1,721,128 research outputs found
Studio della Proteomica Umana
No full textAnalysis of the human genome revealed that the amount of transcribed sequence is an order of magnitude greater than the number of predicted and well-characterized genes. A sizeable fraction of these transcripts is related to alternatively spliced forms of known protein coding genes. Inspection of the alternatively spliced transcripts identified in the pilot phase of the ENCODE project has clearly shown that often their structure might substantially differ from that of other isoforms of the same gene, and therefore that they might perform unrelated functions, or that they might even not correspond to a functional protein. Identifying these cases is obviously relevant for the functional assignment of gene products and for the interpretation of the effect of variations in the corresponding proteins.
It is important to develop a publicly available tool that, given a gene or a protein, retrieves and analyses all its annotated isoforms, provides users with three-dimensional models of the isoform(s) of his/her interest whenever possible and automatically assesses whether homology derived structural models correspond to plausible structures
Critical assessment of methods of protein structure prediction: Progress and new directions in round XI
Full text linkModeling of protein structure from amino acid sequence now plays a major role in structural biology. Here we report new
developments and progress from the CASP11 community experiment, assessing the state of the art in structure modeling.
Notable points include the following: (1) New methods for predicting three dimensional contacts resulted in a few spectacular
template free models in this CASP, whereas models based on sequence homology to proteins with experimental structure
continue to be the most accurate. (2) Refinement of initial protein models, primarily using molecular dynamics related
approaches, has now advanced to the point where the best methods can consistently (though slightly) improve nearly all
models. (3) The use of relatively sparse NMR constraints dramatically improves the accuracy of models, and another type of
sparse data, chemical crosslinking, introduced in this CASP, also shows promise for producing better models. (4) A new
emphasis on modeling protein complexes, in collaboration with CAPRI, has produced interesting results, but also shows the
need for more focus on this area. (5) Methods for estimating the accuracy of models have advanced to the point where they
are of considerable practical use. (6) A first assessment demonstrates that models can sometimes successfully address biological
questions that motivate experimental structure determination. (7) There is continuing progress in accuracy of modeling
regions of structure not directly available by comparative modeling, while there is marginal or no progress in some other
areas
Zinc-selective inhibition of the promiscuous bacterial amide-hydrolase DapE: Implications of metal heterogeneity for evolution and antibiotic drug design
Full text linkThe development of resistance to virtually all current antibiotics makes the discovery of new antimicrobial compounds with novel protein targets an urgent challenge. The dapE-encoded N-succinyl-l,l-diaminopimelic acid desuccinylase (DapE) is an essential metallo-enzyme for growth and proliferation in many bacteria, acting in the desuccinylation of N-succinyl-l,l- diaminopimelic acid (SDAP) in a late stage of the anabolic pathway towards both lysine and a crucial building block of the peptidoglycan cell wall. l-Captopril, which has been shown to exhibit very promising inhibitory activity in vitro against DapE and has attractive drug-like properties, nevertheless does not target DapE in bacteria effectively. Here we show that l-captopril targets only the Zn2+-metallo-isoform of the enzyme, whereas the Mn 2+-enzyme, which is also a physiologically relevant isoform in bacteria, is not inhibited. Our finding provides a rationale for the failure of this promising lead-compound to exhibit any significant antibiotic activity in bacteria and underlines the importance of addressing metallo-isoform heterogeneity in future drug design. Moreover, to our knowledge, this is the first example of metallo-isoform heterogeneity in vivo that provides an evolutionary advantage to bacteria upon drug-challenge. © 2014 The Royal Society of Chemistry
IMMUNOLOGICALLY DISCERNIBLE CELL SURFACE VARIANTS FOR USE IN CELL THERAPY
No full textThe invention relates to a mammalian cell, particularly a human cell, expressing a first isoform of a surface protein, wherein the first isoform is functionally indistinguishable, but immunologically distinguishable from a second isoform, for use in a medical treatment of a patient having cells expressing the second isoform form of the surface protein. The invention further relates to an agent selected from 1) a compound comprising, or consisting of, an antibody or antibody-like molecule and 2) an immune effector cell bearing an antibody-like molecule or an immune effector cell bearing a chimeric antigen receptor, for use in a method of treatment of a medical condition, wherein the agent is specifically reactive to either a first or a second isoform of a surface protein, wherein the first isoform is functionally indistinguishable, but immunologically distinguishable from the second isoform, and wherein the agent is administered to ablate a cell bearing the isoform that the agent is reactive to
Modelling the effects of single point mutations on the structure and function of proteins
Full text linkInsight into the molecular impact of mutations on the structure and function of proteins is of great importance in biology. It helps understand the evolution of proteins, rationalize the molecular causes of disease and, from a practical perspective, aid in planning experiments. In this work, three goals are pursued.
