SwarmDock and the Use of Normal Modes in Protein-Protein Docking

Here is presented an investigation of the use of normal modes in protein-protein docking, both in theory and in practice. Upper limits of the ability of normal modes to capture the unbound to bound conformational change are calculated on a large test set, with particular focus on the binding interface, the subset of residues from which the binding energy is calculated. Further, the SwarmDock algorithm is presented, to demonstrate that the modelling of conformational change as a linear combination of normal modes is an effective method of modelling flexibility in protein-protein docking

Machine learning applications for the topology prediction of transmembrane beta-barrel proteins

The research topic for this PhD thesis focuses on the topology prediction of beta-barrel transmembrane proteins. Transmembrane proteins adopt various conformations that are about the functions that they provide. The two most predominant classes are alpha-helix bundles and beta-barrel transmembrane proteins. Alpha-helix proteins are present in larger numbers than beta-barrel transmembrane proteins in structure databases. Therefore, there is a need to find computational tools that can predict and detect the structure of beta-barrel transmembrane proteins. Transmembrane proteins are used for active transport across the membrane or signal transduction. Knowing the importance of their roles, it becomes essential to understand the structures of the proteins. Transmembrane proteins are also a significant focus for new drug discovery. Transmembrane beta-barrel proteins play critical roles in the translocation machinery, pore formation, membrane anchoring, and ion exchange. In bioinformatics, many years of research have been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB (transmembrane beta-barrel) proteins topology prediction have been overshadowed, and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past to predict TMB proteins topology. Methods developed in the literature that are available include turn identification, hydrophobicity profiles, rule-based prediction, HMM (Hidden Markov model), ANN (Artificial Neural Networks), radial basis function networks, or combinations of methods. The use of cascading classifier has never been fully explored. This research presents and evaluates approaches such as ANN (Artificial Neural Networks), KNN (K-Nearest Neighbors, SVM (Support Vector Machines), and a novel approach to TMB topology prediction with the use of a cascading classifier. Computer simulations have been implemented in MATLAB, and the results have been evaluated. Data were collected from various datasets and pre-processed for each machine learning technique. A deep neural network was built with an input layer, hidden layers, and an output. Optimisation of the cascading classifier was mainly obtained by optimising each machine learning algorithm used and by starting using the parameters that gave the best results for each machine learning algorithm. The cascading classifier results show that the proposed methodology predicts transmembrane beta-barrel proteins topologies with high accuracy for randomly selected proteins. Using the cascading classifier approach, the best overall accuracy is 76.3%, with a precision of 0.831 and recall or probability of detection of 0.799 for TMB topology prediction. The accuracy of 76.3% is achieved using a two-layers cascading classifier. By constructing and using various machine-learning frameworks, systems were developed to analyse the TMB topologies with significant robustness. We have presented several experimental findings that may be useful for future research. Using the cascading classifier, we used a novel approach for the topology prediction of TMB proteins

Activation of the pro-resolving receptor Fpr2 attenuates inflammatory microglial activation

Poster number: P-T099 Theme: Neurodegenerative disorders & ageing Activation of the pro-resolving receptor Fpr2 reverses inflammatory microglial activation Authors: Edward S Wickstead - Life Science & Technology University of Westminster/Queen Mary University of London Inflammation is a major contributor to many neurodegenerative disease (Heneka et al. 2015). Microglia, as the resident immune cells of the brain and spinal cord, provide the first line of immunological defence, but can become deleterious when chronically activated, triggering extensive neuronal damage (Cunningham, 2013). Dampening or even reversing this activation may provide neuronal protection against chronic inflammatory damage. The aim of this study was to determine whether lipopolysaccharide (LPS)-induced inflammation could be abrogated through activation of the receptor Fpr2, known to play an important role in peripheral inflammatory resolution. Immortalised murine microglia (BV2 cell line) were stimulated with LPS (50ng/ml) for 1 hour prior to the treatment with one of two Fpr2 ligands, either Cpd43 or Quin-C1 (both 100nM), and production of nitric oxide (NO), tumour necrosis factor alpha (TNFα) and interleukin-10 (IL-10) were monitored after 24h and 48h. Treatment with either Fpr2 ligand significantly suppressed LPS-induced production of NO or TNFα after both 24h and 48h exposure, moreover Fpr2 ligand treatment significantly enhanced production of IL-10 48h post-LPS treatment. As we have previously shown Fpr2 to be coupled to a number of intracellular signaling pathways (Cooray et al. 2013), we investigated potential signaling responses. Western blot analysis revealed no activation of ERK1/2, but identified a rapid and potent activation of p38 MAP kinase in BV2 microglia following stimulation with Fpr2 ligands. Together, these data indicate the possibility of exploiting immunomodulatory strategies for the treatment of neurological diseases, and highlight in particular the important potential of resolution mechanisms as novel therapeutic targets in neuroinflammation. References Cooray SN et al. (2013). Proc Natl Acad Sci U S A 110: 18232-7. Cunningham C (2013). Glia 61: 71-90. Heneka MT et al. (2015). Lancet Neurol 14: 388-40

