72 research outputs found
A new class of orthosteric uPARĀ·uPA small-molecule antagonists are allosteric inhibitors of the uPARĀ·vitronectin interaction
The urokinase receptor (uPAR) is a GPI-anchored cell surface receptor that is at the center of an intricate network of protein-protein interactions. Its immediate binding partners are the serine proteinase urokinase (uPA), and vitronectin (VTN), a component of the extracellular matrix. uPA and VTN bind at distinct sites on uPAR to promote extracellular matrix degradation and integrin signaling, respectively. Here, we report the discovery of a new class of pyrrolone small-molecule inhibitors of the tight ā¼1 nM uPARĀ·uPA protein-protein interaction. These compounds were designed to bind to the uPA pocket on uPAR. The highest affinity compound, namely 7, displaced a fluorescently labeled Ī±-helical peptide (AE147-FAM) with an inhibition constant Ki of 0.7 Ī¼M and inhibited the tight uPARĀ·uPAATF interaction with an IC50 of 18 Ī¼M. Biophysical studies with surface plasmon resonance showed that VTN binding is highly dependent on uPA. This cooperative binding was confirmed as 7, which binds at the uPARĀ·uPA interface, also inhibited the distal VTNĀ·uPAR interaction. In cell culture, 7 blocked the uPARĀ·uPA interaction in uPAR-expressing human embryonic kidney (HEK-293) cells and impaired cell adhesion to VTN, a process that is mediated by integrins. As a result, 7 inhibited integrin signaling in MDA-MB-231 cancer cells as evidenced by a decrease in focal adhesion kinase (FAK) phosphorylation and Rac1 GTPase activation. Consistent with these results, 7 blocked breast MDA-MB-231 cancer cell invasion with IC50 values similar to those observed in ELISA and surface plasmon resonance competition studies. Explicit-solvent molecular dynamics simulations show that the cooperativity between uPA and VTN is attributed to stabilization of uPAR motion by uPA. In addition, free energy calculations revealed that uPA stabilizes the VTNSMBĀ·uPAR interaction through more favorable electrostatics and entropy. Disruption of the uPARĀ·VTNSMB interaction by 7 is consistent with the cooperative binding to uPAR by uPA and VTN. Interestingly, the VTNSMBĀ·uPAR interaction was less favorable in the VTNSMBĀ·uPARĀ·7 complex suggesting potential cooperativity between 7 and VTN. Compound 7 provides an excellent starting point for the development of more potent derivatives to explore uPAR biology
Structure-Based Target-Specific Screening Leads to Small-Molecule CaMKII Inhibitors
Target-specific scoring methods are more commonly used to identify small-molecule inhibitors among compounds docked to a target of interest. Top candidates that emerge from these methods have rarely been tested for activity and specificity across a family of proteins. In this study we docked a chemical library into CaMKIIĪ“, a member of the Ca2+ /calmodulin (CaM)-dependent protein kinase (CaMK) family, and re-scored the resulting protein-compound structures using Support Vector Machine SPecific (SVMSP), a target-specific method that we developed previously. Among the 35 selected candidates, three hits were identified, such as quinazoline compound 1 (KIN-1; N4-[7-chloro-2-[(E)-styryl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), which was found to inhibit CaMKIIĪ“ kinase activity at single-digit micromolar IC50 . Activity across the kinome was assessed by profiling analogues of 1, namely 6 (KIN-236; N4-[7-chloro-2-[(E)-2-(2-chloro-4,5-dimethoxyphenyl)vinyl]quinazolin-4-yl]-N1,N1-diethylpentane-1,4-diamine), and an analogue of hit compound 2 (KIN-15; 2-[4-[(E)-[(5-bromobenzofuran-2-carbonyl)hydrazono]methyl]-2-chloro-6-methoxyphenoxy]acetic acid), namely 14 (KIN-332; N-[(E)-[4-(2-anilino-2-oxoethoxy)-3-chlorophenyl]methyleneamino]benzofuran-2-carboxamide), against 337 kinases. Interestingly, for compound 6, CaMKIIĪ“ and homologue CaMKIIĪ³ were among the top ten targets. Among the top 25 targets of 6, IC50 values ranged from 5 to 22ā
Ī¼m. Compound 14 was found to be not specific toward CaMKII kinases, but it does inhibit two kinases with sub-micromolar IC50 values among the top 25. Derivatives of 1 were tested against several kinases including several members of the CaMK family. These data afforded a limited structure-activity relationship study. Molecular dynamics simulations with explicit solvent followed by end-point MM-GBSA free-energy calculations revealed strong engagement of specific residues within the ATP binding pocket, and also changes in the dynamics as a result of binding. This work suggests that target-specific scoring approaches such as SVMSP may hold promise for the identification of small-molecule kinase inhibitors that exhibit some level of specificity toward the target of interest across a large number of proteins
AKConv: Convolutional Kernel with Arbitrary Sampled Shapes and Arbitrary Number of Parameters
Neural networks based on convolutional operations have achieved remarkable
results in the field of deep learning, but there are two inherent flaws in
standard convolutional operations. On the one hand, the convolution operation
be confined to a local window and cannot capture information from other
locations, and its sampled shapes is fixed. On the other hand, the size of the
convolutional kernel is fixed to k k, which is a fixed square shape,
and the number of parameters tends to grow squarely with size. It is obvious
that the shape and size of targets are various in different datasets and at
different locations. Convolutional kernels with fixed sample shapes and squares
do not adapt well to changing targets. In response to the above questions, the
Alterable Kernel Convolution (AKConv) is explored in this work, which gives the
convolution kernel an arbitrary number of parameters and arbitrary sampled
shapes to provide richer options for the trade-off between network overhead and
performance. In AKConv, we define initial positions for convolutional kernels
of arbitrary size by means of a new coordinate generation algorithm. To adapt
to changes for targets, we introduce offsets to adjust the shape of the samples
at each position. Moreover, we explore the effect of the neural network by
using the AKConv with the same size and different initial sampled shapes.
