The Chemical Space Project

One of the simplest questions that can be asked about molecular diversity is how many organic molecules are possible in total? To answer this question, my research group has computationally enumerated all possible organic molecules up to a certain size to gain an unbiased insight into the entire chemical space. Our latest database, GDB-17, contains 166.4 billion molecules of up to 17 atoms of C, N, O, S, and halogens, by far the largest small molecule database reported to date. Molecules allowed by valency rules but unstable or nonsynthesizable due to strained topologies or reactive functional groups were not considered, which reduced the enumeration by at least 10 orders of magnitude and was essential to arrive at a manageable database size. Despite these restrictions, GDB-17 is highly relevant with respect to known molecules. Beyond enumeration, understanding and exploiting GDBs (generated databases) led us to develop methods for virtual screening and visualization of very large databases in the form of a “periodic system of molecules” comprising six different fingerprint spaces, with web-browsers for nearest neighbor searches, and the MQN- and SMIfp-Mapplet application for exploring color-coded principal component maps of GDB and other large databases. Proof-of-concept applications of GDB for drug discovery were realized by combining virtual screening with chemical synthesis and activity testing for neurotransmitter receptor and transporter ligands. One surprising lesson from using GDB for drug analog searches is the incredible depth of chemical space, that is, the fact that millions of very close analogs of any molecule can be readily identified by nearest-neighbor searches in the MQN-space of the various GDBs. The chemical space project has opened an unprecedented door on chemical diversity. Ongoing and yet unmet challenges concern enumerating molecules beyond 17 atoms and synthesizing GDB molecules with innovative scaffolds and pharmacophores.

Targeting TRAF6 for Cancer Therapeutical Development

Tumor necrosis factor (TNF)-Receptor Associated Factors (TRAFs) are a family of signal transducer proteins. TRAF6 is a unique member of this family in that it is involved in not only the TNF superfamily, but the toll-like receptor (TLR)/IL-1R (TIR) superfamily. The formation of the complex consisting of Receptor Activator of Nuclear Factor κ B (RANK), with its ligand (RANKL) results in the recruitment of TRAF6, which activates NF-κB, JNK and MAP kinase pathways. TRAF6 is critical in signaling with leading to release of various growth factors in bone, and promotes osteoclastogenesis. TRAF6 has also been implicated as an oncogene in lung cancer and as a target in multiple myeloma. In the hopes of developing small molecule inhibitors of the TRAF6-RANK interaction, multiple steps were carried out. Computational prediction of hot spot residues on the protein-protein interaction of TRAF6 and RANK were examined. Three methods were used: Robetta, KFC2, and HotPoint, each of which uses a different methodology to determine if a residue is a hot spot. These hot spot predictions were considered the basis for resolving the binding site for in silico high-throughput screening using GOLD and the MyriaScreen database of drug/lead-like compounds. Computationally intensive molecular dynamics simulations highlighted the binding mechanism and TRAF6 structural changes upon hit binding. Compounds identified as hits were verified using a GST-pull down assay, comparing inhibition to a RANK decoy peptide. Since many drugs fail due to lack of efficacy and toxicity, predictive models for the evaluation of the LD50 and bioavailability of our TRAF6 hits, and these models can be used towards other drugs and small molecule therapeutics as well. Datasets of compounds and their corresponding bioavailability and LD50 values were curated based, and QSAR models were built using molecular descriptors of these compounds using the k-nearest neighbor (k-NN) method, and quality of these models were cross-validated.