Modelling biomolecules through atomistic graphs: theory, implementation, and applications

Describing biological molecules through computational models enjoys ever-growing popularity. Never before has access to computational resources been easier for scientists across the natural sciences. The need for accurate, efficient, and robust modelling tools is therefore irrefutable. This, in turn, calls for highly interdisciplinary research, which the thesis presented here is a product of. Through the successful marriage of techniques from mathematical graph theory, theoretical insights from chemistry and biology, and the tools of modern computer science, we are able to computationally construct accurate depictions of biomolecules as atomistic graphs, in which individual atoms become nodes and chemical bonds/interactions are represented by weighted edges. When combined with methods from graph theory and network science, this approach has previously been shown to successfully reveal various properties of proteins, such as dynamics, rigidity, multi-scale organisation, allostery, and protein-protein interactions, and is well poised to set new standards in terms of computational feasibility, multi-scale resolution (from atoms to domains) and time-scales (from nanoseconds to milliseconds). Therefore, building on previous work in our research group spanning over 15 years and to further encourage and facilitate research into this growing field, this thesis's main contribution is to provide a formalised foundation for the construction of atomistic graphs. The most crucial aspect of constructing atomistic graphs of large biomolecules compared to small molecules is the necessity to include a variety of different types of bonds and interactions, because larger biomolecules attain their unique structural layout mainly through weaker interactions, e.g. hydrogen bonds, the hydrophobic effect or π-π interactions. Whilst most interaction types are well-studied and have readily available methodology which can be used to construct atomistic graphs, this is not the case for hydrophobic interactions. To fill this gap, the work presented herein includes novel methodology for encoding the hydrophobic effect in atomistic graphs, that accounts for the many-body effect and non-additivity. Then, a standalone software package for constructing atomistic graphs from structural data is presented. Herein lies the heart of this thesis: the combination of a variety of methodologies for a range of bond/interaction types, as well as an implementation that is deterministic, easy-to-use and efficient. Finally, some promising avenues for utilising atomistic graphs in combination with graph theoretical tools such as Markov Stability as well as other approaches such as Multilayer Networks to study various properties of biomolecules are presented.Open Acces

Role interneuronů a dysfunkce nervových okruhů u Alzheimerovy nemoci

Alzheimerova choroba je jednou z nejrozšířenějších neurodegenerativních poruch ve svetě, které vedou ke změně aktivity nervových a neuronových okruhů, zejména ke zhoršení kognitivní funkce. Většina lidí s AD trpí poruchou paměti, špatným úsudkem, dezorientaci a problémy s učením. Několik hypotéz se snaží vysvětlit příčinu nemoci, ale není to zcela pochopeno. Vzhledem k tomu, že změny v mozkové struktuře vznikají roky před vznikem klinických příznaků, dostupná terapeutická léčba může pouze snížit dopad neurodegenerace, nikoliv však zvrátit. Interneurony, jež jsou součástí neuronových okruhů, hrají důležitou roli ve formaci kognitivních schopností. Interneurony v CNS jsou většinou inhibiční a efektivně řídí synchronizaci neurálních sítí. Síťová hypersynchronie je zvýšená synchronizace nervové aktivity a je spojena s patologií AD. Dysfunkce interneuronů má za následek změnu síťové aktivity u pacientů s AD. Klíčová slova: AD, mozek, potkan, interneurony, hypersynchronie.Alzheimer's disease is one of the most common neurodegenerative disorder that results in altered network activity, in particular cognitive decline. Majority people with AD experience memory impairment, poor judgment, disorientation and learning difficulties. Several hypotheses try to explain the cause of the disease, but it's poorly understood. Due to the fact that changes in brain structure arise years before clinical symptoms emerge, the available therapeutic treatments can only reduce the impact of neurodegeneration, but not to reverse. Interneurons, as a part of neural circuits, play an important role in the formation of cognitive abilities. Most of interneurons in CNS are inhibitory and they effectively control the network synchrony. Network hypersynchrony is an increased synchronization of neural activity and it's linked to AD pathology. Dysfunction of interneurons is resulted in altered network activity in patients with AD. Keywords: AD, brain, rat, interneurons, hypersynchrony.Katedra fyziologieDepartment of PhysiologyPřírodovědecká fakultaFaculty of Scienc

Fluidity of functional ensembles in the infralimbic cortex of rats during reward seeking

