Protein-peptide interactions play a major role in several biological processes, such as cellproliferation and cancer cell life-cycles. Accurate computational methods for predictingprotein-protein interactions exist, but few of these method can be extended to predictinginteractions between a protein and a particularly small or intrinsically disordered peptide. In this thesis, PePIP is presented. PePIP is a pipeline for predicting where on a given proteina given peptide will most probably bind. The pipeline utilizes structural aligning to perusethe Protein Data Bank for possible templates for the interaction to be predicted, using thelarger chain as the query. The possible templates are then evaluated as to whether they canrepresent the query protein and peptide using a Random Forest classifier machine learningalgorithm, and the best templates are found by using the evaluation from the Random Forest in combination with hierarchical clustering. These final templates are then combined to givea prediction of binding site. PePIP is proven to be highly accurate when testing on a set of 502 experimentally determinedprotein-peptide structures, suggesting a binding site on the correct part of the protein- surfaceroughly 4 out of 5 times.

In this dissertation a novel approach to protein-ligand docking is presented. First an existing method to predict putative active sites is employed. These predictions are then used to cut down the search space of an algorithm that uses the fast Fourier transform to calculate the geometrical and electrostatic complementarity between a protein and a small organic ligand. A simplified hydrophobicity score is also calculated for each active site. The docking method could be applied either to dock ligands in a known active site or to rank several putative active sites according to their biological feasibility. The method was evaluated on a set of 310 protein-ligand complexes. The results show that with respect to docking the method with its initial parameter settings is too coarse grained. The results also show that with respect to ranking of putative active sites the method works quite well.

Single-cell RNA sequencing and in situ sequencing were combined in a recent study of the developing human heart to explore the transcriptional landscape at three developmental stages. However, the method used in the study to create the spatial cellular maps has some limitations. It relies on image segmentation of the nuclei and cell types defined in advance by single-cell sequencing. In this study, we applied a new unsupervised approach based on graph neural networks on the in situ sequencing data of the human heart to find spatial gene expression patterns and detect novel cell and sub-cell types. In this thesis, we first introduce some relevant background knowledge about the sequencing techniques that generate our data, machine learning in single-cell analysis, and deep learning on graphs. We have explored several graph neural network models and algorithms to learn embeddings for spatial gene expression. Dimensionality reduction and cluster analysis were performed on the embeddings for visualization and identification of biologically functional domains. Based on the cluster gene expression profiles, locations of the clusters in the heart sections, and comparison with cell types defined in the previous study, the results of our experiments demonstrate that graph neural networks can learn meaningful representations of spatial gene expression in the human heart. We hope further validations of our clustering results could give new insights into cell development and differentiation processes of the human heart.

Protein-protein interaction dominates all major biology processes in living cells. Recent studies suggestthat the surface expression and activity of G protein-coupled receptors (GPCRs), which are the largestfamily of receptors in human cells, can be modulated by receptor activity–modifying proteins (RAMPs). Computational tools are essential to complement experimental approaches for the understanding ofmolecular activity of living cells and molecular dynamics simulations are well suited to providemolecular details of proteins function and structure. The classical atom-level molecular modeling ofbiological systems is limited to small systems and short time scales. Therefore, its application iscomplicated for systems such as protein-protein interaction in cell-surface membrane. For this reason, coarse-grained (CG) models have become widely used and they represent an importantstep in the study of large biomolecular systems. CG models are computationally more effective becausethey simplify the complexity of the protein structure allowing simulations to have longer timescales. The aim of this degree project was to determine if the applications of coarse-grained molecularsimulations were suitable for the understanding of the dynamics and structural basis of the GPCRRAMP interactions in a membrane environment. Results indicate that the study of protein-proteininteractions using CG needs further improvement with a more accurate parameterization that will allowthe study of complex systems.

