Structure-Based beta-Secretase (BACE1) Inhibitors

Alois Alzheimer identified first abnormal deformation in the brain of diseased people with mental disorder. The disorder is clinically characterized by a progression from episodic memory problems to a slow global decline of cognitive function, ending with the final stage when patients become bedridden and death occurs on average 9 years after diagnosis. The current standard of care does not cover the approved and effective treatment of both cognitive and non-cognitive symptoms. Tremendous effort was put in investigation of the disease development. The uncovered molecular mechanism shed light on aspartic proteases, the smallest protease class with about 15 members in the human genome. Here we summarise the most important structure-based developments on one of the most popular aspartic protease target BACE1

Multidimensional computational modeling of Potent BACE1 (β-Secretase) inhibitors towards Alzheimer’s disease treatment.

Doctoral Degree. University of KwaZulu-Natal, Durban.Alzheimer’s disease (AD), as a progressive multifactorial neurodegenerative abnormality of the brain, is often connected with loss or death of neurons as its primary pathogenesis. Another kind of dementia is associated with memory loss and unstable and irrational behaviors, especially among the elderly above 60 years. In South Africa, there are over four million people above the age of 60 years, with an approximation of one hundred and eighty-seven thousand living with dementia. The two distinguishing features (hallmarks) of AD are neurofibrillary tangles and β-amyloid plaques. The β-amyloid plaques result when amyloid precursor protein (APP) is cleaved by β-amyloid precursor protein cleaving enzyme1 (BACE1), otherwise known as β-secretase. Since 1999 the first BACE1 was discovered, it has become a major interest in attempting to develop drugs for the inhibition or reduction of the β-amyloid aggregates in the brain. Reducing or inhibiting the accumulation of β-amyloid has long been the target in the design of drugs for AD treatment. Having a good knowledge of the characteristic properties (BACE1) would assist in the design of potent selective BACE1 inhibitors with fewer or no side effects. Hitherto, only five drugs have been approved by the Food and Drug Administration (FDA) for the remediation of Alzheimer’s disease, and none of the approved drugs targets BACE1. In about twenty years of its discovery, several past and ongoing studies have focused on BACE1 therapeutic roles as a target in managing AD. Several attempts have previously beenmade in designing some small drugmolecules capable of good BACE1 inhibition. Some of the initially discovered BACE1 inhibitors include verubecestat, lanabecestat, atabecestat, and umibecestat (CNP-520). Although these inhibitors significantly lowered β-amyloid plaques in persons having neurological Alzheimer’s at its clinical trials (phase 3), they were suddenly terminated for some health concerns. The termination contributed to the reasons why there are insufficient BACE-targeted drugs for AD treatment. Lately, a novel potent, orally effective, and highly selective AM-6494 BACE1 inhibitor was discovered. This novel BACE1 inhibitor exhibited no fur coloration and common skin alteration, as observed with some initial BACE1 inhibitors. AM-6494 with an IC50 value of 0.4 nM in vivo is presently selected and at the preclinical phase trials. Before this study, the inhibition properties of this novel BACE1 inhibitor at the atomistic and molecular level of BACE1 inhibition remained very unclear. The first manuscript (chapter two) is a literature review on Alzheimer's disease and β-secretase inhibition: An update focusing on computer-aided inhibitor design. We provide an introductory background of the subject with a brief discussion on Alzheimer’s pathology. The review features computational methods involved in designing BACE1 inhibitors including the discontinued drugs. Using the topical keywords BACE1, inhibitor design, and computational/theoretical study in theWeb of Science and Scopus database, we retrieved over 49 relevant articles. The search years are from 2010 and 2020, with analysis conducted from May 2020 to March 2021. Our