Chih-Yuan Tseng

Modeling the Yew Tree Tubulin and a Comparison of its Interaction with Paclitaxel to Human Tubulin

ABSTRACTPurposeTo explore possible ways in which yew tree tubulin is naturally resistant to paclitaxel. While the yew produces a potent cytotoxin, paclitaxel, it is immune to paclitaxel’s cytotoxic action.MethodsTubulin sequence data for plant species were obtained from Alberta 1000 Plants Initiative. Sequences were assembled with Trinity de novo assembly program and tubulin identified. Homology modeling using MODELLER software was done to generate structures for yew tubulin. Molecular dynamics simulations and molecular mechanics Poisson–Boltzmann calculations were performed with the Amber package to determine binding affinity of paclitaxel to yew tubulin. ClustalW2 program and PHYLIP package were used to perform phylogenetic analysis on plant tubulin sequences.ResultsWe specifically analyzed several important regions in tubulin structure: the high-affinity paclitaxel binding site, as well as the intermediate binding site and microtubule nanopores. Our analysis indicates that the high-affinity binding site contains several substitutions compared to human tubulin, all of which reduce the binding energy of paclitaxel.ConclusionsThe yew has achieved a significant reduction of paclitaxel’s affinity for its tubulin by utilizing several specific residue changes in the binding pocket for paclitaxel.

Chemotherapy drugs form ion pores in membranes due to physical interactions with lipids.

We demonstrate the effects on membrane of the tubulin‐binding chemotherapy drugs: thiocolchicoside and taxol. Electrophysiology recordings across lipid membranes in aqueous phases containing drugs were used to investigate the drug effects on membrane conductance. Molecular dynamics simulation of the chemotherapy drug–lipid complexes was used to elucidate the mechanism at an atomistic level. Both drugs are observed to induce stable ion‐flowing pores across membranes. Discrete pore current–time plots exhibit triangular conductance events in contrast to rectangular ones found for ion channels. Molecular dynamics simulations indicate that drugs and lipids experience electrostatic and van der Waals interactions for short periods of time when found within each other’s proximity. The energies from these two interactions are found to be similar to the energies derived theoretically using the screened Coulomb and the van der Waals interactions between peptides and lipids due to mainly their charge properties while forming peptide‐induced ion channels in lipid bilayers. Experimental and in silico studies together suggest that the chemotherapy drugs induce ion pores inside lipid membranes due to drug–lipid physical interactions. The findings reveal cytotoxic effects of drugs on the cell membrane, which may aid in novel drug development for treatment of cancer and other diseases.

Entropic Fragment Based Approach for Aptamer Design

Aptamer, a short RNA/DNA sequence, is designed through SELEX (systematic evolution of ligands by exponential enrichment) to bind to specific targets including small molecules, proteins, nucleic acids, and even cells, tissues and organisms. Several advantages such as binding specificity and affinity and non-toxic and non-immunogenic properties make aptamer a promising tool in therapeutic applications. Basically, SELEX starts with preparing a pool of random RNA/DNA sequences and consists of a series of enrichment processes. In each step, the process will identify sequences that have the highest binding affinity. The success of SELEX hinges on synthesizing “good” random sequence pools. A “good” pool should have sufficient sequence diversity and structural complexity. Furthermore, the quality of sequence pools also greatly influences efficiency of SELEX. These criteria discourage the application of the conventional virtual screening approach.Therefore, we propose an entropic fragment based approach that is free from these criteria to design aptamers given a target protein in this work. The crux is to introduce probabilistic description. First, the approach utilizes limited information such as the interactions of nucleotide fragments and target proteins to determine the probability of having such interactions. Afterward, based on the method of maximum entropy (ME), the preferred nucleotide fragment that mostly likely interacts with target proteins is the one that maximizes the entropy of the system. By repeating the same procedure given the fragment determined in previous step, a preferred aptamer then can be constructed. At last, we consider the thrombin aptamer designed from SELEX as a target to investigate the applicability of the proposed approach.

