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

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening

Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening Computer-aided drug design is an essential part of the modern medicinal chemistry, and has led to the acceleration of many projects. The herein described thesis presents examples for its application in the field of lead optimization and lead identification for three metalloproteins. DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent isoprenoid biosynthesis. Structure-activity relationships for 43 DXR inhibitors are established, derived from protein-based docking, ligand-based 3D QSAR and a combination of both approaches as realized by AFMoC. As part of an effort to optimize the properties of the established inhibitor Fosmidomycin, analogues have been synthesized and tested to gain further insights into the primary determinants of structural affinity. Unfortunately, these structures still leave the active Fosmidomycin conformation and detailed reaction mechanism undetermined. This fact, together with the small inhibitor data set provides a major challenge for presently available docking programs and 3D QSAR tools. Using the recently developed protein tailored scoring protocol AFMoC precise prediction of binding affinities for related ligands as well as the capability to estimate the affinities of structurally distinct inhibitors has been achieved. Farnesyltransferase is a zinc-metallo enzyme that catalyzes the posttranslational modification of numerous proteins involved in intracellular signal transduction. The development of farnesyltransferase inhibitors is directed towards the so-called non-thiol inhibitors because of adverse drug effects connected to free thiols. A first step on the way to non-thiol farnesyltransferase inhibitors was the development of an CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its basis bisubstrate analogues were developed as one class of non-thiol farnesyltransferase inhibitors. In further studies two aryl binding and two distinct specificity sites were postulated. Flexible docking of model compounds was applied to investigate the sub-pockets and design highly active non-thiol farnesyltransferase inhibitor. In addition to affinity, special attention was paid towards in vivo activity and species specificity. The second part of this thesis describes a possible strategy for computer-aided lead discovery. Assembling a complex ligand from simple fragments has recently been introduced as an alternative to traditional HTS. While frequently applied experimentally, only a few examples are known for computational fragment-based approaches. Mostly, computational tools are applied to compile the libraries and to finally assess the assembled ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a computational fragment-based screening protocol has been established. Starting with a data set of commercially available chemical compounds, a fragment library has been compiled considering (1) fragment likeness and (2) similarity to known drugs. The library is screened for target specificity, resulting in 112 fragments to target the zinc binding area and 75 fragments targeting the hydrophobic specificity pocket of the enzyme. After analyzing the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking experiments were performed for reference fragment to derive a general applicable crystallization protocol for TLN and subsequently for new protein-fragment complex structures. 3-Methylsaspirin could be determined to bind to TLN. Additional studies addressed a retrospective performance analysis of the applied scoring functions and modification on the screening hit. Curios about the differences of aspirin and 3-methylaspirin, 3-chloroaspirin has been synthesized and affinities could be determined to be 2.42 mM; 1.73 mM und 522 μM respectively. The results of the thesis show, that computer aided drug design approaches could successfully support projects in lead optimization and lead identification. fragments in general, the fragments derived from the screening are docke

MDM2 Case Study: Computational Protocol Utilizing Protein Flexibility Improves Ligand Binding Mode Predictions

Recovery of the P53 tumor suppressor pathway via small molecule inhibitors of onco-protein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs in particular, provide a quantitative ranking of predicted binding geometries based on binding free energy allowing for the screening of large chemical libraries in search of lead compounds for cancer therapeutics. This study found improved binding mode predictions of medicinal compounds to MDM2 using the popular docking programs AutoDock and AutoDock Vina, while adopting a rigid-ligand/flexible-receptor protocol. Crystal structures representing small molecule inhibitors bound to MDM2 were selected and a total of 12 rotatable bonds was supplied to each complex and distributed systematically between the ligand and binding site residues. Docking results were evaluated in terms of the top ranked binding free energy and corresponding RMSD values from the experimentally known binding site. Results show lowest RMSD values coincide with a rigid ligand, while the protein retained the majority of flexibility. This study suggests the future implementation of a rigid-ligand/flexible-receptor protocol may improve accuracy of high throughput screenings of potential cancer drugs targeting the MDM2 protein, while maintaining manageable computational costs

T-cell epitope prediction and immune complex simulation using molecular dynamics: state of the art and persisting challenges

Atomistic Molecular Dynamics provides powerful and flexible tools for the prediction and analysis of molecular and macromolecular systems. Specifically, it provides a means by which we can measure theoretically that which cannot be measured experimentally: the dynamic time-evolution of complex systems comprising atoms and molecules. It is particularly suitable for the simulation and analysis of the otherwise inaccessible details of MHC-peptide interaction and, on a larger scale, the simulation of the immune synapse. Progress has been relatively tentative yet the emergence of truly high-performance computing and the development of coarse-grained simulation now offers us the hope of accurately predicting thermodynamic parameters and of simulating not merely a handful of proteins but larger, longer simulations comprising thousands of protein molecules and the cellular scale structures they form. We exemplify this within the context of immunoinformatics

Large-Scale Analysis of Protein-Ligand Binding Sites using the Binding MOAD Database.