second manuscript (chapter three) reviewed BACE1 exosite-binding antibody and allosteric inhibition as an alternative therapeutic development. We studied BACE1 biological functions, the pathogenesis of the associated diseases, and the enzymatic properties of the APP site cleavage. We suggested an extensive application of advanced computational simulations in the investigation of anti-BACE1 body and allosteric exosites. It is believed that this investigation will further help in reducing the associated challenges with designing BACE1 inhibitors while exploring the opportunities in the design of allosteric antibodies. The review also revealed that some molecules exhibited dual binding sites at the active site and allosteric site. As a result, we recommend an extensive investigation of the binding free energy beyond molecular docking (such as advanced molecular dynamic simulations) as this promises to reveal the actual binding site for the compounds under investigation. Chapter four contains the detailed computational science techniques which cover the application of the vitally essential methods of molecular mechanics (MM), quantum mechanics (QM), hybrid of QM/MM, basis sets, and other computational instruments employed in this study. In the third manuscript (chapter five), we carried out computational simulations of AM-6494 and CNP- 520.CNP520 was one of the earliest BACE1 drugs that were terminated, chosen in this study forcomparative reasons. This simulation was to elucidate and understand the binding affinities of these two inhibitors at the atomistic level. We explored the quantum mechanics (QM) density functional theory (DFT) and hybrid QM/MM of Our Own N-layered Integrated molecular Orbital and Molecular Mechanics (ONIOM) in these simulations. These computational approaches helped in predicting the electronic properties of AM-6494 and CNP-520, including their binding energies when in complex with BACE1. Considering the debates on which protonated forms of Asp 32 and Asp 288 gives a more favorable binding energy, we analysed the two forms which involved the protonation and un-protonation of Asp 32 and Asp 228.The ONIOM protonated model calculation gave binding free energy of -33.463 kcal/mol (CNP-520)and 62.849 kcal/mol (AM-6494) while the binding free energy of -59.758 kcal/mol was observed for the unprotonated AM-6494 model. These results show the protonated model as a more favourable binding free energy when compared with the un-protonation AM-6494 model. Further thermochemistry processes coupled with molecular interaction plots indicate that AM-6494 has better inhibition properties thanCNP-520.However, it was observed that the protonation and the un-protonation of Asp 32 and Asp 228 modelscould adequately illustrate the interatomic binding of the ligands-BACE1 complex. To further explicate the binding mechanism, conformational and structural dynamism of AM-6494 relative to CNP-520 in complex with BACE1, we carried out advanced computational simulations in the fourth manuscript (chapter six). The extensive application of accelerated molecular dynamics simulations, as well as principal component analysis, were involved. From the results, AM-6494 further exhibited higher binding affinity with van der Waals as the predominant contributing energy relative to CNP-520. Furthermore, conformational analysis of the β-hairpin (flap) within the BACE1 active site exhibited efficient closed flap conformations in complex withAM-6494 relative to CNP-520, whichmostly alternated between closed and semi-open conformational dynamics. These observations further elucidate that AM- 6494 shows higher inhibitory potential towards BACE1. The catalytic dyad (Asp32/228), Tyr14, Leu30, Tyr71, and Gly230 constitute essential residues in both AM-6494 potencies CNP-520 at the BACE1 binding interface. The results from these extensive computational simulations and analysis undoubtedly elucidate AM-6494 higher inhibition potentials that will further help develop new molecules with improved potency and selectivity for BACE1. Besides, grasping the comprehensive molecular mechanisms of the selected inhibitors would also help in fundamental pharmacophore investigation when designing BACE1 inhibitors. Finally, the