Firstly, a method for objectively assessing the effect of mutations on protein structure is formulated. The random noise component in the comparison of two structures is quantified by a log linear regression model incorporating information on experimental quality and intrinsic flexibility, which can account for approximately half of all structural variation between alternative structures. Applying this model to the task of isolating the effects of single point mutations, it is shown that subtle changes in structure, induced by mutations from evolutionarily favourable residues to unfavourable ones, can't be observed without correcting for noise.
Secondly, the use of automated prediction tools for generating 3D structures for proteins without experimental structures is assessed. It is found that current state of the art automated modelling methods rival or exceed most expert modelling groups in terms of coverage and accuracy. However, in both cases there is still significant room for improvement until protein structure model reach accuracy comparable to experimental structures for non-trivial target proteins. Computationally cheap methods fare comparatively well and thus represent useful tools for the purpose of providing valuable structural information for systematic analyses, such as the study at hand.
Finally, the use of machine learning methods for predicting the impact of mutations on protein function is assessed, using a large set of single amino acid variants in humans. The contribution of structural and evolutionary information to predicting the phenotype of mutations is tested rigorously and it is found that structural information provides information not present in evolutionary data. A generalised classifier using both sequence and structure derived information outperforms other comparable published methods. By validating the classifier on independent datasets we show that it can be used as a general purpose mutation prediction tool, and that our validation methods give reasonable estimates of its performance.
Zusammenfassung
Einsicht in die Auswirkungen von Mutationen auf die Struktur und Funktion von Proteinen ist im Bereich der Biochemie von grosser Bedeutung. Sie hilft sowohl beim Verständnis der Proteinevolution und den Ursachen von Erbkrankheiten, als auch bei der Planung von Mutageneseexperimenten. In der vorliegenden Arbeit werden drei Ziele verfolgt.
Erstens wird eine Methode vorgestellt, die dazu dient die structurellen Auswirkungen von Mutationen auf Proteinen auf objektive Weise zu charakterisieren. Zufällig auftretende räumliche Variationen zwischen alternativen Proteinstrukturen, d.h. Strukturen des selben Proteins, welche in verschiedenen Experimenten gelösst wurden, werden mithilfe log-linearer Regression in Abhängigkeit von Deskriptoren, die experimentelle Fehler oder strukturelle Flexibilität wiederspiegeln, modelliert. Mit diesem Modell, welches etwa die Hälfte der auftretenden räumlichen Variabilität zwischen alternativen Strukturen erklären kann, lässt sich ein Erwartungswert für zufällige auftretende Abweichungen berechnen. Somit kann die Signifikanz beobachteter Variationen ermittelt werden. Auf Punktmutationen anwandt, kann durch dieses Modell gezeigt werden, dass evolutionär ungünstige Mutationen gegenüber evolutionär Bevorzugten Effekte auf Strukturen haben, die ohne die Korrektur durch das Modell nicht erkennbar sind.
Zweitens wird die Genauigkeit von Methoden zur Proteinstrukturvorhersage bewertet. Die jetztige Generation von vollständig automatisierten Methoden kann durchaus jenen Methoden, die auf menschliche Intervention angewiesen sind, Konkurrenz bieten und in vielen Fällen diese sogar übertreffen. In beiden Fällen sind Verbesserungen nötig, bevor ihre Genauigkeit vergleichbar ist mit experimentellen Strukturen. Dennoch tragen besonders diejenigen Methoden, die wenig Rechenkraft benötigen und dennoch im Vergleich gute Leistung erbringen, dazu bei wertvolle strukturelle Informationen zu gewinnen, mit der biologische Fragenstellungen erörtert werden können.
