Specificity rendering ‘hot-spots’ for aurora kinase inhibitor design: the role of non-covalent interactions and conformational transitions

The present study examines the conformational transitions occurring among the major structural motifs of Aurora kinase (AK) concomitant with the DFG-flip and deciphers the role of non-covalent interactions in rendering specificity. Multiple sequence alignment, docking and structural analysis of a repertoire of 56 crystal structures of AK from Protein Data Bank (PDB) has been carried out. The crystal structures were systematically categorized based on the conformational disposition of the DFG-loop [in (DI) 42, out (DO) 5 and out-up (DOU) 9], G-loop [extended (GE) 53 and folded (GF) 3] and αC-helix [in (CI) 42 and out (CO) 14]. The overlapping subsets on categorization show the inter-dependency among structural motifs. Therefore, the four distinct possibilities a) 2W1C (DI, CI, GE) b) 3E5A (DI, CI, GF) c) 3DJ6 (DI, CO, GF) d) 3UNZ (DOU, CO, GF) along with their co-crystals and apo-forms were subjected to molecular dynamics simulations of 40 ns each to evaluate the variations of individual residues and their impact on forming interactions. The non-covalent interactions formed by the 157 AK co-crystals with different regions of the binding site were initially studied with the docked complexes and structure interaction fingerprints. The frequency of the most prominent interactions was gauged in the AK inhibitors from PDB and the four representative conformations during 40 ns. Based on this study, seven major non-covalent interactions and their complementary sites in AK capable of rendering specificity have been prioritized for the design of different classes of inhibitors

Elicitation of Protein-Protein Interactions from Biomedical Literature Using Association Rule Discovery

Extracting information from a stack of data is a tedious task and the scenario is no different in proteomics. Volumes of research papers are published about study of various proteins in several species, their interactions with other proteins and identification of protein(s) as possible biomarker in causing diseases. It is a challenging task for biologists to keep track of these developments manually by reading through the literatures. Several tools have been developed by computer linguists to assist identification, extraction and hypotheses generation of proteins and protein-protein interactions from biomedical publications and protein databases. However, they are confronted with the challenges of term variation, term ambiguity, access only to abstracts and inconsistencies in time-consuming manual curation of protein and protein-protein interaction repositories. This work attempts to attenuate the challenges by extracting protein-protein interactions in humans and elicit possible interactions using associative rule mining on full text, abstracts and captions from figures available from publicly available biomedical literature databases. Two such databases are used in our study: Directory of Open Access Journals (DOAJ) and PubMed Central (PMC). A corpus is built using articles based on search terms. A dataset of more than 38,000 protein-protein interactions from the Human Protein Reference Database (HPRD) is cross-referenced to validate discovered interactive pairs. A set of an optimal size of possible binary protein-protein interactions is generated to be made available for clinician or biological validation. A significant change in the number of new associations was found by altering the thresholds for support and confidence metrics. This study narrows down the limitations for biologists in keeping pace with discovery of protein-protein interactions via manually reading the literature and their needs to validate each and every possible interaction

Biophysical Properties of Small Molecules Binding to Proteins.

Binding MOAD (Mother of All Databases) is the largest collection of high-quality, protein–ligand complexes. Binding MOAD contains 13138 protein–ligand complexes comprised of 4078 unique protein families and 6210 unique ligands. We have compiled binding data for 4146 of the protein–ligand complexes. The creation of this database and three studies mining the database for biophysical properties of protein-small molecule binding are discussed in this thesis. An additional study is included in the appendix which investigates flexibility upon small molecule binding to MDM2. First, we present the development of GoCav, which allows us to mine properties of the whole database. We have determined that most complexes have well buried binding sites (70-85%), which fits the idea that a large degree of contact between the ligand and protein is significant in molecular recognition. Secondly, we investigate the differences in biophysical properties of binding to enzymes versus non-enzymes. Differences in the sizes of weak versus tight ligands indicate that the addition of complementary functional groups may improve the affinity of an enzyme inhibitor, but the process may not be as fruitful for ligands of non-enzymes. Non-enzymes were found to have greater ligand efficiencies than enzymes, which supports the feasibility of non-enzymes as druggable targets. Most importantly, the differences in ligand efficiencies appear to come from the pockets which yield different amino acid compositions, despite similar overall distributions of amino acids. We then investigate the biophysical properties of the most efficient protein-ligand complexes. All highly efficient small molecules contain one or more charge and are found in binding sites with at least one charge, challenging previous thoughts that hydrophobic properties of ligands lead to better binding. Lastly it is known that affinity for complexes rarely exceeds -15 kcal/mol, and we suggest that ligands do not exceed this values because there is no evolutionary pressure to drive tighter binding.PhDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75969/1/dicksmit_1.pd

Protein ligand interaction database (PLID)

A comprehensive database named, protein ligand interaction database (PLID), is created with 6295 ligands bound to proteins extracted from the protein data bank (PDB). This is by far the most comprehensive database of physico-chemical properties, quantum mechanical descriptors and the residues present in the active site of proteins. It is a publicly available web-based database (via the Internet) at http://203.199.182.73/gnsmmg/databases/plid/