implementation of computational techniques in the designing of BACE1 inhibitors has been quite interesting. Nevertheless, the designing of potent BACE1 inhibitors through the computational application of the QM method such as the density functional theory (DFT), MM, and a hybrid QM/MM method should be extensively explored. We highly recommend that experimentalists should always collaborate with computational chemists to save time and other resources. ISIZULU ABSTRACT Iqoqa Isifo se-Alzheimer (AD), njengoba siqhubeka siyinhlanganisela yezimbangela ze- neurodegenerative engajwayelekile ebuchosheni, isikhathi esiningi kuxhumana nokulahleka noma ukufa kwama-neurons njengongqaphambili we-pathogenesis. Kungolunye uhlobo lwedementia oluhambisana nokulahlekelwa ukukhumbula kanyenokuxenga kanye nokuphanjanelwa ingqondo, ikakhulukazi kubantu abadala esebeneminyaka engaphezulu kuka-60. ENingizimu Afrikha, kunabantu abangaphezulu kwezigidi ezine abangephezulu kweminyaka ewu-60, ngokuhlawumbisela nje abayinkulungwane namashumi ayisishayangolombili nesikhombisa baphila nedemetia. Zimbili izimpawu ezihlukanisekayo ze-AD ziba-ama-neurofibrillary tangles kanye ne-B-amyloid plaques. I-B-amyloid plaques ingumphumela ngesikhathi i-amyloid eyiprotheni egijimayo iqhwakele oketshezini i-enzyme1 (BACEI), ngale kwalokho yaziwanjenge B-secretase. Kusukela ngo 1999 i-BAC1 yatholakala, isiphenduke ungqaphambili emizamweni yokwakha isidakamizwa sokwehlisa i-B-amyloid ngokwezinga lengqondo. Ngokunciphisa ukwanda kwe-B-amyloid isiphenduke okuqondiwe mayelana nokuqopha isidakamizwa ukuze kwelashwe i-AD. Ukuba nolwazi oluhle oluthinta isici sezakhi ze-BACE1 kuzosiza ekubazeni amandla akhethiwe i-BACE1 ukuvimbela imiphumela engaqondiwe. Kuze kube manje mihlanu imithi esiphasisiwe ngabezokuphatha ukudla kanye nezidakamizwa (FDA) ukwelapha isifo se-Alzheimer kanye nokuthi azikho kulezi eziphasisiwe izidakamizwa ebhekana ngqo ne-BACE1. Emva kokuba selitholakele lapho nje eminyakeni engu 20, sekunezinye esikhathini esedlule kanye nezifundo ezisaqhubeka zigxile ngokubheka kakhulu iqhaza lokwelapha i-BAC1 njengokuqondiswe ekungameleni u-AD. Imizamo eminingana yenziwa esikhathini esedlule ukuqopha uketshezi lwezidakamizwa olukwazi ukuvimba kahle i-BACE1. i-B-amyloid plaques kumuntu one-neurological ye-Alzheimer’s kumzamo (isigaba 3), kwabuye kwanqanyulwa ngenxa yokukhathazeka ngokwezempilo. Ukunqanyulwa kwanikela kuzizathu zokusilele kwezidakamizwa okuqondene nokulashwa kwe-AD. Kamuva, i-novel enamandla, ngisho ngawo umlomo kanye neyakhethwa ngezinga eliphezulu i-AM-6494 BACE1 evikelayo yatholakala. Le noveli i-BACE1 evimbayo yabukisa hhayi ukushintsha kombala woboya kanye nokushintsha kwesikhumba okujwayelekile, njengoba kubukwa nezivimbo zokuqala ze-BACE1. I-AM-6494 ne-IC50 enobumqoka buka 0.4nM kuyo i-vivo ekhethwa ngokwamanje kanye nesigaba sembulambethe yemizamo. Ngaphambi kwalesi sifundo, izakhi zesivimbela zale noveli i-BACE1zivimba ngokwe-atomistic kanye neqophelo le-molecular ye-B ACE1evimbayo kusale nje kungacacile. Umqulu wokuqala (isahluko sesibili) ukubuyekezwa kwesifo se-Alzheimer’s kanye no-B-secretase ovimbayo: ezikhumbuzayo ezigxile ngokusizwa yikhompuyutha eyisivimbo ngokwakhiwa. Sethula isendlalelo sesifundo kanye nengxoxo kafushane nezimbangela nemiphumela ye-Alzheimer. Ukubukezwa kwezimpawu zendlela zobukhompuyutha kufaka ekuqopheni isivimbo se-BACE1 nokuqhutshekiswa kwesidakamizwa. Ngokusebenzisa ofeleba begama BACE1, kusho ukwakha isivimbo, kanye nesifundo senjulalwazi kulwembu lobuchwepheshe kanye ne-Scopus sesizindalwazi. Sathola amaphepha acwaningiwe anokuhlobana angaphezulu kuka 49. Unyaka wokuthungatha usukela ku2010 kuya ku2020, nohlaziyo lwenziwa kusukela kuNhlaba 2020 kuya kuNdasa 2021. Umqulu wethu wesibili (isahluko sesithathu) sabuyekeza i-BACE ehlanganisa i-exosite antibody kanye ne-allosteric yokuthuthukisa ukwelashwa. Sakufunda ukusebenza kwesayensi yokuphila ye-BACE1, i-pathogenesis ehambisana nezifo kanye nezakhi zama-enzymatic esizinda sokuhlukana se-APP. Saphakamisa ukufakwa okunzulu nokucokeme kokulinganisa ngobuchwepheshe bekhompuyutha ekuphenyeni ama-anti-BACE1 omzimba kanye ne-allosteric ye-exosites. Kuyakholeka ukuthi uphenyo luzoqhubeka nokusiza ekwehliseni izinselelo ezihambisana