36 research outputs found
The L3MBTL3 Methyl-Lysine Reader Domain Functions As a Dimer
L3MBTL3
recognizes mono- and dimethylated lysine residues on histone
tails. The recently reported X-ray cocrystal structures of the chemical
probe UNC1215 and inhibitor UNC2533 bound to the methyl-lysine reading
MBT domains of L3MBTL3 demonstrate a unique and flexible 2:2 dimer
mode of recognition. In this study, we describe our <i>in vitro</i> analysis of L3MBTL3 dimerization via its MBT domains and additionally
show that this dimerization occurs within a cellular context in the
absence of small molecule ligands. Furthermore, mutations to the first
and second MBT domains abrogated L3MBTL3 dimerization both <i>in vitro</i> and in cells. These observations are consistent
with the hypothesis that L3MBTL3 engages methylated histone tails
as a dimer while carrying out its normal function and provides an
explanation for the presence of repeated MBT domains within L3MBTL3
Table_8_Behind the screen: drug discovery using the big data of phenotypic analysis.DOCX
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p
Table_1_Behind the screen: drug discovery using the big data of phenotypic analysis.XLSX
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p
Table_6_Behind the screen: drug discovery using the big data of phenotypic analysis.XLSX
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p
Table_7_Behind the screen: drug discovery using the big data of phenotypic analysis.XLSX
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p
Table_9_Behind the screen: drug discovery using the big data of phenotypic analysis.docx
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p
Table_3_Behind the screen: drug discovery using the big data of phenotypic analysis.DOCX
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p
Table_5_Behind the screen: drug discovery using the big data of phenotypic analysis.DOCX
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p
Presentation_1_Behind the screen: drug discovery using the big data of phenotypic analysis.pptx
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p
Table_4_Behind the screen: drug discovery using the big data of phenotypic analysis.DOCX
Technological advances in drug discovery are exciting to students, but it is challenging for faculty to maintain the pace with these developments, particularly within undergraduate courses. In recent years, a High-throughput Discovery Science and Inquiry-based Case Studies for Today’s Students (HITS) Research Coordination Network has been assembled to address the mechanism of how faculty can, on-pace, introduce these advancements. As a part of HITS, our team has developed “Behind the Screen: Drug Discovery using the Big Data of Phenotypic Analysis” to introduce students and faculty to phenotypic screening as a tool to identify inhibitors of diseases that do not have known cellular targets. This case guides faculty and students though current screening methods using statistics and can be applied at undergraduate and graduate levels. Tested across 70 students at three universities and a variety of courses, our case utilizes datasets modeled on a real phenotypic screening method as an accessible way to teach students about current methods in drug discovery. Students will learn how to identify hit compounds from a dataset they have analyzed and understand the biological significance of the results they generate. They are guided through practical statistical procedures, like those of researchers engaging in a novel drug discovery strategy. Student survey data demonstrated that the case was successful in improving student attitudes in their ability to discuss key topics, with both undergraduate and graduate students having a significant increase in confidence. Together, we present a case that uses big data to examine the utility of a novel phenotypic screening strategy, a pedagogical tool that can be customized for a wide variety of courses.</p