Collective Dynamics Differentiates Functional Divergence in Protein Evolution

Protein evolution is most commonly studied by analyzing related protein sequences and generating ancestral sequences through Bayesian and Maximum Likelihood methods, and/or by resurrecting ancestral proteins in the lab and performing ligand binding studies to determine function. Structural and dynamic evolution have largely been left out of molecular evolution studies. Here we incorporate both structure and dynamics to elucidate the molecular principles behind the divergence in the evolutionary path of the steroid receptor proteins. We determine the likely structure of three evolutionarily diverged ancestral steroid receptor proteins using the Zipping and Assembly Method with FRODA (ZAMF). Our predictions are within ∼2.7 Å all-atom RMSD of the respective crystal structures of the ancestral steroid receptors. Beyond static structure prediction, a particular feature of ZAMF is that it generates protein dynamics information. We investigate the differences in conformational dynamics of diverged proteins by obtaining the most collective motion through essential dynamics. Strikingly, our analysis shows that evolutionarily diverged proteins of the same family do not share the same dynamic subspace, while those sharing the same function are simultaneously clustered together and distant from those, that have functionally diverged. Dynamic analysis also enables those mutations that most affect dynamics to be identified. It correctly predicts all mutations (functional and permissive) necessary to evolve new function and ∼60% of permissive mutations necessary to recover ancestral function

The Role of Mutations in Protein Structural Dynamics and Function: A Multi-scale Computational Approach

abstract: Proteins are a fundamental unit in biology. Although proteins have been extensively studied, there is still much to investigate. The mechanism by which proteins fold into their native state, how evolution shapes structural dynamics, and the dynamic mechanisms of many diseases are not well understood. In this thesis, protein folding is explored using a multi-scale modeling method including (i) geometric constraint based simulations that efficiently search for native like topologies and (ii) reservoir replica exchange molecular dynamics, which identify the low free energy structures and refines these structures toward the native conformation. A test set of eight proteins and three ancestral steroid receptor proteins are folded to 2.7Å all-atom RMSD from their experimental crystal structures. Protein evolution and disease associated mutations (DAMs) are most commonly studied by in silico multiple sequence alignment methods. Here, however, the structural dynamics are incorporated to give insight into the evolution of three ancestral proteins and the mechanism of several diseases in human ferritin protein. The differences in conformational dynamics of these evolutionary related, functionally diverged ancestral steroid receptor proteins are investigated by obtaining the most collective motion through essential dynamics. Strikingly, this analysis shows that evolutionary diverged proteins of the same family do not share the same dynamic subspace. Rather, those sharing the same function are simultaneously clustered together and distant from those functionally diverged homologs. This dynamics analysis also identifies 77% of mutations (functional and permissive) necessary to evolve new function. In silico methods for prediction of DAMs rely on differences in evolution rate due to purifying selection and therefore the accuracy of DAM prediction decreases at fast and slow evolvable sites. Here, we investigate structural dynamics through computing the contribution of each residue to the biologically relevant fluctuations and from this define a metric: the dynamic stability index (DSI). Using DSI we study the mechanism for three diseases observed in the human ferritin protein. The T30I and R40G DAMs show a loss of dynamic stability at the C-terminus helix and nearby regulatory loop, agreeing with experimental results implicating the same regulatory loop as a cause in cataracts syndrome.Dissertation/ThesisPh.D. Physics 201

Photoconvertible Fluorescent Proteins and the Role of Dynamics in Protein Evolution

abstract: Photoconvertible fluorescent proteins (pcFPs) constitute a large group of fluorescent proteins related to green fluorescent protein (GFP) that, when exposed to blue light, bear the capability of irreversibly switching their emission color from green to red. Not surprisingly, this fascinating class of FPs has found numerous applications, in particular for the visualization of biological processes. A detailed understanding of the photoconversion mechanism appears indispensable in the design of improved variants for applications such as super-resolution imaging. In this article, recent work is reviewed that involves using pcFPs as a model system for studying protein dynamics. Evidence has been provided that the evolution of pcFPs from a green ancestor involved the natural selection for altered dynamical features of the beta-barrel fold. It appears that photoconversion may be the outcome of a long-range positional shift of a fold-anchoring region. A relatively stiff, rigid element appears to have migrated away from the chromophore-bearing section to the opposite edge of the barrel, thereby endowing pcFPs with increased active site flexibility while keeping the fold intact. In this way, the stage was set for the coupling of light absorption with subsequent chemical transformations. The emerging mechanistic model suggests that highly specific dynamic motions are linked to key chemical steps, preparing the system for a concerted deprotonation and β-elimination reaction that enlarges the chromophore’s π-conjugation to generate red color.The final version of this article, as published in International Journal of Molecular Sciences, can be viewed online at: http://www.mdpi.com/1422-0067/18/8/179

