44,399 research outputs found
Structure-based discovery of fiber-binding compounds that reduce the cytotoxicity of amyloid beta.
Amyloid protein aggregates are associated with dozens of devastating diseases including Alzheimer's, Parkinson's, ALS, and diabetes type 2. While structure-based discovery of compounds has been effective in combating numerous infectious and metabolic diseases, ignorance of amyloid structure has hindered similar approaches to amyloid disease. Here we show that knowledge of the atomic structure of one of the adhesive, steric-zipper segments of the amyloid-beta (Aβ) protein of Alzheimer's disease, when coupled with computational methods, identifies eight diverse but mainly flat compounds and three compound derivatives that reduce Aβ cytotoxicity against mammalian cells by up to 90%. Although these compounds bind to Aβ fibers, they do not reduce fiber formation of Aβ. Structure-activity relationship studies of the fiber-binding compounds and their derivatives suggest that compound binding increases fiber stability and decreases fiber toxicity, perhaps by shifting the equilibrium of Aβ from oligomers to fibers. DOI:http://dx.doi.org/10.7554/eLife.00857.001
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Identification of drug-target interactions (DTIs) plays a key role in drug
discovery. The high cost and labor-intensive nature of in vitro and in vivo
experiments have highlighted the importance of in silico-based DTI prediction
approaches. In several computational models, conventional protein descriptors
are shown to be not informative enough to predict accurate DTIs. Thus, in this
study, we employ a convolutional neural network (CNN) on raw protein sequences
to capture local residue patterns participating in DTIs. With CNN on protein
sequences, our model performs better than previous protein descriptor-based
models. In addition, our model performs better than the previous deep learning
model for massive prediction of DTIs. By examining the pooled convolution
results, we found that our model can detect binding sites of proteins for DTIs.
In conclusion, our prediction model for detecting local residue patterns of
target proteins successfully enriches the protein features of a raw protein
sequence, yielding better prediction results than previous approaches.Comment: 26 pages, 7 figure
Targeting kinases with anilinopyrimidines: Discovery of N-phenyl-N'-[4-(pyrimidin-4-ylamino)phenyl]urea derivatives as selective inhibitors of class III receptor tyrosine kinase subfamily
Kinase inhibitors are attractive drugs/drug candidates for the treatment of cancer. The most recent
literature has highlighted the importance of multi target kinase inhibitors, although a correct
balance between specificity and non-specificity is required. In this view, the discovery of multityrosine
kinase inhibitors with subfamily selectivity is a challenging goal. Herein we present the
synthesis and the preliminary kinase profiling of a set of novel 4-anilinopyrimidines. Among the
synthesized compounds, the N-phenyl-N\u2019-[4-(pyrimidin-4-ylamino)phenyl]urea derivatives selectively
targeted some members of class III receptor tyrosine kinase family. Starting from the structure of
hit compound 19 we synthesized a further compound with an improved affinity toward the class III
receptor tyrosine kinase members and endowed with a promising antitumor activity both in vitro
and in vivo in a murine solid tumor model. Molecular modeling simulations were used in order to
rationalize the behavior of the title compounds
Structural and Functional Similarity between the Bacterial Type III Secretion System Needle Protein PrgI and the Eukaryotic Apoptosis Bcl-2 Proteins
Background: Functional similarity is challenging to identify when global sequence and structure similarity is low. Activesites or functionally relevant regions are evolutionarily more stable relative to the remainder of a protein structure and provide an alternative means to identify potential functional similarity between proteins. We recently developed the FASTNMR methodology to discover biochemical functions or functional hypotheses of proteins of unknown function by experimentally identifying ligand binding sites. FAST-NMR utilizes our CPASS software and database to assign a function based on a similarity in the structure and sequence of ligand binding sites between proteins of known and unknown function.
Methodology/Principal Findings: The PrgI protein from Salmonella typhimurium forms the needle complex in the type III secretion system (T3SS). A FAST-NMR screen identified a similarity between the ligand binding sites of PrgI and the Bcl-2 apoptosis protein Bcl-xL. These ligand binding sites correlate with known protein-protein binding interfaces required for oligomerization. Both proteins form membrane pores through this oligomerization to release effector proteins to stimulate cell death. Structural analysis indicates an overlap between the PrgI structure and the pore forming motif of Bcl-xL. A sequence alignment indicates conservation between the PrgI and Bcl-xL ligand binding sites and pore formation regions. This active-site similarity was then used to verify that chelerythrine, a known Bcl-xL inhibitor, also binds PrgI.
