Homology modeling: an important tool for the drug discovery

In the last decades, homology modeling has become a popular tool to access theoretical three-dimensional (3D) structures of molecular targets. So far several 3D models of proteins have been built by this technique and used in a great diversity of structural biology studies. But are those models consistent enough with experimental structures to make this technique an effective and reliable tool for drug discovery? Here we present, briefly, the fundamentals and current state-of-the-art of the homology modeling techniques used to build 3D structures of molecular targets, which experimental structures are not available in databases, and list some of the more important works, using this technique, available in literature today. In many cases those studies have afforded successful models for the drug design of more selective agonists/antagonists to the molecular targets in focus and guided promising experimental works, proving that, when the appropriate templates are available, useful models can be built using some of the several software available today for this purpose. Limitations of the experimental techniques used to solve 3D structures allied to constant improvements in the homology modeling software will maintain the need for theoretical models, establishing the homology modeling as a fundamental tool for the drug discovery.     

Protein kinase C isozymes and their selectivity towards ruboxistaurin

Protein kinase C (PKC) isozymes are an important class of enzymes in cell signaling and as drug targets. They are involved in specific pathways and have selectivity towards certain ligands, despite their high sequence similarities. Ruboxistaurin is a specific inhibitor of PKC-beta. To understand the molecular determinants for the selectivity of ruboxistaurin, we derived the three-dimensional structures of the kinase domains of PKC-alpha, -betaI, and -zeta using homology modeling. Several binding orientations of ruboxistaurin in the binding sites of these PKC catalytic domains were analyzed, and a putative alternative binding site for PKC-zeta was identified in its kinase domain. The calculated free energy of binding correlates well with the IC(50) of the inhibitor against each PKC isozyme. A residue-based energy decomposition analysis attributed the binding free energy to several key residues in the catalytic sites of these enzymes, revealing potential protein-ligand interactions responsible for ligand binding. The contiguous binding site revealed in the catalytic domain of PKC-zeta provides avenues for selective drug design. The details of structural nuances for specific inhibition of PKC isozymes are presented in the context of the three-dimensional structures of this important class of enzymes.     

Structural differences of matrix metalloproteinases. Homology modeling and energy minimization of enzyme-substrate complexes

Matrix metalloproteinases are extracellular enzymes taking part in the remodeling of extracellular matrix. The structures of the catalytic domain of MMP1, MMP3, MMP7 and MMP8 are known, but structures of enzymes belonging to this family still remain to be determined. A general approach to the homology modeling of matrix metalloproteinases, exemplified by the modeling of MMP2, MMP9, MMP12 and MMP14 is described. The models were refined using an energy minimization procedure developed for matrix metalloproteinases. This procedure includes incorporation of parameters for zinc and calcium ions in the AMBER 4.1 force field, applying a non-bonded approach and a full ion charge representation. Energy minimization of the apoenzymes yielded structures with distorted active sites, while reliable three-dimensional structures of the enzymes containing a substrate in active site were obtained. The structural differences between the eight enzyme-substrate complexes were studied with particular emphasis on the active site, and possible sites for obtaining selectivity among the MMP's are discussed. Differences in the P1' pocket are well-documented and have been extensively exploited in inhibitor design. The present work indicates that selectivity could be further improved by considering the P2 pocket as well.     

Bioinformatic and mutational analysis of channelrhodopsin-2 protein cation-conducting pathway

Channelrhodopsin-2 (ChR2) is a light-gated cation channel widely used as a biotechnological tool to control membrane depolarization in various cell types and tissues. Although several ChR2 variants with modified properties have been generated, the structural determinants of the protein function are largely unresolved. We used bioinformatic modeling of the ChR2 structure to identify the putative cationic pathway within the channel, which is formed by a system of inner cavities that are uniquely present in this microbial rhodopsin. Site-directed mutagenesis combined with patch clamp analysis in HeLa cells was used to determine key residues involved in ChR2 conductance and selectivity. Among them, Gln-56 is important for ion conductance, whereas Ser-63, Thr-250, and Asn-258 are previously unrecognized residues involved in ion selectivity and photocurrent kinetics. This study widens the current structural information on ChR2 and can assist in the design of new improved variants for specific biological applications.     

Refinement of unreliable local regions in template-based protein models

Contemporary template-based modeling techniques allow applications of modeling methods to vast biological problems. However, they tend to fail to provide accurate structures for less-conserved local regions in sequence even when the overall structure can be modeled reliably. We call these regions unreliable local regions (ULRs). Accurate modeling of ULRs is of enormous value because they are frequently involved in functional specificity. In this article, we introduce a new method for modeling ULRs in template-based models by employing a sophisticated loop modeling technique. Combined with our previous study on protein termini, the method is applicable to refinement of both loop and terminus ULRs. A large-scale test carried out in a blind fashion in CASP9 (the 9th Critical Assessment of techniques for protein structure prediction) shows that ULR structures are improved over initial template-based models by refinement in more than 70% of the successfully detected ULRs. It is also notable that successful modeling of several long ULRs over 12 residues is achieved. Overall, the current results show that a careful application of loop and terminus modeling can be a promising tool for model refinement in template-based modeling.     

