Residue-residue Contacts Research Articles

Structural insights into the molecular recognition of integrin αVβ3 by RGD-containing ligands: The role of the specificity-determining loop (SDL).

Integrin αVβ3 is a prominent member of the "RGD-recognizing" integrin family of cell surface receptors. αVβ3 binds to various extracellular matrix (ECM) proteins and oxysterols such as 25-hydroxycholesterol, is implicated in several diseases, including cancer metastasis, lung fibrosis, inflammation, and autoimmune diseases, and is pursued as a valuable therapeutic target. Despite enormous efforts to seek a pure antagonist, to date, no single drug candidate has successfully reached clinics due to associated partial agonism and toxicity issues. Developing effective and safe inhibitors require a thorough understanding of the molecular interactions and structural changes related to the receptor's activation and inhibition mechanisms. This study offers a comprehensive residue-residue contact and network analyses of the ligand-binding β-propeller βI domains (headpiece) based on all available experimental structures of integrin αVβ3 in unliganded, agonist-, antagonist-, and antibody-bound states. The analyses reveal many critical interactions that were not reported before and show that specific orientation and interactions of residues from the specificity-determining loop (SDL) are critical in molecular recognition and regulation. Also, the network analysis reveals that residues from the nearby allosteric site (site II) connect to the primary RGD-binding site via SDL, which likely acts as an interface between the two sites. Our results provide valuable insights into molecular interactions, structural changes, distinct features of the active and inactive headpiece conformations, the role of SDL in ligand recognition, and SDL-mediated allostery. Thus, the insights from this study may facilitate the designing of pure antagonists or site II-mediated allosteric modulators to integrin αVβ3 to treat various diseases.

Using Attention-UNet Models to Predict Protein Contact Maps.

Proteins are essential to life, and understanding their intrinsic roles requires determining their structure. The field of proteomics has opened up new opportunities by applying deep learning algorithms to large databases of solved protein structures. With the availability of large data sets and advanced machine learning methods, the prediction of protein residue interactions has greatly improved. Protein contact maps provide empirical evidence of the interacting residue pairs within a protein sequence. Template-free protein structure prediction systems rely heavily on this information. This article proposes UNet-CON, an attention-integrated UNet architecture, trained to predict residue-residue contacts in protein sequences. With the predicted contacts being more accurate than state-of-the-art methods on the PDB25 test set, the model paves the way for the development of more powerful deep learning algorithms for predicting protein residue interactions.

Dynamics of Protein-RNA Interfaces Using All-Atom Molecular Dynamics Simulations.

Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein-RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein-RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein-RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein-RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue-residue contacts with respect to the experimental structure.

ARIP: A Tool for Precise Interatomic Contact Area and Volume Calculation in Proteins.

The interplay patterns of amino acid residues are pivotal in determining the tertiary structure and flexibility of proteins, which in turn are intricately linked to their functionality and interactions with other molecules. Here, we introduce ARIP, a novel tool designed to identify contact residues within proteins. ARIP employs a modified version of the dr_sasa algorithm and an atomic overlap weighted algorithm to directly calculate the contact area and volume between atoms based on their van der Waals radius. It also allows for the selection of solvent radii, recognizing that not every atom in proteins can interact with water molecules. The solvent parameters were derived from the analysis of approximately 5000 protein and nucleic acid structures with water molecules determined using X-ray crystallography. One advantage of the modified algorithm is its capability to analyze multiple models within a single PDB file, making it suitable for molecular dynamic capture. The contact volume is symmetrically distributed between the interacting atoms, providing more informative results than contact area for the analysis of intra- and intermolecular interactions and the development of scoring functions. Furthermore, ARIP has been applied to four distinct cases: capturing key residue-residue contacts in NMR structures of P4HB, protein-drug binding of CYP17A1, protein-DNA binding of SPI1, and molecular dynamic simulations of BRD4.

Structure prediction of linear and cyclic peptides using CABS-flex.

