MUCHA: multiple chemical alignment algorithm to identify building block substructures of orphan secondary metabolites

[Background]In contrast to the increasing number of the successful genome projects, there still remain many orphan metabolites for which their synthesis processes are unknown. Metabolites, including these orphan metabolites, can be classified into groups that share the same core substructures, originated from the same biosynthetic pathways. It is known that many metabolites are synthesized by adding up building blocks to existing metabolites. Therefore, it is proposed that, for any given group of metabolites, finding the core substructure and the branched substructures can help predict their biosynthetic pathway. There already have been many reports on the multiple graph alignment techniques to find the conserved chemical substructures in relatively small molecules. However, they are optimized for ligand binding and are not suitable for metabolomic studies. [Results]We developed an efficient multiple graph alignment method named as MUCHA (Multiple Chemical Alignment), specialized for finding metabolic building blocks. This method showed the strength in finding metabolic building blocks with preserving the relative positions among the substructures, which is not achieved by simply applying the frequent graph mining techniques. Compared with the combined pairwise alignments, this proposed MUCHA method generally reduced computational costs with improving the quality of the alignment. [Conclusions]MUCHA successfully find building blocks of secondary metabolites, and has a potential to complement to other existing methods to reconstruct metabolic networks using reaction patterns

Computational methods for small molecules

Metabolism is the system of chemical reactions sustaining life in the cells of living organisms. It is responsible for cellular processes that break down nutrients for energy and produce building blocks for necessary molecules. The study of metabolism is vital to many disciplines in medicine and pharmacy. Chemical reactions operate on small molecules called metabolites, which form the core of metabolism. In this thesis we propose efficient computational methods for small molecules in metabolic applications. In this thesis we discuss four distinctive studies covering two major themes: the atom-level description of biochemical reactions, and analysis of tandem mass spectrometric measurements of metabolites. In the first part we study atom-level descriptions of organic reactions. We begin by proposing an optimal algorithm for determining the atom-to-atom correspondences between the reactant and product metabolites of organic reactions. In addition, we introduce a graph edit distance based cost as the mathematical formalism to determine optimality of atom mappings. We continue by proposing a compact single-graph representation of reactions using the atom mappings. We investigate the utility of the new representation in a reaction function classification task, where a descriptive category of the reaction's function is predicted. To facilitate the prediction, we introduce the first feasible path-based graph kernel, which describes the reactions as path sequences to high classification accuracy. In the second part we turn our focus on analysing tandem mass spectrometric measurements of metabolites. In a tandem mass spectrometer, an input molecule structure is fragmented into substructures or fragments, whose masses are observed. We begin by studying the fragment identification problem. A combinatorial algorithm is presented to enumerate candidate substructures based on the given masses. We also demonstrate the usefulness of utilising approximated bond energies as a cost function to rank the candidate structures according to their chemical feasibility. We propose fragmentation tree models to describe the dependencies between fragments for higher identification accuracy. We continue by studying a closely related problem where an unknown metabolite is elucidated based on its tandem mass spectrometric fragment signals. This metabolite identification task is an important problem in metabolomics, underpinning the subsequent modelling and analysis efforts. We propose an automatic machine learning framework to predict a set of structural properties of the unknown metabolite. The properties are turned into candidate structures by a novel statistical model. We introduce the first mass spectral kernels and explore three feature classes to facilitate the prediction. The kernels introduce support for high-accuracy mass spectrometric measurements for enhanced predictive accuracy.Tässä väitöskirjassa esitetään tehokkaita laskennallisia menetelmiä pienille molekyyleille aineenvaihduntasovelluksissa. Aineenvaihdunta on kemiallisten reaktioiden järjestelmä, joka ylläpitää elämää solutasolla. Aineenvaihduntaprosessit hajottavat ravinteita energiaksi ja rakennusaineiksi soluille tarpeellisten molekyylien valmistamiseen. Kemiallisten reaktioiden muokkaamia pieniä molekyylejä kutsutaan metaboliiteiksi. Tämä väitöskirja sisältää neljä itsenäistä tutkimusta, jotka jakautuvat teemallisesti biokemiallisten reaktioiden atomitason kuvaamiseen ja metaboliittien massaspektrometriamittausten analysointiin. Väitöskirjan ensimmäisessä osassa käsitellään biokemiallisten reaktioiden atomitason kuvauksia. Väitöskirjassa esitellään optimaalinen algoritmi reaktioiden lähtö- ja tuoteaineiden välisten atomikuvausten määrittämiseen. Optimaalisuus määrittyy verkkojen editointietäisyyteen perustuvalla kustannusfunktiolla. Optimaalinen atomikuvaus mahdollistaa reaktion kuvaamisen yksikäsitteisesti yhdellä verkolla. Uutta reaktiokuvausta hyödynnetään reaktion funktion ennustustehtävässä, jossa pyritään määrittämään reaktiota sanallisesti kuvaava kategoria automaattisesti. Väitöskirjassa esitetään polku-perustainen verkkokerneli, joka kuvaa reaktiot atomien polkusekvensseinä verrattuna aiempiin kulkusekvensseihin saavuttaen paremman ennustustarkkuuden. Väitöskirjan toisessa osassa analysoidaan metaboliittien tandem-massaspektrometriamittauksia. Tandem-massaspektrometri hajottaa analysoitavan syötemolekyylin fragmenteiksi ja mittaa niiden massa-varaus suhteet. Väitöskirjassa esitetään perusteellinen kombinatorinen algoritmi fragmenttien tunnistamiseen. Menetelmän kustannusfunktio perustuu fragmenttien sidosenergioiden vertailuun. Lopuksi väitöskirjassa esitetään fragmentaatiopuut, joiden avulla voidaan mallintaa fragmenttien välisiä suhteita ja saavuttaa parempi tunnistustarkkuus. Fragmenttien tunnistuksen ohella voidaan tunnistaa myös analysoitavia metaboliitteja. Ongelma on merkittävä ja edellytys aineenvaihdunnun analyyseille. Väitöskirjassa esitetään koneoppimismenetelmä, joka ennustaa tuntemattoman metaboliitin rakennetta kuvaavia piirteitä ja muodostaa niiden perusteella rakenne-ennusteita tilastollisesti. Menetelmä esittelee ensimmäiset erityisesti massaspektrometriadataan soveltuvat kernel-funktiot ja saavuttaa hyvän ennustustarkkuuden