nokwakha isithiyo se-BACE1 ngesikhathi kuhlolwa amathuba okwakheka kwe-allosteric yama-antibodies. Ubuyekezo luphinde lwaveza uketshezi olubukisa isizinda sokuhlanganisa kabili kusizinda esikhuthele kanye nesizinda se-allosteric. Umphumela, kube ukwenza isincomo mayelana nocwaningo olunzulu oluzohlanganisa umfutho okhululekile odlulele ku-molecular docking (njengesicokeme se-molecular yokuhlukahlukana kokulinganisa) njengoba lokhu kuthembisa ukuveza isiza esibopha ngempela ama-compounds angaphansi Isahluko sesine siqukethe imininingwane ngamaqhinga e-computational sayensi efaka isicelo esibalulekile sezindlela ezibalulekile ze-molecular mechanics (MM), i-quantum mechanics (QM), i-hybrid ye-QM/MM, ngesisekelo samasethi kanye namanye amathuluzi ekhompuyutha akhethwa kulesi sifundo. Kumqulu wesithathu (isahluko sesihlanu), siqhube isilinganiso se-computational ye-AM-6494 kanye CNP-520.I-CNP-520 kwakungenye yezidakamizwa zokuqala zeBACE1 ezashatshalaliswa, zakhethwa kulesisifundo ngezizathu zokuqhathanisa. Ukulinganisa kwakuchaza kanye nokuqonda ukusondelana ngokuhlanganiswa kwezithiyo ezimbili kusigaba se-atomistic. Kwahlolwa i-quatum mechanics (QM) yesisindo yokusebenza kwenjulalwazi (DFT) kanye ne-hybrid QM/MM yokwethu okuno-N oluwugqinsi lwe-molecular Orbital kanye ne-Molecular Mechanics (ONIOM) kulolu linganiso. Lezi zindlelakwenza ze-computational zasiza ekuqageleni kwezakhiwo zama-electronic e-AM-6494 kanye CNP-520, kungena namandla okuhlanganisa ngesikhathi kuba lukhuni ne-BACE1. Ngokucabanga izinkulumo mpikiswano mayelana nokuma kwe-protonated ye-Asp32 kanye Asp288 kunika ukuvumelana namandla okuhlanganisa, nokuhlaziya izimo ezimbili ezifaka i-protonation kanye ne-unprotonation ye-Asp32 kanye Asp228. I-ONIOM ye-protonated yomfanekiso wokubala wanikeza amandla akhululekile okuhlanganisa -33,463kcal/mol (NP-520) kanye 62.849 kcal /mol kwavela i-unprotonate ye-AM6494. Imiphumela itshengisa ukuthi i-protonated iyisifanekiso njengoba kuyisona esivumela ukuhlanganiswa ngokukhululeka ngesikhathi lapho bekuqhathanisa ne-unprotonation yomfanekiso u-AM-649. Kuqhutshelwa phambili nemisebenzi ye-thermochemistry kuhlangana nokudlelana ne-molecular plots kutshengisa ukuthi i-AM-649 inezakhiwo ezinhle zokuvimba kune CNP-520. Yize kunjalo kwabonakala ukuthi i-protonation kanye ne-unprotonation ye-Asp32 kanye neyomfanekiso owu- Asp228 bekungatshengisa ngokwenele ukuhlanganisa ngokwe-interatomic yama-ligands EBACE1 ebilukhuni. be-BACE1 ngokwedlulele isilinganiso se-computational. Ukwenza ngokujulile kuphangiswa isilinganiso se-molecular ngokuhlukana, kwakakwa nohlaziyo olusemqoka lwezingxenyana. Imiphumela ye-AM-6494 yaqhubeka yatshengisa ukusondelana kokuhlanganiswayo no-van der Waals njengohamba phambili ekunikeleni amandla ahlobene ne-CNP-520. Ukuvuma kohlaziyo lwe-B-hairpin ngaphakathi ku-BACE1 kutshengiswa esizeni esiphilayo esivala ngendlela umnyakazo wokuvuma kobunkimbinkimbi be-AM-6494 ehlobene neCNP-520, ngokuvamile eshitshashintshayo phakathi kwevalekile kanye nezishaya sakuvuleka kokuvuma okunhlobonhlobo. Lokhu kuhlolwa kuqhubeke kwachazwa ngokuthi i-AM-6494 itshengisa ukuvimba okukhulu nokunethemba mayelana ne-BACE1. Isikhuthazizinguquko se-dyad (Asp32/228), Tyr14, Leu 30, Tyr 71, kanye ne-Gly230 kwakha izinsalela ezibalulekile nxazombili kuAM-6494ne-potencies yeCNP-520 kuBACE1 nesixhumanisi esihlanganisayo. Imiphumela ivela kulama-computational anzulu ayisilinganiso kanye nohlaziyo olucacisa ngokungangabazi i-AM-6494 enesivimbelo esiphakeme esingakwazi ukuqhubeka nokusiza intuthuko yama-molecules amasha anamandla athuthukile kanye nakhethelwe i-BACE1. Ngaphandle kwalokhu, ukucosha izinkambiso ezibanzi ze-moleculor mayelana nezivimbo ezikhethiwe kuzosiza mayelana nophenyo olubalulekile lwe- pharmacophore ngesikhathi kuqoshwa izivimbo se-BACE1. Ekugcineni, ukwenziwa kwe-computational ngokwamacebo ekubazeni izivimbo ze-BACE1 kube into ehlaba umxhwele. Nokho ukubaza izivimbo ezinamandla ze-BACE1 ngokusebenzisa i-computational yendlela ye-QM njengenjulalwazi yesisindo esisebenzayo (DFT), MM, kanye nendlela ye-hybrid QM/MM kufanele iphenywe kanzulu. Sincoma kakhulu ukuthi ongoti abenza izibonisi kufanele njalo bahlangane nama-computational chemists ukonga isikhathi kanye nezinye izinsiza

