Computational approaches to shed light on molecular mechanisms in biological processes

Computational approaches based on Molecular Dynamics simulations, Quantum Mechanical methods and 3D Quantitative Structure-Activity Relationships were employed by computational chemistry groups at the University of Milano-Bicocca to study biological processes at the molecular level. The paper reports the methodologies adopted and the results obtained on Aryl hydrocarbon Receptor and homologous PAS proteins mechanisms, the properties of prion protein peptides, the reaction pathway of hydrogenase and peroxidase enzymes and the defibrillogenic activity of tetracyclines. © Springer-Verlag 2007

Probabilistic methods in the analysis of protein interaction networks

Imperial Users onl

Modelling the evolution of biological complexity with a two-dimensional lattice self-assembly process

Self-assembling systems are prevalent across numerous scales of nature, lying at the heart of diverse physical and biological phenomena. Individual protein subunits self-assembling into complexes is often a vital first step of biological processes. Errors during protein assembly, due to mutations or misfolds, can have devastating effects and are responsible for an assortment of protein diseases, known as proteopathies. With proteins exhibiting endless layers of complexity, building any all-encompassing model is unrealistic. Coarse-grained models, despite not faithfully capturing every detail of the original system, have massive potential to assist understanding complex phenomenon. A principal actor in self-assembly is the binding interactions between subunits, and so geometric constraints, polarity, kinetic forces, etc. can often be marginalised. This work explores how self-assembly and its outcomes are inextricably tied to the involved interactions through the use of a two-dimensional lattice polyomino model. %Armed with this tractable model, we can probe how dynamics acting on evolution are reflected in interaction properties. First, this thesis addresses how the interaction characteristics of self-assembly building blocks determine what structures they form. Specifically, if the same structures are consistently produced and remain finite in size. Assembly graphs store subunit interaction information and are used in classifying these two properties, the determinism and boundedness respectively. Arbitrary sets of building blocks are classified without the costly overhead of repeated stochastic assembling, improving both the analysis speed and accuracy. Furthermore, assembly graphs naturally integrate combinatorial and graph techniques, enabling a wider range of future polyomino studies. The second part narrows in on implications of nondeterministic assembly on interaction strength evolution. Generalising subunit binding sites with mutable binary strings introduces such interaction strengths into the polyomino model. Deterministic assemblies obey analytic expectations. Conversely, interactions in nondeterministic assemblies rapidly diverge from equilibrium to minimise assembly inconsistency. Optimal interaction strengths during assembly are also reflected in evolution. Transitions between certain polyominoes are strongly forbidden when interaction strengths are misaligned. The third aspect focuses on genetic duplication, an evolutionary event observed in organisms across all taxa. Through polyomino evolutions, a duplication-heteromerisation pathway emerges as an efficient process. This pathway exploits the advantages of both self-interactions and pairwise-interactions, and accelerates evolution by avoiding complexity bottlenecks. Several simulation predictions are successfully validated against a large data set of protein complexes. These results focus on coarse-grained models rather than quantified biological insight. Despite this, they reinforce existing observations of protein complexes, as well as posing several new mechanisms for the evolution of biological complexity

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics

Improving the resolution of interaction maps: A middleground between high-resolution complexes and genome-wide interactomes

Protein-protein interactions are ubiquitous in Biology and therefore central to understand living organisms. In recent years, large-scale studies have been undertaken to describe, at least partially, protein-protein interaction maps or interactomes for a number of relevant organisms including human. Although the analysis of interaction networks is proving useful, current interactomes provide a blurry and granular picture of the molecular machinery, i.e. unless the structure of the protein complex is known the molecular details of the interaction are missing and sometime is even not possible to know if the interaction between the proteins is direct, i.e. physical interaction or part of functional, not necessary, direct association. Unfortunately, the determination of the structure of protein complexes cannot keep pace with the discovery of new protein-protein interactions resulting in a large, and increasing, gap between the number of complexes that are thought to exist and the number for which 3D structures are available. The aim of the thesis was to tackle this problem by implementing computational approaches to derive structural models of protein complexes and thus reduce this existing gap. Over the course of the thesis, a novel modelling algorithm to predict the structure of protein complexes, V-D2OCK, was implemented. This new algorithm combines structure-based prediction of protein binding sites by means of a novel algorithm developed over the course of the thesis: VORFFIP and M-VORFFIP, data-driven docking and energy minimization. This algorithm was used to improve the coverage and structural content of the human interactome compiled from different sources of interactomic data to ensure the most comprehensive interactome. Finally, the human interactome and structural models were compiled in a database, V-D2OCK DB, that offers an easy and user-friendly access to the human interactome including a bespoken graphical molecular viewer to facilitate the analysis of the structural models of protein complexes. Furthermore, new organisms, in addition to human, were included providing a useful resource for the study of all known interactomes

Profiling interactions of proximal nascent chains reveals a general co-translational mechanism of protein complex assembly