Entropic Fragment-Based Approach to Aptamer Design: Entropic Fragment-Based Approach

Aptamers are short RNA/DNA sequences that are identified through the process of systematic evolution of ligands by exponential enrichment and that bind to diverse biomolecular targets. Aptamers have strong and specific binding through molecular recognition and are promising tools in studying molecular biology. They are recognized as having potential therapeutic and diagnostic clinical applications. The success of the systematic evolution of ligands by exponential enrichment process requires that the RNA/DNA pools used in the process have a sufficient level of sequence diversity and structural complexity. While the systematic evolution of ligands by exponential enrichment technology is well developed, it remains a challenge in the efficient identification of correct aptamers. In this article, we propose a novel information‐driven approach to a theoretical design of aptamer templates based solely on the knowledge regarding the biomolecular target structures. We have investigated both theoretically and experimentally the applicability of the proposed approach by considering two specific targets: the serum protein thrombin and the cell membrane phospholipid phosphatidylserine. Both of these case studies support our method and indicate a promising advancement in theoretical aptamer design. In unfavorable cases where the designed sequences show weak binding affinity, these template sequences can be still modified to enhance their affinities without going through the systematic evolution of ligands by exponential enrichment process.

Homology and Molecular Dynamics Models of Toll-Like Receptor 7 Protein and Its Dimerization

Toll‐like receptor protein 7 is a transmembrane protein playing a crucial role in the signaling pathways involved in innate immunity. Its crystal structure is not yet available, but there are several proteins possessing domains of sufficiently high homology, which enabled us to build a model of the toll‐like receptor protein 7 monomer and gain insights into dimer formation. To obtain a reliable structure prediction, we subjected this model to equilibration using molecular dynamics simulations. Furthermore, the equilibrated monomer structure was used to construct models of dimerization and to predict binding sites for small ligands. Docking studies were performed for some of the known toll‐like receptor protein 7 ligands. We determined that a new homology model generated by the LOOPP server provides a good alternative to a previously reported model. Our docking results indicate that the addition of either imiquimod or 1V209 to a toll‐like receptor protein 7 dimer changes an unfavorable interaction into a favorable one. We found that eight small molecules docked to two pockets in toll‐like receptor protein 7 bind to both pockets at pH 7 and at pH 5.5. This work provides a realistic model that could be used for drug discovery aimed at finding toll‐like receptor protein 7 dimerization activators, with potential clinical applications to a host of diseases, including cancer.

Maximum Entropy in Drug Discovery

Abstract: Drug discovery applies multidisciplinary approaches either experimentally, computationally or both ways to identify lead compounds to treat various diseases. While conventional approaches have yielded many US Food and Drug Administration (FDA)-approved drugs, researchers continue investigating and designing better approaches to increase the success rate in the discovery process. In this article, we provide an overview of the current strategies and point out where and how the method of maximum entropy has been introduced in this area. The maximum entropy principle has its root in thermodynamics, yet since Jaynes’ pioneering work in the 1950s, the maximum entropy principle has not only been used as a physics law, but also as a reasoning tool that allows us to process information in hand with the least bias. Its applicability in various disciplines has been abundantly demonstrated. We give several examples of applications of maximum entropy in different stages of drug discovery. Finally, we discuss a promising new direction in drug discovery that is likely to hinge on the ways of utilizing maximum entropy.

A unified approach to computational drug discovery

It has been reported that a slowdown in the development of new medical therapies is affecting clinical outcomes. The FDA has thus initiated the Critical Path Initiative project investigating better approaches. We review the current strategies in drug discovery and focus on the advantages of the maximum entropy method being introduced in this area. The maximum entropy principle is derived from statistical thermodynamics and has been demonstrated to be an inductive inference tool. We propose a unified method to drug discovery that hinges on robust information processing using entropic inductive inference. Increasingly, applications of maximum entropy in drug discovery employ this unified approach and demonstrate the usefulness of the concept in the area of pharmaceutical sciences.

Entropic analysis reveals a connection between the recurrence of cancer and chemotherapy

In this study, we proposed an entropic analysis to overcome limitations of conventional statistical methods to analyze clinical data for cancer patients who experienced relapse of tumors following chemotherapy. We have applied this entropic method to reveal potential mechanisms that lead to a relapse of Wilms’ tumor in pediatric patients. Results indicate β-tubulin isotype III up-regulation is likely the primary cause of the relapse.

Mathematical and computational modeling in biology at multiple scales

A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined.

Using Entropy Leads to a Better Understanding of Biological Systems

In studying biological systems, conventional approaches based on the laws of physics almost always require introducing appropriate approximations. We argue that a comprehensive approach that integrates the laws of physics and principles of inference provides a better conceptual framework than these approaches to reveal emergence in such systems. The crux of this comprehensive approach hinges on entropy. Entropy is not merely a physical quantity. It is also a reasoning tool to process information with the least bias. By reviewing three distinctive examples from protein folding dynamics to drug design, we demonstrate the developments and applications of this comprehensive approach in the area of biological systems.