Genome-wide Membrane Protein Structure Prediction

Transmembrane proteins allow cells to extensively communicate with the external world in a very accurate and specific way. They form principal nodes in several signaling pathways and attract large interest in therapeutic intervention, as the majority pharmaceutical compounds target membrane proteins. Thus, according to the current genome annotation methods, a detailed structural/functional characterization at the protein level of each of the elements codified in the genome is also required. The extreme difficulty in obtaining high-resolution three-dimensional structures, calls for computational approaches. Here we review to which extent the efforts made in the last few years, combining the structural characterization of membrane proteins with protein bioinformatics techniques, could help describing membrane proteins at a genome-wide scale. In particular we analyze the use of comparative modeling techniques as a way of overcoming the lack of high-resolution three-dimensional structures in the human membrane proteome.     

Molecular modeling of the bacterial outer membrane receptor energizer,ExbBD/TonB,based on homology with the flagellar motor,MotAB

The MotA/MotB proteins serve as the motor that drives bacterial flagellar rotation in response to the proton motive force (pmf). They have been shown to comprise a transmembrane proton pathway. The ExbB/ExbD/TonB protein complex serves to energize transport of iron siderophores and vitamin B12 across the outer membrane of the Gram-negative bacterial cell using the pmf. These two protein complexes have the same topology and are homologous. Based on molecular data for the MotA/MotB proteins, we propose simple three-dimensional channel structures for both MotA/MotB and ExbB/ExbD/TonB using modeling methods. Features of the derived channels are discussed, and two possible proton transfer pathways for the ExbBD/TonB system are proposed. These analyses provide a guide for molecular studies aimed at elucidating the mechanism by which chemiosmotic energy can be transferred either between two adjacent membranes to energize outer membrane transport or to the bacterial flagellum to generate torque.     

Molecular modeling of the bacterial outer membrane receptor energizer, ExbBD/TonB, based on homology with the flagellar motor, MotAB

The MotA/MotB proteins serve as the motor that drives bacterial flagellar rotation in response to the proton motive force (pmf). They have been shown to comprise a transmembrane proton pathway. The ExbB/ExbD/TonB protein complex serves to energize transport of iron siderophores and vitamin B₁₂ across the outer membrane of the Gram-negative bacterial cell using the pmf. These two protein complexes have the same topology and are homologous. Based on molecular data for the MotA/MotB proteins, we propose simple three-dimensional channel structures for both MotA/MotB and ExbB/ExbD/TonB using modeling methods. Features of the derived channels are discussed, and two possible proton transfer pathways for the ExbBD/TonB system are proposed. These analyses provide a guide for molecular studies aimed at elucidating the mechanism by which chemiosmotic energy can be transferred either between two adjacent membranes to energize outer membrane transport or to the bacterial flagellum to generate torque.     

Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures

Predicting the three-dimensional structure of proteins from their amino acid sequences remains a challenging problem in molecular biology. While the current structural coverage of proteins is almost exclusively provided by template-based techniques, the modeling of the rest of the protein sequences increasingly require template-free methods. However, template-free modeling methods are much less reliable and are usually applicable for smaller proteins, leaving much space for improvement. We present here a novel computational method that uses a library of supersecondary structure fragments, known as Smotifs, to model protein structures. The library of Smotifs has saturated over time, providing a theoretical foundation for efficient modeling. The method relies on weak sequence signals from remotely related protein structures to create a library of Smotif fragments specific to the target protein sequence. This Smotif library is exploited in a fragment assembly protocol to sample decoys, which are assessed by a composite scoring function. Since the Smotif fragments are larger in size compared to the ones used in other fragment-based methods, the proposed modeling algorithm, SmotifTF, can employ an exhaustive sampling during decoy assembly. SmotifTF successfully predicts the overall fold of the target proteins in about 50% of the test cases and performs competitively when compared to other state of the art prediction methods, especially when sequence signal to remote homologs is diminishing. Smotif-based modeling is complementary to current prediction methods and provides a promising direction in addressing the structure prediction problem, especially when targeting larger proteins for modeling.     