The structural modeling of peptides can be a useful aid in the discovery of new drugs and a deeper understanding of the molecular mechanisms of life. Here we present a novel multiscale protocol for the structure prediction of linear and cyclic peptides. The protocol combines two main stages: coarse-grained simulations using the CABS-flex standalone package and an all-atom reconstruction-optimization process using the Modeller program. We evaluated the protocol on a set of linear peptides and two sets of cyclic peptides, with cyclization through the backbone and disulfide bonds. A comparison with other state-of-the-art tools (APPTEST, PEP-FOLD, ESMFold and AlphaFold implementation in ColabFold) shows that for most cases, AlphaFold offers the highest resolution. However, CABS-flex is competitive, particularly when it comes to short linear peptides. As demonstrated, the protocol performance can be further improved by combination with the residue-residue contact prediction method or more efficient scoring. The protocol is included in the CABS-flex standalone package along with online documentation to aid users in predicting the structure of peptides and mini-proteins.

Predicting the functional state of protein kinases using interpretable graph neural networks from sequence and structural data.

Protein kinases are central to cellular activities and are actively pursued as drug targets for several conditions including cancer and autoimmune diseases. Despite the availability of a large structural database for kinases, methodologies to elucidate the structure-function relationship of these proteins (without manual intervention) are lacking. Such techniques are essential in structural biology and to accelerate drug discovery efforts. Here, we implement an interpretable graph neural network (GNN) framework for classifying the functionally active and inactive states of a large set of protein kinases by only using their tertiary structure and amino acid sequence. We show that the GNN models can classify kinase structures with high accuracy (>97%). We implement the Gradient-weighted Class Activation Mapping for graphs (Graph Grad-CAM) to automatically identify structurally important residues and residue-residue contacts of the kinases without any a priori input. We show that the motifs identified through the Graph Grad-CAM methodology are functionally critical, consistent with the existing kinase literature. Notably, the highly conserved DFG and HRD motifs of the well-known hydrophobic spine are identified by the interpretable framework in addition to some of the lesser known motifs. Further, using Grad-CAM maps as the vector embedding of the protein structures, we identify the subtle differences in the crystal structures among different sub-classes of kinases in the Protein Data Bank (PDB). Frameworks such as the one implemented here, for high-throughput identification of protein structure-function relationships are essential in designing targeted small molecules therapies as well as in engineering new proteins for novel applications.

ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction.

The latent features extracted from the multiple sequence alignments (MSAs) of homologous protein families are useful for identifying residue-residue contacts, predicting mutation effects, shaping protein evolution, etc. Over the past three decades, a growing body of supervised and unsupervised machine learning methods have been applied to this field, yielding fruitful results. Here, we propose a novel self-supervised model, called encoder-transformation layer-decoder (ETLD) architecture, capable of capturing protein sequence latent features directly from MSAs. Compared to the typical autoencoder model, ETLD introduces a transformation layer with the ability to learn inter-site couplings, which can be used to parse out the two-dimensional residue-residue contacts map after a simple mathematical derivation or an additional supervised neural network. ETLD retains the process of encoding and decoding sequences, and the predicted probabilities of amino acids at each site can be further used to construct the mutation landscapes for mutation effects prediction, outperforming advanced models such as GEMME, DeepSequence and EVmutation in general. Overall, ETLD is a highly interpretable unsupervised model with great potential for improvement and can be further combined with supervised methods for more extensive and accurate predictions.

Deep transfer learning for inter-chain contact predictions of transmembrane protein complexes

Membrane proteins are encoded by approximately a quarter of human genes. Inter-chain residue-residue contact information is important for structure prediction of membrane protein complexes and valuable for understanding their molecular mechanism. Although many deep learning methods have been proposed to predict the intra-protein contacts or helix-helix interactions in membrane proteins, it is still challenging to accurately predict their inter-chain contacts due to the limited number of transmembrane proteins. Addressing the challenge, here we develop a deep transfer learning method for predicting inter-chain contacts of transmembrane protein complexes, named DeepTMP, by taking advantage of the knowledge pre-trained from a large data set of non-transmembrane proteins. DeepTMP utilizes a geometric triangle-aware module to capture the correct inter-chain interaction from the coevolution information generated by protein language models. DeepTMP is extensively evaluated on a test set of 52 self-associated transmembrane protein complexes, and compared with state-of-the-art methods including DeepHomo2.0, CDPred, GLINTER, DeepHomo, and DNCON2_Inter. It is shown that DeepTMP considerably improves the precision of inter-chain contact prediction and outperforms the existing approaches in both accuracy and robustness.