Current structure-based drug design (SBDD) methods require understanding of general tends of protein-ligand interactions. Informative descriptors of ligand-binding sites provide powerful heuristics to improve SBDD methods designed to infer function from protein structure. These descriptors must have a solid statistical foundation for assessing general trends in large sets of protein-ligand complexes. This dissertation focuses on mining the Binding MOAD database of highly curated protein-ligand complexes to determine frequently observed patterns of binding-site composition. An extension to Binding MOAD’s framework is developed to store structural details of binding sites and facilitate large-scale analysis. This thesis uses the framework to address three topics. It first describes a strategy for determining over-representation of amino acids within ligand-binding sites, comparing the trends of residue propensity for binding sites of biologically relevant ligands to those of spurious molecules with no known function. To determine the significance of these trends and to provide guidelines for residue-propensity studies, the effect of the data set size on the variation in propensity values is evaluated. Next, binding-site residue propensities are applied to improve the performance of a geometry-based, binding-site prediction algorithm. Propensity-based scores are found to perform comparably to the native score in successfully ranking correct predictions. For large proteins, propensity-based and consensus scores improve the scoring success. Finally, current protein-ligand scoring functions are evaluated using a new criterion: the ability to discern biologically relevant ligands from “opportunistic binders,” molecules present in crystal structures due to their high concentrations in the crystallization medium. Four different scoring functions are evaluated against a diverse benchmark set. All are found to perform well for ranking biologically relevant sites over spurious ones, and all performed best when penalties for torsional strain of ligands were included. The final chapter describes a structural alignment method, termed HwRMSD, which can align proteins of very low sequence homology based on their structural similarity using a weighted structure superposition. The overall aims of the dissertation are to collect high-quality binding-site composition data within the largest available set of protein-ligand complexes and to evaluate the appropriate applications of this data to emerging methods for computational proteomics.Ph.D.BioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91400/1/nickolay_1.pd

Computational studies on protein-ligand docking

This thesis describes the development and refinement of a number of techniques for molecular docking and ligand database screening, as well as the application of these techniques to predict the structures of several protein-ligand complexes and to discover novel ligands of an important receptor protein. Global energy optimisation by Monte-Carlo minimisation in internal co-ordinates was used to predict bound conformations of eight protein-ligand complexes. Experimental X-ray crystallography structures became available after the predictions were made. Comparison with the X-ray structures showed that the docking procedure placed 30 to 70% of the ligand molecule correctly within 1.5A from the native structure. The discrimination potential for identification of high-affinity ligands was derived and optimised using a large set of available protein-ligand complex structures. A fast boundary-element solvation electrostatic calculation algorithm was implemented to evaluate the solvation component of the discrimination potential. An accelerated docking procedure utilising pre-calculated grid potentials was developed and tested. For 23 receptors and 63 ligands extracted from X-ray structures, the docking and discrimination protocol was capable of correct identification of the majority of native receptor-ligand couples. 51 complexes with known structures were predicted. 35 predictions were within 3A from the native structure, giving correct overall positioning of the ligand, and 26 were within 2A, reproducing a detailed picture of the receptor-ligand interaction. Docking and ligand discrimination potential evaluation was applied to screen the database of more than 150000 commercially available compounds for binding to the fibroblast growth factor receptor tyrosine kinase, the protein implicated in several pathological cell growth aberrations. As expected, a number of compounds selected by the screening protocol turned out to be known inhibitors of the tyrosine kinases. 49 putative novel ligands identified by the screening protocol were experimentally tested and five compounds have shown inhibition of phosphorylation activity of the kinase. These compounds can be used as leads for further drug development

Development and Improvement of Tools and Algorithms for the Problem of Atom Type Perception and for the Assessment of Protein-Ligand-Complex Geometries

In context of the present work, a scoring function for protein-ligand complexes has been developed, not aimed at affinity prediction, but rather a good recognition rate of near native geometries. The developed program DSX makes use of the same formalism as the knowledge-based scoring function DrugScore, hence using the knowledge from crystallographic databases and atom-type specific distance-dependent distribution functions. It is based on newly defined atom-types. Additionally, the program is augmented by two novel potentials which evaluate the torsion angles and (de-)solvation effects. Validation of DSX is based on a literature-known, comprehensive data-set that allows for comparison with other popular scoring functions. DSX is intended for the recognition of near-native binding modes. In this important task, DSX outperforms the competitors, but is also among the best scoring functions regarding the ranking of different compounds. Another essential step in the development of DSX was the automatical assignment of the new atom types. A powerful programming framework was implemented to fulfill this task. Validation was done on a literature-known data-set and showed superior efficiency and quality compared to similar programs where this data was available. The front-end fconv was developed to share this functionality with the scientific community. Multiple features useful in computational drug-design workflows are also included and fconv was made freely available as Open Source Project. Based on the developed potentials for DSX, a number of further applications was created and impemented: The program HotspotsX calculates favorable interaction fields in protein binding pockets that can be used as a starting point for pharmacophoric models and that indicate possible directions for the optimization of lead structures. The program DSFP calculates scores based on fingerprints for given binding geometries. These fingerprints are compared with reference fingerprints that are derived from DSX interactions in known crystal structures of the particular target. Finally, the program DSX_wat was developed to predict stable water networks within a binding pocket. DSX interaction fields are used to calculate the putative water positions