New metrics for comparing and assessing discrepancies between RNA 3D structures and models

To benchmark progress made in RNA three-dimensional modeling and assess newly developed techniques, reliable and meaningful comparison metrics and associated tools are necessary. Generally, the average root-mean-square deviations (RMSDs) are quoted. However, RMSD can be misleading since errors are spread over the whole molecule and do not account for the specificity of RNA base interactions. Here, we introduce two new metrics that are particularly suitable to RNAs: the deformation index and deformation profile. The deformation index is calibrated by the interaction network fidelity, which considers base–base-stacking and base–base-pairing interactions within the target structure. The deformation profile highlights dissimilarities between structures at the nucleotide scale for both intradomain and interdomain interactions. Our results show that there is little correlation between RMSD and interaction network fidelity. The deformation profile is a tool that allows for rapid assessment of the origins of discrepancies.     

Information-driven modeling of biomolecular complexes

Proteins play crucial roles in every cellular process by interacting with each other, nucleic acids, metabolites, and other molecules. The resulting assemblies can be very large and intricate and pose challenges to experimental methods. In the current era of integrative modeling, it is often only by a combination of various experimental techniques and computations that three-dimensional models of those molecular machines can be obtained. Among the various computational approaches available, molecular docking is often the method of choice when it comes to predicting three-dimensional structures of complexes. Docking can generate particularly accurate models when taking into account the available information on the complex of interest. We review here the use of experimental and bioinformatics data in protein-protein docking, describing recent software developments and highlighting applications for the modeling of antibody–antigen complexes and membrane protein complexes, and the use of evolutionary and shape information.     

Modeling of inhibitor–metalloenzyme interactions and selectivity using molecular mechanics grounded in quantum chemistry

We investigated the binding properties of the metalloprotease inhibitors hydroxamate, methanethiolate, and methylphosphoramidate to a model coordination site occurring in several Zn2+ metalloproteases, including thermolysin. This was carried out using both the SIBFA (sum of interactions between fragments ab initio-computed) molecular mechanics and the SCF/MP2 procedures for the purpose of evaluating SIBFA as a metalloenzyme modeling tool. The energy-minimized structures were closely similar to the X-ray crystallographic structures of related thermolysin-inhibitor complexes. We found that selectivity between alternative geometries and between inhibitors usually stemmed from multiple interaction components included in SIBFA. The binding strength sequence is hydroxamate > methanethiolate ≥ methylphosphoramidate from multiple interaction components included in SIBFA. The trends in interaction energy components, rankings, and preferences for mono- or bidentate binding were consistent in both computational procedures. We also compared the Zn2+ vs. Mg2+ selectivities in several other polycoordinated sites having various “hard” and “soft” qualities. This included a hexahydrate, a model representing Mg2+/Ca2+ binding sites, a chlorophyll-like structure, and a zinc finger model. The latter three favor Zn2+ over Mg2+ by a greater degree than the hydrated state, but the selectivity varies widely according to the ligand “softness.” SIBFA was able to match the ab initio binding energies by <2%, with the SIBFA terms representing dispersion and charge-transfer contributing the most to Zn2+/Mg2+ selectivity. These results showed this procedure to be a very capable modeling tool for metalloenzyme problems, in this case giving valuable information about details and limitations of “hard” and “soft” selectivity trends. Proteins 31:42–60, 1998. © 1998 Wiley-Liss, Inc.     

Enriching the human apoptosis pathway by predicting the structures of protein-protein complexes

Apoptosis is a matter of life and death for cells and both inhibited and enhanced apoptosis may be involved in the pathogenesis of human diseases. The structures of protein-protein complexes in the apoptosis signaling pathway are important as the structural pathway helps in understanding the mechanism of the regulation and information transfer, and in identifying targets for drug design. Here, we aim to predict the structures toward a more informative pathway than currently available. Based on the 3D structures of complexes in the target pathway and a protein-protein interaction modeling tool which allows accurate and proteome-scale applications, we modeled the structures of 29 interactions, 21 of which were previously unknown. Next, 27 interactions which were not listed in the KEGG apoptosis pathway were predicted and subsequently validated by the experimental data in the literature. Additional interactions are also predicted. The multi-partner hub proteins are analyzed and interactions that can and cannot co-exist are identified. Overall, our results enrich the understanding of the pathway with interactions and provide structural details for the human apoptosis pathway. They also illustrate that computational modeling of protein-protein interactions on a large scale can help validate experimental data and provide accurate, structural atom-level detail of signaling pathways in the human cell.     

A Proto-Architecture for Innate Directionally Selective Visual Maps

Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example robotics, where the ability to extract and learn features from the environment in an unsupervised way is important. In this computational study we explore a DS map that is already latent in a simple artificial network. This latent selectivity arises purely from the cortical architecture without any explicit coding for DS and prior to any self-organising process facilitated by spontaneous activity or training. We find DS maps with local patchy regions that exhibit features similar to maps derived experimentally and from previous modeling studies. We explore the consequences of changes to the afferent and lateral connectivity to establish the key features of this proto-architecture that support DS.     