A novel free energy evaluation scheme based on energetic decomposition of molecular dynamics simulations

能夠準確預測小分子與生物分子的結合在電腦輔助藥物設計上扮演了相當重要的角色，因為這些方法能加速先導化合物的產生與優化。這些方法在現在大約可分為兩大類，包含了分子嵌合與評分與自由能之方法。自由能方法需要構形取樣，反之，嵌合與評分的方式往往都只聚焦在單一的構型上面。也因此自由能方法需要較多的電腦計算時間與資源。們認為在預測蛋白質與配位體的結合能力仍然需要考慮到動態的影響，因為在結合的過程當中蛋白質與配位都是可運動的狀態。因此我們使用了linear interaction energy方法做為起點，因為這個方法是自由能方法中的一種而且只需要兩個分子模擬就能得到結合的能量。這個方法跟其他一樣屬於自由能方法的FEP與MM-PBSA來的更為經濟。我們的研究當中，利用GROMACS來做分子模擬且使用GROMACS的參數。預測結合的自由能我們除了使用最原始的LIE方法的公式外，還加入了配位體與配位體之間的能量項進去試著去得到更好的預測結果。另外，我們也使用了許多的評分的函數來與我們的結果做比較。Accurate methods for predicting the affinity of a small molecule with a protein or other biomolecule play a crucial role in computational drug design because these predictions can speed the lead generation or lead optimization. Nowadays these methods can be categorized as docking and scoring and free energy method. Free energy method, in contrast with docking and scoring, which focus on a single bound conformation use conformation sampling to generate thermodynamic averages. Therefore, free energy methods are required more computer time than docking and scoring approaches.e consider that the dynamic effect of the protein-ligand complex should be still included in evaluating binding affinities because in the binding process, protein and ligand are flexible. Therefore, our study started from the linear interaction energy (LIE) method, which is one free energy method and only required two simulations, complex and ligad only. Besides that, it is also more economical than other free energy methods, such as the FEP and the MM-PBSA method.n our study, we used GROMACS to perform simulations with GROMACS force field parameters. For predicting the free energy of binding, we not only used the original LIE equation, but also we added ligand-ligand interaction into the evaluating scheme trying to obtain a better prediction model. In addition, we used several scoring functions, ChemScore, DrugScore, XScore, etc, to compare with our results.口試委員會審定書 III謝 V文摘要 VIbstract VIIIigure List XIIIable List XVIhapter 1: Introduction 1.1 Computational aid for Drug Discovery 1.2 Theory for Calculating Affinity 2.2.1 Potential Energy and Solvation Energy 4.3 Docking and Scoring 6.3.1 The Scoring function of GOLD (G-Score) 6.3.2 LigScore 7.3.3 PLP 8.3.4 The Scoring Function of FlexX (F-Score) 9.3.5 ChemScore 9.3.6 XScore 10.3.7 Potential of Mean Force (PMF) Score 11.3.8 DrugScore 11.4 Free Energy Method 12.4.1 The Linear Interaction Energy (LIE) Method 13.4.2 MM-PBSA Method 14.4.3 Free Energy Perturbation (FEP) Method 15.4.4 Comparison of Free Energy Methods 16.5 Protein-Ligand Interaction Database (PLID) 17hapter 2: Materials and Methods 19.1 Study Cases 19.1.1 Fk506 Binding Protein (FKBP) 19.1.2 HIV-1 Protease 21.1.3 Trypsin 23.2 Simulation Details 25.2.1 Protein Preparation 25.2.2 Ligand Preparation 26.2.3 Complex System Preparation 26.2.4 Ligand only system Preparation 27.2.5 Simulation Protocol 27.3 Energy Calculation and Energy Decomposition 29.4 Free Energy Calculations 33.5 Scoring Functions 37.5.1 Preparation of G-Score (GOLD) and ChemScore 37.5.2 Preparation of DrugScoreONLINE 38.5.3 Preparation of F-Score (FlexX) 38.5.4 Preparation of LigScore, PLP and PMF 38.5.5 Preparation of Xscore 39hapter 3: Results and Discussions 40.1 Results of FKBP Complexes 40.1.1 Protein RMSD of FKBP 40.1.2 The Free energy of FKBP complexes 45.1.3 Results of Scoring Functions for FKBP Complexes 55.2 Results of HIV-1 Protease Complexes 73.2.1 Protein RMSD of HIV-1 Protease 73.2.2 The Free energy of HIV-1 Protease Complexes 78.2.3 Results of Scoring Functions for HIV-1 Protease Complexes 85.3 Results of Trypsin Complexes 104.3.1 Protein RMSD of Trypsin Protease 104.3.2 The Free Energy of Trypsin Complexes 109.3.3 Results of Scoring Functions for Trypsin Complexes 116hapter 4: Conclusions 131eferences 13