Slc11 Synapomorphy: A Conserved 3D Framework Articulating Carrier Conformation Switch

Transmembrane carriers of the Slc11 family catalyze proton (H+)-dependent uptake of divalent metal ions (Me2+) such as manganese and iron-vital elements coveted during infection. The Slc11 mechanism of high-affinity Me2+ cell import is selective and conserved between prokaryotic (MntH) and eukaryotic (Nramp) homologs, though processes coupling the use of the proton motive force to Me2+ uptake evolved repeatedly. Adding bacterial piracy of Nramp genes spread in distinct environmental niches suggests selective gain of function that may benefit opportunistic pathogens. To better understand Slc11 evolution, Alphafold (AF2)/Colabfold (CF) 3D predictions for bacterial sequences from sister clades of eukaryotic descent (MCb and MCg) were compared using both native and mutant templates. AF2/CF model an array of native MCb intermediates spanning the transition from outwardly open (OO) to inwardly open (IO) carriers. In silico mutagenesis targeting (i) a set of (evolutionarily coupled) sites that may define Slc11 function (putative synapomorphy) and (ii) residues from networked communities evolving during MCb transition indicates that Slc11 synapomorphy primarily instructs a Me2+-selective conformation switch which unlocks carrier inner gate and contributes to Me2+ binding site occlusion and outer gate locking. Inner gate opening apparently proceeds from interaction between transmembrane helix (h) h5, h8 and h1a. MCg1 xenologs revealed marked differences in carrier shape and plasticity, owing partly to an altered intramolecular H+ network. Yet, targeting Slc11 synapomorphy also converted MCg1 IO models to an OO state, apparently mobilizing the same residues to control gates. But MCg1 response to mutagenesis differed, with extensive divergence within this clade correlating with MCb-like modeling properties. Notably, MCg1 divergent epistasis marks the emergence of the genus Bordetella-Achromobacter. Slc11 synapomorphy localizes to the 3D areas that deviate least among MCb and MCg1 models (either IO or OO) implying that it constitutes a 3D network of residues articulating a Me2+-selective carrier conformation switch which is maintained in fast-evolving clades at the cost of divergent epistatic interactions impacting carrier shape and dynamics.

Intrinsically Disordered Protein Ensembles Shape Evolutionary Rates Revealing Conformational Patterns

Intrinsically disordered proteins (IDPs) lack stable tertiary structure under physiological conditions. The unique composition and complex dynamical behaviour of IDPs make them a challenge for structural biology and molecular evolution studies. Using NMR ensembles, we found that IDPs evolve under a strong site-specific evolutionary rate heterogeneity, mainly originated by different constraints derived from their inter-residue contacts. Evolutionary rate profiles correlate with the experimentally observed conformational diversity of the protein, allowing the description of different conformational patterns possibly related to their structure-function relationships. The correlation between evolutionary rates and contact information improves when structural information is taken not from any individual conformer or the whole ensemble, but from combining a limited number of conformers. Our results suggest that residue contacts in disordered regions constrain evolutionary rates to conserve the dynamic behaviour of the ensemble and that evolutionary rates can be used as a proxy for the conformational diversity of IDPs.

Exploring Conformational Space with Thermal Fluctuations Obtained by Normal-Mode Analysis

Proteins in their native states can be represented as ensembles of conformers in dynamical equilibrium. Thermal fluctuations are responsible for transitions between these conformers. Normal-modes analysis (NMA) using elastic network models (ENMs) provides an efficient procedure to explore global dynamics of proteins commonly associated with conformational transitions. In the present work, we present an iterative approach to explore protein conformational spaces by introducing structural distortions according to their equilibrium dynamics at room temperature. The approach can be used either to perform unbiased explorations of conformational space or to explore guided pathways connecting two different conformations, e.g., apo and holo forms. In order to test its performance, four proteins with different magnitudes of structural distortions upon ligand binding have been tested. In all cases, the conformational selection model has been confirmed and the conformational space between apo and holo forms has been encompassed. Different strategies have been tested that impact on the efficiency either to achieve a desired conformational change or to achieve a balanced exploration of the protein conformational multiplicity.