Drittens werden "Machine Learning" Methoden benutzt um mithilfe struktureller und evolutionärer Information die phänotypischen Konsequenzen von Mutationen im Menschen vorherzusagen. Der Beitrag dieser beiden Imformationsquellen zur Vorhersage wird dabei rigoros getested, und es wird festgestellt, dass strukturelle Information durchaus evolutionäre Information ergänzen kann. Ein Modell, dass der Klassifizierung der Effekte von Mutationen dient, wird vorgestellt, welches vergleichbare publizierte Methoden in Genauigkeit übertreffen kann. Indem die hier vorgestellte Methode auf unabhängigen Datensätzen getestet wird, ist gewährleistet, dass sie generell anwendbar ist und dass deren Leistung mit unseren Validierungsmethoden zuverlässig eingeschätzt wird
Computational approaches for investigating protein-ligand interactions - towards an in-depth understanding of the dengue virus methyltransferase
Full text linkInteractions between proteins and their ligands play crucial roles in many
biological processes, such as metabolism, signaling, transport, regulation or
molecular recognition. Understanding the molecular basis of protein-ligand
interactions is thus of great interest, not only for modeling complex biological
systems but also for applications in drug discovery. However, structural details
for most of these interactions have not been characterized experimentally.
Therefore, computational methods have become increasingly important for
investigating biological systems at an atomistic level.
This work aims at a better understanding of the molecular basis of disease
related viral methyltransferases, their interactions with small molecules and
the catalytic mechanism, which may on the long perspective help to develop a
treatment against neglected tropical diseases. Furthermore, we aim to advance
the current methods for the computational prediction of a protein's molecular
function and its biological role in the cell. In addition, we aim to complement
currently available computational strategies for estimating protein ligand
interaction energies.
Dengue fever is a rapidly emerging, still neglected tropical disease which
causes significant mortality and morbidity in humans. For the discovery of novel
classes of compounds inhibiting dengue virus methyltransferase, a combination of
structure-based virtual screening and enzymatic inhibition assays is employed.
From the shortlist of 263 candidates selected by virtual screening, ten
compounds are found to specifically inhibit the target enzyme with IC50
values in the low uM range. Promising compounds are selected for further
experimental characterization and the inhibitory activity of the two most active
compounds is confirmed.
For obtaining a better understanding of the molecular basis of the target
enzyme's function, molecular dynamics simulations and mixed quantum
mechanics/molecular mechanics calculations are employed to investigate the
mechanisms of the enzymatically catalyzed reaction at an atomistic level. Based
on a structural model of the target protein in complex with its RNA substrate,
the impact of mutations on ligand binding, geometric arrangements and reaction
energy barriers are evaluated computationally. In addition, for a detailed
characterization of the underlying chemical reactions, ab initio electronic
structure calculations are performed on model systems approximating the
biological structure.
The reliable prediction of ligand binding sites is crucial for characterizing
proteins with unknown function. Therefore, the use of computational predictions
of protein function and ligand binding sites for proteins without experimental
structures are assessed in a blind and objective way. Limitations in the current
prediction methods are analyzed and suggestions for a more reliable evaluation
are given. Following those suggestions, an extended and fully automated
assessment is implemented in the Continuous Automated Model EvaluatiOn (CAMEO)
framework.
Computational identification of protein-ligand interactions can greatly
facilitate the drug discovery process. Thus, we establish a straightforward,
rapid scoring function that aims to identify the best poses out of an ensemble
of pre-docked poses, by quantifying the degree of burial and the electrostatic
interactions of the ligand in a binding site. The scoring function is evaluated
on a set of high quality protein-ligand complex structures, where the results
show promisingly high retrieval rates for selecting the best poses from a pool
of decoy poses.
Finally, a novel human-computer interface device is described which facilitates
the interaction with the computational representation of complex biological
systems by employing natural and intuitive movements
Promiscuous behaviour of the bacterial metallohydrolase DapE : an evolutionary and mechanistic perspective
Full text linkEnzyme promiscuity, defined as functional properties other than those for which they are evolved, is considered a key factor in the evolution of new enzyme functions. Many metalloproteins can be alternatively metallated, which may lead to metal-dependent promiscuity. The mechanisms and evolutionary implications of metal-mediated promiscuity appear to be underexplored, especially considering that approximately one-third of structurally characterized proteins are thought to be metalloproteins.
