Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Multiple functional neurosteroid binding sites on GABAA receptors

Neurosteroids are endogenous modulators of neuronal excitability and nervous system development and are being developed as anesthetic agents and treatments for psychiatric diseases. While gamma amino-butyric acid Type A (GABAA) receptors are the primary molecular targets of neurosteroid action, the structural details of neurosteroid binding to these proteins remain ill defined. We synthesized neurosteroid analogue photolabeling reagents in which the photolabeling groups were placed at three positions around the neurosteroid ring structure, enabling identification of binding sites and mapping of neurosteroid orientation within these sites. Using middle-down mass spectrometry (MS), we identified three clusters of photolabeled residues representing three distinct neurosteroid binding sites in the human α1β3 GABAA receptor. Novel intrasubunit binding sites were identified within the transmembrane helical bundles of both the α1 (labeled residues α1-N408, Y415) and β3 (labeled residue β3-Y442) subunits, adjacent to the extracellular domains (ECDs). An intersubunit site (labeled residues β3-L294 and G308) in the interface between the β3(+) and α1(-) subunits of the GABAA receptor pentamer was also identified. Computational docking studies of neurosteroid to the three sites predicted critical residues contributing to neurosteroid interaction with the GABAA receptors. Electrophysiological studies of receptors with mutations based on these predictions (α1-V227W, N408A/Y411F, and Q242L) indicate that both the α1 intrasubunit and β3-α1 intersubunit sites are critical for neurosteroid action

Nilotinib, an approved leukemia drug, inhibits smoothened signaling in Hedgehog-dependent medulloblastoma.

Dysregulation of the seven-transmembrane (7TM) receptor Smoothened (SMO) and other components of the Hedgehog (Hh) signaling pathway contributes to the development of cancers including basal cell carcinoma (BCC) and medulloblastoma (MB). However, SMO-specific antagonists produced mixed results in clinical trials, marked by limited efficacy and high rate of acquired resistance in tumors. Here we discovered that Nilotinib, an approved inhibitor of several kinases, possesses an anti-Hh activity, at clinically achievable concentrations, due to direct binding to SMO and inhibition of SMO signaling. Nilotinib was more efficacious than the SMO-specific antagonist Vismodegib in inhibiting growth of two Hh-dependent MB cell lines. It also reduced tumor growth in subcutaneous MB mouse xenograft model. These results indicate that in addition to its known activity against several tyrosine-kinase-mediated proliferative pathways, Nilotinib is a direct inhibitor of the Hh pathway. The newly discovered extension of Nilotinib's target profile holds promise for the treatment of Hh-dependent cancers

A novel transport mechanism for MOMP in Chlamydophila pneumoniae and its putative role in immune-therapy

Major outer membrane proteins (MOMPs) of Gram negative bacteria are one of the most intensively studied membrane proteins. MOMPs are essential for maintaining the structural integrity of bacterial outer membranes and in adaptation of parasites to their hosts. There is evidence to suggest a role for purified MOMP from Chlamydophila pneumoniae and corresponding MOMP-derived peptides in immune-modulation, leading to a reduced atherosclerotic phenotype in apoE−/− mice via a characteristic dampening of MHC class II activity. The work reported herein tests this hypothesis by employing a combination of homology modelling and docking to examine the detailed molecular interactions that may be responsible. A three-dimensional homology model of the C. pneumoniae MOMP was constructed based on the 14 transmembrane β-barrel crystal structure of the fatty acid transporter from Escherichia coli, which provides a plausible transport mechanism for MOMP. Ligand docking experiments were used to provide details of the possible molecular interactions driving the binding of MOMP-derived peptides to MHC class II alleles known to be strongly associated with inflammation. The docking experiments were corroborated by predictions from conventional immuno-informatic algorithms. This work supports further the use of MOMP in C. pneumoniae as a possible vaccine target and the role of MOMP-derived peptides as vaccine candidates for immune-therapy in chronic inflammation that can result in cardiovascular events

Scoring functions for protein docking and drug design

textPredicting the structure of complexes formed by two interacting proteins is an important problem in computation structural biology. Proteins perform many of their functions by binding to other proteins. The structure of protein-protein complexes provides atomic details about protein function and biochemical pathways, and can help in designing drugs that inhibit binding. Docking computationally models the structure of protein-protein complexes, given three-dimensional structures of the individual chains. Protein docking methods have two phases. In the first phase, a comprehensive, coarse search is performed for optimally docked models. In the second refinement and reranking phase, the models from the first phase are refined and reranked, with the expectation of extracting a small set of accurate models from the pool of thousands of models obtained from the first phase. In this thesis, new algorithms are developed for the refinement and reranking phase of docking. New scoring functions, or potentials, that rank models are developed. These potentials are learnt using large-scale machine learning methods based on mathematical programming. The procedure for learning these potentials involves examining hundreds of thousands of correct and incorrect models. In this thesis, hierarchical constraints were introduced into the learning algorithm. First, an atomic potential was developed using this learning procedure. A refinement procedure involving side-chain remodeling and conjugate gradient-based minimization was introduced. The refinement procedure combined with the atomic potential was shown to improve docking accuracy significantly. Second, a hydrogen bond potential, was developed. Molecular dynamics-based sampling combined with the hydrogen bond potential improved docking predictions. Third, mathematical programming compared favorably to SVMs and neural networks in terms of accuracy, training and test time for the task of designing potentials to rank docking models. The methods described in this thesis are implemented in the docking package DOCK/PIERR. DOCK/PIERR was shown to be among the best automated docking methods in community wide assessments. Finally, DOCK/PIERR was extended to predict membrane protein complexes. A membrane-based score was added to the reranking phase, and shown to improve the accuracy of docking. This docking algorithm for membrane proteins was used to study the dimers of amyloid precursor protein, implicated in Alzheimer's disease.R. DOCK/PIERR was shown to be among the best automated docking methods in community wide assessments. Finally, DOCK/PIERR was extended to predict membrane protein complexes. A membrane-based score was added to the reranking phase, and shown to improve the accuracy of docking. This docking algorithm for membrane proteins was used to study the dimers of amyloid precursor protein, implicated in Alzheimer’s disease.Computer Science