Effect of sequence pattern on conformation of DOPA-Peptide conjugate aggregates: a discontinuous molecular dynamics simulation study

ABSTRACT Underwater adhesives are critical for many applications, including marine coatings, sealants, and medical devices. Research on natural underwater adhesives has shown that L-3,4-dihydroxyphenylalanine (DOPA) and amyloid nanostructures are vital to their adhesive abilities. The fusion of DOPA-containing chains and amyloid-forming peptides creates a new space for designing underwater adhesives capable of multi-surface adhesion. One critical question for this design is the interplay between the DOPA and amyloid-forming peptide regions. Here we investigate the effect of the sequence pattern of DOPA-containing chains on the aggregation conformation of conjugates. Discontinuous molecular dynamics simulations were performed for fourteen DOPA-amyloid conjugates with different sequence patterns along the DOPA-containing portion. The amyloid-forming portion is represented by KLVFFAE from the Aβ42 peptide. The structural properties of the DOPA-amyloid conjugates are characterised by the percentages of ordered secondary structures and residue-residue contact maps. The results showed that certain patterns of DOPA and glycine in the DOPA-containing tail allowed the KLVFFAE portions of the conjugates to form distinct ordered β-sheets, and the DOPA-containing portion and the KLVFFAE portion of the conjugates to remain separated both within the same chain and amongst different chains. Among the designs, the most promising sequences are KLVFFAE-G-YYGYYGYY (where Y represents DOPA) and KLVFFAE-G-YYYYGGGG.

GPCRana: A web server for quantitative analysis of GPCR structures

G protein-coupled receptors (GPCRs) attract tremendous attention from both industrial and academic researchers with currently over 900 released structures. Structural analysis is widely used to understand receptor functionality and pharmacology, but more user-friendly tools are needed. Residue-residue contact score (RRCS) is an atomic distance-based method that allows a quantitative description of GPCR structures. Here, we present GPCRana, a web server that provides a user-friendly interface to analyze GPCR structures. After uploading selected structures, GPCRana immediately generates a comprehensive report covering four aspects: (i) RRCS for all residue pairs incorporated with real-time 3D visualization; (ii) ligand-receptor interactions; (iii) activation pathway analysis; and (iv) RRCS_TMs that indicates the global movements of transmembrane helices. Moreover, conformational changes between two structures can be analyzed. Applying GPCRana on AlphaFold2-predicted models reveals differentiated inter-helical packing forms in a receptor-dependent manner. Our web server offers a fast and precise way to study GPCR structures and is freely available at http://gpcranalysis.com/#/.

Inhibitive and Destructive Mechanisms of Chronic Traumatic Encephalopathy-Related R3-R4 Tau Peptide Chains and Protofibril by Epigallocatechin Gallate: Evidence from Molecular Dynamics Simulation.