Here, we investigated the bacterial binuclear metallohydrolase, N-Succinyl-L-LDiaminopimelic acid desuccinylase DapE (EC 3.5.1.18) of S.enterica. DapE is an essential enzyme in the late stage of the lysine biosynthetic pathway that also provides a crucial building block of the peptidoglycan cell wall. Since DapE is essential for most Gram-negative and many Gram-positive bacteria and it is not present in humans, it has been proposed as a very good target for antibiotic development. It was also reported that DapE has a metal dependent promiscuous aspartyl dipeptidase activity, in which incorporation of either zinc or manganese to the enzyme leads to activity with different substrates and this phenomenon occurs both in vivo and in vitro.
We addressed the reaction mechanism of the native desuccinylase activity as well as the Mn2+-dependent aspartyl dipeptidase promiscuous activity of DapE, by investigating a series of substrate analogues and potential inhibitors. We postulated a plausible mechanism for metal-dependent promiscuity, based on subtle differences in coordination preferences between Mn2+ and Zn2+,which may be widely applicable to other enzymes. We revealed why a promising inhibitor of the enzyme in vitro, L-captopril, fails to exert antibiotic activity and
propose broad practical implications of this discovery for drug-design, as well as fundamental evolutionary implications. We described kinetic cooperativity in DapE, which offers clues on structural rearrangements that occur during catalysis and is also of relevance to inhibitor design.
Finally, we explored the evolutionary aspects of functional robustness of native activity over the promiscuous activity using DapE as a model enzyme and addressed the molecular mechanisms underlying the emergence of functional robustness through laboratory evolution
Modeling of tertiary and quaternary protein structures by homology
Full text linkThe structure of a protein is crucial to understand its function. Despite this importance,
experimentally solved structures are only available for a small portion of the currently known protein
sequences. Comparative or homology modeling is currently the most powerful method used in order
to predict the structure from sequence by the use of homologous template structures. Models,
hence, need to be accurate regarding their three-dimensional coordinates and must represent the
biological active state of the target protein in order to be useful for scientists.
Four goals are pursued in this work in this area of research. Firstly, we increase the coverage of
homology modeling by introducing a method which is able to identify and align evolutionary distant
template structures. The resulting template search and selection procedure is hierarchical. Closely
related template structures are identified accurately and efficiently by standard tools.
A computationally more complex method is invoked in order to identify evolutionary more distant
template structures with high precision and accuracy. Integrated into an automated modeling
pipeline, the developed method is competitive compared to other protein structure prediction
methods.
Secondly, the automated modeling pipeline is applied to a large set of protein sequences to increase
the structural coverage of sequence space. The resulting models and associated annotation data are
stored in a relational database and can be accessed online in order to allow scientists to query for
their protein of interest. Efforts are made to update a selected set of sequences regularly by
shortening the update process without losing accuracy. It is found that the structural coverage of
seven proteomes is increased considerably by this large scale modeling approach.
Thirdly, the modeling of quaternary structure is addressed. Significant room for improvement in the
field of quaternary structure prediction is found when assessing the current state-of-the-art methods
in a double blind prediction experiment. Novel similarity measures are therefore developed to
distinguish proteins with different quaternary structure. We further create a template library built of
structures in their previously defined most likely oligomeric state, to extent the concept of homology
modeling towards the prediction of oligomeric protein structures. In order to select template
structures which share the same quaternary structure with the target structure, a variety of
evolutionary and structural features are investigated. It is shown, that using a combination of these
features for the first time predicts the quaternary structure with high accuracy.
Finally, the performances of methods which predict non-folded (intrinsically disordered) protein
segments are assessed. Current issues are addressed in a field of very active research as more and
more proteins are found to be hubs in interaction networks with considerable disordered portions in
their tertiary structure. In general it is found that such methods perform well, even within the limits
of the test set
Computational investigation of function of membrane proteins : Amt/Rh Ammonium transporters and SecY translocon
Full text linkIn this thesis, we studied the function of the Amt/Rh family of proteins and of the SecY/Sec61 translocons using computational methods. The Amt/Rh proteins mediate transport of ammonium across the lipid bilayer. SecY and Sec61 translocons facilitate the insertion of membrane proteins or translocation of secreted proteins in prokaryotes and eukaryotes, respectively. We investigated on the molecular details of ammonium transport in E.Coli AmtB and human RhCG proteins, and the effect of the hydrophobicity of the SecY translocon pore in membrane protein insertion.