Ancestral sequence reconstruction: accounting for structural information by averaging over replacement matrices

MOTIVATION

Ancestral sequence reconstruction (ASR) is widely used to understand protein evolution, structure and function. Current ASR methodologies do not fully consider differences in evolutionary constraints among positions imposed by the three-dimensional (3D) structure of the protein. Here, we developed an ASR algorithm that allows different protein sites to evolve according to different mixtures of replacement matrices. We show that assigning replacement matrices to protein positions based on their solvent accessibility leads to ASR with higher log-likelihoods compared to naïve models that assume a single replacement matrix for all sites. Improved ASR log-likelihoods are also demonstrated when solvent accessibility is predicted from protein sequences rather than inferred from a known 3D structure. Finally, we show that using such structure-aware mixture models results in substantial differences in the inferred ancestral sequences.

AVAILABILITY AND IMPLEMENTATION

http://fastml.tau.ac.il.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Difference contact maps: From what to why in the analysis of the conformational flexibility of proteins

Protein structures, usually visualized in various highly idealized forms focusing on the three-dimensional arrangements of secondary structure elements, can also be described as lists of interacting residues or atoms and visualized as two-dimensional distance or contact maps. We show that contact maps provide an ideal tool to describe and analyze differences between structures of proteins in different conformations. Expanding functionality of the PDBFlex server and database developed previously in our group, we describe how analysis of difference contact maps (DCMs) can be used to identify critical interactions stabilizing alternative protein conformations, recognize residues and positions controlling protein functions and build hypotheses as to molecular mechanisms of disease mutations.

Shared Signature Dynamics Tempered by Local Fluctuations Enables Fold Adaptability and Specificity

Recent studies have drawn attention to the evolution of protein dynamics, in addition to sequence and structure, based on the premise structure-encodes-dynamics-encodes-function. Of interest is to understand how functional differentiation is accomplished while maintaining the fold, or how intrinsic dynamics plays out in the evolution of structural variations and functional specificity. We performed a systematic computational analysis of 26,899 proteins belonging to 116 CATH superfamilies. Characterizing cooperative mechanisms and convergent/divergent features that underlie the shared/differentiated dynamics of family members required a methodology that lends itself to efficient analyses of large ensembles of proteins. We therefore introduced, SignDy, an integrated pipeline for evaluating the signature dynamics of families based on elastic network models. Our analysis confirmed that family members share conserved, highly cooperative (global) modes of motion. Importantly, our analysis discloses a subset of motions that sharply distinguishes subfamilies, which lie in a low-to-intermediate frequency regime of the mode spectrum. This regime has maximal impact on functional differentiation of families into subfamilies, while being evolutionarily conserved among subfamily members. Notably, the high-frequency end of the spectrum also reveals evolutionary conserved features across and within subfamilies; but in sharp contrast to global motions, high-frequency modes are minimally collective. Modulation of robust/conserved global dynamics by low-to-intermediate frequency fluctuations thus emerges as a versatile mechanism ensuring the adaptability of selected folds and the specificity of their subfamilies. SignDy further allows for dynamics-based categorization as a new layer of information relevant to distinctive mechanisms of action of subfamilies, beyond sequence or structural classifications.

Network analysis of dynamically important residues in protein structures mediating ligand-binding conformational changes

According to the generalized conformational selection model, ligand binding involves the co-existence of at least two conformers with different ligand-affinities in a dynamical equilibrium. Conformational transitions between them should be guaranteed by intramolecular vibrational dynamics associated to each conformation. These motions are, therefore, related to the biological function of a protein. Positions whose mutations are found to alter these vibrations the most can be defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. In a previous study, we have shown that these positions are evolutionarily conserved. They correspond to buried aliphatic residues mostly localized in regular structured regions of the protein like β-sheets and α-helices. In the present paper, we perform a network analysis of these key positions for a large dataset of paired protein structures in the ligand-free and ligand-bound form. We observe that networks of interactions between these key positions present larger and more integrated networks with faster transmission of the information. Besides, networks of residues result that are robust to conformational changes. Our results reveal that the conformational diversity of proteins seems to be guaranteed by a network of strongly interconnected key positions rather than individual residues.

Targeting uracil-DNA glycosylases for therapeutic outcomes using insights from virus evolution

Ung-type uracil-DNA glycosylases are frontline defenders of DNA sequence fidelity in bacteria, plants and animals; Ungs also directly assist both innate and humoral immunity. Critically important in viral pathogenesis, whether acting for or against viral DNA persistence, Ungs also have therapeutic relevance to cancer, microbial and parasitic diseases. Ung catalytic specificity is uniquely conserved, yet selective antiviral drugging of the Ung catalytic pocket is tractable. However, more promising precision therapy approaches present themselves via insights from viral strategies, including sequestration or adaptation of Ung for noncanonical roles. A universal Ung inhibition mechanism, converged upon by unrelated viruses, could also inform design of compounds to inhibit specific distinct Ungs. Extrapolating current developments, the character of such novel chemical entities is proposed.

