47 research outputs found
Towards Personalized Medicine: Computational Approaches to Support Drug Design and Clinical Decision Making
The future looks bright for a clinical practice that tailors the
therapy with the best efficacy and highest safety to a patient. Substantial
amounts of funding have resulted in technological advances regarding
patient-centered data acquisition --- particularly genetic data. Yet, the
challenge of translating this data into clinical practice remains open.
To support drug target characterization, we developed a global maximum
entropy-based method that predicts protein-protein complexes including the
three-dimensional structure of their interface from sequence data. To further
speed up the drug development process, we present methods to reposition drugs
with established safety profiles to new indications leveraging paths in
cellular interaction networks. We validated both methods on known data,
demonstrating their ability to recapitulate known protein complexes and
drug-indication pairs, respectively.
After studying the extent and characteristics of genetic variation with a
predicted impact on protein function across 60,607 individuals, we showed that
most patients carry variants in drug-related genes. However, for the majority
of variants, their impact on drug efficacy remains unknown. To inform
personalized treatment decisions, it is thus crucial to first collate knowledge
from open data sources about known variant effects and to then close the
knowledge gaps for variants whose effect on drug binding is still not
characterized. Here, we built an automated annotation pipeline for
patient-specific variants whose value we illustrate for a set of patients with
hepatocellular carcinoma. We further developed a molecular modeling protocol to
predict changes in binding affinity in proteins with genetic variants which we
evaluated for several clinically relevant protein kinases.
Overall, we expect that each presented method has the potential to advance
personalized medicine by closing knowledge gaps about protein interactions and
genetic variation in drug-related genes. To reach clinical applicability,
challenges with data availability need to be overcome and prediction
performance should be validated experimentally.Therapien mit der besten Wirksamkeit und höchsten
Sicherheit werden in Zukunft auf den Patienten zugeschnitten werden. Hier haben
erhebliche finanzielle Mittel zu technologischen Fortschritten bei der
patientenzentrierten Datenerfassung geführt, aber diese Daten in die
klinische Praxis zu übertragen, bleibt aktuell noch eine Herausforderung.
Um die Wirkstoffforschung in der Charakterisierung therapeutischer Zielproteine
zu unterstützen, haben wir eine Maximum-Entropie-Methode entwickelt,
die Protein-Interaktionen und ihre dreidimensionalen Struktur
aus Sequenzdaten vorhersagt. Darüber hinaus, stellen wir Methoden
zur Repositionierung von etablierten Arzneimitteln auf
neue Indikationen vor, die Pfade in zellulären Interaktionsnetze nutzen.
Diese Methoden haben wir anhand bekannter Daten validiert und ihre Fähigkeit
demonstriert, bekannte Proteinkomplexe bzw. Wirkstoff-Indikations-Paare zu
rekapitulieren.
Unsere Analyse genetischer Variation mit einem Einfluss auf die
Proteinfunktion in 60,607 Individuen konnte zeigen, dass nahezu jeder Patient
funktionsverändernde Varianten in Medikamenten-assoziierten Genen
trägt. Der direkte Einfluss der meisten beobachteten Varianten auf die
Medikamenten-Wirksamkeit ist jedoch noch unbekannt. Um dennoch personalisierte
Behandlungsentscheidungen treffen zu können, präsentieren wir eine Annotationspipeline für genetische
Varianten, deren Wert wir für Patienten mit hepatozellulärem
Karzinom illustrieren konnten. Darüber hinaus haben wir ein molekulares
Modellierungsprotokoll entwickelt, um die Veränderungen in der
Bindungsaffinität von Proteinen mit genetischen Varianten voraussagen.
