Unraveling the Catalytic Pathway of Metalloenzyme Farnesyltransferase through QM/MM Computation

The protein farnesyltransferase (FTase) is a Zn2+-metalloenzyme that catalyzes the farnesylation reaction, i.e., the transfer of the 15-carbon atom farnesyl group from farnesyl diphosphate (FPP) to a specific cysteine of protein substrates. Oncogenic Ras proteins, which are among the FTase substrates, are observed in about 20−30% of human cancer cells. Thus, FTase represents a target for anticancer drug design. Herein, we present a classical force-field-based and quantum mechanics/molecular mechanics (QM/MM) computational study of the FTase reaction mechanism. Our findings offer a detailed picture of the FTase catalytic pathway, describing structural features and the energetics of its saddle points. A moderate dissociation of the diphosphate group from the FPP is observed during the nucleophilic attack of the zinc-bound thiolate. At the transition state, a resonance structure is observed, which indicates the formation of a metastable carbocation. However, no stable intermediate is found along the reaction pathway. Thus, the reaction occurs via an associative mechanism with dissociative character, in agreement with the mechanism proposed by Fierke et al. ( Biochemistry 2000, 39, 2593−2602 and Biochemistry 2003, 42, 9741−9748 ). Moreover, a fluorine-substituted FPP analogue (CF3-FPP) is used to investigate the inhibitory effect of fluorine, which in turn provides additional agreement with experimental data.

Computational Investigation of Intracellular Signalling Cascades

Siri Camee van Keulen

In order to adjust to changes in the environment, cells can communicate via signalling cascades. A common pathway for intercellular communication is the G-protein-coupled- receptor (GPCR) signal transduction cascade which is triggered by a (chemical or physical) extracellular signal, such as light or small molecules. This external interaction induces conformational changes that can lead either to GPCR activation or deactivation. In the active form, GPCRs can induce the activation of trimeric G proteins, which results in the dissociation of the Galpha from the beta and gamma subunits. Subsequently, active Galpha can interact with other cytosolic proteins, such as adenylyl cyclase (AC), which converts adenosine triphosphate to cyclic adenosine monophosphate (cAMP) upon stimulation. AC activation can be induced via stimulatory Galpha subunit (Galpha_s) interactions, while inhibition is related to inhibitory Galpha (Galpha_i) association. X-ray crystallography is crucial for the computational study of the GPCR signal transduction pathway. However, sometimes important molecules or moieties can become lost or cannot be incorporated during crystallisation procedures, which can have a significant effect on a protein’s conformation and function. In this thesis, three steps in the GPCR signal transduction cascade have been studied, rhodopsin activation (i), the conformation of active Galpha_i (ii) and the interaction between Galpha_i and AC (iii). In all three cases some groups or molecules that are absent in the X-ray structures play a vital role in the function of the proteins. In the first project, rhodopsin’s early intermediates after photon exposure are investigated together with a deprotonation reaction that takes place in the later stages of the activation pathway. The classical molecular dynamics (MD) and mixed quantum mechanics/molecular mechanics MD results suggest that photon exposure induces active site rearrangements which increase the stability of rhodopsin’s deprotonated Schiff base state. A bridging water molecule in the active site that is absent in the X-ray structure appears to play an important role during the deprotonation mechanism, which demonstrates the dependence of the protein’s function on its environment. The second study investigates the conformation of the active soluble form of Galpha_i, which has not yet been fully resolved via experimental studies due to crystallisation difficulties of the lipidated N-terminus. To investigate the effect of lipidation via classical MD simulations, a model of soluble lipid-bound Galpha_i was constructed. The simulations show that Galpha_i can form a hydrophobic pocket for the lipid on the protein surface. Other regions of the protein, important for protein-protein interaction, are adjusted as well. Hence, the post-translational modification appears to have a significant impact on Galpha_i’s active conformation and function, which was not captured via the unlipidated X-ray structures. The last project is devoted to a complex of active soluble Galpha_i and AC (Galpha_i :AC). While the interaction site for Galpha_s on AC is known, the interaction site for Galpha_i has not yet been identified. Classical MD simulations were performed on a docked Galpha_i:AC complex to understand the inhibition mechanisms as well as the differentiation between AC stimulation/inhibition. The results show that Galpha_i binding not only leads to AC inhibition via impeding ATP binding but also by preventing re-activation via Galpha_s through closure of the subunit’s interaction site on AC. Keywords: GPCR signal transduction pathway, G-protein-coupled recepter, rhodopsin, G protein, complex formation, cis-trans isomerisation, adenylyl cyclase.

EPFL2017

Algorithms and flowchart for the design of synthetic biochemical networks

Homa Mohammadi Peyhani

New drugs are needed to assure effective therapies for previously untreated diseases, emerging diseases, and personalized medicine, but the process of drug development is complex, costly, and time-consuming. This is especially problematic considering that 90% of drug candidates in clinical trials are discarded due to unexpected toxicity or other secondary effects. This inefficiency threatens our health care system and economy. Despite the advances in the cellular metabolism, our knowledge of the mechanisms governing enzymatic biotransformations in cells is far from complete, in particular regarding degradation pathways, mode of action, or side effects of drugs. Examining the mechanisms of enzymatic reactions at the cellular scale could improve our fundamental understanding of their catalytic capability, and facilitate identifying and filling the knowledge gaps. The scale and the complexity of metabolic data is ever-expanding, requiring scientists to apply more advanced computational methods to systematically store, explore, and interpret the enzymatic potential of cells. The first step toward simulating enzymes in silico is to learn from their biochemical reactions in nature. To do this, we use distilled knowledge of known biochemistry in the form of generalized enzymatic reaction rules. Enzymatic rules are mathematical representations of enzymatic action mimicking the catalytic function of enzymes. They are formulated in a less specific manner (more promiscuous) to act on a broad range of substrates. In addition to reconstructing known biochemistry, the application of these reaction rules paves the way toward the discovery of novel enzymatic interactions. In this thesis, I developed computational models, tools, and methodologies to facilitate the study of metabolism and catalytic action of enzymes. We analyzed different aspects of metabolism through five distinct studies: In a first study, in order to provide a holistic view of currently known biochemistry, we gathered biochemical data from 14 sources, covering the known metabolic networks of all species. We integrated all biological data into a high-performance database based on ontology, named LCSB DB. We further expanded the scope of LCSB DB to cover all bioactive and chemicals. LCSB DB offers fast and efficient searching of biochemical data and serves as a platform for sharing, storing, and analyzing biochemical data. In a second study, we used enzymatic reaction rules to predict all theoretically possible metabolic reactions between biological and bioactive compounds in LCSB DB. In a third study, we developed a method to find enzymes are able to catalyze orphan and predicted reactions, called BridgIT. BridgIT uses the knowledge of reactive sites on substrates to find the most similar, known biochemical reactions. We then validated the utility of BridgIT in enzyme discovery for the design of de novo synthetic pathways producing tetrahydropalmatine and adipic acid. In the last study, we propose a workflow for rational drug design and systems-level analysis of drug metabolism, called NICEdrug.ch. NICEdrug.ch allows large-scale computational analysis of drug biochemistry (metabolic precursors or prodrugs and metabolic fate or degradation), enzymatic targets, and toxicity in the context of cellular metabolism. Finally, in the conclusion chapter, we discuss the contribution and the potential further applications of the computational tools that were developed in this thesis.

EPFL2020

Computational Investigation of Intracellular Signalling Cascades

Siri Camee van Keulen

EPFL2017

Algorithms and flowchart for the design of synthetic biochemical networks

Homa Mohammadi Peyhani

EPFL2020