Abstract
BackgroundProtein loops are flexible structures that are intimately tied to function, but understanding loop motion and generating loop conformation ensembles remain significant computational challenges. Discrete search techniques scale poorly to large loops, optimization and molecular dynamics techniques are prone to local minima, and inverse kinematics techniques can only incorporate structural preferences in adhoc fashion. This paper presents Sub-Loop Inverse Kinematics Monte Carlo (SLIKMC), a new Markov chain Monte Carlo algorithm for generating conformations of closed loops according to experimentally available, heterogeneous structural preferences.ResultsOur simulation experiments demonstrate that the method computes high-scoring conformations of large loops (>10 residues) orders of magnitude faster than standard Monte Carlo and discrete search techniques. Two new developments contribute to the scalability of the new method. First, structural preferences are specified via a probabilistic graphical model (PGM) that links conformation variables, spatial variables (e.g., atom positions), constraints and prior information in a unified framework. The method uses a sparse PGM that exploits locality of interactions between atoms and residues. Second, a novel method for sampling sub-loops is developed to generate statistically unbiased samples of probability densities restricted by loop-closure constraints.ConclusionNumerical experiments confirm that SLIKMC generates conformation ensembles that are statistically consistent with specified structural preferences. Protein conformations with 100+ residues are sampled on standard PC hardware in seconds. Application to proteins involved in ion-binding demonstrate its potential as a tool for loop ensemble generation and missing structure completion.
Highlights
Protein loops are flexible structures that are intimately tied to function, but understanding loop motion and generating loop conformation ensembles remain significant computational challenges
Sub-Loop Inverse Kinematics Monte Carlo (SLIKMC) is a Markov chain Monte Carlo (MCMC) method that takes as input an experimental conformation scoring function F, a protein structure from the Protein Data Bank (PDB), the beginning and ending residues of the loop, and outputs a sequence of perturbed loop conformations such that the sequence asymptotically approaches a probability distribution proportional to F
Result and discussion The SLIKMC algorithm implements a scalable framework for Monte Carlo sampling of kinematic chains
Summary
Protein loops are flexible structures that are intimately tied to function, but understanding loop motion and generating loop conformation ensembles remain significant computational challenges. Discrete search techniques scale poorly to large loops, optimization and molecular dynamics techniques are prone to local minima, and inverse kinematics techniques can only incorporate structural preferences in adhoc fashion. Sampling poses a challenging computational problem when chains are large and must satisfy a variety of constraints and statistical preferences. Loop sampling has been used in missing fragment reconstruction, generating fluctuations in equilibrium conformations, and generating decoy sets for function prediction Such applications typically require methods for sampling energetically-likely and diverse configuration ensembles rather than optimizing a single point estimate. Inverse kinematics (IK) techniques from the robotics field have been adopted to sample conformations with exact loop closure [3,4,5,9] These methods prioritize the loop closure constraint and do not take energies into account during sampling. Some authors employ a secondary energy optimization step to generate more plausible conformations [3,6,10]
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