Chronic traumatic encephalopathy (CTE), a unique tauopathy, is pathologically associated with the aggregation of hyperphosphorylated tau protein into fibrillar aggregates. Inhibiting tau aggregation and disaggregating tau protofibril might be promising strategies to prevent or delay the development of CTE. Newly resolved tau fibril structures from deceased CTE patients' brains show that the R3-R4 fragment of tau forms the core of the fibrils and the structures are distinct from other tauopathies. An in vitro experiment finds that epigallocatechin gallate (EGCG) can effectively inhibit human full-length tau aggregation and disaggregate preformed fibrils. However, its inhibitive and destructive effects on the CTE-related R3-R4 tau and the underlying molecular mechanisms remain elusive. In this study, we performed extensive all-atom molecular dynamics simulations on the CTE-related R3-R4 tau dimer/protofibril with and without EGCG. The results reveal that EGCG could reduce the β-sheet structure content of the dimer, induce the dimer to form loosely packed conformations, and impede the interchain interactions, thus inhibiting the further aggregation of the two peptide chains. Besides, EGCG could reduce the structural stability, decrease the β-sheet structure content, reduce the structural compactness, and weaken local residue-residue contacts of the protofibril, hence making the protofibril disaggregated. We also identified the dominant binding sites and pivotal interactions. EGCG preferentially binds with hydrophobic, aromatic, and positively/negatively charged residues of the dimer, while it tends to bind with polar, hydrophobic, aromatic, and positively charged residues of the protofibril. Hydrophobic, hydrogen-bonding, π-π stacking, and cation-π interactions synergistically drive the binding of EGCG on both the dimer and the protofibril, but anion-π interaction only exists in the interaction of EGCG with the dimer. Our work unravels EGCG's inhibitive and destructive effects on the CTE-related R3-R4 tau dimer/protofibril and the underlying molecular mechanisms, which provides useful implications for the design of drugs to prevent or delay the progression of CTE.

Cholesterol Biases the Conformational Landscape of the Chemokine Receptor CCR3: A MAS SSNMR-Filtered Molecular Dynamics Study.

Cholesterol directs the pathway of ligand-induced G protein-coupled receptor (GPCR) signal transduction. The GPCR C-C motif chemokine receptor 3 (CCR3) is the principal chemotactic receptor for eosinophils, with roles in cancer metastasis and autoinflammatory conditions. Recently, we discovered a direct correlation between bilayer cholesterol and increased agonist-triggered CCR3 signal transduction. However, the allosteric molecular mechanism escalating ligand affinity and G protein coupling is unknown. To study cholesterol-guided CCR3 conformational selection, we implement comparative, objective measurement of protein architectures by scoring shifts (COMPASS) to grade model structures from molecular dynamics simulations. In this workflow, we scored predicted chemical shifts against 2-dimensional solid-state NMR 13C-13C correlation spectra of U-15N,13C-CCR3 samples prepared with and without cholesterol. Our analysis of trajectory model structures uncovers that cholesterol induces site-specific conformational restraint of extracellular loop (ECL) 2 and conserved motion in transmembrane helices and ECL3 not observed in simulations of bilayers with only phosphatidylcholine lipids. PyLipID analysis implicates direct cholesterol agency in CCR3 conformational selection and dynamics. Residue-residue contact scoring shows that cholesterol biases the conformational selection of the orthosteric pocket involving Y411.39, Y1133.32, and E2877.39. Lastly, we observe contact remodeling in activation pathway residues centered on the initial transmission switch, Na+ pocket, and R3.50 in the DRY motif. Our observations have unique implications for understanding of CCR3 ligand recognition and specificity and provide mechanistic insight into how cholesterol functions as an allosteric regulator of CCR3 signal transduction.

Structural basis for the molecular recognition of various RGD-containing ligands by integrin αVβ3: The role of the specificity-determining loop (SDL).

Integrins are multifunctional cell surface receptors that play critical roles in normal physiological processes such as proliferation, survival, migration, and cell fate transitions. Integrins are implicated in several pathological conditions, including inflammation, tumor metastasis, lung fibrosis, and autoimmune disorders. Recent studies show that apart from their endogenous ligands, αVβ3 and α5β1 integrins bind directly to the hydroxylated form of cholesterol (25HC and 24HC) to upregulate pro-inflammatory response. Among the integrins, αVβ3 integrin has been considered a viable target for treating many diseases. Despite enormous efforts, currently available RGD-based anti-integrin drugs lack specificity, exhibit partial agonism, and cause serious side effects, including tumor growth and angiogenesis. The development of safe and efficient inhibitors requires a thorough understanding of the structural basis for outside-in activation and inhibition mechanism of integrin αvβ3. In this study, we performed a comprehensive residue-residue contact and network analysis of the αvβ3 integrin headpiece using all available experimental structures. Surprisingly, our analyses show that specific orientation and interactions of residues from the specificity-determining loop (SDL) are critical in the molecular recognition and regulation of integrin αvβ3. In addition, network analysis reveals that residues from the allosteric site (site II) connect to the primary RGD-binding site via SDL, which acts as an interface for the allosteric modulation. The obtained results provide a comprehensive structural perspective, useful insights into the distinct features of active and inactive conformations, and the role of SDL in the ligand recognition process and allostery.

