Substrate Specificity and Plasticity of FERM-Containing Protein Tyrosine Phosphatases

SummaryEpidermal growth factor receptor (EGFR) pathway substrate 15 (Eps15) is a newly identified substrate for protein tyrosine phosphatase N3 (PTPN3), which belongs to the FERM-containing PTP subfamily comprising five members including PTPN3, N4, N13, N14, and N21. We solved the crystal structures of the PTPN3-Eps15 phosphopeptide complex and found that His812 of PTPN3 and Pro850 of Eps15 are responsible for the specific interaction between them. We defined the critical role of the additional residue Tyr676 of PTPN3, which is replaced by Ile939 in PTPN14, in recognition of tyrosine phosphorylated Eps15. The WPD loop necessary for catalysis is present in all members but not PTPN21. We identified that Glu instead of Asp in the WPE loop contributes to the catalytic incapability of PTPN21 due to an extended distance beyond protonation targeting a phosphotyrosine substrate. Together with in vivo validations, our results provide novel insights into the substrate specificity and plasticity of FERM-containing PTPs

Plasticity of cerebellar Purkinje cells in behavioral training of body balance control

Neural responses to sensory inputs caused by self-generated movements (reafference) and external passive stimulation (exafference) differ in various brain regions. The ability to differentiate such sensory information can lead to movement execution with better accuracy. However, how sensory responses are adjusted in regard to this distinguishability during motor learning is still poorly understood. The cerebellum has been hypothesized to analyze the functional significance of sensory information during motor learning, and is thought to be a key region of reafference computation in the vestibular system. In this study, we investigated Purkinje cell (PC) spike trains as cerebellar cortical output when rats learned to balance on a suspended dowel. Rats progressively reduced the amplitude of body swing and made fewer foot slips during a 5-min balancing task. Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference. Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning. In response to unexpected perturbations and under anesthesia, SS coding capability of these PCs recovered. By plotting SS and CS firing frequencies over 15-s time windows in double-logarithmic plots, a negative correlation between SS and CS was found in awake, but not anesthetized, rats. PCs with prominent SS coding attenuation during motor learning showed weaker SS-CS correlation. Hence, we demonstrate that neural plasticity for filtering out sensory reafference from active motion occurs in the cerebellar cortex in rats during balance learning. SS-CS interaction may contribute to this rapid plasticity as a form of receptive field plasticity in the cerebellar cortex between two receptive maps of sensory inputs from the external world and of efference copies from the will center for volitional movements

Dynamic neural networks for real-time water level predictions of sewerage systems-covering gauged and ungauged sites

[[abstract]]In this research, we propose recurrent neural networks (RNNs) to build a relationship between rainfalls and water level patterns of an urban sewerage system based on historical torrential rain/storm events. The RNN allows signals to propagate in both forward and backward directions, which offers the network dynamic memories. Besides, the information at the current time-step with a feedback operation can yield a time-delay unit that provides internal input information at the next time-step to effectively deal with time-varying systems. The RNN is implemented at both gauged and ungauged sites for 5-, 10-, 15-, and 20-min-ahead water level predictions. The results show that the RNN is capable of learning the nonlinear sewerage system and producing satisfactory predictions at the gauged sites. Concerning the ungauged sites, there are no historical data of water level to support prediction. In order to overcome such problem, a set of synthetic data, generated from a storm water management model (SWMM) under cautious verification process of applicability based on the data from nearby gauging stations, are introduced as the learning target to the training procedure of the RNN and moreover evaluating the performance of the RNN at the ungauged sites. The results demonstrate that the potential role of the SWMM coupled with nearby rainfall and water level information can be of great use in enhancing the capability of the RNN at the ungauged sites. Hence we can conclude that the RNN is an effective and suitable model for successfully predicting the water levels at both gauged and ungauged sites in urban sewerage systems.[[incitationindex]]SCI[[booktype]]紙

A sequence-based hybrid predictor for identifying conformationally ambivalent regions in proteins

<p>Abstract</p> <p>Background</p> <p>Proteins are dynamic macromolecules which may undergo conformational transitions upon changes in environment. As it has been observed in laboratories that protein flexibility is correlated to essential biological functions, scientists have been designing various types of predictors for identifying structurally flexible regions in proteins. In this respect, there are two major categories of predictors. One category of predictors attempts to identify conformationally flexible regions through analysis of protein tertiary structures. Another category of predictors works completely based on analysis of the polypeptide sequences. As the availability of protein tertiary structures is generally limited, the design of predictors that work completely based on sequence information is crucial for advances of molecular biology research.</p> <p>Results</p> <p>In this article, we propose a novel approach to design a sequence-based predictor for identifying conformationally ambivalent regions in proteins. The novelty in the design stems from incorporating two classifiers based on two distinctive supervised learning algorithms that provide complementary prediction powers. Experimental results show that the overall performance delivered by the hybrid predictor proposed in this article is superior to the performance delivered by the existing predictors. Furthermore, the case study presented in this article demonstrates that the proposed hybrid predictor is capable of providing the biologists with valuable clues about the functional sites in a protein chain. The proposed hybrid predictor provides the users with two optional modes, namely, the <it>high-sensitivity </it>mode and the <it>high-specificity </it>mode. The experimental results with an independent testing data set show that the proposed hybrid predictor is capable of delivering sensitivity of 0.710 and specificity of 0.608 under the <it>high-sensitivity </it>mode, while delivering sensitivity of 0.451 and specificity of 0.787 under the <it>high-specificity </it>mode.</p> <p>Conclusion</p> <p>Though experimental results show that the hybrid approach designed to exploit the complementary prediction powers of distinctive supervised learning algorithms works more effectively than conventional approaches, there exists a large room for further improvement with respect to the achieved performance. In this respect, it is of interest to investigate the effects of exploiting additional physiochemical properties that are related to conformational ambivalence. Furthermore, it is of interest to investigate the effects of incorporating lately-developed machine learning approaches, e.g. the random forest design and the multi-stage design. As conformational transition plays a key role in carrying out several essential types of biological functions, the design of more advanced predictors for identifying conformationally ambivalent regions in proteins deserves our continuous attention.</p