Progress and challenges in protein structure prediction

Depending on whether similar structures are found in the PDB library, the protein structure prediction can be categorized into template-based modeling and free modeling. Although threading is an efficient tool to detect the structural analogs, the advancements in methodology development have come to a steady state. Encouraging progress is observed in structure refinement which aims at drawing template structures closer to the native; this has been mainly driven by the use of multiple structure templates and the development of hybrid knowledge-based and physics-based force fields. For free modeling, exciting examples have been witnessed in folding small proteins to atomic resolutions. However, predicting structures for proteins larger than 150 residues still remains a challenge, with bottlenecks from both force field and conformational search.     

STRUCLA: a WWW meta-server for protein structure comparison and evolutionary classification

MOTIVATION: Evolutionary relationships of proteins have long been derived from the alignment of protein sequences. But from the view of function, most restraints of evolutionary divergence operate at the level of tertiary structure. It has been demonstrated that quantitative measures of dissimilarity in families of structurally similar proteins can be applied to the construction of trees from a comparison of their three-dimensional structures. However, no convenient tool is publicly available to carry out such analyses. RESULTS: We developed STRUCLA (STRUcture CLAssification), a WWW tool for generation of trees based on evolutionary distances inferred from protein structures according to various methods. The server takes as an input a list of PDB files or the initial alignment of protein coordinates provided by the user (for instance exported from SWISS PDB VIEWER). The user specifies the distance cutoff and selects the distance measures. The server returns series of unrooted trees in the NEXUS format and corresponding distance matrices, as well as a consensus tree. The results can be used as an alternative and a complement to a fixed hierarchy of current protein structure databases. It can complement sequence-based phylogenetic analysis in the 'twilight zone of homology', where amino acid sequences are too diverged to provide reliable relationships.     

Probing the outer mouth structure of the HERG channel with peptide toxin footprinting and molecular modeling

Previous studies have shown that the unusually long S5-P linker lining human ether a-go-go related gene's (hERG's) outer vestibule is critical for its channel function: point mutations at high-impact positions here can interfere with the inactivation process and, in many cases, also reduce the pore's K+ selectivity. Because no data are available on the equivalent region in the available K channel crystal structures to allow for homology modeling, we used alternative approaches to model its three-dimensional structure. The first part of this article describes mutant cycle analysis used to identify residues on hERG's outer vestibule that interact with specific residues on the interaction surface of BeKm-1, a peptide toxin with known NMR structure and a high binding affinity to hERG. The second part describes molecular modeling of hERG's pore domain. The transmembrane region was modeled after the crystal structure of KvAP pore domain. The S5-P linker was docked to the transmembrane region based on data from previous NMR and mutagenesis experiments, as well as a set of modeling criteria. The models were further restrained by contact points between hERG's outer vestibule and the bound BeKm-1 toxin molecule deduced from the mutant cycle analysis. Based on these analyses, we propose a working model for the open conformation of the outer vestibule of the hERG channel, in which the S5-P linkers interact with the pore loops to influence ion flux through the pore.     

Practical tools for molecular modeling of complex carbohydrates and their interactions with proteins

Computer modeling has become a valuable component of studies of carbohydrate three-dimensional structures and their relationship to function and properties. In this paper we examine the methods required for conformational modeling of carbohydrates, and we present a series of tools that have been developed to this end. These tools can be integrated into three-dimensional real-time molecular modeling software. A data base of pre-optimized carbohydrate fragments has been established to be used further in the construction of much more complex molecules. In addition we describe some possible uses of a data base of three dimensional structures of the disaccharide fragments present in the glycan moiety ofN-glycoprotein. A molecular mechanical force field appropriate for the conformational analysis of oligosaccharides has been derived by the addition of new parameters to the Tripos force field and is compatible with protein simulations. The new parametrization has been assessed in three stages of increasing complexity: computations of potential energy surfaces, conformational refinement of relevant oligosaccharides, modeling at the atomic level of a protein/carbohydrate complex.     

Molecular Dynamics,Flexible Docking,Virtual Screening,ADMET Predictions,and Molecular Interaction Field Studies to Design Novel Potential MAO-B Inhibitors

Abstract

Monoamine oxidase is a flavoenzyme bound to the mitochondrial outer membranes of the cells, which is responsible for the oxidative deamination of neurotransmitter and dietary amines. It has two distinct isozymic forms, designated MAO-A and MAO-B, each displaying different substrate and inhibitor specificities. They are the well-known targets for antidepressant, Parkinson's disease, and neuroprotective drugs. Elucidation of the x-ray crystallographic structure of MAO-B has opened the way for the molecular modeling studies. In this work we have used molecular modeling, density functional theory with correlation, virtual screening, flexible docking, molecular dynamics, ADMET predictions, and molecular interaction field studies in order to design new molecules with potential higher selectivity and enzymatic inhibitory activity over MAO-B.