The medial prefrontal cortex (mPFC), specifically the prelimbic (PL) and the infralimbic region (IL), plays a crucial role during reward seeking behaviour. The IL specifically is involved in the control of reward seeking and has been implicated in the representation of different rewards. However, the precise representation of reward seeking behaviour on a neuronal network level within the IL remains elusive. To investigate neuronal ensembles during reward seeking in the IL of the mPFC in rats, an operant conditioning paradigm was combined with imaging of the intracellular calcium concentration ([Ca2+]i) as a proxy for neuronal activity. The latter is achieved using GRIN lenses and miniaturized head-mounted fluorescence microscopes. A frame trigger was used to synchronize the operant conditioning chambers with the [Ca2+]i data and an analysis pipeline was developed using a combination of custom designed Matlab classes and available open source software. Neurons were identified for three saccharin self-administration (SA) and one reinstatement (RE) session and then matched across sessions. Periods during which rats interacted with the operant conditioning setup were identified (e.g. lever presses and head entries into the reward port) and the corresponding [Ca2+]i transients were used to identify neurons coactive during distinct phases of reward seeking behaviour. Neurons were classified according to the time point of their activity relative to the sequence of actions consisting of the lever press and the time before, during, and after the head entry. This analysis revealed that subsets of neurons are preferentially active during distinct events of the reward seeking. Also, cells tuned to time points during the reward seeking did not appear or show tuning in all of the sessions. If they did show tuning, however, the phase of the reward seeking to which they showed tuning generally remained the same. Hence, the specific ensemble which is active during the reward seeking in each session changes. Individual neurons that are recruited into these ensembles, however, keep their tuning. Also, the composition of tuned neurons active during a specific behavioural phase remains stable. In addition, neurons that are active and tuned in multiple sessions do not appear to be arranged in a topology that can be identified with the methods used. In conclusion, the sequence of the reward seeking behaviour is encoded in neuronal ensembles of the IL cortex. These ensembles are formed from a larger pool of available neurons in each session. Neurons participating in these ensembles preferentially keep their tuning to a phase of the reward seeking, but may not be recruited to each of the ensembles. Thus, ensembles representing identical behavioural episodes in different sessions are not stable, but fluidly change their composition

The <i>N</i>-myristoylome of <i>Trypanosoma cruzi</i>

Protein N-myristoylation is catalysed by N-myristoyltransferase (NMT), an essential and druggable target in Trypanosoma cruzi, the causative agent of Chagas’ disease. Here we have employed whole cell labelling with azidomyristic acid and click chemistry to identify N-myristoylated proteins in different life cycle stages of the parasite. Only minor differences in fluorescent-labelling were observed between the dividing forms (the insect epimastigote and mammalian amastigote stages) and the non-dividing trypomastigote stage. Using a combination of label-free and stable isotope labelling of cells in culture (SILAC) based proteomic strategies in the presence and absence of the NMT inhibitor DDD85646, we identified 56 proteins enriched in at least two out of the three experimental approaches. Of these, 6 were likely to be false positives, with the remaining 50 commencing with amino acids MG at the N-terminus in one or more of the T. cruzi genomes. Most of these are proteins of unknown function (32), with the remainder (18) implicated in a diverse range of critical cellular and metabolic functions such as intracellular transport, cell signalling and protein turnover. In summary, we have established that 0.43–0.46% of the proteome is N-myristoylated in T. cruzi approaching that of other eukaryotic organisms (0.5–1.7%)

Approaches for studying allostery using network theory

Allostery is the process whereby binding of a substrate at a site other than the active site modulates the function of a protein. Allostery is thus one of the myriad of biological processes that keeps cells under tight regulatory control, specifically one that acts at the level of the protein rather than through changes in gene transciption or translation of mRNA. Despite over 50 years of investigation, allostery has remained a difficult phenomenon to elucidate. Structural changes are often too subtle for many experimental methods to capture and it has become increasingly obvious that a range of timescales are involved, from extremely fast pico- to nanosecond local fluctuations all the way up to the millisecond or even second timescales over which the biological effects of allostery are observed. As a result, computational methods have arisen to become a powerful means of studying allostery, aided greatly by the staggering increases in computational power over the last 70 years. A field that has experienced a surge in interest over the last 20 years or so is \emph{network theory}, perhaps stimulated by the development of the internet and the Web, two examples of immensely important networks in our everyday life. One of the reasons for the popularity of networks in modelling is their comparative simplicity: a network consists of \emph{nodes}, representing a set of objects in a system, and \emph{edges}, that capture the relations between them. In this thesis, we both apply existing ideas and methods from network theory and develop new computational network methods to study allostery in proteins. We attempt to tackle this problem in three distinct ways, each representing a protein using a different form of a network. Our initial work follows on logically from previous work in the group, representing proteins as \emph{graphs} where atoms are nodes and bonds are energy weighted edges. In effect we disregard the 3-dimensional structure of the protein and instead focus on how the bond \emph{connectivity} can be used to explain potential long range communication between allosteric and active sites in a multimeric protein. We then focus on a class of protein models known as \emph{elastic network models}, in which our edges now correspond to mechanical Hooke springs between either atoms or residues, in order to attempt to understand the physical, mechanistic basis of allostery.Open Acces