Structural characterization and docking studies of acetylcholine binding proteins.

Neuronal Nicotinic Receptors (NNRs) are ligand gated ion channels located both pre- and postsynaptically in the peripheral and central nervous systems. NNRs are important pharmaceutical targets for schizophrenia, pain, epilepsy, tobacco dependence, Tourette’s syndrome, Alzheimer’s disease, Parkinson’s disease, myasthenia gravis, and depression.1, 2 Rational drug design for NNRs has been hampered by the lack of crystallographic information about this important target. Currently, there exist two atomic level structures representing NNR subtypes. The first a cryoelectron micrograph of a muscle NNR3 at 4 Å resolution provided initial structural information about the complete receptor. A more recent crystal structure of the extracellular domain of the mouse nicotinic acetylcholine receptor (NNR) a1 subunit bound to a-bungarotoxin at 1.94 Å resolution is the first atomic-resolution view of a NNR subunit extracellular domain.2 Other receptor structural data has arisen from acetylcholine binding proteins (AChBPs) 4, 5 isolated from freshwater and marine snails. AChBPs are water-soluble proteins, which are homologues of the extracellular domain of the NNRs. Information collected during this project will be used to aid in the development of homology models for various NNR subtypes, based on a more complete understanding of AChBPs. To this end, available co-crystal structures were analyzed through measurement of distances and angles between residues that make up the ligand binding domain (LBD). Further, to evaluate the utility of various docking/scoring algorithms docking studies were performed on these AChBP co-crystal structures. Nineteen NNR ligands were docked into the AChBPs using Schrodinger’s Glide 5.0 software. A few of the key findings of this research are as follows. First, careful examination of the various geometric parameters shows that large changes occur to the AChBP LBD as a ligand binds. These changes include a 15 residue C-loop closing over the LBD with a concomitant movement of the two subunits that make up the LBD relative to each other to accommodate the ligand. The latter is illustrated by changes in the chi 1 and chi 2 of tyrosine (Y) 55 (from the complementary face) and changes in the chi 1 of tyrosine (Y) 93 in the lobeline AChBP to make room in the LBD for one of the phenyl rings on lobeline. Second, results from the docking studies on all available AChBP-cocrystal structure suggest that the AChBP lobeline structure is the best template for homology modeling based on the following: (1) Glide 5.0 was able to dock most of a diverse set of 19 NNR ligands into this structure, in contrast to more limited success for other AChBP starting points; (2) in cases where the crystal structure had been determined, poses similar to those found for the actual co-crystal structure could be reproduced; (3) the correlation between the Glide score (Gscore) for these expected poses and experimental pKd values was (while still modest) best for this structure (correlation, 0.30); (4) correlation between the best Gsore and the pKd was highest for the lobeline AChBP structure (R2 = 0.38). The lobeline AChBP structure is now under investigation as a template to generate homology models to aid in drug discovery at Targacept

Nicotinic acetylcholine receptors and their interactions with allosteric ligands

Nicotinic acetylcholine receptors (nAChRs) are pentameric ligand gated ion channels (pLGICs) expressed widely throughout the body, including in the peripheral nervous system, central nervous system and at the neuromuscular junction. nAChRs are of therapeutic interest due to their involvement in several pathophysiological conditions. The most widely expressed nAChR subtypes, α7 and α4β2 have attracted a lot of attention and many allosteric ligands have been pharmacologically and chemically characterised for these receptors. However, much remains to be understood about where and how these ligands bind to the receptors and modulate their function. This thesis has focussed on a set of transmembrane binding allosteric modulators for the α7 nAChR and sought to aid understanding of their interactions with their target receptor by building models of nAChRs in physiologically relevant states. A transmembrane error in the only example of a pLGIC structure determined in a native lipid membrane environment, the T. marmorata nAChR, has been corrected through modelling and refinement into previously determined electron cryo-microscopy density maps, in putative closed and open conformations. The refined models offer important reference structures for anyone working in the pLGIC field and here have been used as templates to model the α7 nAChR. A consensus docking protocol has been developed and was utilised in conjunction with the α7 models to predict binding modes for a set of allosteric modulators and provide insight into how they may elicit distinct pharmacology. Based on binding modes of allosteric modulators predicted by the consensus docking protocol, pharmacophores were generated for use in ligand-based virtual screening and allosteric modulators have been uncovered for α7 and α4β2 nAChRs from the existing pharmacopeia. Further to this, novel reactive chemical probes have been developed and synthesised to study the covalent incorporation of allosteric modulators into nAChRs