Cavity-based negative images in molecular docking

In drug development, computer-based methods are constantly evolving as a result of increasing computing power and cumulative costs of generating new pharmaceuticals. With virtual screening (VS), it is possible to screen even hundreds of millions of compounds and select the best molecule candidates for in vitro testing instead of investing time and resources in analysing all molecules systematically in laboratories. However, there is a constant need to generate more reliable and effective software for VS. For example, molecular docking, one of the most central methods in structure-based VS, can be a very successful approach for certain targets while failing completely with others. However, it is not necessarily the docking sampling but the scoring of the docking poses that is the bottleneck. In this thesis, a novel rescoring method, negative image-based rescoring (R-NiB), is introduced, which generates a negative image of the ligand binding cavity and compares the shape and electrostatic similarity between the generated model and the docked molecule pose. The performance of the method is tested comprehensively using several different protein targets, benchmarking sets and docking software. Additionally, it is compared to other rescoring methods. R-NiB is shown to be a fast and effective method to rescore the docking poses producing notable improvement in active molecule recognition. Furthermore, the NIB model optimization method based on a greedy algorithm is introduced that uses a set of known active and inactive molecules as a training set. This approach, brute force negative image-based optimization (BR-NiB), is shown to work remarkably well producing impressive in silico results even with very limited active molecule training sets. Importantly, the results suggest that the in silico hit rates of the optimized models in docking rescoring are on a level needed in real-world VS and drug discovery projects.Tietokoneiden laskentatehojen ja lääketutkimuksen tuotekehityskulujen kasvaessa tietokonepohjaiset menetelmät kehittyvät jatkuvasti lääkekehityksessä. Virtuaaliseulonnalla voidaan seuloa jopa satoja miljoonia molekyylejä ja valita vain parhaat molekyyliehdokkaat laboratoriotestaukseen sen sijaan, että tuhlattaisiin aikaa ja resursseja analysoimalla järjestelmällisesti kaikki molekyylit laboratoriossa. Tästä huolimatta on koko ajan jatkuva tarve kehittää luotettavampia ja tehokkaampia menetelmiä virtuaaliseulontaan. Esimerkiksi telakointi, yksi keskeisimmistä työkaluista rakennepohjaisessa lääkeainekehityksessä, saattaa toimia erinomaisesti yhdellä kohteella ja epäonnistua täysin toisella. Ongelma ei välttämättä ole telakoitujen molekyylien luonnissa vaan niiden pisteytyksessä. Tässä väitöskirjassa tähän ongelmaan esitellään ratkaisuksi uudenlainen pisteytysmenetelmä R-NiB, jossa verrataan ligandinsitomisalueen negatiivikuvan muodon ja sähköstaattisen potentiaalin samankaltaisuutta telakoituihin molekyyleihin. Menetelmän suorituskykyä testataan usealla eri molekyylisarjalla, lääkeainekohteella, telakointiohjelmalla ja vertaamalla tuloksia muihin pisteytysmenetelmiin. R-NiB:n näytetään olevan nopea ja tehokas menetelmä telakointiasentojen pisteytykseen tuottaen huomattavan parannuksen aktiivisten molekyylien tunnistukseen. Tämän lisäksi esitellään ns. ahneeseen algoritmiin perustuva negatiivikuvan optimointimenetelmä, joka käyttää sarjaa tunnettuja aktiivisia ja inaktiivisia molekyylejä harjoitusjoukkona. Tämän BR-NiB-menetelmän näytetään toimivan ainakin tietokonemallinnuksessa todella hyvin tuottaen vaikuttavia tuloksia jopa silloin, kun harjoitusjoukko koostuu vain muutamista aktiivisista molekyyleistä. Mikä tärkeintä, in silico -tulokset viittaavat optimointimenetelmän osumaprosentin telakoinnin uudelleenpisteytyksessä olevan riittävän korkea myös oikeisiin virtuaaliseulontaprojekteihin

High-Performance Drug Discovery: Computational Screening by Combining Docking and Molecular Dynamics Simulations

Virtual compound screening using molecular docking is widely used in the discovery of new lead compounds for drug design. However, this method is not completely reliable and therefore unsatisfactory. In this study, we used massive molecular dynamics simulations of protein-ligand conformations obtained by molecular docking in order to improve the enrichment performance of molecular docking. Our screening approach employed the molecular mechanics/Poisson-Boltzmann and surface area method to estimate the binding free energies. For the top-ranking 1,000 compounds obtained by docking to a target protein, approximately 6,000 molecular dynamics simulations were performed using multiple docking poses in about a week. As a result, the enrichment performance of the top 100 compounds by our approach was improved by 1.6–4.0 times that of the enrichment performance of molecular dockings. This result indicates that the application of molecular dynamics simulations to virtual screening for lead discovery is both effective and practical. However, further optimization of the computational protocols is required for screening various target proteins

