Structural quality of unrefined models in protein docking

Structural characterization of protein‐protein interactions is essential for understanding life processes at the molecular level. However, only a fraction of protein interactions have experimentally resolved structures. Thus, reliable computational methods for structural modeling of protein interactions (protein docking) are important for generating such structures and understanding the principles of protein recognition. Template‐based docking techniques that utilize structural similarity between target protein‐protein interaction and cocrystallized protein‐protein complexes (templates) are gaining popularity due to generally higher reliability than that of the template‐free docking. However, the template‐based approach lacks explicit penalties for intermolecular penetration, as opposed to the typical free docking where such penalty is inherent due to the shape complementarity paradigm. Thus, template‐based docking models are commonly assumed to require special treatment to remove large structural penetrations. In this study, we compared clashes in the template‐based and free docking of the same proteins, with crystallographically determined and modeled structures. The results show that for the less accurate protein models, free docking produces fewer clashes than the template‐based approach. However, contrary to the common expectation, in acceptable and better quality docking models of unbound crystallographically determined proteins, the clashes in the template‐based docking are comparable to those in the free docking, due to the overall higher quality of the template‐based docking predictions. This suggests that the free docking refinement protocols can in principle be applied to the template‐based docking predictions as well. Proteins 2016; 85:39–45. © 2016 Wiley Periodicals, Inc.     

Structural insights into the interaction of human S100B and basic fibroblast growth factor (FGF2): Effects on FGFR1 receptor signaling

S100B is a calcium sensing protein belonging to the S100 protein family with intracellular and extracellular roles. It is one of the EF hand homodimeric proteins, which is known to interact with various protein targets to regulate varied biological functions. Extracellular S100B has been recently reported to interact with FGF2 in a RAGE-independent manner. However, the recognition mechanism of S100B–FGF2 interaction at the molecular level remains unclear. In this study, the critical residues on S100B–FGF2 interface were mapped by combined information derived from NMR spectroscopy and site directed mutagenesis experiments. Utilizing NMR titration data, we generated the structural models of S100B–FGF2 complex from the computational docking program, HADDOCK which were further proved stable during 15 ns unrestrained molecular dynamics (MD) simulations. Isothermal titration calorimetry studies indicated S100B interaction with FGF2 is an entropically favored process implying dominant role of hydrophobic contacts at the protein–protein interface. Residue level information of S100B interaction with FGF2 was useful to understand the varied target recognition ability of S100B and further explained its role in effecting extracellular signaling diversity. Mechanistic insights into the S100B–FGF2 complex interface and cell-based assay studies involving mutants led us to conclude the novel role of S100B in FGF2 mediated FGFR1 receptor inactivation.     

Simulated Interactions between Endothelin Converting Enzyme and Aβ Peptide: Insights into Subsite Recognition and Cleavage Mechanism

Alzheimer’s disease (AD) is a most common form of dementia caused due to aggregation of amyloid beta (Aβ) peptides in brain. The AD brain exhibits extracellular deposition of Aβ-peptides which triggers neuronal death. Thus, degradation of Aβ peptides has evaluated a promising therapeutic target in AD. Human endothelin converting enzyme (hECE-1) has been implicated in Aβ-peptide degradation. In this study, we have performed molecular docking between three different conformations of Aβ peptides and hECE-1 coupled with molecular dynamics to investigate subsite recognition and cleavage mechanism. Molecular docking and MD simulation studies show that β-sheet conformation with particular orientation of Aβ-peptide residues selectively entrap in substrate binding cavity of hECE-1. However, unusual orientation of Aβ-peptide residues and helical conformation undergoes substantial fluctuations resulted in the reduction of enzyme-substrate interactions. Zn ion coordinates with Aβ-peptide near the scissile peptide bond. Based on this information we have proposed catalytic mechanism of hECE-1 for Aβ-peptide degradation in which residue E 608 of hECE-1 plays an important role as a proton shuttle. The molecular basis of Aβ peptide cleavage by hECE-1 could aid in designing enzyme based therapies to control Aβ concentration in AD.     