une approche computationnelle pour le développement d'agents thérapeutiques

Over the last few decades, computer-aided drug design (CADD) has established as a strong tool for developing novel therapeutic compounds. In computer-aided drug design, two methodologies are typically used: structure-based drug design and ligand-based drug design. Molecular docking combined with molecular dynamics is one of the most important tools of drug discovery and drug design, which it used to examine the type of binding between the ligand and its protein enzyme. Global reactivity has important properties, which enable chemists to understand the chemical reactivity and kinetic stability of compounds. The recent new contagion coronavirus 2019 (COVID-19) disease is a new generation of severe acute respiratory syndrome coronavirus-2 SARS-CoV-2 which infected millions confirmed cases and hundreds of thousands death cases around the world so far. In this study, molecular docking and reactivity were applied for eighteen drugs, which are similar in structure to chloroquine and hydroxychloroquine, the potential inhibitors to angiotensinconverting enzyme (ACE2). Those drugs were selected from DrugBank. The reactivity, molecular docking and molecular dynamics were performed for two receptors ACE2 and Crystal structure SARS-CoV-2 spike receptor-binding with ACE2 complex receptor in two active sites to find a ligand, which may inhibit COVID-19. The results obtained from this study showed that Ramipril, Delapril and Lisinopril could bind with ACE2 receptor andCrystal structure SARS-CoV-2 spike receptor-binding with ACE2 complex better than chloroquine and hydroxychloroquine. The tyrosine kinase inhibitors gefitinib and erlotinib activated mutations of the epidermal growth factor receptor (EGFR) in non-small cell lung cancer. Quinazolines and pyridopyrimidines are antibacterial, antifungal, and cancer-fighting compounds. The goal of this study is to look into the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of a series of quinazolines and pyrido[3,4-d]pyrimidines as irreversible inhibitors of wild-type (WT) and L858R and T790M EGFR kinase domain mutants, as well as their reactivity, molecular docking, and molecular dynamics simulation. The 27 heterocycles under examination show a wide range of affinities for WT, L858R, and T790M, as well as strong chemical reactivity and kinetic stability. The compounds were found to have high ADMET characteristics, and pyrido[3,4-d]pyrimidines had good reactivity and affinity towards WT, L858R, and T790M mutations. New, powerful, irreversible tyrosine kinase inhibitors have been discovered

In Silico Strategies for Prospective Drug Repositionings

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions

Investigating the inhibition mechanism of L,D- transpeptidase 5 from Mycobacterium tuberculosis computational methods.

