Computational Treatment of Metalloproteins

Metalloproteins present a considerable challenge for modeling, especially when the starting point is far from thermodynamic equilibrium. Examples include formidable problems such as metalloprotein folding and structure prediction upon metal addition, removal, or even just replacement; metalloenzyme design, where stabilization of a transition state of the catalyzed reaction in the specific binding pocket around the metal needs to be achieved; docking to metal-containing sites and design of metalloenzyme inhibitors. Even more conservative computations, such as elucidations of the mechanisms and energetics of the reaction catalyzed by natural metalloenzymes, are often nontrivial. The reason is the vast span of time and length scales over which these proteins operate, and thus the resultant difficulties in estimating their energies and free energies. It is required to perform extensive sampling, properly treat the electronic structure of the bound metal or metals, and seamlessly merge the required techniques to assess energies and entropies, or their changes, for the entire system. Additionally, the machinery needs to be computationally affordable. Although a great advancement has been made over the years, including some of the seminal works resulting in the 2013 Nobel Prize in chemistry, many aforementioned exciting applications remain far from reach. We review the methodology on the forefront of the field, including several promising methods developed in our lab that bring us closer to the desired modern goals. We further highlight their performance by a few examples of applications

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Metal-dependent activity of Fe and Ni acireductone dioxygenases: how two electrons reroute the catalytic pathway.

Two virtually identical acireductone dioxygenases, ARD and ARD', catalyze completely different oxidation reactions of the same substrate, 1,2-dihydroxy-3-keto-5-(methylthio)pentene, depending exclusively on the nature of the bound metal. Fe(2+)-dependent ARD' produces the α-keto acid precursor of methionine and formate and allows for the recycling of methionine in cells. Ni(2+)-dependent ARD instead produces methylthiopropionate, CO, and formate, and exits the methionine salvage cycle. This mechanistic difference has not been understood to date but has been speculated to be due to the difference in coordination of the substrate to Fe(2+)versus Ni(2+): forming a five-membered ring versus a six-membered ring, respectively, thus exposing different carbon atoms for the attack by O2. Here, using mixed quantum-classical molecular dynamics simulations followed by the density functional theory mechanistic investigation, we show that, contrary to the old hypothesis, both metals preferentially bind the substrate as a six-membered ring, exposing the exact same sites to the attack by O2. It is the electronic properties of the metals that are then responsible for the system following different reaction paths, to yield the respective products. We fully explain the puzzling metal-induced difference in functionality between ARD and ARD' and, in particular, propose a new mechanism for ARD'. All results are in agreement with available isotopic substitution and other experimental data

Computational treatment of metalloproteins.

Metalloproteins present a considerable challenge for modeling, especially when the starting point is far from thermodynamic equilibrium. Examples include formidable problems such as metalloprotein folding and structure prediction upon metal addition, removal, or even just replacement; metalloenzyme design, where stabilization of a transition state of the catalyzed reaction in the specific binding pocket around the metal needs to be achieved; docking to metal-containing sites and design of metalloenzyme inhibitors. Even more conservative computations, such as elucidations of the mechanisms and energetics of the reaction catalyzed by natural metalloenzymes, are often nontrivial. The reason is the vast span of time and length scales over which these proteins operate, and thus the resultant difficulties in estimating their energies and free energies. It is required to perform extensive sampling, properly treat the electronic structure of the bound metal or metals, and seamlessly merge the required techniques to assess energies and entropies, or their changes, for the entire system. Additionally, the machinery needs to be computationally affordable. Although a great advancement has been made over the years, including some of the seminal works resulting in the 2013 Nobel Prize in chemistry, many aforementioned exciting applications remain far from reach. We review the methodology on the forefront of the field, including several promising methods developed in our lab that bring us closer to the desired modern goals. We further highlight their performance by a few examples of applications

Computational treatment of metalloproteins.

Metalloproteins present a considerable challenge for modeling, especially when the starting point is far from thermodynamic equilibrium. Examples include formidable problems such as metalloprotein folding and structure prediction upon metal addition, removal, or even just replacement; metalloenzyme design, where stabilization of a transition state of the catalyzed reaction in the specific binding pocket around the metal needs to be achieved; docking to metal-containing sites and design of metalloenzyme inhibitors. Even more conservative computations, such as elucidations of the mechanisms and energetics of the reaction catalyzed by natural metalloenzymes, are often nontrivial. The reason is the vast span of time and length scales over which these proteins operate, and thus the resultant difficulties in estimating their energies and free energies. It is required to perform extensive sampling, properly treat the electronic structure of the bound metal or metals, and seamlessly merge the required techniques to assess energies and entropies, or their changes, for the entire system. Additionally, the machinery needs to be computationally affordable. Although a great advancement has been made over the years, including some of the seminal works resulting in the 2013 Nobel Prize in chemistry, many aforementioned exciting applications remain far from reach. We review the methodology on the forefront of the field, including several promising methods developed in our lab that bring us closer to the desired modern goals. We further highlight their performance by a few examples of applications

Why Urease Is a Di-Nickel Enzyme whereas the CcrA β‑Lactamase Is a Di-Zinc Enzyme

Ureases and metallo-β-lactamases are amide hydrolases closely related in function and structure. However, one major difference between them is that the former uses two nickel cations, and the latter uses two zinc cations to do similar catalytic jobs. What is the reason for this choice that Nature made for the catalytic metals? Is it dictated by electronic or structural reasons in the two catalyzed reactions, or some other evolutionary factors? Are both enzymes “perfect” catalysts, as far as just catalysis is concerned, and if they are, then why? Here, we address these questions through a joint quantum mechanical/molecular mechanical dynamics approach and <i>ab initio</i> mechanistic investigation. Five enzyme/substrate systems are considered: urease/urea, CcrA β-lactamase/β-lactam antibiotic model, urease/β-lactam antibiotic model, CcrA β-lactamase/urea, and di-Ni-substituted CcrA β-lactamase/β-lactam antibiotic model. The mechanisms and rates of the metal-facilitated nucleophilic attack are assessed. Both urease and Ni-substituted β-lactamase catalyze the attack on the β-lactam ring with the efficiency surpassing that of natural di-Zn β-lactamase, whereas β-lactamase is unable to hydrolyze urea. These results suggest that in β-lactamases the use of zinc does not provide maximal possible efficiency of the enzyme. Thus, β-lactamases operate by the principle of “good enough”; i.e., the choice for Zn in them leads to a performance that is just satisfactory for its biological purpose but can be evolutionarily improved via replacement of Zn with Ni