AKConv completes the process of efficient feature extraction by irregular
convolutional operations and brings more exploration options for convolutional
sampling shapes. Object detection experiments on representative datasets
COCO2017, VOC 7+12 and VisDrone-DET2021 fully demonstrate the advantages of
AKConv. AKConv can be used as a plug-and-play convolutional operation to
replace convolutional operations to improve network performance. The code for
the relevant tasks can be found at https://github.com/CV-ZhangXin/AKConv.Comment: 10 pages, 5 figure
Profiling Good Leakage Models For Masked Implementations
Leakage model plays a very important role in side channel attacks. An accurate leakage model greatly improves the efficiency of attacks. However, how to profile a good enough leakage model, or how to measure the accuracy of a leakage model, is seldom studied. Durvaux et al. proposed leakage certification tests to profile good enough leakage model for unmasked implementations. However, they left the leakage model profiling for protected implementations as an open problem. To solve this problem, we propose the first practical higher-order leakage model certification tests for masked implementations. First and second order attacks are performed on the simulations of serial and parallel implementations of a first-order fixed masking. A third-order attack is performed on another simulation of a second-order random masked implementation. The experimental results show that our new tests can profile the leakage models accurately
Towards Optimal Pre-processing in Leakage Detection
An attacker or evaluator can detect more information leakages if he improves the Signal-to-Noise Ratio (SNR) of power traces in his tests. For this purpose, pre-processings such as de-noise, distribution-based traces biasing are used. However, the existing traces biasing schemes can\u27t accurately express the characteristics of power traces with high SNR, making them not ideal for leakage detections. Moreover, if the SNR of power traces is very low, it is very difficult to use the existing de-noise schemes and traces biasing schemes to enhance leakage detection. In this paper, a known key based pre-processing tool named Traces Linear Optimal Biasing (TLOB) is proposed, which performs very well even on power traces with very low SNR. It can accurately evaluate the noise of time samples and give reliable traces optimal biasing. Experimental results show that TLOB significantly reduces number of traces used for detection; correlation coefficients in -tests using TLOB approach 1.00, thus the confidence of tests is significantly improved. As far as we know, there is no pre-processing tool more efficient than TLOB. TLOB is very simple, and only brings very limited time and memory consumption. We strongly recommend to use it to pre-process traces in side channel evaluations
Towards Easy Key Enumeration
Key enumeration solutions are post-processing schemes for the output sequences of side channel distinguishers, the application of which are prevented by very large key candidate space and computation power requirements. The attacker may spend several days or months to enumerate a huge key space (e.g. ). In this paper, we aim at pre-processing and reducing the key candidate space by deleting impossible key candidates before enumeration. A new distinguisher named Group Collision Attack (GCA) is given. Moreover, we introduce key verification into key recovery and a new divide and conquer strategy named Key Grouping Enumeration (KGE) is proposed. KGE divides the huge key space into several groups and uses GCA to delete impossible key combinations and output possible ones in each group. KGE then recombines the remaining key candidates in each group using verification. The number of remaining key candidates becomes much smaller through these two impossible key candidate deletion steps with a small amount of computation. Thus, the attacker can use KGE as a pre-processing tool of key enumeration and enumerate the key more easily and fast in a much smaller candidate space
A Computational Investigation of Small-Molecule Engagement of Hot Spots at ProteināProtein Interaction Interfaces
The binding affinity of a proteināprotein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt proteināprotein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule proteināprotein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of proteināprotein interactions (primary, secondary, and tertiary) were considered, namely, BRD4ā¢H4, XIAPā¢Smac, MDM2ā¢p53, Bcl-xLā¢Bak, and IL-2ā¢IL-2RĪ±. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of proteināprotein interactions do not optimally mimic proteināligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials
Small-molecule CaVĪ±1ā CaVĪ² antagonist suppresses neuronal voltage-gated calcium-channel trafficking
Extracellular calcium flow through neuronal voltage-gated CaV2.2 calcium channels converts action potential-encoded information to the release of pronociceptive neurotransmitters in the dorsal horn of the spinal cord, culminating in excitation of the postsynaptic central nociceptive neurons. The CaV2.2 channel is composed of a pore-forming Ī±1 subunit (CaVĪ±1) that is engaged in protein-protein interactions with auxiliary Ī±2/Ī“ and Ī² subunits. The high-affinity CaV2.2Ī±1ā
CaVĪ²3 protein-protein interaction is essential for proper trafficking of CaV2.2 channels to the plasma membrane. Here, structure-based computational screening led to small molecules that disrupt the CaV2.2Ī±1ā
CaVĪ²3 protein-protein interaction. The binding mode of these compounds reveals that three substituents closely mimic the side chains of hot-spot residues located on the Ī±-helix of CaV2.2Ī±1 Site-directed mutagenesis confirmed the critical nature of a salt-bridge interaction between the compounds and CaVĪ²3 Arg-307. In cells, compounds decreased trafficking of CaV2.2 channels to the plasma membrane and modulated the functions of the channel. In a rodent neuropathic pain model, the compounds suppressed pain responses. Small-molecule Ī±-helical mimetics targeting ion channel protein-protein interactions may represent a strategy for developing nonopioid analgesia and for treatment of other neurological disorders associated with calcium-channel trafficking
- ā¦