Exploring Protein Conformational Diversity

The native state of proteins is composed of conformers in dynamical equilibrium. In this chapter, different issues related to conformational diversity are explored using a curated and experimentally based database called CoDNaS (Conformational Diversity in the Native State). This database is a collection of redundant structures for the same sequence. CoDNaS estimates the degree of conformational diversity using different global and local structural similarity measures. It allows the user to explore how structural differences among conformers change as a function of several structural features providing further biological information. This chapter explores the measurement of conformational diversity and its relationship with sequence divergence. Also, it discusses how proteins with high conformational diversity could affect homology modeling techniques.

Structural database resources for biological macromolecules

This Briefing reviews the widely used, currently active, up-to-date databases derived from the worldwide Protein Data Bank (PDB) to facilitate browsing, finding and exploring its entries. These databases contain visualization and analysis tools tailored to specific kinds of molecules and interactions, often including also complex metrics precomputed by experts or external programs, and connections to sequence and functional annotation databases. Importantly, updates of most of these databases involves steps of curation and error checks based on specific expertise about the subject molecules or interactions, and removal of sequence redundancy, both leading to better data sets for mining studies compared with the full list of raw PDB entries. The article presents the databases in groups such as those aimed to facilitate browsing through PDB entries, their molecules and their general information, those built to link protein structure with sequence and dynamics, those specific for transmembrane proteins, nucleic acids, interactions of biomacromolecules with each other and with small molecules or metal ions, and those concerning specific structural features or specific protein families. A few webservers directly connected to active databases, and a few databases that have been discontinued but would be important to have back, are also briefly commented on. Along the Briefing, sample cases where these databases have been used to aid structural studies or advance our knowledge about biological macromolecules are referenced. A few specific examples are also given where using these databases is easier and more informative than using raw PDB data.

Homology modeling in a dynamical world

A key concept in template-based modeling (TBM) is the high correlation between sequence and structural divergence, with the practical consequence that homologous proteins that are similar at the sequence level will also be similar at the structural level. However, conformational diversity of the native state will reduce the correlation between structural and sequence divergence, because structural variation can appear without sequence diversity. In this work, we explore the impact that conformational diversity has on the relationship between structural and sequence divergence. We find that the extent of conformational diversity can be as high as the maximum structural divergence among families. Also, as expected, conformational diversity impairs the well-established correlation between sequence and structural divergence, which is nosier than previously suggested. However, we found that this noise can be resolved using a priori information coming from the structure-function relationship. We show that protein families with low conformational diversity show a well-correlated relationship between sequence and structural divergence, which is severely reduced in proteins with larger conformational diversity. This lack of correlation could impair TBM results in highly dynamical proteins. Finally, we also find that the presence of order/disorder can provide useful beforehand information for better TBM performance.

Biophysical Models of Protein Evolution: Understanding the Patterns of Evolutionary Sequence Divergence

For decades, rates of protein evolution have been interpreted in terms of the vague concept of functional importance. Slowly evolving proteins or sites within proteins were assumed to be more functionally important and thus subject to stronger selection pressure. More recently, biophysical models of protein evolution, which combine evolutionary theory with protein biophysics, have completely revolutionized our view of the forces that shape sequence divergence. Slowly evolving proteins have been found to evolve slowly because of selection against toxic misfolding and misinteractions, linking their rate of evolution primarily to their abundance. Similarly, most slowly evolving sites in proteins are not directly involved in function, but mutating these sites has a large impact on protein structure and stability. In this article, we review the studies in the emerging field of biophysical protein evolution that have shaped our current understanding of sequence divergence patterns. We also propose future research directions to develop this nascent field.

Selection on protein structure, interaction, and sequence

Characterizing the probabilities of observing amino acid substitutions at specific sites in a protein over evolutionary time is a major goal in the field of molecular evolution. While purely statistical approaches at different levels of complexity exist, approaches rooted in underlying biological processes are necessary to characterize both the context-dependence of sequence changes (epistasis) and to extrapolate to sequences not observed in biological databases. To develop such approaches, an understanding of the different selective forces that act on amino acid substitution is necessary. Here, an overview of selection on and corresponding modeling of folding stability, folding specificity, binding affinity and specificity for ligands, the evolution of new binding sites on protein surfaces, protein dynamics, intrinsic disorder, and protein aggregation as well as the interplay with protein expression level (concentration) and biased mutational processes are presented.