Basal Ganglia are evolutionarily conserved brain nuclei involved in several physiologically important animal behaviors like motor control and reward learning. Striatum, which is the input nuclei of basal ganglia, integrates inputs from several neurons, like cortical and thalamic glutamatergic input and local GABAergic inputs. Several neuromodulators, such as dopamine, accetylcholine and serotonin modulate the functional properties of striatal neurons. Aberrations in the intracellular signaling of these neurons lead to several debilitating neurodegenerative diseases, like Parkinson’s disease. In order to understand these aberrations we should first identify the role of different molecular players in the normal physiology. The long term goal of this research is to understand the molecular mechanisms responsible for the integration of different neuromodulatory signals by striatal medium spiny neurons (MSN). This signal integration is known to play important role in learning. This is manifested via changes in the synaptic weights between different neurons. The group of synpases taken into consideration for the current work is the corticostriatal one, which are synapses between the cortical projection neurons and MSNs. One of the molecular processes of considerable interest is the interaction between dopaminergic and cholinergic inputs. In this thesis I have investigated the interactions between the biochemical cascades triggered by dopaminergic, cholinergic (ACh) and glutamatergic inputs to the striatal MSN. The dopamine induced signaling increases the levels of cAMP in the striatonigral MSNs. The sources of dopamine and acetylcholine are dopaminergic neurons (DAN) from midbrain and tonically active cholinergic interneurons (TAN) of striatum, respectively. A sub-second burst activity in DAN along with a simultaneous pause in TAN is a characteristic effect elicited by a salient stimulus. This, in turn, leads to a dopamine peak and, possibly, an acetylcholine (ACh) dip in striatum. I have looked into the possibility of sensing this ACh dip and the dopamine peak at striatonigral MSNs. These neurons express D1 dopamine receptor (D1R) coupled to Golf and M4 Muscarinic receptor (M4R) coupled to Gi/o . These receptors are expressed significantly in the dendritic spines of these neurons where the Adenylate Cyclase 5 (AC5) is a point of convergence for these two signals. Golf stimulates the production of cAMP by AC5 whereas Gi/o inhibits the Golf mediated cAMP production. I have performed a kinetic-modeling exercise to explore how dopamine and ACh interacts with each other via these receptors and what are the effects on the downstream signaling events. The results of model simulation suggest that the striatonigral MSNs are able to sense the ACh dip via M4R. They integrate the dip with the dopamine peak to activate AC5 synergistically. We also found that the ACh tone may act as a potential noise filter against noisy dopamine signals. The parameters for the G-protein GTPase activity indicate towards an important role of GTPase Activating Proteins (GAPs), like RGS, in this process. Besides this we also hypothesize that M4R may have therapeutic potential. / <p>QC 20140325</p>

Parameter inference for stochastic systems is considered as one of the fundamental classical problems in the domain of computational systems biology. The problem becomes challenging and often analytically intractable with the large number of uncertain parameters. In this scenario, Markov Chain Monte Carlo (MCMC) algorithms have been proved to be highly effective. For a stochastic system, the most accurate description of the kinetics is given by the Chemical Master Equation (CME). Unfortunately, analytical solution of CME is often intractable even for considerably small amount of chemically reacting species due to its super exponential state space complexity. As a solution, Stochastic Simulation Algorithm (SSA) using Monte Carlo approach was introduced to simulate the chemical process defined by the CME. SSA is an exact stochastic method to simulate CME but it also suffers from high time complexity due to simulation of every reaction. Therefore computation of likelihood function (based on exact CME) in MCMC becomes expensive which alternately makes the rejection step expensive. In this generic work, we introduce different approximations of CME as a pre-conditioning step to the full MCMC to make rejection cheaper. The goal is to avoid expensive computation of exact CME as far as possible. We show that, with effective pre-conditioning scheme, one can save a considerable amount of exact CME computations maintaining similar convergence characteristics. Additionally, we investigate three different sampling schemes (dense sampling, longer sampling and i.i.d sampling) under which convergence for MCMC using exact CME for parameter estimation can be analyzed. We find that under i.i.d sampling scheme, better convergence can be achieved than that of dense sampling of the same process or sampling the same process for longer time. We verify our theoretical findings for two different processes: linear birth-death and dimerization.Apart from providing a framework for parameter inference using CME, this work also provides us the reasons behind avoiding CME (in general) as a parameter estimation technique for so long years after its formulation

Host-adapted bacteria are pathogens that, through evolutionary time and host-adaptive events, acquired the ability to manipulate hosts into assisting their own reproduction and spread. Through these host-adaptive events, free-living pathogens may be rendered unable to reproduce without their host, which is an irreversible step in evolution. Francisellaceae and Legionellales, two orders of Gammaproteobacteria, are cases where host-adaptation has lead to an intracellular lifestyle. Both orders use secretion systems, in combination with effector proteins, to invade and control their hosts. A current view is that Francisellaceae and Legionellales went through host-adaptive events at two separate time points. However, F. hongkongensis, a member of Francisellaceae shares the same secretion system as the order of Legionellales. Additionally, two host-adapted Gammaproteobacteria, Piscirickettsia spp. and Berkiella spp., swaps phylogenetic positions between Legionellales and Francisellaceae depending on methods applied - indicating shared features of Francisellaceae and Legionellales. In this study, we set up a workflow to screen public metagenomic data for candidate host-adaptive bacteria. Using this data, we attempted to assert the phylogenetic position and possibly resolve evolutionary events that occurred in Legionellales, F. hongkongensis, Francisellaceae, Piscirickettsia spp. and Berkiella spp. We successfully acquired 23 candidate host-adapted MAGs by (i) scanning for genes, among reads before assembly, using PhyloMagnet, and (ii) screening for complete secretion systems with MacSyFinder. The phylogenetic results turned out indecisive in the placement ofBerkiella spp. and Piscirickettsia. However, results found in this study indicate that, contrary to previous beliefs, it is possible that it was one intracellularization event of a common ancestor that gave rise to the intracellular lifestyle of Francisellaceae and Legionellales.

