Camel Whey Protein Attenuates Acute Heat Stress-Induced Kidney Injury in Rats by Up-Regulating CYP2J Activity and Activating PI3K/AKT/eNOS to Inhibit Oxidative Stress.

Camel whey protein (CWP) is a potent natural antioxidant, noted for its abundance of antioxidant amino acids. Despite its promising properties, the precise mechanisms underlying its effects remain inadequately explored. This study aims to investigate the impact of CWP on kidney damage induced by acute heat stress in rats, as well as to elucidate its mechanism of action.

Transcriptomic profiling of different developmental stages reveals parasitic strategies of Wohlfahrtia magnifica, a myiasis-causing flesh fly.

Background: Wohlfahrtia magnifica is an obligatory parasite that causes myiasis in several warm-blooded vertebrates. Adult females deposit the first-stage larvae directly onto wounds or natural body orifices (e.g.

Genome and Transcriptome Analyses Facilitate Genetic Control of , a Myiasis-Causing Flesh Fly.

Myiasis caused by is a widespread parasitic infestation in mammals. The infested host suffers from damage as the developing larvae feed on its tissues. For the control of myiasis infestation, genetic methods have been shown to be effective and promising as an alternative to insecticides.

Identification of TNO155, an Allosteric SHP2 Inhibitor for the Treatment of Cancer.

SHP2 is a nonreceptor protein tyrosine phosphatase encoded by the gene and is involved in cell growth and differentiation via the MAPK signaling pathway. SHP2 also plays an important role in the programed cell death pathway (PD-1/PD-L1). As an oncoprotein as well as a potential immunomodulator, controlling SHP2 activity is of high therapeutic interest.

Systematic Chemogenetic Library Assembly.

Chemogenetic libraries, collections of well-defined chemical probes, provide tremendous value to biomedical research but require substantial effort to ensure diversity as well as quality of the contents. We have assembled a chemogenetic library by data mining and crowdsourcing institutional expertise. We are sharing our approach, lessons learned, and disclosing our current collection of 4,185 compounds with their primary annotated gene targets (https://github.

A Novel Evidence Conflict Measurement for Multi-Sensor Data Fusion Based on the Evidence Distance and Evidence Angle.

As an important method for uncertainty modeling, Dempster-Shafer (DS) evidence theory has been widely used in practical applications. However, the results turned out to be almost counter-intuitive when fusing the different sources of highly conflicting evidence with Dempster's combination rule. In previous researches, most of them were mainly dependent on the conflict measurement method between the evidence represented by the evidence distance.

Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases.

The non-receptor protein tyrosine phosphatase SHP2, encoded by PTPN11, has an important role in signal transduction downstream of growth factor receptor signalling and was the first reported oncogenic tyrosine phosphatase. Activating mutations of SHP2 have been associated with developmental pathologies such as Noonan syndrome and are found in multiple cancer types, including leukaemia, lung and breast cancer and neuroblastoma. SHP2 is ubiquitously expressed and regulates cell survival and proliferation primarily through activation of the RAS–ERK signalling pathway.

Allosteric Inhibition of SHP2: Identification of a Potent, Selective, and Orally Efficacious Phosphatase Inhibitor.

SHP2 is a nonreceptor protein tyrosine phosphatase (PTP) encoded by the PTPN11 gene involved in cell growth and differentiation via the MAPK signaling pathway. SHP2 also purportedly plays an important role in the programmed cell death pathway (PD-1/PD-L1). Because it is an oncoprotein associated with multiple cancer-related diseases, as well as a potential immunomodulator, controlling SHP2 activity is of significant therapeutic interest.

Rethinking molecular similarity: comparing compounds on the basis of biological activity.

Since the advent of high-throughput screening (HTS), there has been an urgent need for methods that facilitate the interrogation of large-scale chemical biology data to build a mode of action (MoA) hypothesis. This can be done either prior to the HTS by subset design of compounds with known MoA or post HTS by data annotation and mining. To enable this process, we developed a tool that compares compounds solely on the basis of their bioactivity: the chemical biological descriptor "high-throughput screening fingerprint" (HTS-FP).

A lead discovery strategy driven by a comprehensive analysis of proteases in the peptide substrate space.

We present here a comprehensive analysis of proteases in the peptide substrate space and demonstrate its applicability for lead discovery. Aligned octapeptide substrates of 498 proteases taken from the MEROPS peptidase database were used for the in silico analysis. A multiple-category naïve Bayes model, trained on the two-dimensional chemical features of the substrates, was able to classify the substrates of 365 (73%) proteases and elucidate statistically significant chemical features for each of their specific substrate positions.