Advanced Protein Modeling Method: Benchmarking and Applications in Computer-Aided Drug Discovery

Development of homology modeling methods will remain an area of active research. These methods aim to develop and model increasingly accurate three-dimensional structures of yet uncrystallized therapeutically relevant proteins e.g. Class A G-Protein Coupled Receptors. Incorporating protein flexibility is one way to achieve this goal. Here, I will discuss the enhancement and validation of the ligand-steered modeling, originally developed by Dr. Claudio Cavasotto, via cross modeling of the newly crystallized GPCR structures. This method uses known ligands and known experimental information to optimize relevant protein binding sites by incorporating protein flexibility. The ligand-steered models were able to model, reasonably reproduce binding sites and the co-crystallized native ligand poses of the β2 adrenergic and Adenosine 2A receptors using a single template structure. They also performed better than the choice of template, and crude models in a small scale high-throughput docking experiments and compound selectivity studies. Next, the application of this method to develop high-quality homology models of Cannabinoid Receptor 2, an emerging non-psychotic pain management target, is discussed. These models were validated by their ability to rationalize structure activity relationship data of two, inverse agonist and agonist, series of compounds. The method was also applied to improve the virtual screening performance of the β2 adrenergic crystal structure by optimizing the binding site using β2 specific compounds. These results show the feasibility of optimizing only the pharmacologically relevant protein binding sites and applicability to structure-based drug design projects.

Modified PNA design and synthesis: a novel approach using Molecular Dynamics and Metadynamics

Gli acidi peptido nucleici sono potenti strumenti utilizzati in ambito biotecnologico per colpire DNA o RNA. PNA contenenti basi o backbone modificati sono attualmente studiati per migliorarne le proprietà in ambito biologico. Bersagliare i micro RNA (anti-miR) è particolarmente interessante nell’ottica di future applicazioni terapeutiche, ma strumenti computazionali che aiutino nel design di nuovi PNA anti-miR non sono stati ancora completamente sviluppati. Le proprietà conformazionali del singolo filamento di PNA (non modificato o recante modificazioni in γ) e dei duplex PNA:RNA e i processi di re-annealing e melting sono stati studiati tramite Dinamica Molecolare e Metadinamica. L’approccio computazionale consolidato, assieme a un programma modificato per la generazione delle strutture dei duplex contenenti PNA, è stato utilizzato per il virtual screening di PNA contenenti basi modificate. Sono state inoltre sintetizzate le unità per l’ottenimento del composto più promettente e una funzione idrolitica da legare al monomero finale.

Design and synthesis of new multitarget inhibitors of dengue virus replication

Attraverso una combinazione di virtual screening/virtual library generation abbiamo identificato una nuova serie di inibitori della replicazione del virus Dengue, attivi sia su un target virale (NS5) sia su uno cellulare (Src chinasi)

Multi-scale approaches to Food Sciences: computational QM and MM explorations at the microscopic level

L'obiettivo principale della politica di sicurezza alimentare è quello di garantire la salute dei consumatori attraverso regole e protocolli di sicurezza specifici. Al fine di rispondere ai requisiti di sicurezza alimentare e standardizzazione della qualità, nel 2002 il Parlamento Europeo e il Consiglio dell'UE (Regolamento (CE) 178/2002 (CE, 2002)), hanno cercato di uniformare concetti, principi e procedure in modo da fornire una base comune in materia di disciplina degli alimenti e mangimi provenienti da Stati membri a livello comunitario. La formalizzazione di regole e protocolli di standardizzazione dovrebbe però passare attraverso una più dettagliata e accurata comprensione ed armonizzazione delle proprietà globali (macroscopiche), pseudo-locali (mesoscopiche), ed eventualmente, locali (microscopiche) dei prodotti alimentari. L'obiettivo principale di questa tesi di dottorato è di illustrare come le tecniche computazionali possano rappresentare un valido supporto per l'analisi e ciò tramite (i) l’applicazione di protocolli e (ii) miglioramento delle tecniche ampiamente applicate. Una dimostrazione diretta delle potenzialità già offerte dagli approcci computazionali viene offerta nel primo lavoro in cui un virtual screening basato su docking è stato applicato al fine di valutare la preliminare xeno-androgenicità di alcuni contaminanti alimentari. Il secondo e terzo lavoro riguardano lo sviluppo e la convalida di nuovi descrittori chimico-fisici in un contesto 3D-QSAR. Denominata HyPhar (Hydrophobic Pharmacophore), la nuova metodologia così messa a punto è stata usata per esplorare il tema della selettività tra bersagli molecolari strutturalmente correlati e ha così dimostrato di possedere i necessari requisiti di applicabilità e adattabilità in un contesto alimentare. Nel complesso, i risultati ci permettono di essere fiduciosi nel potenziale impatto che le tecniche in silico potranno avere nella identificazione e chiarificazione di eventi molecolari implicati negli aspetti tossicologici e nutrizionali degli alimenti.

