Program to compute protein abundance in mass spectrometry

The program is to compute protein abundance from spectral counts from mass spectrometry. It computes protein abundance from unique peptides and hared peptides among proteins considered. It is also available at http://kiharalab.org/RACalculator

Energetic Coupling between Ligand Binding and Dimerization in <i>Escherichia coli</i> Phosphoglycerate Mutase

Energetic coupling of two molecular events in a protein molecule is ubiquitous in biochemical reactions mediated by proteins, such as catalysis and signal transduction. Here, we investigate energetic coupling between ligand binding and folding of a dimer using a model system that shows three-state equilibrium unfolding of an exceptional quality. The homodimeric <i>Escherichia coli</i> cofactor-dependent phosphoglycerate mutase (dPGM) was found to be stabilized by ATP in a proteome-wide screen, although dPGM does not require or utilize ATP for enzymatic function. We investigated the effect of ATP on the thermodynamic stability of dPGM using equilibrium unfolding. We found that, in the absence of ATP, dPGM populates a partially unfolded, monomeric intermediate during equilibrium unfolding. However, addition of 1.0 mM ATP drastically reduces the population of the intermediate by selectively stabilizing the native dimer. Using a computational ligand docking method, we predicted ATP binds to the active site of the enzyme using the triphosphate group. By performing equilibrium unfolding and isothermal titration calorimetry with active-site variants of dPGM, we confirmed that active-site residues are involved in ATP binding. Our findings show that ATP promotes dimerization of the protein by binding to the active site, which is distal from the dimer interface. This cooperativity suggests an energetic coupling between the active site and the dimer interface. We also propose a structural link to explain how ligand binding to the active site is energetically coupled with dimerization

PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation

Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/

Modeling disordered protein interactions from biophysical principles

<div><p>Disordered protein-protein interactions (PPIs), those involving a folded protein and an intrinsically disordered protein (IDP), are prevalent in the cell, including important signaling and regulatory pathways. IDPs do not adopt a single dominant structure in isolation but often become ordered upon binding. To aid understanding of the molecular mechanisms of disordered PPIs, it is crucial to obtain the tertiary structure of the PPIs. However, experimental methods have difficulty in solving disordered PPIs and existing protein-protein and protein-peptide docking methods are not able to model them. Here we present a novel computational method, IDP-LZerD, which models the conformation of a disordered PPI by considering the biophysical binding mechanism of an IDP to a structured protein, whereby a local segment of the IDP initiates the interaction and subsequently the remaining IDP regions explore and coalesce around the initial binding site. On a dataset of 22 disordered PPIs with IDPs up to 69 amino acids, successful predictions were made for 21 bound and 18 unbound receptors. The successful modeling provides additional support for biophysical principles. Moreover, the new technique significantly expands the capability of protein structure modeling and provides crucial insights into the molecular mechanisms of disordered PPIs.</p></div