Insgesamt sind wir davon überzeugt, dass jede der vorgestellten Methoden das
Potential hat, Wissenslücken über Proteininteraktionen und
genetische Variationen in medikamentenbezogenen Genen zu schlie{\ss}en und
somit das Feld der personalisierten Medizin voranzubringen. Um klinische
Anwendbarkeit zu erreichen, gilt es in der Zukunft, verbleibende
Herausforderungen bei der Datenverfügbarkeit zu bewältigen und unsere
Vorhersagen experimentell zu validieren
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Structure, Dynamics and Implied Gating Mechanism of a Human Cyclic Nucleotide-Gated Channel
Cyclic nucleotide-gated (CNG) ion channels are nonselective cation channels, essential for visual and olfactory sensory transduction. Although the channels include voltage-sensor domains (VSDs), their conductance is thought to be independent of the membrane potential, and their gating regulated by cytosolic cyclic nucleotide–binding domains. Mutations in these channels result in severe, degenerative retinal diseases, which remain untreatable. The lack of structural information on CNG channels has prevented mechanistic understanding of disease-causing mutations, precluded structure-based drug design, and hampered in silico investigation of the gating mechanism. To address this, we built a 3D model of the cone tetrameric CNG channel, based on homology to two distinct templates with known structures: the transmembrane (TM) domain of a bacterial channel, and the cyclic nucleotide-binding domain of the mouse HCN2 channel. Since the TM-domain template had low sequence-similarity to the TM domains of the CNG channels, and to reconcile conflicts between the two templates, we developed a novel, hybrid approach, combining homology modeling with evolutionary coupling constraints. Next, we used elastic network analysis of the model structure to investigate global motions of the channel and to elucidate its gating mechanism. We found the following: (i) In the main mode of motion, the TM and cytosolic domains counter-rotated around the membrane normal. We related this motion to gating, a proposition that is supported by previous experimental data, and by comparison to the known gating mechanism of the bacterial KirBac channel. (ii) The VSDs could facilitate gating (supplementing the pore gate), explaining their presence in such ‘voltage-insensitive’ channels. (iii) Our elastic network model analysis of the CNGA3 channel supports a modular model of allosteric gating, according to which protein domains are quasi-independent: they can move independently, but are coupled to each other allosterically
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Antiparallel protocadherin homodimers use distinct affinity- and specificity-mediating regions in cadherin repeats 1-4
Protocadherins (Pcdhs) are cell adhesion and signaling proteins used by neurons to develop and maintain neuronal networks, relying on trans homophilic interactions between their extracellular cadherin (EC) repeat domains. We present the structure of the antiparallel EC1-4 homodimer of human PcdhγB3, a member of the γ subfamily of clustered Pcdhs. Structure and sequence comparisons of α, β, and γ clustered Pcdh isoforms illustrate that subfamilies encode specificity in distinct ways through diversification of loop region structure and composition in EC2 and EC3, which contains isoform-specific conservation of primarily polar residues. In contrast, the EC1/EC4 interface comprises hydrophobic interactions that provide non-selective dimerization affinity. Using sequence coevolution analysis, we found evidence for a similar antiparallel EC1-4 interaction in non-clustered Pcdh families. We thus deduce that the EC1-4 antiparallel homodimer is a general interaction strategy that evolved before the divergence of these distinct protocadherin families. DOI: http://dx.doi.org/10.7554/eLife.18449.00
Sequence co-evolution gives 3D contacts and structures of protein complexes
Protein–protein interactions are fundamental to many biological processes. Experimental screens have identified tens of thousands of interactions, and structural biology has provided detailed functional insight for select 3D protein complexes. An alternative rich source of information about protein interactions is the evolutionary sequence record. Building on earlier work, we show that analysis of correlated evolutionary sequence changes across proteins identifies residues that are close in space with sufficient accuracy to determine the three-dimensional structure of the protein complexes. We evaluate prediction performance in blinded tests on 76 complexes of known 3D structure, predict protein–protein contacts in 32 complexes of unknown structure, and demonstrate how evolutionary couplings can be used to distinguish between interacting and non-interacting protein pairs in a large complex. With the current growth of sequences, we expect that the method can be generalized to genome-wide elucidation of protein–protein interaction networks and used for interaction predictions at residue resolution. DOI: http://dx.doi.org/10.7554/eLife.03430.00
Structure, Dynamics and Implied Gating Mechanism of a Human Cyclic Nucleotide-Gated Channel
Population-specific design of de-immunized protein biotherapeutics
Immunogenicity is a major problem during the development of biotherapeutics since it can lead to rapid clearance of the drug and adverse reactions. The challenge for biotherapeutic design is therefore to identify mutants of the protein sequence that minimize immunogenicity in a target population whilst retaining pharmaceutical activity and protein function. Current approaches are moderately successful in designing sequences with reduced immunogenicity, but do not account for the varying frequencies of different human leucocyte antigen alleles in a specific population and in addition, since many designs are non-functional, require costly experimental post-screening. Here, we report a new method for de-immunization design using multi-objective combinatorial optimization. The method simultaneously optimizes the likelihood of a functional protein sequence at the same time as minimizing its immunogenicity tailored to a target population. We bypass the need for three-dimensional protein structure or molecular simulations to identify functional designs by automatically generating sequences using probabilistic models that have been used previously for mutation effect prediction and structure prediction. As proof-of-principle we designed sequences of the C2 domain of Factor VIII and tested them experimentally, resulting in a good correlation with the predicted immunogenicity of our model
Genetic variation in human drug-related genes
Background: Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classic pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied. Methods: Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional variants in 806 drug-related genes, including 628 known drug targets. We further computed the likelihood of 1236 FDA-approved drugs to be affected by functional variants in their targets in the whole ExAC population as well as different geographic sub-populations. Results: We find that most genetic variants in drug-related genes are very rare (f < 0.1%) and thus will likely not be observed in clinical trials. Furthermore, we show that patient risk varies for many drugs and with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is, at 44%, high enough to suggest that not only somatic alterations but also germline variants carried over into the tumor genome could affect the response to antineoplastic agents. Conclusions: This study indicates that even though many variants are very rare and thus likely not observed in clinical trials, four in five patients are likely to carry a variant with possibly functional effects in a target for commonly prescribed drugs. Such variants could potentially alter drug efficacy. Electronic supplementary material The online version of this article (doi:10.1186/s13073-017-0502-5) contains supplementary material, which is available to authorized users