HLA-DP2 binding prediction by molecular dynamics simulations

Major histocompatibility complex (MHC) II proteins bind peptide fragments derived from pathogen antigens and present them at the cell surface for recognition by T cells. MHC proteins are divided into Class I and Class II. Human MHC Class II alleles are grouped into three loci: HLA-DP, HLA-DQ, and HLA-DR. They are involved in many autoimmune diseases. In contrast to HLA-DR and HLA-DQ proteins, the X-ray structure of the HLA-DP2 protein has been solved quite recently. In this study, we have used structure-based molecular dynamics simulation to derive a tool for rapid and accurate virtual screening for the prediction of HLA-DP2-peptide binding. A combinatorial library of 247 peptides was built using the "single amino acid substitution" approach and docked into the HLA-DP2 binding site. The complexes were simulated for 1 ns and the short range interaction energies (Lennard-Jones and Coulumb) were used as binding scores after normalization. The normalized values were collected into quantitative matrices (QMs) and their predictive abilities were validated on a large external test set. The validation shows that the best performing QM consisted of Lennard-Jones energies normalized over all positions for anchor residues only plus cross terms between anchor-residues.

In silico identified CCR4 antagonists target regulatory T cells and exert adjuvant activity in vaccination

Adjuvants are substances that enhance immune responses and thus improve the efficacy of vaccination. Few adjuvants are available for use in humans, and the one that is most commonly used (alum) often induces suboptimal immunity for protection against many pathogens. There is thus an obvious need to develop new and improved adjuvants. We have therefore taken an approach to adjuvant discovery that uses in silico modeling and structure-based drug-design. As proof-of-principle we chose to target the interaction of the chemokines CCL22 and CCL17 with their receptor CCR4. CCR4 was posited as an adjuvant target based on its expression on CD4(+)CD25(+) regulatory T cells (Tregs), which negatively regulate immune responses induced by dendritic cells (DC), whereas CCL17 and CCL22 are chemotactic agents produced by DC, which are crucial in promoting contact between DC and CCR4(+) T cells. Molecules identified by virtual screening and molecular docking as CCR4 antagonists were able to block CCL22- and CCL17-mediated recruitment of human Tregs and Th2 cells. Furthermore, CCR4 antagonists enhanced DC-mediated human CD4(+) T cell proliferation in an in vitro immune response model and amplified cellular and humoral immune responses in vivo in experimental models when injected in combination with either Modified Vaccinia Ankara expressing Ag85A from Mycobacterium tuberculosis (MVA85A) or recombinant hepatitis B virus surface antigen (rHBsAg) vaccines. The significant adjuvant activity observed provides good evidence supporting our hypothesis that CCR4 is a viable target for rational adjuvant design.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.

Improving drug discovery using a neural networks based parallel scoring function

Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface to find new hotspots, where ligands might potentially interact with, and which is implemented in massively parallel Graphics Processing Units, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to solve this problem, we propose a novel approach where neural networks are trained with databases of known active (drugs) and inactive compounds, and later used to improve VS predictions.

Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.

Ecological validity of virtual reality daily living activities screening for early dementia: longitudinal study