Analytical Formulation and Field-Theoretic Simulation of Sequence-Specific Phase Separation of Protein-Like Heteropolymers with Short- and Long-Spatial-Range Interactions.

A theory for sequence-dependent liquid-liquid phase separation (LLPS) of intrinsically disordered proteins (IDPs) in the study of biomolecular condensates is formulated by extending the random phase approximation (RPA) and field-theoretic simulation (FTS) of heteropolymers with spatially long-range Coulomb interactions to include the fundamental effects of short-range, hydrophobic-like interactions between amino acid residues. To this end, short-range effects are modeled by Yukawa interactions between multiple nonelectrostatic charges derived from an eigenvalue decomposition of pairwise residue-residue contact energies. Chain excluded volume is afforded by incompressibility constraints. A mean-field approximation leads to an effective Flory-Huggins χ parameter, which, in conjunction with RPA, accounts for the contact-interaction effects of amino acid composition and the sequence-pattern effects of long-range electrostatics in IDP LLPS, whereas FTS based on the formulation provides full sequence dependence for both short- and long-range interactions. This general approach is illustrated here by applications to variants of a natural IDP in the context of several different amino-acid interaction schemes as well as a set of different model hydrophobic-polar sequences sharing the same composition. Effectiveness of the methodology is verified by coarse-grained explicit-chain molecular dynamics simulations.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations.

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Protein Residue Contact Prediction Based on Deep Learning and Massive Statistical Features from Multi-Sequence Alignment

Sequence-based protein tertiary structure prediction is of fundamental importance because the function of a protein ultimately depends on its 3D structure. An accurate residue-residue contact map is one of the essential elements for current ab initio prediction protocols of 3D structure prediction. Recently, with the combination of deep learning and direct coupling techniques, the performance of residue contact prediction has achieved significant progress. However, a considerable number of current Deep-Learning (DL)-based prediction methods are usually time-consuming, mainly because they rely on different categories of data types and third-party programs. In this research, we transformed the complex biological problem into a pure computational problem through statistics and artificial intelligence. We have accordingly proposed a feature extraction method to obtain various categories of statistical information from only the multi-sequence alignment, followed by training a DL model for residue-residue contact prediction based on the massive statistical information. The proposed method is robust in terms of different test sets, showed high reliability on model confidence score, could obtain high computational efficiency and achieve comparable prediction precisions with DL methods that relying on multi-source inputs.

Identification of PARP12 Inhibitors By Virtual Screening and Molecular Dynamics Simulations.

Poly [adenosine diphosphate (ADP)-ribose] polymerases (PARPs) are members of a family of 17 enzymes that performs several fundamental cellular processes. Aberrant activity (mutation) in PARP12 has been linked to various diseases including inflammation, cardiovascular disease, and cancer. Herein, a large library of compounds (ZINC-FDA database) has been screened virtually to identify potential PARP12 inhibitor(s). The best compounds were selected on the basis of binding affinity scores and poses. Molecular dynamics (MD) simulation and binding free energy calculation (MMGBSA) were carried out to delineate the stability and dynamics of the resulting complexes. To this end, root means deviations, relative fluctuation, atomic gyration, compactness, covariance, residue-residue contact map, and free energy landscapes were studied. These studies have revealed that compounds ZINC03830332, ZINC03830554, and ZINC03831186 are promising agents against mutated PARP12.

