ConfJump : a fast biomolecular sampling method which drills tunnels through high mountains

In order to compute the thermodynamic weights of the different metastable conformations of a molecule, we want to approximate the molecule’s Boltzmann distribution π in a reasonable time. This is an essential issue in computational drug design. The energy landscape of active biomolecules is generally very rough with a lot of high barriers and low regions. Many of the algorithms that perform such samplings (e.g. the hybrid Monte Carlo method) have difficulties with such landscapes. They are trapped in low-energy regions for a very long time and cannot overcome high barriers. Moving from one low-energy region to another is a very rare event. For these reasons, the distribution of the generated sampling points converges very slowly against the thermodynamically correct distribution of the molecule. The idea of ConfJump is to use a priori knowledge of the localization of low-energy regions to enhance the sampling with artificial jumps between these low-energy regions. The artificial jumps are combined with the hybrid Monte Carlo method. This allows the computation of some dynamical properties of the molecule. In ConfJump, the detailed balance condition is satisfied and the mathematically correct molecular distribution is sampled

Flexible molecular alignment: an industrial case study on quantum algorithmic techniques

Dissertação de mestrado em Engenharia FísicaFlexible molecular alignment is a complex and challenging problem in the area of Medic inal Chemistry. The current approach to this problem does not test all possible alignments, but makes a previous analysis of all the variables and chooses the ones with potentially greater impact in the posterior alignment. This procedure can lead to wrong ”best align ments” since not every data is considered. Quantum computation, due to its natural parallelism, may improve algorithmic solutions for this kind of problems because it may test and/or simulate all possible solutions in an execution cycle. As a case study proposed by BIAL and in collaboration with IBM, the main goal of this dissertation was to study and create quantum algorithms able to refactor the problem of molecular alignment in the new setting of quantum computation. Additionally, the comparison between both classical and quantum solutions was defined as a subsequent goal. During this dissertation and due to its complexity, in order to produce a practical solu tion to this problem, we resorted to a manageable number of conformations per molecule, revisited the classical solution and elaborated a corresponding quantum algorithm. Such algorithm was then tested in both a quantum simulator and a real device. Despite the privileged collaboration with IBM, the quantum simulations were not pro duced in viable time, making them impractical for industry applications. Nonetheless, tak ing in consideration the current point of development of quantum hardware, the suggested solutions still has potential for the future.O alinhamento de moléculas flexíveis é um problema complexo na área de Química Medicinal, onde, mesmo hoje em dia, é um desafio encontrar uma solução. A atual abordagem para este problema não testa todos os possíveis alinhamentos. Em vez disso, realiza uma análise prévia de todas as variáveis e escolhe aquelas com maior potencial de impacto no posterior alinhamento. Este procedimento pode levar a falsos “melhores alinhamentos” visto que nem todos os dados são considerados. A computação quântica, devido ao seu natural paralelismo, pode melhorar as soluções algorítmicas deste tipo de problemas visto que poderá testar e/ou simular todas as possíveis soluções num ciclo de execução. Partindo de um caso de estudo proposto pela BIAL, e em colaboração com a IBM, o objetivo principal desta dissertação foi estudar e criar algoritmos quânticos capazes reformular no contexto de computação quântica o problema de alinhamento de moléculas. Adicionalmente, e como objetivo subsequente, foi prevista a comparação entre os algoritmos clássicos e quânticos. Durante esta dissertação e devido à sua complexidade, de modo a produzir uma solução prática para este problema, foi utilizado um número tratável de conformações por molécula, revisitada a solução clássica e desenvolvido um algoritmo quântico correspondente. Tal algoritmo foi depois testado tanto num simulador quântico como num dispositivo real. Apesar da colaboração privilegiada com a IBM, as simulações quânticas não foram produzidas em tempo viável, tornando-as impraticáveis para aplicações industriais. Não obstante, tendo em consideração o ponto atual de desenvolvimento dos dispositivos quânticos, as soluções propostas terão potencial para o futuro

Multiple Semi-flexible 3D Superposition of Drug-sized Molecules

In this paper we describe a new algorithm for multiple semi-flexible superpositioning of drug-sized molecules. The algorithm identifies structural similarities of two or more molecules. When comparing a set of molecules on the basis of their three-dimensional structures, one is faced with two main problems. (1) Molecular structures are not fixed but flexible, i.e., a molecule adopts different forms. To address this problem, we consider a set of conformers per molecule. As conformers we use representatives of conformational ensembles, generated by the program ZIBMol. (2) The degree of similarity may vary considerably among the molecules. This problem is addressed by searching for similar substructures present in arbitrary subsets of the given set of molecules. The algorithm requires to preselect a reference molecule. All molecules are compared to this reference molecule. For this pairwise comparison we use a two-step approach. Clique detection on the correspondence graph of the molecular structures is used to generate start transformations, which are then iteratively improved to compute large common substructures. The results of the pairwise comparisons are efficiently merged using binary matching trees. All common substructures that were found, whether they are common to all or only a few molecules, are ranked according to different criteria, such as number of molecules containing the substructure, size of substructure, and geometric fit. For evaluating the geometric fit, we extend a known scoring function by introducing weights which allow to favor potential pharmacophore points. Despite considering the full atomic information for identifying multiple structural similarities, our algorithm is quite fast. Thus it is well suited as an interactive tool for the exploration of structural similarities of drugsized molecules

Advanced Immunoinformatics Approaches for Precision Medicine

Genomic sequencing and other ’-omic’ technologies are slowly changing biomedical practice. As a result, patients now can be treated based on their molecular profile. Especially the immune system’s variability, in particular that of the human leukocyte antigen (HLA) gene cluster, makes such a paradigm indispensable when treating illnesses such as cancer, autoimmune diseases, or infectious diseases. It can be, however, costly and time-consuming to determine the HLA genotype with traditional means, as these methods do not utilize often pre-existing sequencing data. We therefore proposed an algorithmic approach that can use these data sources to infer the HLA genotype. HLA genotyping inference can be cast into a set covering problem under special biological constraints and can be solved efficiently via integer linear programming. Our proposed approach outperformed previously published methods and remains one of the most accurate methods to date. We then introduced two applications in which a HLA-based stratification is vital for the efficacy of the treatment and the reduction of its adverse effects. In the first example, we dealt with the optimal design of string-of-beads vaccines (SOB). We developed a mathematical model that maximizes the efficacy of such vaccines while minimizing their side effects based on a given HLA distribution. Comparisons of our optimally designed SOB with experimentally tested designs yielded promising results. In the second example, we considered the problem of anti-drug antibody (ADA) formation of biotherapeutics caused by HLA presented peptides. We combined a new statistical model for mutation effect prediction together with a quantitative measure of immunogenicity to formulate an optimization problem that finds alterations to reduce the risk of ADA formation. To efficiently solve this bi-objective problem, we developed a distributed solver that is up to 25-times faster than state-of-the art solvers. We used our approach to design the C2 domain of factor VIII, which is linked to ADA formation in hemophilia A. Our experimental evaluations of the proposed designs are encouraging and demonstrate the prospects of our approach. Bioinformatics is an integral part of modern biomedical research. The translation of advanced methods into clinical use is often complicated. To ease the translation, we developed a programming library for computational immunology and used it to implement a Galaxy-based web server for vaccine design and a KNIME extension for desktop PCs. These platforms allow researchers to develop their own immunoinformatics workflows utilizing the platform’s graphical programming capabilities