-Prune (textit{i}TBP) method. Both methods transform interval distance information into angular constraints on circular arcs; textit{i}ABP uses these constraints to reduce explicit feasibility checks during branching, while textit{i}TBP further incorporates prescribed torsion-angle intervals to encode local chirality and planarity information. We develop the corresponding geometric foundations and describe a systematic construction of biologically meaningful textit{i}DDGP instances from Protein Data Bank structures. Computational experiments indicate that textit{i}ABP improves the embedding-normalized feasible-solution yield relative to interval BP (textit{i}BP), a standard enumerative BP baseline for textit{i}DDGP instances, and that both angular methods find feasible realizations for more instances than textit{i}BP. Moreover, textit{i}TBP attains the lowest minimum RMSD on all instances for which both angular methods find feasible realizations, highlighting the benefit of incorporating torsion-angle information.
Source: An Angle-Based Algorithmic Framework for the Interval Discretizable Distance Geometry Problem