Alexandre Barrozo

Force Field Independent Metal Parameters Using a Nonbonded Dummy Model

The cationic dummy atom approach provides a powerful nonbonded description for a range of alkaline-earth and transition-metal centers, capturing both structural and electrostatic effects. In this work we refine existing literature parameters for octahedrally coordinated Mn2+, Zn2+, Mg2+, and Ca2+, as well as providing new parameters for Ni2+, Co2+, and Fe2+. In all the cases, we are able to reproduce both M2+–O distances and experimental solvation free energies, which has not been achieved to date for transition metals using any other model. The parameters have also been tested using two different water models and show consistent performance. Therefore, our parameters are easily transferable to any force field that describes nonbonded interactions using Coulomb and Lennard-Jones potentials. Finally, we demonstrate the stability of our parameters in both the human and Escherichia coli variants of the enzyme glyoxalase I as showcase systems, as both enzymes are active with a range of transition metals. The parameters presented in this work provide a valuable resource for the molecular simulation community, as they extend the range of metal ions that can be studied using classical approaches, while also providing a starting point for subsequent parametrization of new metal centers.

Cooperative Electrostatic Interactions Drive Functional Evolution in the Alkaline Phosphatase Superfamily

It is becoming widely accepted that catalytic promiscuity, i.e., the ability of a single enzyme to catalyze the turnover of multiple, chemically distinct substrates, plays a key role in the evolution of new enzyme functions. In this context, the members of the alkaline phosphatase superfamily have been extensively studied as model systems in order to understand the phenomenon of enzyme multifunctionality. In the present work, we model the selectivity of two multiply promiscuous members of this superfamily, namely the phosphonate monoester hydrolases from Burkholderia caryophylli and Rhizobium leguminosarum. We have performed extensive simulations of the enzymatic reaction of both wild-type enzymes and several experimentally characterized mutants. Our computational models are in agreement with key experimental observables, such as the observed activities of the wild-type enzymes, qualitative interpretations of experimental pH-rate profiles, and activity trends among several active site mutants. In all cases the substrates of interest bind to the enzyme in similar conformations, with largely unperturbed transition states from their corresponding analogues in aqueous solution. Examination of transition-state geometries and the contribution of individual residues to the calculated activation barriers suggest that the broad promiscuity of these enzymes arises from cooperative electrostatic interactions in the active site, allowing each enzyme to adapt to the electrostatic needs of different substrates. By comparing the structural and electrostatic features of several alkaline phosphatases, we suggest that this phenomenon is a generalized feature driving selectivity and promiscuity within this superfamily and can be in turn used for artificial enzyme design.

Challenges in computational studies of enzyme structure, function and dynamics

In this review we give an overview of the field of Computational enzymology. We start by describing the birth of the field, with emphasis on the work of the 2013 chemistry Nobel Laureates. We then present key features of the state-of-the-art in the field, showing what theory, accompanied by experiments, has taught us so far about enzymes. We also briefly describe computational methods, such as quantum mechanics-molecular mechanics approaches, reaction coordinate treatment, and free energy simulation approaches. We finalize by discussing open questions and challenges.

Computational Protein Engineering: Bridging the Gap between Rational Design and Laboratory Evolution

Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation “hotspots” with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies.

The Competing Mechanisms of Phosphate Monoester Dianion Hydrolysis

Despite the numerous experimental and theoretical studies on phosphate monoester hydrolysis, significant questions remain concerning the mechanistic details of these biologically critical reactions. In the present work we construct a linear free energy relationship for phosphate monoester hydrolysis to explore the effect of modulating leaving group pKa on the competition between solvent- and substrate-assisted pathways for the hydrolysis of these compounds. Through detailed comparative electronic-structure studies of methyl phosphate and a series of substituted aryl phosphate monoesters, we demonstrate that the preferred mechanism is dependent on the nature of the leaving group. For good leaving groups, a strong preference is observed for a more dissociative solvent-assisted pathway. However, the energy difference between the two pathways gradually reduces as the leaving group pKa increases and creates mechanistic ambiguity for reactions involving relatively poor alkoxy leaving groups. Our calculations show that the transition-state structures vary smoothly across the range of pKas studied and that the pathways remain discrete mechanistic alternatives. Therefore, while not impossible, a biological catalyst would have to surmount a significantly higher activation barrier to facilitate a substrate-assisted pathway than for the solvent-assisted pathway when phosphate is bonded to good leaving groups. For poor leaving groups, this intrinsic preference disappears.

Cryptic genetic variation defines the adaptive evolutionary potential of enzymes

Genetic variation among orthologous proteins can cause cryptic phenotypic properties that only manifest in changing environments. Such variation may also impact the evolutionary potential of proteins, but the molecular basis for this remains unclear. Here we perform comparative directed evolution in which four orthologous metallo-β-lactamases were evolved toward a new function. We found that genetic variation between these enzymes resulted in distinct evolutionary outcomes. The ortholog with the lower initial activity reached a 20-fold higher fitness plateau exclusively via increasing catalytic activity. By contrast, the ortholog with the highest initial activity evolved to a less-optimal and phenotypically distinct outcome through changes in expression, oligomerization and activity. We show that the cryptic molecular properties and conformational variation of residues in the initial genotypes cause epistasis, thereby constraining evolutionary outcomes. Our work highlights that understanding the molecular details relating genetic variation to protein functions is essential to predicting the evolution of proteins.

Multiheme Cytochrome Mediated Redox Conduction Through Shewanella oneidensis MR-1 Cells

Multiheme cytochromes function as extracellular electron transfer (EET) conduits that extend the metabolic reach of microorganisms to external solid surfaces. These conduits are also proposed to facilitate long-distance electron transport along cellular membranes and across multiple cells. Here we report electrochemical gating measurements of Shewanella oneidensis MR-1 cells linking interdigitated electrodes. The dependence of the source-drain current on gate potential demonstrates a redox conduction mechanism, which we link to the presence of multiheme cytochromes of the Mtr pathway. We also find that the measured thermal activation energy of 0.29 ± 0.03 eV is consistent with these obtained from electron hopping calculations through the S. oneidensis Mtr outer-membrane decaheme cytochromes. Our measurements and calculations have implications for understanding and controlling micrometer-scale electron transport in microbial systems.