Tik Hang Soong

University of Arizona

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Isaac Nies

Fargo VA Health Care System

Wesley M. Botello‐Smith

Western University of Health Sciences

Yun Luo

Western University of Health Sciences

Bradley T. Andresen

Western University of Health Sciences

Priscilla Santos

Western University of Health Sciences

Yin Win Thu

Western University of Health Sciences

Shane D. Anderson

Western University of Health Sciences

Jae Kyung Yeon

Western University of Health Sciences

Andrew Chandler

University of Chichester

Rhye‐Samuel Kanassatega

University of Arizona

Cooperative Institutions

Western University of Health Sciences

National Agriculture and Food Research Organization

National Institute for Agro-Environmental Sciences

University of Arizona

University of Health Science

The University of Tokyo

Tohoku Agricultural Research Center

Environmental Education Exchange

University of California, San Francisco

Taiyo Yuden (Japan)

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

High mesophyll conductance in the high-yielding rice cultivar Takanari quantified with the combined gas exchange and chlorophyll fluorescence measurements under free-air CO₂ enrichment

Plant Production Science (2019)

Hiroki Ikawa Hidemitsu Sakai Charles P. Chen Tik Hang Soong Seiichiro Yonemura

An effective strategy for increasing crop production is increasing the rate of photosynthesis. In this study, we conducted gas exchange and chlorophyll fluorescence measurements for a high-yielding rice cultivar, Takanari, to identify the leaf physiological properties that contribute to high capacity for photosynthesis of the uppermost leaves before (panicle initiation stage) and after heading (grain-filling stage) in the Tsukuba free-air CO2 enrichment (FACE) facility. The higher photosynthesis rate of Takanari compared with that of the commonly cultivated cultivar, Koshihikari, was mainly attributed to the greater stomatal conductance for CO2 (gsc) at the panicle initiation stage and to the greater mesophyll conductance (gm) at the grain-filling stage in both current and elevated atmospheric CO2 concentrations [CO2]. Takanari had a higher level of leaf nitrogen content (Nl) compared with Koshihikari at the grain-filling stage, which led to greater gm and maximum carboxylation rate (Vc,max), but Nl alone did not explain the variations of gm within the variety. A clear correlation was found between Vc,max and Nl. Calculating Vc,max taking gm into consideration removed the artifact of Vc,max25 in relation to Nl that was observed when gm was assumed to be infinite. Our results emphasize the need to separate the roles of Vc,max and gm to accurately understand the ecophysiological processes that control leaf photosynthesis in Takanari.

Panicle

Stomatal Conductance

Carboxylation

10.1080/1343943x.2019.1626253

Cite

Citations (14)

In silico prediction of ARB resistance: A first step in creating personalized ARB therapy

PLoS Computational Biology (2020)

Shane D. Anderson Asna Tabassum Jae Kyung Yeon Garima Sharma Priscilla Santos

Angiotensin II type 1 receptor (AT 1 R) blockers (ARBs) are among the most prescribed drugs. However, ARB effectiveness varies widely, which may be due to non-synonymous single nucleotide polymorphisms (nsSNPs) within the AT 1 R gene. The AT 1 R coding sequence contains over 100 nsSNPs; therefore, this study embarked on determining which nsSNPs may abrogate the binding of selective ARBs. The crystal structure of olmesartan-bound human AT 1 R (PDB:4ZUD) served as a template to create an inactive apo-AT 1 R via molecular dynamics simulation (n = 3). All simulations resulted in a water accessible ligand-binding pocket that lacked sodium ions. The model remained inactive displaying little movement in the receptor core; however, helix 8 showed considerable flexibility. A single frame representing the average stable AT 1 R was used as a template to dock Olmesartan via AutoDock 4.2, MOE, and AutoDock Vina to obtain predicted binding poses and mean Boltzmann weighted average affinity. The docking results did not match the known pose and affinity of Olmesartan. Thus, an optimization protocol was initiated using AutoDock 4.2 that provided more accurate poses and affinity for Olmesartan (n = 6). Atomic models of 103 of the known human AT 1 R polymorphisms were constructed using the molecular dynamics equilibrated apo-AT 1 R. Each of the eight ARBs was then docked, using ARB-optimized parameters, to each polymorphic AT 1 R (n = 6). Although each nsSNP has a negligible effect on the global AT 1 R structure, most nsSNPs drastically alter a sub-set of ARBs affinity to the AT 1 R. Alterations within N298 –L314 strongly effected predicted ARB affinity, which aligns with early mutagenesis studies. The current study demonstrates the potential of utilizing in silico approaches towards personalized ARB therapy. The results presented here will guide further biochemical studies and refinement of the model to increase the accuracy of the prediction of ARB resistance in order to increase overall ARB effectiveness.

AutoDock

Olmesartan

Docking (animal)

10.1371/journal.pcbi.1007719

Cite

Citations (5)

In silico prediction of ARB resistance: A first step in creating personalized ARB therapy

bioRxiv (Cold Spring Harbor Laboratory) (2020)