Promiscuity in the Enzymatic Catalysis of Phosphate and Sulfate Transfer

The enzymes that facilitate phosphate and sulfate hydrolysis are among the most proficient natural catalysts known to date. Interestingly, a large number of these enzymes are promiscuous catalysts that exhibit both phosphatase and sulfatase activities in the same active site and, on top of that, have also been demonstrated to efficiently catalyze the hydrolysis of other additional substrates with varying degrees of efficiency. Understanding the factors that underlie such multifunctionality is crucial both for understanding functional evolution in enzyme superfamilies and for the development of artificial enzymes. In this Current Topic, we have primarily focused on the structural and mechanistic basis for catalytic promiscuity among enzymes that facilitate both phosphoryl and sulfuryl transfer in the same active site, while comparing this to how catalytic promiscuity manifests in other promiscuous phosphatases. We have also drawn on the large number of experimental and computational studies of selected model systems in the literature to explore the different features driving the catalytic promiscuity of such enzymes. Finally, on the basis of this comparative analysis, we probe the plausible origins and determinants of catalytic promiscuity in enzymes that catalyze phosphoryl and sulfuryl transfer.

Addressing the Role of Conformational Diversity in Protein Structure Prediction

Computational modeling of tertiary structures has become of standard use to study proteins that lack experimental characterization. Unfortunately, 3D structure prediction methods and model quality assessment programs often overlook that an ensemble of conformers in equilibrium populates the native state of proteins. In this work we collected sets of publicly available protein models and the corresponding target structures experimentally solved and studied how they describe the conformational diversity of the protein. For each protein, we assessed the quality of the models against known conformers by several standard measures and identified those models ranked best. We found that model rankings are defined by both the selected target conformer and the similarity measure used. 70% of the proteins in our datasets show that different models are structurally closest to different conformers of the same protein target. We observed that model building protocols such as template-based or ab initio approaches describe in similar ways the conformational diversity of the protein, although for template-based methods this description may depend on the sequence similarity between target and template sequences. Taken together, our results support the idea that protein structure modeling could help to identify members of the native ensemble, highlight the importance of considering conformational diversity in protein 3D quality evaluations and endorse the study of the variability of the native structure for a meaningful biological analysis.

CoDNaS 2.0: a comprehensive database of protein conformational diversity in the native state

CoDNaS (conformational diversity of the native state) is a protein conformational diversity database. Conformational diversity describes structural differences between conformers that define the native state of proteins. It is a key concept to understand protein function and biological processes related to protein functions. CoDNaS offers a well curated database that is experimentally driven, thoroughly linked, and annotated. CoDNaS facilitates the extraction of key information on small structural differences based on protein movements. CoDNaS enables users to easily relate the degree of conformational diversity with physical, chemical and biological properties derived from experiments on protein structure and biological characteristics. The new version of CoDNaS includes ∼70% of all available protein structures, and new tools have been added that run sequence searches, display structural flexibility profiles and allow users to browse the database for different structural classes. These tools facilitate the exploration of protein conformational diversity and its role in protein function.

Database URL:http://ufq.unq.edu.ar/codnas

Evolutionary Conserved Positions Define Protein Conformational Diversity

Conformational diversity of the native state plays a central role in modulating protein function. The selection paradigm sustains that different ligands shift the conformational equilibrium through their binding to highest-affinity conformers. Intramolecular vibrational dynamics associated to each conformation should guarantee conformational transitions, which due to its importance, could possibly be associated with evolutionary conserved traits. Normal mode analysis, based on a coarse-grained model of the protein, can provide the required information to explore these features. Herein, we present a novel procedure to identify key positions sustaining the conformational diversity associated to ligand binding. The method is applied to an adequate refined dataset of 188 paired protein structures in their bound and unbound forms. Firstly, normal modes most involved in the conformational change are selected according to their corresponding overlap with structural distortions introduced by ligand binding. The subspace defined by these modes is used to analyze the effect of simulated point mutations on preserving the conformational diversity of the protein. We find a negative correlation between the effects of mutations on these normal mode subspaces associated to ligand-binding and position-specific evolutionary conservations obtained from multiple sequence-structure alignments. Positions whose mutations are found to alter the most these subspaces are defined as key positions, that is, dynamically important residues that mediate the ligand-binding conformational change. These positions are shown to be evolutionary conserved, mostly buried aliphatic residues localized in regular structural regions of the protein like β-sheets and α-helix.