The study of enzyme binding sites is an essential but rather demanding process of increased complexity since the amino acids lining these areas are not rigid. At the same time, the minimization of side effects and the specificity of new ligands is a great challenge in the structure-based drug design approach. Using glycogen phosphorylase - a validated target for the development of new antidiabetic agents - as a case study, this project focuses on the examination of side-chain conformations of amino acids that play a key role in the catalytic site of the enzyme. Specifically, different rotamers of each amino acid were collected to build a dataset of different conformations of the catalytic site. The rotamers were filtered by their probability of occurrence and subsequently, all rotamers that create steric clashes were rejected. Then, these conformations were clustered based on their similarity. Three different clustering algorithms and multiple numbers of clusters were tested using the silhouette scores evaluation for the clustering process. In order to measure the similarity, the Euclidean metric was used which due to the correspondence of the coordinates between the conformations was very similar to the cRMSD metric. Two-level clustering was applied to the dataset for more in-depth observations. According to the clustering results, specific aminoacids with major geometrical variations in their rotamers play the most important role in the separation of the clusters. Additionally, all rotamers of an amino acid can be grouped based on their structure, something that was confirmed using “Chimera” software as a visualization tool. To this end, the ultimate aim of this study is to examine whether the clustering of conformations produces clusters with points geometrically similar to each other, in order to identify near neighbors, i.e. conformations that are quite similar in structure but do not play a determinant role in the function and those that are quite diverse and could be further exploited.

With the advent of fluorescence microscopy and image analysis, quantitative information from images can be extracted and changes in cell morphology can be studied. Microscopy-based morphological profiling assays with multiplexed fluorescent dyes, like Cell Painting, can be used for this purpose. It has been shown that morphological profiles can be used to train AI models to classify images into different biological mechanisms. Hence, the goal of this project was to study the possibilities for Deep Learning models and Convolutional Neural Networks to distinguish between different classes of kinase inhibitors based on their morphological profiles. Three different Convolutional Neural Network architectures were used: ResNet50, MobileNetV2, and VGG16. They were trained with two different inputs and two different optimisers: Adam and SGD. Also, a comparison between the performances with and without Transfer Learning through ImageNet weights was executed. The results indicate that MobileNetV2 with Adam as an optimiser performed the best, with a micro average of 0.93 and higher ROC areas compared to the other models. The study also highlighted the importance of utilizing Transfer Learning.

Knee osteoarthritis is a growing problem due to increasing risk factors such as age and obesity. It is a common task for a radiologist to grade osteoarthritis in three compartments (medial tibiofemoral (MTF), lateral tibiofemoral (LTF) and patellofemoral (PF)) in a knee from different image views of X-ray images, to decide if osteoarthritis is the cause of pain for the patient. Reasons for automating this process are to decrease subjectivity, time for diagnosis and reduce workload for radiologists. The aim with this project was to grade osteoarthritis using machine learning by training convolutional neural networks on around 5000 double annotated examinations by radiologists and one orthopaedic surgeon at Nyköping Hospital. Different methods were evaluated and the models were then optimised with hyperparameter tuning. The aim with the project is to contribute to a future software that could be tested at Nyköping Hospital. The project found that using transfer learning with DenseNet for MTF and PF, and using a MTF model as transfer learning model for the LTF model was the best performing transfer learning networks to use. Also, cropping the images around the region of interest for MTF and LTF improved the models. The best method to make predictions from the model outputs appeared to be to train a model on a merged set of training- and validation data for making predictions. Comparisons of final models with the radiologist initial annotations showed that the MTF and LTF models give fewer misclassifications of more than one grade, if compared to the disagreements of more than one grade by the two radiologists. While for the PF model the radiologists still have an advantage and more data is probably needed for both the PF model and the LTF model since grade 0 is very overrepresented for those grades.