Conclusions/Significance: A structural and functional relationship between the bacterial T3SS and eukaryotic apoptosis was identified using our FAST-NMR ligand affinity screen in combination with a bioinformatic analysis based on our CPASS program. A similarity between PrgI and Bcl-xL is not readily apparent using traditional global sequence and structure analysis, but was only identified because of conservation in ligand binding sites. These results demonstrate the unique opportunity that ligand-binding sites provide for the identification of functional relationships when global sequence and structural information is limited
Surface plasmon resonance: a versatile technique for biosensor applications
Surface plasmon resonance (SPR) is a label-free detection method which has emerged during the last two decades as a suitable and reliable platform in clinical analysis for biomolecular interactions. The technique makes it possible to measure interactions in real-time with high sensitivity and without the need of labels. This review article discusses a wide range of applications in optical-based sensors using either surface plasmon resonance (SPR) or surface plasmon resonance imaging (SPRI). Here we summarize the principles, provide examples, and illustrate the utility of SPR and SPRI through example applications from the biomedical, proteomics, genomics and bioengineering fields. In addition, SPR signal amplification strategies and surface functionalization are covered in the review.open1
Lead optimization for new antimalarials and Successful lead identification for metalloproteinases: A Fragment-based approach Using Virtual Screening
Lead optimization for new antimalarials and Successful lead identification
for metalloproteinases: A Fragment-based approach Using Virtual Screening
Computer-aided drug design is an essential part of the modern medicinal
chemistry, and has led to the acceleration of many projects. The herein
described thesis presents examples for its application in the field of lead
optimization and lead identification for three metalloproteins.
DOXP-reductoisomerase (DXR) is a key enzyme of the mevalonate independent
isoprenoid biosynthesis. Structure-activity relationships for 43 DXR
inhibitors are established, derived from protein-based docking, ligand-based
3D QSAR and a combination of both approaches as realized by AFMoC. As part
of an effort to optimize the properties of the established inhibitor
Fosmidomycin, analogues have been synthesized and tested to gain further
insights into the primary determinants of structural affinity.
Unfortunately, these structures still leave the active Fosmidomycin
conformation and detailed reaction mechanism undetermined. This fact,
together with the small inhibitor data set provides a major challenge for
presently available docking programs and 3D QSAR tools. Using the recently
developed protein tailored scoring protocol AFMoC precise prediction of
binding affinities for related ligands as well as the capability to estimate
the affinities of structurally distinct inhibitors has been achieved.
Farnesyltransferase is a zinc-metallo enzyme that catalyzes the
posttranslational modification of numerous proteins involved in
intracellular signal transduction. The development of farnesyltransferase
inhibitors is directed towards the so-called non-thiol inhibitors because of
adverse drug effects connected to free thiols. A first step on the way to
non-thiol farnesyltransferase inhibitors was the development of an
CAAX-benzophenone peptidomimetic based on a pharmacophore model. On its
basis bisubstrate analogues were developed as one class of non-thiol
farnesyltransferase inhibitors. In further studies two aryl binding and two
distinct specificity sites were postulated. Flexible docking of model
compounds was applied to investigate the sub-pockets and design highly
active non-thiol farnesyltransferase inhibitor. In addition to affinity,
special attention was paid towards in vivo activity and species specificity.
The second part of this thesis describes a possible strategy for
computer-aided lead discovery. Assembling a complex ligand from simple
fragments has recently been introduced as an alternative to traditional HTS.
While frequently applied experimentally, only a few examples are known for
computational fragment-based approaches. Mostly, computational tools are
applied to compile the libraries and to finally assess the assembled
ligands. Using the metalloproteinase thermolysin (TLN) as a model target, a
computational fragment-based screening protocol has been established.
Starting with a data set of commercially available chemical compounds, a
fragment library has been compiled considering (1) fragment likeness and (2)
similarity to known drugs. The library is screened for target specificity,
resulting in 112 fragments to target the zinc binding area and 75 fragments
targeting the hydrophobic specificity pocket of the enzyme. After analyzing
the performance of multiple docking programs and scoring functions forand the most 14 candidates are selected for further analysis. Soaking
experiments were performed for reference fragment to derive a general
applicable crystallization protocol for TLN and subsequently for new
protein-fragment complex structures. 3-Methylsaspirin could be determined to
bind to TLN. Additional studies addressed a retrospective performance
analysis of the applied scoring functions and modification on the screening
hit. Curios about the differences of aspirin and 3-methylaspirin,
3-chloroaspirin has been synthesized and affinities could be determined to
be 2.42 mM; 1.73 mM und 522 μM respectively.
The results of the thesis show, that computer aided drug design approaches
could successfully support projects in lead optimization and lead
identification.
fragments in general, the fragments derived from the screening are docke
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RyR1-targeted drug discovery pipeline integrating FRET-based high-throughput screening and human myofiber dynamic Ca2+ assays.
Elevated cytoplasmic [Ca2+] is characteristic in severe skeletal and cardiac myopathies, diabetes, and neurodegeneration, and partly results from increased Ca2+ leak from sarcoplasmic reticulum stores via dysregulated ryanodine receptor (RyR) channels. Consequently, RyR is recognized as a high-value target for drug discovery to treat such pathologies. Using a FRET-based high-throughput screening assay that we previously reported, we identified small-molecule compounds that modulate the skeletal muscle channel isoform (RyR1) interaction with calmodulin and FK506 binding protein 12.6. Two such compounds, chloroxine and myricetin, increase FRET and inhibit [3H]ryanodine binding to RyR1 at nanomolar Ca2+. Both compounds also decrease RyR1 Ca2+ leak in human skinned skeletal muscle fibers. Furthermore, we identified compound concentrations that reduced leak by > 50% but only slightly affected Ca2+ release in excitation-contraction coupling, which is essential for normal muscle contraction. This report demonstrates a pipeline that effectively filters small-molecule RyR1 modulators towards clinical relevance
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