Biophysics of protein evolution and evolutionary protein biophysics

The study of molecular evolution at the level of protein-coding genes often entails comparing large datasets of sequences to infer their evolutionary relationships. Despite the importance of a protein's structure and conformational dynamics to its function and thus its fitness, common phylogenetic methods embody minimal biophysical knowledge of proteins. To underscore the biophysical constraints on natural selection, we survey effects of protein mutations, highlighting the physical basis for marginal stability of natural globular proteins and how requirement for kinetic stability and avoidance of misfolding and misinteractions might have affected protein evolution. The biophysical underpinnings of these effects have been addressed by models with an explicit coarse-grained spatial representation of the polypeptide chain. Sequence-structure mappings based on such models are powerful conceptual tools that rationalize mutational robustness, evolvability, epistasis, promiscuous function performed by 'hidden' conformational states, resolution of adaptive conflicts and conformational switches in the evolution from one protein fold to another. Recently, protein biophysics has been applied to derive more accurate evolutionary accounts of sequence data. Methods have also been developed to exploit sequence-based evolutionary information to predict biophysical behaviours of proteins. The success of these approaches demonstrates a deep synergy between the fields of protein biophysics and protein evolution.

Evolution of oligomeric state through allosteric pathways that mimic ligand binding

Evolution and design of protein complexes are almost always viewed through the lens of amino acid mutations at protein interfaces. We showed previously that residues not involved in the physical interaction between proteins make important contributions to oligomerization by acting indirectly or allosterically. In this work, we sought to investigate the mechanism by which allosteric mutations act, using the example of the PyrR family of pyrimidine operon attenuators. In this family, a perfectly sequence-conserved helix that forms a tetrameric interface is exposed as solvent-accessible surface in dimeric orthologs. This means that mutations must be acting from a distance to destabilize the interface. We identified 11 key mutations controlling oligomeric state, all distant from the interfaces and outside ligand-binding pockets. Finally, we show that the key mutations introduce conformational changes equivalent to the conformational shift between the free versus nucleotide-bound conformations of the proteins.

Protein evolution along phylogenetic histories under structurally constrained substitution models

MOTIVATION

Models of molecular evolution aim at describing the evolutionary processes at the molecular level. However, current models rarely incorporate information from protein structure. Conversely, structure-based models of protein evolution have not been commonly applied to simulate sequence evolution in a phylogenetic framework, and they often ignore relevant evolutionary processes such as recombination. A simulation evolutionary framework that integrates substitution models that account for protein structure stability should be able to generate more realistic in silico evolved proteins for a variety of purposes.

RESULTS

We developed a method to simulate protein evolution that combines models of protein folding stability, such that the fitness depends on the stability of the native state both with respect to unfolding and misfolding, with phylogenetic histories that can be either specified by the user or simulated with the coalescent under complex evolutionary scenarios, including recombination, demographics and migration. We have implemented this framework in a computer program called ProteinEvolver. Remarkably, comparing these models with empirical amino acid replacement models, we found that the former produce amino acid distributions closer to distributions observed in real protein families, and proteins that are predicted to be more stable. Therefore, we conclude that evolutionary models that consider protein stability and realistic evolutionary histories constitute a better approximation of the real evolutionary process.

BeEP Server: Using evolutionary information for quality assessment of protein structure models

The BeEP Server (http://www.embnet.qb.fcen.uba.ar/embnet/beep.php) is an online resource aimed to help in the endgame of protein structure prediction. It is able to rank submitted structural models of a protein through an explicit use of evolutionary information, a criterion differing from structural or energetic considerations commonly used in other assessment programs. The idea behind BeEP (Best Evolutionary Pattern) is to benefit from the substitution pattern derived from structural constraints present in a set of homologous proteins adopting a given protein conformation. The BeEP method uses a model of protein evolution that takes into account the structure of a protein to build site-specific substitution matrices. The suitability of these substitution matrices is assessed through maximum likelihood calculations from which position-specific and global scores can be derived. These scores estimate how well the structural constraints derived from each structural model are represented in a sequence alignment of homologous proteins. Our assessment on a subset of proteins from the Critical Assessment of techniques for protein Structure Prediction (CASP) experiment has shown that BeEP is capable of discriminating the models and selecting one or more native-like structures. Moreover, BeEP is not explicitly parameterized to find structural similarities between models and given targets, potentially helping to explore the conformational ensemble of the native state.