Identification of DNA-binding protein based multiple kernel model

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs. In detail, we first extract features from the protein sequence using six methods. Second, the Radial Basis Function (RBF) kernel function is utilized to construct pre-defined kernel metrics. Then, these metrics are combined linearly by weights calculated by LapLKA. Finally, the fused kernel is input to RKM for training and prediction. Independent tests and leave-one-out cross-validation were used to validate the performance of our method on a small dataset and two large datasets. Importantly, we built an online platform to represent our model, which is now freely accessible via http://8.130.69.121:8082/

Application of Machine Learning for Drug–Target Interaction Prediction

Exploring drug–target interactions by biomedical experiments requires a lot of human, financial, and material resources. To save time and cost to meet the needs of the present generation, machine learning methods have been introduced into the prediction of drug–target interactions. The large amount of available drug and target data in existing databases, the evolving and innovative computer technologies, and the inherent characteristics of various types of machine learning have made machine learning techniques the mainstream method for drug–target interaction prediction research. In this review, details of the specific applications of machine learning in drug–target interaction prediction are summarized, the characteristics of each algorithm are analyzed, and the issues that need to be further addressed and explored for future research are discussed. The aim of this review is to provide a sound basis for the construction of high-performance models

Markov field models of molecular kinetics

Computer simulations such as molecular dynamics (MD) provide a possible means to understand protein dynamics and mechanisms on an atomistic scale. The resulting simulation data can be analyzed with Markov state models (MSMs), yielding a quantitative kinetic model that, e.g., encodes state populations and transition rates. However, the larger an investigated system, the more data is required to estimate a valid kinetic model. In this work, we show that this scaling problem can be escaped when decomposing a system into smaller ones, leveraging weak couplings between local domains. Our approach, termed independent Markov decomposition (IMD), is a first-order approximation neglecting couplings, i.e., it represents a decomposition of the underlying global dynamics into a set of independent local ones. We demonstrate that for truly independent systems, IMD can reduce the sampling by three orders of magnitude. IMD is applied to two biomolecular systems. First, synaptotagmin-1 is analyzed, a rapid calcium switch from the neurotransmitter release machinery. Within its C2A domain, local conformational switches are identified and modeled with independent MSMs, shedding light on the mechanism of its calcium-mediated activation. Second, the catalytic site of the serine protease TMPRSS2 is analyzed with a local drug-binding model. Equilibrium populations of different drug-binding modes are derived for three inhibitors, mirroring experimentally determined drug efficiencies. IMD is subsequently extended to an end-to-end deep learning framework called iVAMPnets, which learns a domain decomposition from simulation data and simultaneously models the kinetics in the local domains. We finally classify IMD and iVAMPnets as Markov field models (MFM), which we define as a class of models that describe dynamics by decomposing systems into local domains. Overall, this thesis introduces a local approach to Markov modeling that enables to quantitatively assess the kinetics of large macromolecular complexes, opening up possibilities to tackle current and future computational molecular biology questions

Computational approaches for improving treatment and prevention of viral infections

The treatment of infections with HIV or HCV is challenging. Thus, novel drugs and new computational approaches that support the selection of therapies are required. This work presents methods that support therapy selection as well as methods that advance novel antiviral treatments. geno2pheno[ngs-freq] identifies drug resistance from HIV-1 or HCV samples that were subjected to next-generation sequencing by interpreting their sequences either via support vector machines or a rules-based approach. geno2pheno[coreceptor-hiv2] determines the coreceptor that is used for viral cell entry by analyzing a segment of the HIV-2 surface protein with a support vector machine. openPrimeR is capable of finding optimal combinations of primers for multiplex polymerase chain reaction by solving a set cover problem and accessing a new logistic regression model for determining amplification events arising from polymerase chain reaction. geno2pheno[ngs-freq] and geno2pheno[coreceptor-hiv2] enable the personalization of antiviral treatments and support clinical decision making. The application of openPrimeR on human immunoglobulin sequences has resulted in novel primer sets that improve the isolation of broadly neutralizing antibodies against HIV-1. The methods that were developed in this work thus constitute important contributions towards improving the prevention and treatment of viral infectious diseases.Die Behandlung von HIV- oder HCV-Infektionen ist herausfordernd. Daher werden neue Wirkstoffe, sowie neue computerbasierte Verfahren benötigt, welche die Therapie verbessern. In dieser Arbeit wurden Methoden zur Unterstützung der Therapieauswahl entwickelt, aber auch solche, welche neuartige Therapien vorantreiben. geno2pheno[ngs-freq] bestimmt, ob Resistenzen gegen Medikamente vorliegen, indem es Hochdurchsatzsequenzierungsdaten von HIV-1 oder HCV Proben mittels Support Vector Machines oder einem regelbasierten Ansatz interpretiert. geno2pheno[coreceptor-hiv2] bestimmt den HIV-2 Korezeptorgebrauch dadurch, dass es einen Abschnitt des viralen Oberflächenproteins mit einer Support Vector Machine analysiert. openPrimeR kann optimale Kombinationen von Primern für die Multiplex-Polymerasekettenreaktion finden, indem es ein Mengenüberdeckungsproblem löst und auf ein neues logistisches Regressionsmodell für die Vorhersage von Amplifizierungsereignissen zurückgreift. geno2pheno[ngs-freq] und geno2pheno[coreceptor-hiv2] ermöglichen die Personalisierung antiviraler Therapien und unterstützen die klinische Entscheidungsfindung. Durch den Einsatz von openPrimeR auf humanen Immunoglobulinsequenzen konnten Primersätze generiert werden, welche die Isolierung von breit neutralisierenden Antikörpern gegen HIV-1 verbessern. Die in dieser Arbeit entwickelten Methoden leisten somit einen wichtigen Beitrag zur Verbesserung der Prävention und Therapie viraler Infektionskrankheiten