Background: Dementia is a multifaceted disorder that impairs cognitive functions, such as memory, language, and executive functions necessary to plan, organize, and prioritize tasks required for goal-directed behaviors. In most cases, individuals with dementia experience difficulties interacting with physical and social environments. The purpose of this study was to establish ecological validity and initial construct validity of a fire evacuation Virtual Reality Day-Out Task (VR-DOT) environment based on performance profiles as a screening tool for early dementia. Objective: The objectives were (1) to examine the relationships among the performances of 3 groups of participants in the VR-DOT and traditional neuropsychological tests employed to assess executive functions, and (2) to compare the performance of participants with mild Alzheimer’s-type dementia (AD) to those with amnestic single-domain mild cognitive impairment (MCI) and healthy controls in the VR-DOT and traditional neuropsychological tests used to assess executive functions. We hypothesized that the 2 cognitively impaired groups would have distinct performance profiles and show significantly impaired independent functioning in ADL compared to the healthy controls. Methods: The study population included 3 groups: 72 healthy control elderly participants, 65 amnestic MCI participants, and 68 mild AD participants. A natural user interface framework based on a fire evacuation VR-DOT environment was used for assessing physical and cognitive abilities of seniors over 3 years. VR-DOT focuses on the subtle errors and patterns in performing everyday activities and has the advantage of not depending on a subjective rating of an individual person. We further assessed functional capacity by both neuropsychological tests (including measures of attention, memory, working memory, executive functions, language, and depression). We also evaluated performance in finger tapping, grip strength, stride length, gait speed, and chair stands separately and while performing VR-DOTs in order to correlate performance in these measures with VR-DOTs because performance while navigating a virtual environment is a valid and reliable indicator of cognitive decline in elderly persons. Results: The mild AD group was more impaired than the amnestic MCI group, and both were more impaired than healthy controls. The novel VR-DOT functional index correlated strongly with standard cognitive and functional measurements, such as mini-mental state examination (MMSE; rho=0.26, P=.01) and Bristol Activities of Daily Living (ADL) scale scores (rho=0.32, P=.001). Conclusions: Functional impairment is a defining characteristic of predementia and is partly dependent on the degree of cognitive impairment. The novel virtual reality measures of functional ability seem more sensitive to functional impairment than qualitative measures in predementia, thus accurately differentiating from healthy controls. We conclude that VR-DOT is an effective tool for discriminating predementia and mild AD from controls by detecting differences in terms of errors, omissions, and perseverations while measuring ADL functional ability.

Colonoscopia virtual e DNA fecal : estudo de viabilidade como testes de rastreio do cancro colorretal

Tese de doutoramento, Cirurgia Geral (Medicina), Universidade de Lisboa, Faculdade de Medicina, 2014

Virtual reality Post Traumatic Stress Disorder (PTSD) exposure therapy results with active duty OIF/OEF service members

Posttraumatic stress disorder (PTSD) is reported to be caused by exposure to traumatic events including (but not limited to) military combat, violent personal assault, being kidnapped or taken hostage and terrorist attacks. Initial data suggest that at least 1 out of 6 Iraq War veterans are exhibiting symptoms of depression, anxiety and PTSD. Virtual reality (VR) delivered exposure therapy for PTSD has been used with reports of positive outcomes. The aim of the current paper, is to present the rationale and brief description of a Virtual Iraq/Afghanistan PTSD VR therapy application and present initial findings from its use with PTSD patients. Thus far, Virtual Iraq/Afghanistan consists of a series of customizable virtual scenarios designed to represent relevant Middle Eastern VR contexts for exposure therapy, including a city and desert road convoy environment. User-centered design feedback, needed to iteratively evolve the system, was gathered from returning Iraq War veterans in the USA and from a system deployed in Iraq and tested by an Army Combat Stress Control Team. Results from an open clinical trial at San Diego Naval Medical Center of the first 20 treatment completers indicate that 16 no longer met PTSD screening criteria at post-treatment, with only one not maintaining treatment gains at 3 month follow-up.

Evaluating relative impact of virtual reality system variables on architectural design comprehension and presence - A variable-centered approach using fractional factorial experiment

The relative contributions of five variables (Stereoscopy, screen size, field of view, level of realism and level of detail) of virtual reality systems on spatial comprehension and presence are evaluated here. Using a variable-centered approach instead of an object-centric view as its theoretical basis, the contributions of these five variables and their two-way interactions are estimated through a 25-1 fractional factorial experiment (screening design) of resolution V with 84 subjects. The experiment design, procedure, measures used, creation of scales and indices, results of statistical analysis, their meaning and agenda for future research are elaborated.