Refinement and rescoring of virtual screening results

High-throughput docking is an established computational screening approach in drug design. This methodology enables a rapid identification of biologically active hit compounds, providing an efficient and cost-effective complement or alternative to experimental high-throughput screenings. However, limitations inherent to the methodology make docking results inevitably approximate. Two major Achille’s heels include the use of approximated scoring functions and the limited sampling of the ligand-target complexes. Therefore, docking results require careful evaluation and further post-docking analyses. In this article, we will overview our approach to post-docking analysis in virtual screenings. BEAR (Binding Estimation After Refinement) was developed as a post-docking processing tool that refines docking poses by means of molecular dynamics (MD) and then rescores the ligands based on more accurate scoring functions (MM-PB(GB)SA). The tool has been validated and used prospectively in drug discovery applications. Future directions regarding refinement and rescoring in virtual screening are discussed

Rescoring Virtual Screening Results with the MM-PBSA Methods: Beware of Internal Dielectric Constants

With the potential of improving virtual screening outcome, MM-PB/GBSA has become a disputed method that requires extensive testing and tuning to provide the optimal results. One of the tuning factors is the internal or solute dielectric constant. We have applied three test sets with receptors of different categories and libraries from different sources to investigate the underlying issue related to this constant. We discovered that increasing internal dielectric value does not improve the virtual screening enrichment qualitatively. More interestingly, nonpolar and polar calculated energies act differently in libraries with different molecular weight distributions. From this work, the performance of MM-PBSA rescoring in virtual screening is more library- than receptor-dependent

Novel cruzain inhibitors for the treatment of Chagas' disease.

The protozoan parasite Trypanosoma cruzi, the etiological agent of Chagas' disease, affects millions of individuals and continues to be an important global health concern. The poor efficacy and unfavorable side effects of current treatments necessitate novel therapeutics. Cruzain, the major cysteine protease of T. cruzi, is one potential novel target. Recent advances in a class of vinyl sulfone inhibitors are encouraging; however, as most potential therapeutics fail in clinical trials and both disease progression and resistance call for combination therapy with several drugs, the identification of additional classes of inhibitory molecules is essential. Using an exhaustive virtual-screening and experimental validation approach, we identify several additional small-molecule cruzain inhibitors. Further optimization of these chemical scaffolds could lead to the development of novel drugs useful in the treatment of Chagas' disease

AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening

<p>Abstract</p> <p>Background</p> <p>Virtual or <it>in silico </it>ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization.</p> <p>Results</p> <p>The program AMMOS carries out an automatic procedure that allows for the structural refinement of compound collections and energy minimization of protein-ligand complexes using the open source program AMMP. The performance of our package was evaluated by comparing the structures of small chemical entities minimized by AMMOS with those minimized with the Tripos and MMFF94s force fields. Next, AMMOS was used for full flexible minimization of protein-ligands complexes obtained from a mutli-step virtual screening. Enrichment studies of the selected pre-docked complexes containing 60% of the initially added inhibitors were carried out with or without final AMMOS minimization on two protein targets having different binding pocket properties. AMMOS was able to improve the enrichment after the pre-docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection.</p> <p>Conclusion</p> <p>The open source AMMOS program can be helpful in a broad range of <it>in silico </it>drug design studies such as optimization of small molecules or energy minimization of pre-docked protein-ligand complexes. Our enrichment study suggests that AMMOS, designed to minimize a large number of ligands pre-docked in a protein target, can successfully be applied in a final post-processing step and that it can take into account some receptor flexibility within the binding site area.</p

Combining molecular dynamics and docking simulations to develop targeted protocols for performing optimized virtual screening campaigns on the HTRPM8 channel

Background: There is an increasing interest in TRPM8 ligands of medicinal interest, the rational design of which can be nowadays supported by structure-based in silico studies based on the recently resolved TRPM8 structures. Methods: The study involves the generation of a reliable hTRPM8 homology model, the reliability of which was assessed by a 1.0 \u3bcs MD simulation which was also used to generate multiple receptor conformations for the following structure-based virtual screening (VS) campaigns; docking simulations utilized different programs and involved all monomers of the selected frames; the so computed docking scores were combined by consensus approaches based on the EFO algorithm. Results: The obtained models revealed very satisfactory performances; LiGen\u2122 provided the best results among the tested docking programs; the combination of docking results from the four monomers elicited a markedly beneficial effect on the computed consensus models. Conclusions: The generated hTRPM8 model appears to be amenable for successful structure-based VS studies; cross-talk modulating effects between interacting monomers on the binding sites can be accounted for by combining docking simulations as performed on all the monomers; this strategy can have general applicability for docking simulations involving quaternary protein structures with multiple identical binding pockets