Developing a framework for semi-automated rule-based modelling for neuroscience research

Dynamic modelling has significantly improved our understanding of the complex molecular mechanisms underpinning neurobiological processes. The detailed mechanistic insights these models offer depend on the availability of a diverse range of experimental observations. Despite the huge increase in biomolecular data generation from novel high-throughput technologies and extensive research in bioinformatics and dynamical modelling, efficient creation of accurate dynamical models remains highly challenging. To study this problem, three perspectives are considered: comparison of modelling methods, prioritisation of results and analysis of primary data sets. Firstly, I compare two models of the DARPP-32 signalling network: a classically defined model with ordinary differential equations (ODE) and its equivalent, defined using a novel rule-based (RB) paradigm. The RB model recapitulates the results of the ODE model, but offers a more expressive and flexible syntax that can efficiently handle the “combinatorial complexity” commonly found in signalling networks, and allows ready access to fine-grain details of the emerging system. RB modelling is particularly well suited to encoding protein-centred features such as domain information and post-translational modification sites. Secondly, I propose a new pipeline for prioritisation of molecular species that arise during model simulation using a recently developed algorithm based on multivariate mutual information (CorEx) coupled with global sensitivity analysis (GSA) using the RKappa package. To efficiently evaluate the importance of parameters, Hilber-Schmidt Independence Criterion (HSIC)-based indices are aggregated into a weighted network that allows compact analysis of the model across conditions. Finally, I describe an approach for the development of disease-specific dynamical models using genes known to be associated with Attention Deficit Hyperactivity Disorder (ADHD) as an exemplar. Candidate disease genes are mapped to a selection of datasets that are potentially relevant to the modelling process (e.g. interactions between proteins and domains, protein-domain and kinase-substrates mappings) and these are jointly analysed using network clustering and pathway enrichment analyses to evaluate their coverage and utility in developing rule-based models

Characterisation of a parallel microbioreactor system and its application to accelerate cell culture process development

Recent advancements in the field of microbioreactor technologies have transformed early- and mid-stage process development. Microbioreactor systems benefit from monitoring and control capabilities akin to larger scale bioreactors that have been realised at a much smaller working volume, thereby promoting the quick accumulation of process knowledge early on in the development timeline. Furthermore, the control of key process parameters at the small scale led to improved scalability when compared to traditionally used systems such as shake flasks and microtitre plates. Ultimately, the successful implementation of microbioreactor systems in a bioprocess development workflow can reduce the time to market and decrease the price of biotechnology products. This thesis sought to investigate whether the micro-Matrix microbioreactor (Applikon-Biotechnology BV) is a suitable instrument for cell culture process development. The system is based on a shaken 24 deep square well microtitre plate format with a working volume between 2 - 5 mL in which each well can be individually controlled for temperature, pH, and dissolved oxygen (DO) concentration. The system was first characterised to gain a better understanding of the cultivation environment within each well and to identify a suitable scaling criterion to a reference benchtop-scale bioreactor system. Mixing times were found to range between 1 – 42 s, while the volumetric mass transfer coefficient (kLa) was 2.4 – 240.8 h-1. Computational fluid dynamics was used to derive the power input, which was found to range between 4 W m-3 – 765 W m-3. Initial cell cultivations revealed considerable evaporation rates and well-to-well variabilities, which were successfully addressed by establishing a method for the periodic replacement of evaporated liquid. Mixing time was identified as suitable scaling criterion and a GS-CHO fed-batch process was scaled down from a reference stirred tank reactor (STR) with a working volume of 5 L to the micro-Matrix system. A problem specific to pH controlled small- and benchtop-scale bioreactors was highlighted, where the removal of CO2 from the cultivation broth is very efficient and eventually CO2 is stripped out completely. A low CO2 concentration was shown to negatively affect the maximum viable cell concentration in either system. A combination of matched mixing time and matched minimum CO2 fraction in the inflowing gas was therefore proposed as suitable scaling criterion. Scalability of growth and production kinetics as well as antibody glycosylation were demonstrated between micro-Matrix and the 5 L reference STR system. Subsequently, the micro-Matrix was used for the rapid optimisation of a GS-CHO feeding strategy. First, several bolus, continuous, and dynamic feed addition strategies were compared and bolus feeding was shown to be sufficient for the cell line under investigation. With the help of response surface methodology, the bolus feeding regime was optimised, which led to a 25.4% increase of the space-time yield and a 25 % increase of the final titre. Following a highly replicated validation of the results in the micro-Matrix, the optimised feeding strategy was scaled up to the 5 L STR system and shown to yield equivalent results. In the final chapter of the thesis, the focus was shifted to an emerging application for small-scale process development systems by investigating the effect of the cultivation environment on the growth and differentiation of the primary T cells. Initially, a perfusion-mimic process was identified as suitable mode of operation, necessitated by the need to repeatedly replenish nutrients and remove waste metabolites. Using this perfusion-mimic process, pH, DO, and shaking speed setpoints were then investigated as part of a full-factorial design and explored for their effect on growth kinetics and differentiation of primary T cells. The numerical optimisation established a locally optimal final cell concentration and differentiation profile for a shaking speed of 200 rpm, a pH of 7.4, and a DO of 25%. In summary, this work demonstrates the utility of the micro-Matrix microbioreactor in two bioprocess development applications and provides a toolbox and framework to aide with the implementation of the micro-Matrix in further bioprocessing applications