Doctoral Degree, University of KwaZulu-Natal, Durban.Abstract available in pdf

Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature

Enumeration, conformation sampling and population of libraries of peptide macrocycles for the search of chemotherapeutic cardioprotection agents

Peptides are uniquely endowed with features that allow them to perturb previously difficult to drug biomolecular targets. Peptide macrocycles in particular have seen a flurry of recent interest due to their enhanced bioavailability, tunability and specificity. Although these properties make them attractive hit-candidates in early stage drug discovery, knowing which peptides to pursue is non‐trivial due to the magnitude of the peptide sequence space. Computational screening approaches show promise in their ability to address the size of this search space but suffer from their inability to accurately interrogate the conformational landscape of peptide macrocycles. We developed an in‐silico compound enumerator that was tasked with populating a conformationally laden peptide virtual library. This library was then used in the search for cardio‐protective agents (that may be administered, reducing tissue damage during reperfusion after ischemia (heart attacks)). Our enumerator successfully generated a library of 15.2 billion compounds, requiring the use of compression algorithms, conformational sampling protocols and management of aggregated compute resources in the context of a local cluster. In the absence of experimental biophysical data, we performed biased sampling during alchemical molecular dynamics simulations in order to observe cyclophilin‐D perturbation by cyclosporine A and its mitochondrial targeted analogue. Reliable intermediate state averaging through a WHAM analysis of the biased dynamic pulling simulations confirmed that the cardio‐protective activity of cyclosporine A was due to its mitochondrial targeting. Paralleltempered solution molecular dynamics in combination with efficient clustering isolated the essential dynamics of a cyclic peptide scaffold. The rapid enumeration of skeletons from these essential dynamics gave rise to a conformation laden virtual library of all the 15.2 Billion unique cyclic peptides (given the limits on peptide sequence imposed). Analysis of this library showed the exact extent of physicochemical properties covered, relative to the bare scaffold precursor. Molecular docking of a subset of the virtual library against cyclophilin‐D showed significant improvements in affinity to the target (relative to cyclosporine A). The conformation laden virtual library, accessed by our methodology, provided derivatives that were able to make many interactions per peptide with the cyclophilin‐D target. Machine learning methods showed promise in the training of Support Vector Machines for synthetic feasibility prediction for this library. The synergy between enumeration and conformational sampling greatly improves the performance of this library during virtual screening, even when only a subset is used

A computational study of the substrate conversion and selective inhibition of aldosterone synthase

When a functional or structural impairment of cardiac output has occurred, the cardiovascular system will attempt to compensate for the reduced blood flow. Unfortunately, many of the resulting processes, such as the renin angiotensin aldosterone system, will progressively weaken the heart, resulting in the condition called heart failure. The renin angiotensin aldosterone regulatory system is currently targeted with medicine for heart failure. Many successes for the prolongation of patient age have been achieved by inhibition of angiotensin II synthesis and action. It has become apparent that this approach is suboptimal. Antagonists of aldosterone have provided better treatment options, however, side-effects are still observed. In the search for an alternative therapeutic application, we have studied a novel treatment involving the selective inhibition of aldosterone biosynthesis. The scope of this study has involved the in silico design and prediction of novel inhibitors, the synthesis of these inhibitors and analogues, and finally the in vitro measurement of their potency. The biosynthesis of aldosterone is performed by two cytochrome p450 enzymes, 11B1 and 11B2, denoted as CYP11B1 and CYP11B2, respectively. From these two family members, only CYP11B2 can perform the final synthesis step that converts 18-hydroxycorticosterone into aldosterone. CYP11B1 performs the synthesis of glucocorticoids that are responsible for metabolic, immunologic and homeostatic functions. Because these glucocorticoid actions should not be inhibited, the newly designed medicine must be CYP11B2 selective. Since CYP11B1 is highly homologous to CYP11B2, we have performed an in silico study that allows us to model the interactions of substrates and inhibitors in both the active sites of CYP11B1 and CYP11B2. Using comparative modelling, we have constructed models for the three dimensional architecture of both proteins. These models have been validated by investigating the torsional properties of the protein backbone and residue side chains, the overall protein packing and the dynamic behaviour of the protein models. Subsequently, the models have been used to evaluate the binding mechanisms and conversion mechanisms for the natural steroidal ligands of CYP11B1 and CYP11B2. A hypothetical binding mode has been proposed for 18-hydroxycorticosterone in CYP11B2, featuring the presence of stabilising hydrogen bonding interactions required for its conversion. Quantum mechanical analyses on the conversion of the steroids involved have shown a favourable conversion for this conformation, thereby supporting our hypothesis. In addition, the quantum mechanical analyses have provided insights on steroid conformations in the active sites during conversion. The suitability of the protein models for inhibitor design has been tested by subjecting the models to a case study with four known inhibitors of CYP11B1 and CYP11B2. Using molecular dynamics and molecular docking, the inhibitor potencies for CYP11B1 and CYP11B2 have been predicted, and their interactions with the proteins have been evaluated. The trends in inhibitor potency found by these computational methods have been confirmed by in vitro inhibition measurements. As a next step, the molecular docking study has been expanded to improve the confidence in the predictive power of the models. Using the protein states evaluated by the molecular dynamics study, the molecular docking results of inhibitor analogues have been investigated and the predictive power of the models has been qualitatively improved. In a final approach, we have performed a ligand-based investigation of the inhibitor analogues to determine which ligand characteristics are important for the potency for CYP11B1 and CYP11B2. To this end, we have conducted decision tree analyses on the physico-chemical properties of inhibitor substituents, resulting in a collection of descriptors that can be used for the prediction and design of novel inhibitors. We have shown that a combination of synthesis, molecular modelling and experimental measurements form a promising approach towards the design of potentially new inhibitors

Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

A powerful combination of computational methods on the road toward potent non-steroidal inhibitors of steroidogenic enzymes involved in hormone-dependent diseases

Different computational methods have been applied to the development of non-steroidal inhibitors of steroidogenic enzymes for the treatment of hormone-sensitive diseases, like breast and prostate cancer and hypertension. A high-quality homology model of CYP17 was created and used for docking studies. Three binding modes were identified and SAR for different CYP17-inhibitor classes, substantiated by ab initio calculations, could be derived and used in further drug design leading to improved potency. Docking studies and ligand cluster analysis for a series of CYP19 inhibitors resulted in a binding mode, well explaining their different inhibitory potencies. The derived SAR were used in ongoing drug design, resulting so far in highly potent CYP19 inhibitors. The kinetic cycle of 17β-HSD1 was hypothesized based on biochemical data, analysis of the crystal structures and a multi-trajectory MD approach and provided insights in protein motion. These were translated into the drug design process. An ensemble docking study was performed for bis(hydroxyphenyl)-arenes, potent 17β-HSD1 inhibitors, and two conformation-dependent binding modes were identified. MD simulations and quantum-chemical calculations identified one of them as the more plausible, suggesting this class of compounds to dysfunction the enzyme dynamics. Two pharmacophore models were derived from CYP11B2 inhibitors and combined into a ligand- and structure-based approach, which led to a new class of potent CYP11B2 inhibitors.Verschiedene computergestützte Methoden wurden in der Entwicklung von nicht-steroidalen Hemmstoffen steroidogener Enzyme angewandt. Diese Inhibitoren sollen zur Behandlung hormon-abhängiger Krankheiten eingesetzt werden. Ein qualitativ-hochwertiges CYP17 Homologiemodell wurde erstellt und in Docking Studien verwendet. Drei Bindungsmodi konnten identifiziert werden und Struktur-Wirkungsbeziehungen wurden für verschiedene CYP17 Hemmstoffklassen abgeleitet und erfolgreich in eine weitere Hemmstoffentwicklung eingebaut. Dockingstudien und Clusteranalysen einer Reihe von CYP19 Inhibitoren ergaben einen plausiblen Bindungsmodus und die daraus gewonnenen Erkenntnisse führten in laufenden Projekten zu höchst potenten Hemmstoffen. Der kinetische Zyklus von 17β-HSD1 wurde postuliert basierend auf biochemischen Literaturdaten, Kristallstrukturenanalyse und einem multiplen MD-Ansatz, welcher wichtige Einblicke in die Dynamik des Enzyms lieferte. Diese wurden als ensemble docking Ansatz in die Entwicklung einer Klasse hochpotenter 17β-HSD1 Inhibitoren eingebaut und ergaben zwei Enzymkonformations-abhängige Bindungsmodi. MD-Simulationen und quantenchemische Methoden identifizierten einen davon als plausibler. Dabei scheinen Substanzen dieser Klasse die Enzymdynamik zu stören. Zwei Pharmakophormodelle wurden basierend auf CYP11B2-Hemmstoffen erstellt und in einen Ligand- und Struktur-basierten Ansatz eingebaut. Dieser führte zu einer neuen Klasse von potenten und selektiven CYP11B2-Inhibitoren