A better prediction of conformational changes of proteins using minimally connected network models

Abstract

Elastic network models have recently been used for studying low-frequency collective motions of proteins. These models simplify the complexity that arises from normal mode analysis by considering a simplified potential involving a few parameters. Two common parameters in most of the elastic network models are cutoff radius and force constant. Although the latter has been studied extensively and even elaborate new models were introduced, for the former usually an ad-hoc cutoff radius is considered. Moreover, the quality of the network models is usually assessed by evaluating their prediction against experimental B-factors. In this work, we consider various common elastic network models with different cutoff radii and assess them by their ability to predict conformational changes of proteins in complexes from unbound to bound state. This prediction is performed by perturbing the unbound structure using a number of low-frequency normal modes of its network model to optimally fit the bound structure. We evaluated a dataset of 30 proteins with distinct unbound and bound structures using this criterion. The results showed that, opposed to the common calibration process based on B-factors, a meaningful relationship exists between the quality of the prediction and model parameters. It was shown that the cutoff radius has a major role in this prediction and minimally connected network models, which use the shortest cutoff radius for which the network is stable, give the best results. It was also shown that by considering the first ten normal modes, the conformational changes can be predicted by about 25 percent. Hence, the evaluation process was extended to the case of considering the contribution of all normal modes in the prediction. The results indicated that minimally connected network models are superior, despite their simplicity, when any number of modes are considered in the prediction.

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models

“…In the past decades, a large number of studies have shown the usefulness of NMA and Principal Component Analysis (PCA) for the prediction and analysis of protein co-operative motions [ 13 , 14 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. Many of these studies were conducted using computationally simple ENM that use uniform harmonic potentials for interacting atom (or residue) pairs instead of more complicated potentials.…”

“…Except as mentioned above, ENMs have been successfully used in many combinations with different input information and methods, such as, for example, data from electron microscopy [ 14 ], atomistic MD simulations [ 17 , 77 ], Brownian simulations [ 78 ], structure-based models [ 79 , 80 ], and many other combinations that have been thoroughly reviewed [ 13 , 14 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ].…”

Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-grained Simulations and Elastic Network Models

“…In the last decades, a large number of studies have shown the usefulness of Normal Mode Analysis (NMA) and Principal Component Analysis (PCA) for the prediction and analysis of protein cooperative motions [13,14,[54][55][56][57][58][59][60][61][62][63]. Many of these studies were conducted using computationally simple Elastic Network Models (ENM) which use uniform harmonic potentials for interacting atom (or residue) pairs instead of more complicated potentials.…”

“…Except as mentioned above, ENMs have been successfully used in many combinations with different input information and methods, such as for example data from electron microscopy [14], atomistic MD simulations [17,78], Brownian simulations [79], structure-based models [80,81] and many other combinations that have been thoroughly reviewed [13,14,[54][55][56][57][58][59][60][61][62][63]. When the range of protein structural dynamics studies can be limited to a roughly defined vicinity of a specific folded structure (determined by experiment), ENM, CG models or their modifications can be very effective in predicting protein flexibility [8,36,[82][83][84].…”

Protein conformational switch discerned via network centrality properties

“…We focus on advancing PSN analysis from three new aspects. First, we examine whether network analysis results can discriminate between subtle (yet significantly different function-wise) protein conformations [45] , [46] , [47] . This perspective has not been extensively explored previously.…”