3510-V 390-m Omega . cm(2) 4H-SiC Lateral JFET on a Semi-Insulating Substrate

The performance of high-voltage 4H-SiC lateral JFETs on a semi-insulating substrate is reported in this letter. The design of the voltage-supporting layers is based on the charge compensation of p- and n-type epilayers. The best measured breakdown voltage is 3510 V, which, to the authors\u27 knowledge, is the highest value ever reported for SiC lateral switching devices. The R-on of this device is 390 m Omega . cm(2), in which 61% is due to the drift-region resistance. The BV2/R-on is 32 MW/cm(2), which is typical among other reported SiC lateral devices

Bis{1-[(E)-(2-methyl­phen­yl)diazen­yl]-2-naphtho­lato}palladium(II)

In the title compound, [Pd(C17H13N2O)2], the PdII atom is tetra­coordinated by two N atoms and two O atoms from two bidentate methylphenyl­diazenylnaphtolate ligands, forming a square-planar complex. The two N atoms and two O atoms around the PdII atom are trans to each other (as the PdII atom lies on a crystallographic inversion centre) with O—Pd—N bond angles of 89.60 (11) and 90.40 (11)°. The distances between the PdII atom and the coordinated O and N atoms are 1.966 (3) and 2.009 (3) Å, respectively

Involvement of the Cav3.2 T-type calcium channel in thalamic neuron discharge patterns

<p>Abstract</p> <p>Background</p> <p>Mice that have defects in their low-threshold T-type calcium channel (T-channel) genes show altered pain behaviors. The changes in the ratio of nociceptive neurons and the burst firing property of reticular thalamic (RT) and ventroposterior (VP) neurons in Cav3.2 knockout (KO) mice were studied to test the involvement of thalamic T-channel and burst firing activity in pain function.</p> <p>Results</p> <p>Under pentobarbital or urethane anesthesia, the patterns of tonic and burst firings were recorded in functionally characterized RT and VPL neurons of Cav3.2 KO mice. Many RT neurons were nociceptive (64% under pentobarbital anesthesia and 50% under urethane anesthesia). Compared to their wild-type (WT) controls, fewer nociceptive RT neurons were found in Cav3.2 KO mice. Both nociceptive and tactile RT neurons showed fewer bursts in Cav3.2 KO mice. Within a burst, RT neurons of Cav3.2 KO mice had a lower spike frequency and less-prominent accelerando-decelerando change. In contrast, VP neurons of Cav3.2 KO mice showed a higher ratio of bursts and a higher discharge rate within a burst than those of the WT control. In addition, the long-lasting tonic firing episodes in RT neurons of the Cav3.2 KO had less stereotypic regularity than their counterparts in WT mice.</p> <p>Conclusions</p> <p>RT might be important in nociception of the mouse. In addition, we showed an important role of Cav3.2 subtype of T-channel in RT burst firing pattern. The decreased occurrence and slowing of the bursts in RT neurons might cause the increased VP bursts. These changes would be factors contributing to alternation of pain behavior in the Cav3.2 KO mice.</p

Enterovirus type 71 2A protease functions as a transcriptional activator in yeast

Enterovirus type 71 (EV71) 2A protease exhibited strong transcriptional activity in yeast cells. The transcriptional activity of 2A protease was independent of its protease activity. EV71 2A protease retained its transcriptional activity after truncation of 40 amino acids at the N-terminus but lost this activity after truncation of 60 amino acids at the N-terminus or deletion of 20 amino acids at the C-terminus. Thus, the acidic domain at the C-terminus of this protein is essential for its transcriptional activity. Indeed, deletion of amino acids from 146 to 149 (EAME) in this acidic domain lost the transcriptional activity of EV71 2A protein though still retained its protease activity. EV71 2A protease was detected both in the cytoplasm and nucleus using confocal microscopy analysis. Coxsackie virus B3 2A protease also exhibited transcriptional activity in yeast cells. As expected, an acidic domain in the C-terminus of Coxsackie virus B3 2A protease was also identified. Truncation of this acidic domain resulted in the loss of transcriptional activity. Interestingly, this acidic region of poliovirus 2A protease is critical for viral RNA replication. The transcriptional activity of the EV71 or Coxsackie virus B3 2A protease should play a role in viral replication and/or pathogenesis