Multiple molecular recognition mechanisms. Cytochrome P450--a case study

Biomolecular recognition is complex. The balance between the different molecular properties that contribute to molecular recognition, such as shape, electrostatics, dynamics and entropy, varies from case to case. This, along with the extent of experimental characterization, influences the choice of appropriate computational approaches to study biomolecular interactions. Here, we present computational studies of cytochrome P450 enzymes and their interactions with small molecules and with other proteins. These interactions exemplify some of the diversity of molecular determinants of binding affinity and specificity observed for proteins and we discuss some of the challenges that they pose for molecular modelling and simulation.     

工业酶研究中的计算化学方法

文中介绍用于工业酶研究特别是用于指导酶工程的主要计算化学方法,包括分子力学力场和分子动力学模拟、量子力学以及量子力学/分子力学结合模型、连续介质静电模型以及分子对接等。文中从以下两个角度分别概要地介绍这些方法:一是方法本身的基本概念、原始计算结果、适用条件和优缺点等;二是通过计算得到有价值的信息,指导突变体和突变库设计。     

Oligomerization states of Bowman-Birk inhibitor by atomic force microscopy and computational approaches

Several methods have been applied to study protein-protein interaction from structural and thermodynamic point of view. The present study reveals that atomic force microscopy (AFM), molecular modeling, and docking approaches represent alternative methods offering new strategy to investigate structural aspects in oligomerization process of proteinase inhibitors. The topography of the black-eyed pea trypsin/chymotrypsin inhibitor (BTCI) was recorded by AFM and compared with computational rigid-bodies docking approaches. Multimeric states of BTCI identified from AFM analysis showed globular-ellipsoidal shapes. Monomers, dimers, trimers, and hexamers were the most prominent molecular arrays observed in AFM images as evaluated by molecular volume calculations and corroborated by in silico docking and theoretical approaches. We therefore propose that BTCI adopts stable and well-packed self-assembled states in monomer-dimer-trimer-hexamer equilibrium. Although there are no correlation between specificity and packing efficiency among proteinases and proteinase inhibitors, the AFM and docked BTCI analyses suggest that these assemblies may exist in situ to play their potential function in oligomerization process.     

Molecular docking: The role of noncovalent interactions in the formation of protein-nucleotide and protein-peptide complexes

Knowledge of the spatial structure of complexes formed by cellular proteins and membrane receptors with their respective ligands is an important step towards understanding the mechanisms of their functioning. Rational drug design and the search for new therapeutically active compounds also require structural information on the interaction of prototypic drugs with the target protein. The present review briefly describes the main computational methods of molecular docking that are used to predict the conformation of a ligand bound to the active center of a protein. Approaches enabling an increase of the precision and efficiency of the currently used docking algorithms are exemplified by the recent projects of the Laboratory of Biomolecular Modeling of IBCh RAS. Special attention is paid to hydrophilic and hydrophobic interactions, as well as to the stacking phenomena that account for the molecular recognition of specific ligand fragments. These types of contacts are often inadequately described by the algorithms of the estimation of the intermolecular interaction energy of the existing docking programs (scoring functions), this ultimately leading to erroneous predictions of the three-dimensional structure of complexes. Therefore, a thorough consideration of these interactions is one of the most important tasks of molecular modeling.     

Integrating statistical pair potentials into protein complex prediction

The biophysical study of protein-protein interactions and docking has important implications in our understanding of most complex cellular signaling processes. Most computational approaches to protein docking involve a tradeoff between the level of detail incorporated into the model and computational power required to properly handle that level of detail. In this work, we seek to optimize that balance by showing that we can reduce the complexity of model representation and thus make the computation tractable with minimal loss of predictive performance. We also introduce a pair-wise statistical potential suitable for docking that builds on previous work and show that this potential can be incorporated into our fast fourier transform-based docking algorithm ZDOCK. We use the Protein Docking Benchmark to illustrate the improved performance of this potential compared with less detailed other scoring functions. Furthermore, we show that the new potential performs well on antibody-antigen complexes, with most predictions clustering around the Complementarity Determining Regions of antibodies without any manual intervention.     