On the Complexity of Various Parameterizations of Common Induced Subgraph Isomorphism

In the Maximum Common Induced Subgraph problem (henceforth MCIS), given two graphs $G_1$ and $G_2$, one looks for a graph with the maximum number of vertices being both an induced subgraph of $G_1$ and $G_2$. MCIS is among the most studied classical NP-hard problems. It remains NP-hard on many graph classes including forests. In this paper, we study the parameterized complexity of MCIS. As a generalization of \textsc{Clique}, it is W[1]-hard parameterized by the size of the solution. Being NP-hard even on forests, most structural parameterizations are intractable. One has to go as far as parameterizing by the size of the minimum vertex cover to get some tractability. Indeed, when parameterized by $k := \text{vc}(G_1)+\text{vc}(G_2)$ the sum of the vertex cover number of the two input graphs, the problem was shown to be fixed-parameter tractable, with an algorithm running in time $2^{O(k \log k)}$. We complement this result by showing that, unless the ETH fails, it cannot be solved in time $2^{o(k \log k)}$. This kind of tight lower bound has been shown for a few problems and parameters but, to the best of our knowledge, not for the vertex cover number. We also show that MCIS does not have a polynomial kernel when parameterized by $k$, unless $NP \subseteq \mathsf{coNP}/poly$. Finally, we study MCIS and its connected variant MCCIS on some special graph classes and with respect to other structural parameters.Comment: This version introduces new result