CpxDeepMSA: A Deep Cascade Algorithm for Constructing Multiple Sequence Alignments of Protein-Protein Interactions.

Protein–protein interactions (PPIs) are fundamental to many biological processes. The coevolution-based prediction of interacting residues has made great strides in protein complexes that are known to interact. A multiple sequence alignment (MSA) is the basis of coevolution analysis. MSAs have recently made significant progress in the protein monomer sequence analysis. However, no standard or efficient pipelines are available for the sensitive protein complex MSA (cpxMSA) collection. How to generate cpxMSA is one of the most challenging problems of sequence coevolution analysis. Although several methods have been developed to address this problem, no standalone program exists. Furthermore, the number of built-in properties is limited; hence, it is often difficult for users to analyze sequence coevolution according to their desired cpxMSA. In this article, we developed a novel cpxMSA approach (cpxDeepMSA. We used different protein monomer databases and incorporated the three strategies (genomic distance, phylogeny information, and STRING interaction network) used to join the monomer MSA results of protein complexes, which can prevent using a single method fail to the joint two-monomer MSA causing the cpxMSA construction failure. We anticipate that the cpxDeepMSA algorithm will become a useful high-throughput tool in protein complex structure predictions, inter-protein residue-residue contacts, and the biological sequence coevolution analysis.

Structural Determinants of Yeast Protein-Protein Interaction Interface Evolution at the Residue Level

Interfaces of contact between proteins play important roles in determining the proper structure and function of protein–protein interactions (PPIs). Therefore, to fully understand PPIs, we need to better understand the evolutionary design principles of PPI interfaces. Previous studies have uncovered that interfacial sites are more evolutionarily conserved than other surface protein sites. Yet, little is known about the nature and relative importance of evolutionary constraints in PPI interfaces. Here, we explore constraints imposed by the structure of the microenvironment surrounding interfacial residues on residue evolutionary rate using a large dataset of over 700 structural models of baker’s yeast PPIs. We find that interfacial residues are, on average, systematically more conserved than all other residues with a similar degree of total burial as measured by relative solvent accessibility (RSA). Besides, we find that RSA of the residue when the PPI is formed is a better predictor of interfacial residue evolutionary rate than RSA in the monomer state. Furthermore, we investigate four structure-based measures of residue interfacial involvement, including change in RSA upon binding (ΔRSA), number of residue-residue contacts across the interface, and distance from the center or the periphery of the interface. Integrated modeling for evolutionary rate prediction in interfaces shows that ΔRSA plays a dominant role among the four measures of interfacial involvement, with minor, but independent contributions from other measures. These results yield insight into the evolutionary design of interfaces, improving our understanding of the role that structure plays in the molecular evolution of PPIs at the residue level.

Determinants of Multiheme Cytochrome Extracellular Electron Transfer Uncovered by Systematic Peptide Insertion.

The multiheme cytochrome MtrA enables microbial respiration by transferring electrons across the outer membrane to extracellular electron acceptors. While structural studies have identified residues that mediate the binding of MtrA to hemes and to other cytochromes that facilitate extracellular electron transfer (EET), the relative importance of these interactions for EET is not known. To better understand EET, we evaluated how insertion of an octapeptide across all MtrA backbone locations affects Shewanella oneidensis MR-1 respiration on Fe(III). The EET efficiency was found to be inversely correlated with the proximity of the insertion to the heme prosthetic groups. Mutants with decreased EET efficiencies also arose from insertions in a subset of the regions that make residue-residue contacts with the porin MtrB, while all sites contacting the extracellular cytochrome MtrC presented high peptide insertion tolerance. MtrA variants having peptide insertions within the CXXCH motifs that coordinate heme cofactors retained some ability to support respiration on Fe(III), although these variants presented significantly decreased EET efficiencies. Furthermore, the fitness of cells expressing different MtrA variants under Fe(III) respiration conditions correlated with anode reduction. The peptide insertion profile, which represents the first comprehensive sequence-structure-function map for a multiheme cytochrome, implicates MtrA as a strategic protein engineering target for the regulation of EET.