Enzymatic Degradation of GlycosaminogIycans

Abstract

Glycosarninoglycans (GAGs) play an intricate role in the extracellular matrix (ECM), not only as soluble components and polyelectrolytes, but also by specific interactions with growth factors and other transient components of the ECM. Modifications of GAG chains, such as isomerization, sulfation, and acetylation, generate the chemical specificity of GAGs. GAGS can be depolymerized enzymatically either by eliminative cleavage with lyases (EC 4.2.2.-) or by hydrolytic cleavage with hydrolases (EC 3.2.1.-). Often, these enzymes are specific for residues in the polysaccharide chain with certain modifications. As such, the enzymes can serve as tools for studying the physiological effect of residue modifications and as models at the molecular level of protein-GAG recognition. This review examines the structure of the substrates, the properties of enzymatic degradation, and the enzyme substrate-interactions at a molecular level. The primary structure of several GAGS is organized macro-scopicallyby segregation into alternating blocks of specific sulfation patterns and microscopicallyby formation of oligosaccharide sequences with specific binding functions. Among GAGs, considerable dermatan sulfate, heparin and heparan sulfate show conformational flexibility in solution. They elicit sequence-specific interactions with enzymes that degrade them, as well as with other proteins, however, the effect of conformational flexibility on protein-GAG interactions is not clear. Recent findings have established empirical rules of substrate specificity and elucidated molecular mechanisms of enzyme-substrate interactions for enzymes that degrade GAGs. Here we propose that local formation of polysaccharide secondary structure is determined by the immediate sequence environment within the GAG polymer, and that this secondary structure, in turn, governs the binding and catalytic interactions between proteins and GAGs.     

Protein-Protein Docking: From Interaction to Interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.     

A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.     

Structural templates for comparative protein docking

Structural characterization of protein‐protein interactions is important for understanding life processes. Because of the inherent limitations of experimental techniques, such characterization requires computational approaches. Along with the traditional protein‐protein docking (free search for a match between two proteins), comparative (template‐based) modeling of protein‐protein complexes has been gaining popularity. Its development puts an emphasis on full and partial structural similarity between the target protein monomers and the protein‐protein complexes previously determined by experimental techniques (templates). The template‐based docking relies on the quality and diversity of the template set. We present a carefully curated, nonredundant library of templates containing 4950 full structures of binary complexes and 5936 protein‐protein interfaces extracted from the full structures at 12 Å distance cut‐off. Redundancy in the libraries was removed by clustering the PDB structures based on structural similarity. The value of the clustering threshold was determined from the analysis of the clusters and the docking performance on a benchmark set. High structural quality of the interfaces in the template and validation sets was achieved by automated procedures and manual curation. The library is included in the Dockground resource for molecular recognition studies at http://dockground.bioinformatics.ku.edu . Proteins 2015; 83:1563–1570. © 2014 Wiley Periodicals, Inc.     

Protein-Protein Docking: From Interaction to Interactome

The protein-protein docking problem is one of the focal points of activity in computational biophysics and structural biology. The three-dimensional structure of a protein-protein complex, generally, is more difficult to determine experimentally than the structure of an individual protein. Adequate computational techniques to model protein interactions are important because of the growing number of known protein structures, particularly in the context of structural genomics. Docking offers tools for fundamental studies of protein interactions and provides a structural basis for drug design. Protein-protein docking is the prediction of the structure of the complex, given the structures of the individual proteins. In the heart of the docking methodology is the notion of steric and physicochemical complementarity at the protein-protein interface. Originally, mostly high-resolution, experimentally determined (primarily by x-ray crystallography) protein structures were considered for docking. However, more recently, the focus has been shifting toward lower-resolution modeled structures. Docking approaches have to deal with the conformational changes between unbound and bound structures, as well as the inaccuracies of the interacting modeled structures, often in a high-throughput mode needed for modeling of large networks of protein interactions. The growing number of docking developers is engaged in the community-wide assessments of predictive methodologies. The development of more powerful and adequate docking approaches is facilitated by rapidly expanding information and data resources, growing computational capabilities, and a deeper understanding of the fundamental principles of protein interactions.     