A treatment of stereochemistry in computer aided organic synthesis

This thesis describes the author’s contributions to a new stereochemical processing module constructed for the ARChem retrosynthesis program. The purpose of the module is to add the ability to perform enantioselective and diastereoselective retrosynthetic disconnections and generate appropriate precursor molecules. The module uses evidence based rules generated from a large database of literature reactions. Chapter 1 provides an introduction and critical review of the published body of work for computer aided synthesis design. The role of computer perception of key structural features (rings, functions groups etc.) and the construction and use of reaction transforms for generating precursors is discussed. Emphasis is also given to the application of strategies in retrosynthetic analysis. The availability of large reaction databases has enabled a new generation of retrosynthesis design programs to be developed that use automatically generated transforms assembled from published reactions. A brief description of the transform generation method employed by ARChem is given. Chapter 2 describes the algorithms devised by the author for handling the computer recognition and representation of the stereochemical features found in molecule and reaction scheme diagrams. The approach is generalised and uses flexible recognition patterns to transform information found in chemical diagrams into concise stereo descriptors for computer processing. An algorithm for efficiently comparing and classifying pairs of stereo descriptors is described. This algorithm is central for solving the stereochemical constraints in a variety of substructure matching problems addressed in chapter 3. The concise representation of reactions and transform rules as hyperstructure graphs is described. Chapter 3 is concerned with the efficient and reliable detection of stereochemical symmetry in both molecules, reactions and rules. A novel symmetry perception algorithm, based on a constraints satisfaction problem (CSP) solver, is described. The use of a CSP solver to implement an isomorph‐free matching algorithm for stereochemical substructure matching is detailed. The prime function of this algorithm is to seek out unique retron locations in target molecules and then to generate precursor molecules without duplications due to symmetry. Novel algorithms for classifying asymmetric, pseudo‐asymmetric and symmetric stereocentres; meso, centro, and C2 symmetric molecules; and the stereotopicity of trigonal (sp2) centres are described. Chapter 4 introduces and formalises the annotated structural language used to create both retrosynthetic rules and the patterns used for functional group recognition. A novel functional group recognition package is described along with its use to detect important electronic features such as electron‐withdrawing or donating groups and leaving groups. The functional groups and electronic features are used as constraints in retron rules to improve transform relevance. Chapter 5 details the approach taken to design detailed stereoselective and substrate controlled transforms from organised hierarchies of rules. The rules employ a rich set of constraints annotations that concisely describe the keying retrons. The application of the transforms for collating evidence based scoring parameters from published reaction examples is described. A survey of available reaction databases and the techniques for mining stereoselective reactions is demonstrated. A data mining tool was developed for finding the best reputable stereoselective reaction types for coding as transforms. For various reasons it was not possible during the research period to fully integrate this work with the ARChem program. Instead, Chapter 6 introduces a novel one‐step retrosynthesis module to test the developed transforms. The retrosynthesis algorithms use the organisation of the transform rule hierarchy to efficiently locate the best retron matches using all applicable stereoselective transforms. This module was tested using a small set of selected target molecules and the generated routes were ranked using a series of measured parameters including: stereocentre clearance and bond cleavage; example reputation; estimated stereoselectivity with reliability; and evidence of tolerated functional groups. In addition a method for detecting regioselectivity issues is presented. This work presents a number of algorithms using common set and graph theory operations and notations. Appendix A lists the set theory symbols and meanings. Appendix B summarises and defines the common graph theory terminology used throughout this thesis

Molecular Similarity and Xenobiotic Metabolism

MetaPrint2D, a new software tool implementing a data-mining approach for predicting sites of xenobiotic metabolism has been developed. The algorithm is based on a statistical analysis of the occurrences of atom centred circular fingerprints in both substrates and metabolites. This approach has undergone extensive evaluation and been shown to be of comparable accuracy to current best-in-class tools, but is able to make much faster predictions, for the first time enabling chemists to explore the effects of structural modifications on a compound’s metabolism in a highly responsive and interactive manner.MetaPrint2D is able to assign a confidence score to the predictions it generates, based on the availability of relevant data and the degree to which a compound is modelled by the algorithm.In the course of the evaluation of MetaPrint2D a novel metric for assessing the performance of site of metabolism predictions has been introduced. This overcomes the bias introduced by molecule size and the number of sites of metabolism inherent to the most commonly reported metrics used to evaluate site of metabolism predictions.This data mining approach to site of metabolism prediction has been augmented by a set of reaction type definitions to produce MetaPrint2D-React, enabling prediction of the types of transformations a compound is likely to undergo and the metabolites that are formed. This approach has been evaluated against both historical data and metabolic schemes reported in a number of recently published studies. Results suggest that the ability of this method to predict metabolic transformations is highly dependent on the relevance of the training set data to the query compounds.MetaPrint2D has been released as an open source software library, and both MetaPrint2D and MetaPrint2D-React are available for chemists to use through the Unilever Centre for Molecular Science Informatics website.----Boehringer-Ingelhie