Shane D. Anderson Asna Tabassum Jae Kyung Yeon Garima Sharma Priscilla Santos

Abstract Angiotensin II type 1 receptor (AT 1 R) blockers (ARBs) are among the most prescribed drugs. However, ARB effectiveness varies widely, and some patients do not respond to ARB therapy. One reason for the variability between patients is non-synonymous single nucleotide polymorphisms (nsSNPs) within agtr1 , the AT 1 R gene. There are over 100 nsSNPs in the AT 1 R; therefore, this study embarked on determining which nsSNPs may abrogate the binding of selective ARBs. The crystal structure of olmesartan-bound human AT 1 R (PDB:4ZUD) served as a template to create an inactive empty AT 1 R via molecular dynamics simulation (n = 3). All simulations resulted in a smaller ligand-binding pocket than 4ZUD due to the absence of olmesartan in the simulation yet remained inactive with little movement in the receptor core. A single frame representing the average stable AT 1 R was used as a template to thrice (n = 3) dock each ARB via AutoDock to obtain a predicted affinity from the weighted average of 100 docking simulations. The results were far from known values; thus, an optimization protocol was initiated, resulting in the predicted binding affinities within experimentally determined ranges (n = 6). The empty model AT 1 R was altered and minimized in Molecular Operating Environment software to represent 103 of the known human AT 1 R polymorphisms. Each of the eight ARBs was then docked, using the optimized parameters, to each polymorphic AT 1 R (n = 6). Although each nsSNP has little effect on global AT 1 R structure, most nsSNPs drastically alter a sub-set of ARBs affinity to the AT 1 R. Comparisons to previous binding studies suggest that the results have a 60% chance of predicting ARB resistance. Although more biochemical studies and refinement of the model are required to increase the accuracy of the prediction of ARB resistance, personalized ARB therapy based on agtr1 sequence could increase overall ARB effectiveness. Author Summary The term “personalized medicine” was coined at the turn of the century, but most medicines are currently prescribed based on disease categories and occasionally racial demographics, but not personalized attributes. In cardiovascular medicine, the personalization of medication is minimal; however, it is accepted that not all patients respond equally to common cardiovascular medications. Here we chose one prominent cardiovascular drug target, the angiotensin receptor, and, using computer modeling, created preliminary models of over 100 known alterations to the angiotensin receptor to determine if the alterations changed the ability of clinically used drugs to interact with the angiotensin receptor. The strength of interaction was compared to the unaltered angiotensin receptor, generating a map predicting which alteration affected each drug. It is expected that in the future, a patient’s receptors can be sequenced, and maps, such as the one presented here, can be used to select the optimum medication based on the patient’s genetics. Such a process would allow for the personalization of current medication therapy.

Olmesartan

AutoDock

DOCK

Docking (animal)

10.1101/2020.02.12.945402

Cite

Citations (0)

Computational approaches to understanding ARB resistance: a route to personalized ARB therapy

The FASEB Journal (2017)

Shane D. Anderson Isaac Nies Priscilla Santos Tik Hang Soong Yin Win Thu

The binding of Angiotensin II to the Angiotensin II type 1 receptor (AT 1 Rs) plays a pivotal role in the regulation of blood pressure and other physiological processes. AT 1 R blockers (ARBs) are commonly prescribed anti‐hypertensive drugs; however, not all patients respond equally to ARBs and some patients are ARB resistant. One documented reason behind these clinical observations is non‐synonymous polymorphisms within the AT 1 R that alter ARB affinity. There are 103 known polymorphisms within the crystallized AT 1 R structure that we utilized for computational approaches to predict which polymorphisms contribute to ARB resistance. To accomplish this, a 150 ns molecular dynamic (MD) simulation was performed to create a stable‐empty AT 1 R in an 87% POPC and 13% cholesterol membrane. Each polymorphism was made in the MD generated AT 1 R via MOE and minimized prior to docking performed with autodock4. Each ARB, including the active metabolite of Losartan, was docked using an experimentally derived grid that produced affinities in the wild‐type AT 1 R comparable to published reports. In total, 93,600 drug‐receptor complexes were analyzed and the data indicates that polymorphisms fall into two general categories: ARB specific resistance and total ARB resistance. Polymorphic AT 1 Rs displaying ARB specific resistance display wild‐type affinities for a subset of ARBs and in some cases, such as C101Y, G196V, and F313C, not the common ARBs valsartan and losartan; whereas, total ARB resistance, such as W84C, V108I, H132Y, and C289W, indicates that all ARBs are likely ineffective. In conclusion, patients that appear ARB resistant should have their AT 1 R sequenced and the sequence could be used to determine which ARB may be effective or suggest discontinuing ARB therapy. Support or Funding Information This project is funded by internal WesternU funds.

POPC

Docking (animal)

10.1096/fasebj.31.1_supplement.829.4

Cite

Citations (0)

Structural Basis for Oxidized Glutathione Recognition by the Yeast Cadmium Factor 1

bioRxiv (Cold Spring Harbor Laboratory) (2024)

Tik Hang Soong Clare Hotze Nitesh Khandelwal Thomas Tomasiak

Abstract Transporters from the ABCC family have an essential role in detoxifying electrophilic compounds including metals, drugs, and lipids, often through conjugation with glutathione complexes. The Yeast Cadmium Factor 1 (Ycf1) transports glutathione alone as well as glutathione conjugated to toxic heavy metals including Cd 2+ , Hg 2+ , and As 3+ . To understand the complicated selectivity and promiscuity of heavy metal substrate binding, we determined the cryo-EM structure of Ycf1 bound to the substrate, oxidized glutathione. We systematically tested binding determinants with cellular survival assays against cadmium to determine how the substrate site accommodates differentsized metal complexes. We identify a “flex-pocket” for substrate binding that binds glutathione complexes asymmetrically and flexes to accommodate different size complexes. Significance Statement The molecular mechanism by which Ycf1 transports a broad array of substrates that are essential for cellular detoxification and redox homeostasis remains unknown in the field of cellular biology. Here, guided by the novel substrate bound structure of Ycf1, we discovered a bipartite binding mechanism that accommodates substrates of varying sizes while maintaining specificity. Four crucial ionic interactions govern substrate specificity by recognizing ligands with a glutathione moiety, complemented by a sizable pocket on the adjacent side for different glutathione complexes.

Basis (linear algebra)

Factor (programming language)

10.1101/2024.01.31.578287

Cite

Citations (0)