Modeling protein recognition of carbohydrates

We have a limited understanding of the details of molecular recognition of carbohydrates by proteins, which is critical to a multitude of biological processes. Furthermore, carbohydrate-modifying proteins such as glycosyl hydrolases and phosphorylases are of growing importance as potential drug targets. Interactions between proteins and carbohydrates have complex thermodynamics, and in general the specific positioning of only a few hydroxyl groups determines their binding affinities. A thorough understanding of both carbohydrate and protein structures is thus essential to predict these interactions. An atomic-level view of carbohydrate recognition through structures of carbohydrate-active enzymes complexed with transition-state inhibitors reveals some of the distinctive molecular features unique to protein-carbohydrate complexes. However, the inherent flexibility of carbohydrates and their often water-mediated hydrogen bonding to proteins makes simulation of their complexes difficult. Nonetheless, recent developments such as the parameterization of specific force fields and docking scoring functions have greatly improved our ability to predict protein-carbohydrate interactions. We review protein-carbohydrate complexes having defined molecular requirements for specific carbohydrate recognition by proteins, providing an overview of the different computational techniques available to model them.     

Pentameric ligand-gated ion channels: insights from computation

Pentameric ligand-gated ion channels (pLGICs) conduct upon the binding of an agonist and are fundamental to neurotransmission. New insights into the complex mechanisms underlying pLGIC gating, ion selectivity and modulation have recently been gained via a series of crystal structures in prokaryotes and Caenorhabditis elegans, as well as computational studies relying on these structures. Here, we review contributions from a variety of computational approaches, including normal-mode analysis, automated docking and fully atomistic molecular dynamics simulation. Examples from our own research, particularly concerning interactions with general anaesthetics and lipids, are used to illustrate predictive results complementary to crystallographic studies.     

Pharmacophore-based virtual screening of catechol-o-methyltransferase (COMT) inhibitors to combat Alzheimer’s disease

Alzheimer’s disease (AD) is one of the most significant neurodegenerative disorders and its symptoms mostly appear in aged people. Catechol-o-methyltransferase (COMT) is one of the known target enzymes responsible for AD. With the use of 23 known inhibitors of COMT, a query has been generated and validated by screening against the database of 1500 decoys to obtain the GH score and enrichment value. The crucial features of the known inhibitors were evaluated by the online ZINC Pharmer to identify new leads from a ZINC database. Five hundred hits were retrieved from ZINC Pharmer and by ADMET (absorption, distribution, metabolism, excretion, and toxicity) filtering by using FAF-Drug-3 and 36 molecules were considered for molecular docking. From the COMT inhibitors, opicapone, fenoldopam, and quercetin were selected, while ZINC63625100_413 ZINC39411941_412, ZINC63234426_254, ZINC63637968_451, and ZINC64019452_303 were chosen for the molecular dynamics simulation analysis having high binding affinity and structural recognition. This study identified the potential COMT inhibitors through pharmacophore-based inhibitor screening leading to a more complete understanding of molecular-level interactions.     

Rapid Structural Characterization of Human Antibody-Antigen Complexes through Experimentally Validated Computational Docking

If we understand the structural rules governing antibody (Ab)-antigen (Ag) interactions in a given virus, then we have the molecular basis to attempt to design and synthesize new epitopes to be used as vaccines or optimize the antibodies themselves for passive immunization. Comparing the binding of several different antibodies to related Ags should also further our understanding of general principles of recognition.To obtain and compare the three-dimensional structure of a large number of different complexes, however, we need a faster method than traditional experimental techniques. While biocomputational docking is fast, its results might not be accurate. Combining experimental validation with computational prediction may be a solution.As a proof of concept, here we isolated a monoclonal Ab from the blood of a human donor recovered from dengue virus infection, characterized its immunological properties, and identified its epitope on domain III of dengue virus E protein through simple and rapid NMR chemical shift mapping experiments. We then obtained the three-dimensional structure of the Ab/Ag complex by computational docking, using the NMR data to drive and validate the results. In an attempt to represent the multiple conformations available to flexible Ab loops, we docked several different starting models and present the result as an ensemble of models equally agreeing with the experimental data. The Ab was shown to bind a region accessible only in part on the viral surface, explaining why it cannot effectively neutralize the virus.     

Efficient incorporation of protein flexibility and dynamics into molecular docking simulations

Flexibility and dynamics are protein characteristics that are essential for the process of molecular recognition. Conformational changes in the protein that are coupled to ligand binding are described by the biophysical models of induced fit and conformational selection. Different concepts that incorporate protein flexibility into protein-ligand docking within the context of these two models are reviewed. Several computational studies that discuss the validity and possible limitations of such approaches will be presented. Finally, different approaches that incorporate protein dynamics, e.g., configurational entropy, and solvation effects into docking will be highlighted.