Orientia and Rickettsia: different flowers from the same garden

Recent discoveries of basal extracellular Rickettsiales have illuminated divergent evolutionary paths to host dependency in later-evolving lineages. Family Rickettsiaceae, primarily comprised of numerous protist- and invertebrate-associated species, also includes human pathogens from two genera, Orientia and Rickettsia. Once considered sister taxa, these bacteria form distinct lineages with newly appreciated lifestyles and morphological traits. Contrasting other rickettsial human pathogens in Family Anaplasmataceae, Orientia and Rickettsia species do not reside in host-derived vacuoles and lack glycolytic potential. With only a few described mechanisms, strategies for commandeering host glycolysis to support cytosolic growth remain to be discovered. While regulatory systems for this unique mode of intracellular parasitism are unclear, conjugative transposons unique to Orientia and Rickettsia species provide insights that are critical for determining how these obligate intracellular pathogens overtake eukaryotic cytosol

Design and Development of Software Tools for Bio-PEPA

This paper surveys the design of software tools for the Bio-PEPA process algebra. Bio-PEPA is a high-level language for modelling biological systems such as metabolic pathways and other biochemical reaction networks. Through providing tools for this modelling language we hope to allow easier use of a range of simulators and model-checkers thereby freeing the modeller from the responsibility of developing a custom simulator for the problem of interest. Further, by providing mappings to a range of different analysis tools the Bio-PEPA language allows modellers to compare analysis results which have been computed using independent numerical analysers, which enhances the reliability and robustness of the results computed.

Hinge-shift mechanism as a protein design principle for the evolution of β-lactamases from substrate promiscuity to specificity

W.D.V.H. acknowledges support from National Institutes of Health (Grant: R01GM112077). S.B.O. acknowledges support from the Gordon and Betty Moore Foundations and National Science Foundation (Awards: 1715591 and 1901709). J.M.S.R. acknowledges support from Spanish Ministry of Economy and Competitiveness/FEDER Funds (Grants BIO2015-66426-R and RTI2018-097142-B-100) and the Human Frontier Science Program (Grant RGP0041/2017). V.A.R. acknowledges support from FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento (Grant E.FQM.113.UGR18). We would like to thank the beamline staff of ID30B of the ESRF (European Synchrotron Radiation Facility, Grenoble, France) for their assistance during data collection and the ESRF for the provision of time through proposals MX-2064.TEM-1 β-lactamase degrades β-lactam antibiotics with a strong preference for penicillins. Sequence reconstruction studies indicate that it evolved from ancestral enzymes that degraded a variety of β-lactam antibiotics with moderate efficiency. This generalist to specialist conversion involved more than 100 mutational changes, but conserved fold and catalytic residues, suggesting a role for dynamics in enzyme evolution. Here, we develop a conformational dynamics computational approach to rationally mold a protein flexibility profile on the basis of a hinge-shift mechanism. By deliberately weighting and altering the conformational dynamics of a putative Precambrian β-lactamase, we engineer enzyme specificity that mimics the modern TEM-1 β-lactamase with only 21 amino acid replacements. Our conformational dynamics design thus re-enacts the evolutionary process and provides a rational allosteric approach for manipulating function while conserving the enzyme active site.United States Department of Health & Human Services National Institutes of Health (NIH) - USA R01GM112077Gordon and Betty Moore FoundationsNational Science Foundation (NSF) 1715591 1901709Spanish Ministry of Economy and Competitiveness/FEDER Funds BIO2015-66426-R RTI2018-097142-B-100Human Frontier Science Program RGP0041/2017FEDER/Junta de Andalucia-Consejeria de Economia y Conocimiento E.FQM.113.UGR1

Emergent phenomena in living systems: a statistical mechanical perspective

A natural phenomenon occurring in a living system is an outcome of the dynamics of the specific biological network underlying the phenomenon. The collective dynamics have both deterministic and stochastic components. The stochastic nature of the key processes like gene expression and cell differentiation give rise to fluctuations (noise) in the levels of the biomolecules and this combined with nonlinear interactions give rise to a number of emergent phenomena. In this review, we describe and discuss some of these phenomena which have the character of phase transitions in physical systems. We specifically focus on noise-induced transitions in a stochastic model of gene expression and in a population genetics model which have no analogs when the dynamics are solely deterministic in nature. Some of these transitions exhibit critical-point phenomena belonging to the mean-field Ising universality class of equilibrium phase transitions. A number of other examples, ranging from biofilms to homeostasis in adult tissues, are also discussed which exhibit behavior similar to critical phenomena in equilibrium and nonequilbrium phase transitions. The examples illustrate how the subject of statistical mechanics provides a bridge between theoretical models and experimental observations.Comment: 29 pages, 4 figure