The association of proteins into functional oligomeric complexes is crucial for nearly all cellular processes. Despite rapid progress in characterizing the structure of native assemblies, the underlying mechanisms that guide faithful complex formation in the crowded cellular environment are understood only superficially. To secure efficient complex biogenesis and limit the exposure of aggregation-prone intermediates, many proteins assemble co-translationally, via interaction of a fully synthetized and a nascent protein subunit (co-post assembly). Here, we explore the prevalence and the mechanistic principles of a putative co-translational assembly mechanism, which involves the direct interaction of nascent subunits emerging from proximal ribosomes (co-co assembly). To obtain direct evidence of this putative assembly mode, we apply a newly developed method based on Ribosome Profiling, named Disome Selective Profiling (DiSP), which allows to monitor the conversion of single ribosomes to nascent chain connected ribosome pairs across the proteome with high resolution. We use this approach to analyse co-co assembly in two human cell lines and demonstrate that it constitutes a general mechanism inside cells that is employed by hundreds of high confidence and thousands of low confidence candidates, comprising 11 to 32% of all complex subunits. Analysing the features of the co-co assembly proteome, we reveal that this mechanism guides formation of mostly homomeric complexes and typically relies on interaction of N-terminal nascent chain segments. We further identify five dimerization domains mediating the majority of co-co interactions, which are either partially or completely exposed at the onset of nascent chain dimerization, implying different folding and assembly mechanisms. The detectable fraction of each candidate’s nascent chains that co-co assemble is in median 40% and in some cases exceeds 90%, suggesting that this co-translational assembly path may be employed as the main route for complex formation. To gain deeper insights into the mechanistic basis of co-co assembly, we took a series of experimental approaches that distinguish between interactions of nascent chains emerging from the same or different polysomes (termed assembly in cis and in trans, respectively). These experiments could not support a model of assembly in trans. Conversely, we find indications supporting a cis assembly model for nuclear lamin C, one of our high confidence candidates. This mechanism provides a simple explanation for the remarkable specificity of lamin homodimer formation in vivo, where splice variants with largely overlapping sequences do not mix. We propose that assembly in cis more generally secures specific homomer formation of isoforms and structurally-related proteins which are highly prone to promiscuous interactions inside cells. In conclusion, this study provides a global annotation of nascent chain interactions across the human proteome and elucidates the basic principles of this widespread assembly pathway. Our findings raise a number of fundamental questions concerning the mechanisms ensuring high-fidelity protein biogenesis, including the implications of co-co assembly on polysome structure, the possible consequences of co-co assembly failure, the inter-dependence with co-translational folding and the synchronization and coordination with translation kinetics

Lipid-free Antigen B subunits from echinococcus granulosus: oligomerization, ligand binding, and membrane interaction properties

Background: The hydatid disease parasite Echinococcus granulosus has a restricted lipid metabolism, and needs to harvest essential lipids from the host. Antigen B (EgAgB), an abundant lipoprotein of the larval stage (hydatid cyst), is thought to be important in lipid storage and transport. It contains a wide variety of lipid classes, from highly hydrophobic compounds to phospholipids. Its protein component belongs to the cestode-specific Hydrophobic Ligand Binding Protein family, which includes five 8-kDa isoforms encoded by a multigene family (EgAgB1-EgAgB5). How lipid and protein components are assembled into EgAgB particles remains unknown. EgAgB apolipoproteins self-associate into large oligomers, but the functional contribution of lipids to oligomerization is uncertain. Furthermore, binding of fatty acids to some EgAgB subunits has been reported, but their ability to bind other lipids and transfer them to acceptor membranes has not been studied.<p></p> Methodology/Principal Findings: Lipid-free EgAgB subunits obtained by reverse-phase HPLC were used to analyse their oligomerization, ligand binding and membrane interaction properties. Size exclusion chromatography and cross-linking experiments showed that EgAgB8/2 and EgAgB8/3 can self-associate, suggesting that lipids are not required for oligomerization. Furthermore, using fluorescent probes, both subunits were found to bind fatty acids, but not cholesterol analogues. Analysis of fatty acid transfer to phospholipid vesicles demonstrated that EgAgB8/2 and EgAgB8/3 are potentially capable of transferring fatty acids to membranes, and that the efficiency of transfer is dependent on the surface charge of the vesicles.<p></p> Conclusions/Significance: We show that EgAgB apolipoproteins can oligomerize in the absence of lipids, and can bind and transfer fatty acids to phospholipid membranes. Since imported fatty acids are essential for Echinococcus granulosus, these findings provide a mechanism whereby EgAgB could engage in lipid acquisition and/or transport between parasite tissues. These results may therefore indicate vulnerabilities open to targeting by new types of drugs for hydatidosis therapy.<p></p&gt

Identification of interface residues involved in protein-protein and protein-DNA interactions from sequence using machine learning approaches

Identification of interface residues involved in protein-protein and protein-DNA interactions is critical for understanding the functions of biological systems. Because identifying interface residues using experimental methods cannot catch up with the pace at which protein sequences are determined, computational methods that can identify interface residues are urgently needed. In this study, we apply machine-learning methods to identify interface residues with the focus on the methods using amino acid sequence information alone. We have developed classifiers for identification of the residues involved in protein-protein and protein-DNA interactions using a window of primary sequence as input. The classifiers were evaluated using both representative datasets and specific cases of interest based on multiple measurements. The results have shown the feasibility of identifying interface residues from sequence. We have also explored information besides primary sequence to improve the performance of sequence-based classifiers. The results show that the performance of sequence-based classifiers can be improved by using solvent accessibility and sequence entropy of the target residue as additional inputs. We have developed a database of protein-protein interfaces that consists of all the protein-protein interfaces derived from the Protein Data Bank. This database, for the first time, makes possible the quick and flexible retrieval of interface sets and various interface features. We have systematically analyzed the characteristics of interfaces using the largest dataset available. In particular, we compared interfaces with the samples that had the same solvent accessibility as the interfaces. This strategy excludes the effect of solvent accessibility on the distributions of residues, secondary structure, and sequence entropy