Efficient Algorithms for Graph Optimization Problems

A doktori értekezés hatékony algoritmusokat mutat be gráfokon értelmezett nehéz kombinatorikus optimalizálási feladatok megoldására. A kutatás legfontosabb eredményét különböző megoldási módszerekhez kidolgozott javítások jelentik, amelyek magukban foglalnak új heurisztikákat, valamint gráfok és fák speciális reprezentációit is. Az elvégzett elemzések igazolták, hogy a szerző által adott leghatékonyabb algoritmusok az esetek többségében gyorsabbak, illetve jobb eredményeket adnak, mint más elérhető implementációk. A dolgozat első fele hét különböző algoritmust és számos hasznos javítást mutat be a minimális költségű folyam feladatra, amely a legtöbbet vizsgált és alkalmazott gráfoptimalizálási problémák egyike. Az implementációinkat egy átfogó tapasztalati elemzés keretében összehasonlítottuk nyolc másik megoldóprogrammal, köztük a leggyakrabban használt és legelismertebb implementációkkal. A hálózati szimplex algoritmusunk lényegesen hatékonyabbnak és robusztusabbnak bizonyult, mint a módszer más implementációi, továbbá a legtöbb tesztadaton ez az algoritmus a leggyorsabb. A bemutatott költségskálázó algoritmus szintén rendkívül hatékony; nagy méretű ritka gráfokon felülmúlja a hálózati szimplex implementációkat. Az értekezésben tárgyalt másik optimalizálási feladat a legnagyobb közös részgráf probléma. Ezt a feladatot kémiai alkalmazások szempontjából vizsgáltuk. Hatékony heurisztikákat dolgoztunk ki, amelyek jelentősen javítják két megoldási módszer pontosságát és sebességét, valamint kémiailag relevánsabb módon rendelik egymáshoz molekulagráfok atomjait és kötéseit. Az algoritmusainkat összehasonlítottuk két ismert megoldóprogrammal, amelyeknél lényegesen jobb eredményeket sikerült elérnünk. A kifejlesztett implementációk bekerültek a ChemAxon Kft. több szoftvertermékébe, melyek vezető nemzetközi gyógyszercégek használatában állnak. Ezen kívül az értekezés röviden bemutatja a LEMON nevű nyílt forrású C++ gráfoptimalizációs programkönyvtárat, amely magában foglalja a minimális költségű folyam feladatra adott algoritmusokat. Ezek az implementációk nagy mértékben hozzájárultak a programcsomag népszerűségének növekedéséhez

Structure generation and de novo design using reaction networks

This project is concerned with de novo molecular design whereby novel molecules are built in silico and evaluated against properties relevant to biological activity, such as physicochemical properties and structural similarity to active compounds. The aim is to encourage cost-effective compound design by reducing the number of molecules requiring synthesis and analysis. One of the main issues in de novo design is ensuring that the molecules generated are synthesisable. In this project, a method is developed that enables virtual synthesis using rules derived from reaction sequences. Individual reactions taken from reaction databases were connected to form reaction networks. Reaction sequences were then extracted by tracing paths through the network and used to create ‘reaction sequence vectors’ (RSVs) which encode the differences between the start and end points of th esequences. RSVs can be applied to molecules to generate virtual products which are based on literature precedents. The RSVs were applied to structure-activity relationship (SAR) exploration using examples taken from the literature. They were shown to be effective in expanding the chemical space that is accessible from the given starting materials. Furthermore, each virtual product is associated with a potential synthetic route. They were then applied in de novo design scenarios with the aim of generating molecules that are predicted to be active using SAR models. Using a collection of RSVs with a set of small molecules as starting materials for de novo design proved that the method was capable of producing many useful, synthesisable compounds worthy of future study. The RSV method was then compared with a previously published method that is based on individual reactions (reaction vectors or RVs). The RSV approach was shown to be considerably faster than de novo design using RVs, however, the diversity of products was more limited

Improving the Unification of Software Clones using Tree and Graph Matching Algorithms

Code duplication is common in all kind of software systems and is one of the most troublesome hurdles in software maintenance and evolution activities. Even though these code clones are created for the reuse of some functionality, they usually go through several modifications after their initial introduction. This has a serious negative impact on the maintainability, comprehensibility, and evolution of software systems. Existing code duplication can be eliminated by extracting the common functionality into a single module. In the past, several techniques have been developed for the detection and management of software clones. However, the unification and refactoring of software clones is still a challenging problem, since the existing tools are mostly focused on clone detection and there is no tool to find particularly refactoring-oriented clones. The programmers need to manually understand the clones returned by the clone detection tools, decide whether they should be refactored, and finally perform their refactoring. This obvious gap between the clone detection tools and the clone analysis tools, makes the refactoring tedious and the programmers reluctant towards refactoring duplicate codes. In this thesis, an approach for the unification and refactoring of software clones that overcomes the limitations of previous approaches is presented. More specifically, the proposed technique is able to detect and parameterize non-trivial differences between the clones. Moreover, it can find a mapping between the statements of the clones that minimizes the number of differences. We have also defined preconditions in order to determine whether the duplicated code can be safely refactored to preserve the behavior of the existing code. We compared the proposed technique with a competitive clone refactoring tool and concluded that our approach is able to find a significantly larger number of refactorable clones