On the Applicability of Elastic Network Normal Modes in Small-Molecule Docking

Incorporating backbone flexibility into protein–ligand docking is still a challenging problem. In protein–protein docking, normal mode analysis (NMA) has become increasingly popular as it can be used to describe the collective motions of a biological system, but the question of whether NMA can also be useful in predicting the conformational changes observed upon small-molecule binding has only been addressed in a few case studies. Here, we describe a large-scale study on the applicability of NMA for protein–ligand docking using 433 apo/holo pairs of the Astex data sets. On the basis of sets of the first normal modes from the apo structure, we first generated for each paired holo structure a set of conformations that optimally reproduce its C_α trace with respect to the underlying normal mode subspace. Using AutoDock, GOLD, and FlexX we then docked the original ligands into these conformations to assess how the docking performance depends on the number of modes used to reproduce the holo structure. The results of our study indicate that, even for such a best-case scenario, the use of normal mode analysis in small-molecule docking is restricted and that a general rule on how many modes to use does not seem to exist or at least is not easy to find

The Role of Conformational Changes in Molecular Recognition

Conformational changes of molecules are crucial elements in many biochemical processes, and also in molecular recognition. Here, we present a novel exact mathematical equation for the binding free energy of a receptor–ligand pair. It shows that the energetic contribution due to conformational changes upon molecular recognition is defined by the so-called Kullback–Leibler (KL) divergence between the probability distributions of the conformational ensemble of the biomolecule in the bound and free states. We show that conformational changes always contribute positively to the change in free energy and therefore disfavor the association process. Using the example of ligands binding to a flexible cavity of T4 lysozyme, we illustrate that, due to enthalpy–entropy compensation, the conformational entropy is a misleading quantity for assessing the conformational contribution to the binding free energy, in contrast to the KL divergence, which is the correct quantity to use in this context

How Molecular Conformational Changes Affect Changes in Free Energy

A simple quantitative relationship between the molecular conformational changes and the corresponding changes in the free energy is presented. The change in free energy is the sum of that part of the enthalpic change that is due to the externally applied work (perturbation) and of that part of the entropic change, termed dissipative entropy, that is related to the conformational changes. The dissipative entropy is equivalent to the relative entropy, a concept from information theory, between the distributions of the conformations in the initial and the final states. The remaining change in entropy (nondissipative) cancels exactly with the remaining enthalpic change. The calculation of the dissipative entropy is demonstrated to pose the main difficulty in free energy computation. The straightforward decomposition of the dissipative entropy into contributions from different parts of the system promises to improve the understanding of the role of conformational changes in biochemical reactions

Both Δdistances (a) and correlation coefficients (b) are shown for each pair of interaction types

<p><b>Copyright information:</b></p><p>Taken from "NOXclass: prediction of protein-protein interaction types"</p><p>BMC Bioinformatics 2006;7():27-27.</p><p>Published online 19 Jan 2006</p><p>PMCID:PMC1386716.</p><p>Copyright © 2006 Zhu et al; licensee BioMed Central Ltd.</p

Analysis of Physicochemical and Structural Properties Determining HIV-1 Coreceptor Usage

<div><p>The relationship of HIV tropism with disease progression and the recent development of CCR5-blocking drugs underscore the importance of monitoring virus coreceptor usage. As an alternative to costly phenotypic assays, computational methods aim at predicting virus tropism based on the sequence and structure of the V3 loop of the virus gp120 protein. Here we present a numerical descriptor of the V3 loop encoding its physicochemical and structural properties. The descriptor allows for structure-based prediction of HIV tropism and identification of properties of the V3 loop that are crucial for coreceptor usage. Use of the proposed descriptor for prediction results in a statistically significant improvement over the prediction based solely on V3 sequence with 3 percentage points improvement in AUC and 7 percentage points in sensitivity at the specificity of the 11/25 rule (95%). We additionally assessed the predictive power of the new method on clinically derived ‘bulk’ sequence data and obtained a statistically significant improvement in AUC of 3 percentage points over sequence-based prediction. Furthermore, we demonstrated the capacity of our method to predict therapy outcome by applying it to 53 samples from patients undergoing Maraviroc therapy. The analysis of structural features of the loop informative of tropism indicates the importance of two loop regions and their physicochemical properties. The regions are located on opposite strands of the loop stem and the respective features are predominantly charge-, hydrophobicity- and structure-related. These regions are in close proximity in the bound conformation of the loop potentially forming a site determinant for the coreceptor binding. The method is available via server under <a href="http://structure.bioinf.mpi-inf.mpg.de/" target="_blank">http://structure.bioinf.mpi-inf.mpg.de/</a>.</p> </div

Comparison of the performance of three existing gene TSS prediction algorithms along with our proposed method in predicting brain-tissue specific miRNA TSS.

<p>Best mean values of the percentage accuracy, sensitivity, specificity, precision and are shown in bold.</p

Performance of the brain-tissue specific miRNA TSS prediction model with and without methylation-based features alongside the other features.

<p>The and denote mean and standard deviation values of the respective performance metrics.</p

Five largest clusters in the NSI/SI and R5/X4 dataset clustering.

<p>Numbers indicate what fraction of the whole dataset is grouped in a given cluster (column “all”) and what is the ratio of the sequences of a given phenotype to all sequences in the respective cluster.</p

Analysis of the importance of features by VWMRmR feature selection.

<p>Analysis of the importance of features by VWMRmR feature selection.</p

Performance of sequence space-based coreceptor prediction methods.

<p>Performance of the individual coreceptor prediction methods (A) and their selected combinations (B) on the R5/X4 dataset is illustrated by the ROC curves. The AUC of each method is indicated in the inserted box.</p