BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

https://www.linkgroup.hu/docs/13PharmTher.pdf

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review

Compared to the available protein sequences of different organisms, the number of revealed protein– protein interactions (PPIs) is still very limited. So many computational methods have been developed to facilitate the identification of novel PPIs. However, the methods only using the information of protein sequences are more universal than those that depend on some additional information or predictions about the proteins. In this article, a sequence-based method is proposed by combining a new feature representation using auto covariance (AC) and support vector machine (SVM). AC accounts for the interactions between residues a certain distance apart in the sequence, so this method adequately takes the neighbouring effect into account. When performed on the PPI data of yeast Saccharomyces cerevisiae, the method achieved a very promising prediction result. An independent data set of 11 474 yeast PPIs was used to evaluate this prediction model and the prediction accuracy is 88.09%. The performance of this method is superior to those of the existing sequence-based methods, so it can be a useful supplementary tool for future proteomics studies. The prediction software and all data sets used in this article are freely available at http://www. scucic.cn/Predict_PPI/index.htm.

/pdf/using-support-vector-machine-combined-with-auto-covariance-4tdcnvvyzk.pdf

Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences

Summary Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection and dimensionality reduction algorithms, greatly facilitating training, analysis and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. Availability and implementation http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. Supplementary information Supplementary data are available at Bioinformatics online.

iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences.

Summary: Sequence-derived structural and physiochemical features have been frequently used for analysing and predicting structural, functional, expression and interaction profiles of proteins and peptides. To facilitate extensive studies of proteins and peptides, we developed a freely available, open source python package called protein in python (propy) for calculating the widely used structural and physicochemical features of proteins and peptides from amino acid sequence. It computes five feature groups composed of 13 features, including amino acid composition, dipeptide composition, tripeptide composition, normalized Moreau–Broto autocorrelation, Moran autocorrelation, Geary autocorrelation, sequence-order-coupling number, quasi-sequence-order descriptors, composition, transition and distribution of various structural and physicochemical properties and two types of pseudo amino acid composition (PseAAC) descriptors. These features could be generally regarded as different Chou’s PseAAC modes. In addition, it can also easily compute the previous descriptors based on user-defined properties, which are automatically available from the AAindex database. Availability: The python package, propy, is freely available via http:// code.google.com/p/protpy/downloads/list, and it runs on Linux and MS-Windows.

/pdf/propy-a-tool-to-generate-various-modes-of-chou-s-pseaac-l1djqfvvds.pdf

propy: a tool to generate various modes of Chou’s PseAAC

Protein–protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and protein interaction networks, the application of most existing methods is limited because they need information about protein homology or the interaction marks of the protein partners. In the present work, we propose a method for PPI prediction using only the information of protein sequences. This method was developed based on a learning algorithm-support vector machine combined with a kernel function and a conjoint triad feature for describing amino acids. More than 16,000 diverse PPI pairs were used to construct the universal model. The prediction ability of our approach is better than that of other sequence-based PPI prediction methods because it is able to predict PPI networks. Different types of PPI networks have been effectively mapped with our method, suggesting that, even with only sequence information, this method could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, such supplementary experimental information can enhance the prediction ability of the method.

https://www.pnas.org/content/pnas/104/11/4337.full.pdf

Predicting protein-protein interactions based only on sequences information.

Both opioids and nonsteroidal anti-inflammatory drugs (NSAIDS) produce deleterious side effects and fail to provide sustained relief in patients with chronic inflammatory pain. Peripheral neuroinflammation (PN) is critical for initiation and development of inflammatory pain. A better understanding of molecular mechanisms underlying PN would facilitate the discovery of new analgesic targets and the development of new therapeutics. Emerging evidence suggests that peripheral sensory neurons are not only responders to painful stimuli, but are also actively engaged in inflammation and immunity, whereas the intrinsic regulatory mechanism is poorly understood. Here we report the expression of proton-selective ion channel Hv1 in peripheral sensory neurons in rodents and humans, which was previously shown as selectively expressed in microglia in mammalian central nervous system. Neuronal Hv1 was up-regulated by PN or depolarizing stimulation, which in turn aggravates inflammation and nociception. Inhibiting neuronal Hv1 genetically or by a newly discovered selective inhibitor YHV98-4 reduced intracellular alkalization and ROS production in inflammatory pain, mitigated the imbalance in downstream SHP-1-pAKT signaling, and also diminished pro-inflammatory chemokine release to alleviate nociception and morphine-induced hyperalgesia and tolerance. Thus, our data reveal neuronal Hv1 as a novel target in analgesia strategy and managing opioids-related side effects. 

Inhibiting Hv1 channel in peripheral sensory neurons attenuates chronic inflammatory pain and opioid side effects

ABSTRACT Two-pore domain potassium (K2P) channels gate primarily within the selectivity filter, termed ‘C-type’ gating. Due to the lack of structural insights into the nonconductive (closed) state, ‘C-type’ gating mechanisms remain elusive. Here, molecular dynamics (MD) simulations on TREK-1, a K2P channel, revealed that M4 helix movements induce filter closing in a novel ‘deeper-down’ structure that represents a ‘C-type’ closed state. The ‘down’ structure does not represent the closed state as previously proposed and instead acts as an intermediate state in gating. The study identified the allosteric ‘seesaw’ mechanism of M4 helix movements in modulating filter closing. Finally, guided by this recognition of K2P gating mechanisms, MD simulations revealed that gain-of-function mutations and small-molecule activators activate TREK-1 by perturbing state transitions from open to closed states. Together, we reveal a ‘C-type’ closed state and provide mechanical insights into gating procedures and allosteric regulations for K2P channels.

/pdf/c-type-closed-state-and-gating-mechanisms-of-k2p-channels-1h1j9o8c.pdf

‘C-type’ closed state and gating mechanisms of K2P channels revealed by conformational changes of the TREK-1 channel

Under the known pharmacological activation mechanisms, activators allosterically or directly open potassium channel gates. However, herein, molecular dynamics simulations on TREK-1, a member of the channel class gated at the filter, suggested that negatively charged activators act with a gate-independent mechanism where compounds increase currents by promoting ions passing through the central cavity. Then, based on studies of KCNQ2, we uncovered that this noncanonical activation mechanism is shared by the other channel class gated at the helix-bundle crossing. Rational drug design found a novel KCNQ2 agonist, CLE030, which stably binds to the central cavity. Functional analysis, molecular dynamics simulations, and calculations of the potential of mean force revealed that the carbonyl oxygen of CLE030 influences permeant ions in the central cavity to contribute to its activation effects. Together, this study discovered a ligand-to-ion activation mechanism for channels that bypasses their gates and thus is conserved across subfamilies with different gates.

General Pharmacological Activation Mechanism of K+ Channels Bypassing Channel Gates.

A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients. The authors use a DNA-framework-based molecular classifier to perform biomarker panel screening and analyse six biomarkers across three-dimensional datatypes to obtain a molecular taxonomy for prostate cancer diagnosis. 

/pdf/dna-framework-based-multidimensional-molecular-classifiers-3b8v4mnz.pdf

Juwen Shen

Papers

Predicting protein-protein interactions based only on sequences information.

Inhibiting Hv1 channel in peripheral sensory neurons attenuates chronic inflammatory pain and opioid side effects

‘C-type’ closed state and gating mechanisms of K2P channels revealed by conformational changes of the TREK-1 channel

General Pharmacological Activation Mechanism of K+ Channels Bypassing Channel Gates.

DNA-framework-based multidimensional molecular classifiers for cancer diagnosis