Investigating and manipulating the reaction mechanism of reductive carboxylases

Efficient capture and conversion of atmospheric carbon dioxide (CO2) is a prerequisite to develop a carbon-neutral, circular future economy. Carbon fixation is the process by which inorganic carbon is fixed into biomass. In Nature, enzymes called caboxlyases are able to capture atmospheric carbon dioxide under mild conditions and catalyze its incorporation into organic molecules. It is estimated that 400 Gt of CO2 are fixed annually solely by the enzyme ribulose-1,5-bisphophate-carboxylase/oxygenase (RuBisCO), the key enzyme of photosynthesis. In comparison, CO2 utilization by chemical industries accounts for only 0.1 Gt of carbon annually and utilizes pressurized CO2, which emphasizes our need to understand the molecular mechanism that allow carboxylases to selectively interact with a CO2 at atmospheric concentrations (0.04% vol) during catalysis. Enoyl-CoA carboxylases/reductases (ECRs) represent the fastest carboxylases known to date and is, in contrast to RuBisCO, completely specific for CO2. These enzymes catalyze the reductive carboxylation of enoyl-CoAs by oxidizing one equivalent of NADPH. ECRs represent a good case study for the understanding of the CO2 chemistry that carboxylases use. In this work, we try to gain a better understanding of the underlying catalytic principles that enable ECRs to achieve high catalytic rates. Initially we focus on understanding how the precise interaction between protein and CO2 takes place at the active site of ECRs. We were able to identify and assign a function to four conserved amino acid residues found at the active site of ECRs. Three residues are responsible for the precise positioning of CO2 for nucleophilic attack by the enolate intermediate. Additionally, one residue is able to shield the active site from water thereby preventing the irreversible protonation of the enolate. These two mechanistic principles are at the base of the efficient carboxylation in ECRs. The following chapter briefly describes how the enzyme is able to accept other electrophiles than CO2. We show that ECRs can utilize formaldehyde as an alternative electrophile to CO2 thereby yielding beta-hydroxy thioesters. The exquisite stereospecificity together with the vast range of small electrophiles make ECR a potential biocatalyst for the production of various α-substituted thioesters. The last two chapters of this work focus on the structural aspects of ECR catalysis. We were able to obtain four new crystal structures of an ECR from Kitasatospora setae and to propose a model for the catalytic cycle of this enzyme. We show that the communication between and within the dimers that compose the functional homotetramer is crucial for the fast catalytic rates observed in this ECR. A separate study aims at developing an in vivo directed evolution screen to improve the catalytic properties of an ECR from Burkholderia ambifaria. Our approach yields an evolved variant, with mutations distant from the active site. The observed improved catalytic supports the importance of the residues for the catalytic rate. Both studies revealed the importance of the residues at the interface of the ECR monomers by their impact on catalytic rates of this enzyme

Elucidating the effects of mutation and evolutionary divergence upon protein structure quantitative stability/flexibility relationships

The importance of flexibility and stability on protein function has been recognized for over five decades. A protein must be flexible enough to mediate a reaction pathway, yet rigid enough to achieve high fidelity in molecular recognition. To understand these relationships, the main focus of our research has been a comparative investigation of proteins' dynamics and thermodynamics across both "depth" and "breadth". Specifically, we compare stability and flexibility properties across a set of human c-type lysozyme point mutations (depth), as well as across a set of functionally related ß-lactamase protein orthologs (breadth). To accomplish these tasks we employ a Distance Constraint Model (DCM), which provides a robust statistical mechanical description of proteins and the relationships therein. The DCM is based on network rigidity that provides mechanical mechanism for enthalpy-entropy compensation, from which Quantitative Stability/Flexibility Relationships (QSFR) can be calculated. Our results suggest that DCM can be used for predicting stability of proteins with an average percent error of 4.3%. Deciphering changes in flexibility, DCM results suggest that the influence of mutations can lead to frequent, large and long-range effects in protein dynamics. Our breadth analyses indicate that QSFR and physiochemical property characterization of orthologs in a protein family parallel evolutionary relationship. Going further, we present protocols for clustering protein structures using their QSFR properties, thus paving way for comprehensive quantitative stability/flexibility relationship analysis across protein families and superfamilies. To summarize, the results presented in this work provide a complete description of proteins that account for their stability, flexibility and function