Which aspects of HTS are empirically correlated with downstream success?

High-throughput screening (HTS) is a well-established hit-finding approach used in the pharmaceutical industry. In this article, recent experience at Novartis with respect to factors influencing the success of HTS campaigns is discussed. An inherent measure of HTS quality could be defined by the assay Z and Z' factors, the number of hits and their biological potencies; however, such measures of quality do not always correlate with the advancement of hits to the later stages of drug discovery.

"Plate cherry picking": a novel semi-sequential screening paradigm for cheaper, faster, information-rich compound selection.

This work describes a novel semi-sequential technique for in silico enhancement of high-throughput screening (HTS) experiments now employed at Novartis. It is used in situations in which the size of the screen is limited by the readout (e.g.

Flexible 3D pharmacophores as descriptors of dynamic biological space.

Development of a pharmacophore hypothesis related to small-molecule activity is pivotal to chemical optimization of a series, since it defines features beneficial or detrimental to activity. Although crystal structures may provide detailed 3D interaction information for one molecule with its receptor, docking a different ligand to that model often leads to unreliable results due to protein flexibility. Graham Richards' lab was one of the first groups to utilize "fuzzy" pattern recognition algorithms taken from the field of image processing to solve problems in protein modeling.

Bridging chemical and biological space: "target fishing" using 2D and 3D molecular descriptors.

Bridging chemical and biological space is the key to drug discovery and development. Typically, cheminformatics methods operate under the assumption that similar chemicals have similar biological activity. Ideally then, one could predict a drug's biological function(s) given only its chemical structure by similarity searching in libraries of compounds with known activities.

"Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: when are multitarget drugs a feasible concept?

Conventional similarity searching of molecules compares single (or multiple) active query structures to each other in a relative framework, by means of a structural descriptor and a similarity measure. While this often works well, depending on the target, we show here that retrieval rates can be improved considerably by incorporating an external framework describing ligand bioactivity space for comparisons ("Bayes affinity fingerprints"). Structures are described by Bayes scores for a ligand panel comprising about 1000 activity classes extracted from the WOMBAT database.

A kinase-focused compound collection: compilation and screening strategy.

Lead identification by high-throughput screening of large compound libraries has been supplemented with virtual screening and focused compound libraries. To complement existing approaches for lead identification at Biogen Idec, a kinase-focused compound collection was designed, developed and validated. Two strategies were adopted to populate the compound collection: a ligand shape-based virtual screening and a receptor-based approach (structural interaction fingerprint).

Structural interaction fingerprints: a new approach to organizing, mining, analyzing, and designing protein-small molecule complexes.

The combination of advances in structure-based drug design efforts in the pharmaceutical industry in parallel with structural genomics initiatives in the public domain has led to an explosion in the number of structures of protein-small molecule complexes structures. This information has critical importance to both the understanding of the structural basis for molecular recognition in biological systems and the design of better drugs. A significant challenge exists in managing this vast amount of data and fully leveraging it.

Knowledge-based design of target-focused libraries using protein-ligand interaction constraints.

Here we present a new strategy for designing and filtering potentially massive combinatorial libraries using structural information of a binding site. We have developed a variation of the structural interaction fingerprint (SIFt) named r-SIFt, which incorporates the binding interactions of variable fragments in a combinatorial library. This method takes into account the 3D structure of the active site of the target molecule and translates desirable ligand-target binding interactions into library filtering constraints.

Interaction profiles of protein kinase-inhibitor complexes and their application to virtual screening.

A major challenge facing structure-based drug discovery efforts is how to leverage the massive amount of experimental (X-ray and NMR) and virtual structural information generated from drug discovery projects. Many important drug targets have large numbers of protein-inhibitor complexes, necessitating tools to compare and contrast their similarities and differences. This information would be valuable for understanding potency and selectivity of inhibitors and could be used to define target constraints to assist virtual screening.

Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions.

Representing and understanding the three-dimensional (3D) structural information of protein-ligand complexes is a critical step in the rational drug discovery process. Traditional analysis methods are proving inadequate and inefficient in dealing with the massive amount of structural information being generated from X-ray crystallography, NMR, and in silico approaches such as structure-based docking experiments. Here, we present SIFt (structural interaction fingerprint), a novel method for representing and analyzing 3D protein-ligand binding interactions.