Protein-Protein Docking Using Map Objects

Protein-protein docking is a molecular modeling strategy to predict biomolecular complexes and assemblies. Traditional protein-protein docking is performed at atomic resolution, which relies on X-ray and NMR experiments to provide structural information. When dealing with biomolecular assemblies of millions of atoms, atomic description of molecular objects becomes very computational inefficient. This article describes a development work that introduces map objects to molecular modeling studies to efficiently derive complex structures through map-map conformational search. This method has been implemented into CHARMM as the EMAP command and into AMBER in its SANDER program. This development enables molecular modeling and simulation to manipulate map objects, including map input, output, comparison, docking, etc. Through map objects, users can efficiently construct complex structures through protein-protein docking as well as from electron microscopy maps according to low map energies. Using a T-cell receptor (TCR) variable domain and acetylcholine binding protein (AChBP) as example systems, we showed the application to model an energetic optimized complex structure according to a complex map. The map objects serve as a bridge between high-resolution atomic structures and low-resolution image data

Structure and Dynamics of Macromolecular Assemblies from Electron Microscopy Maps

Electron microscopy (EM) and electron tomography (ET) have found extensive application to probe structural and dynamic properties of macromolecular assemblies. As an important complementary to X-ray and NMR in atomic structural determination, EM has reached a milestone resolution of 2.2Å, enough for understanding atomic interactions, interpreting mechanism of functions, and for structure-based drug design. This work describes approaches to derive structural information from EM/ET images and methods to study dynamics of macromolecular systems using EM/ET images as starting or ending targets. For low-resolution EM/ET maps, X-ray or NMR atomic structures of molecular components are needed to reduce the number of degrees of uncertainty. Depending on the resolution of EM/ET maps and the conformational differences from the X-ray or NMR structures, either rigid fitting or flexible fitting is used to obtain atomic structures. To illustrate the procedures of the atomic structure derivation, this work describes the core-weighted grid-threading Monte Carlo (CW-GTMC) rigid fitting and the map-restrained self-guided Langevin dynamics (MapSGLD) flexible fitting methods. Their applications are highlighted with four examples: architecture of an icosahedral pyruvate dehydrogenase complex, dynamics of a group II chaperonin, high-resolution structure of the cell-permeant inhibitor phenylethyl β-D-thiogalactopyranoside, and the mechanism of kinesin walking on microtube

Normal mode analysis of macromolecular systems with the Mobile Block Hessian method

Until recently, normal mode analysis (NMA) was limited to small proteins, not only because the required energy minimization is a computationally exhausting task, but also because NMA requires the expensive diagonalization of a 3Na 3Na matrix with Na the number of atoms. A series of simplified models has been proposed, in particular the Rotation-Translation Blocks (RTB) method by Tama et al. for the simulation of proteins. It makes use of the concept that a peptide chain or protein can be seen as a subsequent set of rigid components, i.e. the peptide units. A peptide chain is thus divided into rigid blocks with six degrees of freedom each. Recently we developed the Mobile Block Hessian (MBH) method, which in a sense has similar features as the RTB method. The main difference is that MBH was developed to deal with partially optimized systems. The position/orientation of each block is optimized while the internal geometry is kept fixed at a plausible – but not necessarily optimized – geometry. This reduces the computational cost of the energy minimization. Applying the standard NMA on a partially optimized structure however results in spurious imaginary frequencies and unwanted coordinate dependence. The MBH avoids these unphysical effects by taking into account energy gradient corrections. Moreover the number of variables is reduced, which facilitates the diagonalization of the Hessian. In the original implementation of MBH, atoms could only be part of one rigid block. The MBH is now extended to the case where atoms can be part of two or more blocks. Two basic linkages can be realized: (1) blocks connected by one link atom, or (2) by two link atoms, where the latter is referred to as the hinge type connection. In this work we present the MBH concept and illustrate its performance with the crambin protein as an example

An efficient approach for the calculation of frequencies in macromolecules

I. INTRODUCTION. Conformational changes of macromolecules are essential in the understanding of e.g. proteins and drug design. The theoretical prediction is far from trivial, especially for large molecules. In many cases, collective motions are present which occur on a timescale (~ms) that is too long to be accessible through molecular dynamics simulations. Normal mode analysis (NMA) has been proven succesful in exploring the potential energy surface (PES) within the harmonic oscillator approximation. The lowest frequency modes contribute the most to a conformational change. This paper presents a computationally attractive method that selects modes from the lower spectrum

A new model defines the minimal set of polymorphism in HLA-DQ and -DR that determines susceptibility and resistance to autoimmune diabetes

Abstract Background The mechanism underlying autoimmune diabetes has been difficult to define. There is a strong genetic contribution and numerous studies associate the major histocompatibility complex, especially the class II region, with predisposition or resistance. However, how these molecules are implicated remains obscure. Presentation of the hypothesis We have supplemented structural analysis with computational biophysical and sequence analyses and propose an heuristic for distinguishing between human leukocyte antigen molecules that predispose to insulin dependent diabetes mellitus and those that are protective. Polar residues at both β37 and β9 suffice to distinguish accurately between class II alleles that predispose to type 1 diabetes and those that do not. The electrostatic potential within the peptide binding pocket exerts a strong influence on diabetogenic epitopes with basic residues. Diabetes susceptibility alleles are predicted to bind autoantigens strongly with tight affinity, prolonged association and altered cytokine expression profile. Protective alleles bind moderately, and neutral alleles poorly or not at all. Non-Asp β57 is a modifier that supplements disease risk but only in the presence of the polymorphic, polar pair at β9 and β37. The nature of β37 determines resistance on one hand, and susceptibility or dominant protection on the other. Conclusion The proposed ideas are illustrated with structural, functional and population studies from the literature. The hypothesis, in turn, rationalizes their results. A plausible mechanism of immune mediated diabetes based on binding affinity and peptide kinetics is discussed. The number of the polymorphic markers present correlates with onset of disease and severity. The molecular elucidation of disease susceptibility and resistance paves the way for risk prediction, treatment and prevention of disease based on analogue peptides. Reviewers This article was reviewed by Eugene V. Koonin, Michael Lenardo, Hossam Ashour, and Bhagirath Singh. For the full reviews, please go to the Reviewers' comments section.</p