Functional studies have revealed that Amt proteins transport the charged form of ammonium (NH4+) while Rh proteins transport neutral ammonia (NH3). However, permeation mechanisms at a molecular level have not been understood clearly. Here, we present molecular details of ammonium transport in AmtB and RhCG proteins. Our calculations show that ammonium ion binds and deprotonates at the hydrophobic pore of AmtB. Then, ammonia diffuses down the hydrophobic pore while the excess proton is transported with the help of a highly conserved histidine dyad (H168 and H318). Ammonia gets re-protonated when it reaches the bottom of the pore and leaves the channel as ammonium. To recruit a new ammonium substrate the protonation states of the histidine dyad has to be reset. This is achieved through water molecules forming a single-file chain in the pore. Thus, hydration of the pore plays an important role in the transport mechanism in AmtB protein. Our simulations of RhCG protein have revealed that the pore of RhCG protein is not hydrated. Lack of hydration in the pore suggests that the excess proton cannot be transported across the hydrophobic pore as it is proposed for AmtB. We show that ammonium binds and deprotonates at a histidine residue (H185) lining the hydrophobic pore of RhCG. After deprotonation, ammonia diffuses down the pore. Then, the excess proton is circulated back to the extracellular site through a network of hydrogen bonds connecting H185 to D177. In conclusion, our calculations suggest that RhCG protein transports neutral ammonia while AmtB transports charged ammonium.
Experimental findings showed that mutation of the pore-ring residues of Sec61 translocon changed the hydrophobicity threshold for membrane integration. Our free energy calculations suggested that mutation of the pore-ring residues influences the stability of peptides in the pore, thus affecting the probability of membrane integration. In addition, insertion experiments of oligo alanine peptides, which contain a cluster of three leucines at various positions, revealed an asymmetry in the membrane integration profile. In particular, a significant drop in membrane integration was observed when the three-leucine cluster aligns with the pore-ring residues. We simulated the wild-type SecY and its pore-ring mutants with the oligo-alanine peptides initially placed into the pores. Analysis of these simulations suggested that hydration of the leucine side-chains drops dramatically when the three-leucine cluster is aligned with the pore-ring residues. The reduced hydration of the leucine residues stabilizes the peptide in the translocon pore and favors its translocation
Computational analysis of protein-ligand binding : from single continuous trajectories to multiple parallel simulations
Full text linkThe interaction of proteins with other proteins or small molecules is essential for biological
functions. Understanding the molecular basis of protein-ligand binding is of
a vast interest for drug discovery, and computational methods to estimate proteinligand
binding are starting to play an increasingly important role. In order to apply
atomistic computational methods to the drug discovery process it is necessary
to have accurate three-dimensional structures of the target protein and a fast and
reliable method to estimate the binding affinity between the target protein and potential
inhibitors. Unfortunately, three-dimensional structures are not available for
all proteins of interest, but often their coordinates can be predicted by computational
methods such as homology modeling.
In this thesis we study the effect of inaccuracies of homology models to ligand binding
using HIV-1 protease as a model system. Homology models of decreasing accuracy
are built and additional errors are introduced by misplacing side chains during
rotamer modeling. We establish a MM-GBSA approach to estimate protein-ligand
binding free energies, and apply this method to the different homology models.
Although MM-GBSA methods are significantly faster than traditional MM-PBSA
methods, still the required computational effort is significant as it is based on the
calculation of a continuous molecular dynamics trajectory. In this study, we establish
a novel approach based on multiple independent short simulations, which is suitable
for execution of a distributed grid of computers and thereby dramatically reduces
the computation time needed. This workflow is validated using the HIV-1 protease
model system, and then applied to the estrogen receptor. Novel methods to assess the
sampling of the different trajectory approaches and potential application to docking
problems are presented and discussed
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