Knowledge Based Membrane Protein Structure Prediction: From X-Ray Crystallography to Bioinformatics and Back to Molecular Biology

Integral membrane proteins play a key role in detecting and conveying outside signals into cells, allowing them to interact and respond to their environment in a specific manner. They form principal nodes in several signaling pathways and attract large interest in therapeutic interventions as the majority of drug targets are associated to the cell's membrane. The original human genome sequence project estimated 20% of the total gene count of 31,778 genes to code for membrane proteins[1]. Thus membrane proteins constitute a very large set of yet-to be-characterized proteins mediating all the relevant life-related functions both in prokaryotes and eukaryotes. Estimates are suggesting that in whole genomes the content of this protein type may vary from 10% to 40% of the whole proteome, depending on the organism. As of today, this may change on time, while the rough amount of protein sequences is ~ 6,000,000 (in the Non Redundant data base [http://www.ncbi.nlm.nih.gov/]), the sequences annotated as \u201cmembrane protein\u201d are just 45,281 in Swiss-Prot (http://expasy.org/sprot/ where the annotation is manually curated), and the solved atomic structures of membrane proteins are about 350 in the Protein Data Bank [http://www.rcsb.org/pdb/]. This is a very small number considering that we may consider a rough average of 30% of membrane proteins per genome (as derived from sequence similarity search) and end up with an approximate number of about 2,000,000 membrane proteins in the data bases. We can then easily evaluate that less than ~0.6% of membrane proteins are annotated and that ~0.001% of all the membrane protein sequences are known with atomic resolution , giving the idea of an enormous gap that should be filled in order to fully characterize the functioning of membrane proteins. The main reason behind these small numbers is that membrane proteins are very difficult to study as they are inserted into lipid bilayers surrounding the cell and its subcompartments, and expose to the polar outer and inner environments portions of different sizes. When isolated from membranes, membrane proteins are generally less stable than globular ones. It is therefore difficult to purify them in the native, functional form, and more difficult to crystallize them. Thus, crystallization of this type of proteins is yet a very difficult process, given the fact that they expose two different chemico-physical surfaces to the environment: water- and lipid-like. Still, in the last few years, and after great improvements in the techniques underlying X-ray crystallography, several new membrane proteins were solved in different activation states, offering to the entire scientific community a fundamental contribution to the characterization of astonishing mechanisms of signal transduction. Although the improvements in the technologies allowed the determination of several new structures in the last few years, the gap between the known membrane proteins and those with solved structures is still enormous. Thus, a deep combination of X-ray crystallography techniques, computational biology techniques and molecular biology validating experiments, is the key to face the challenge of bridging the gap existing between membrane proteins with and those without known structures. This and other issues may be resolved in the post-genomic era by taking advantage of the all the theoretical and experimental efforts aiming at developing tools based on our present knowledge that are capable of extracting selected structural/functional features from known sequences/structures and of computing the likelihood of their presence in never-seen before sequences/structures. Indeed, some of state-of-the-art tools, are based in the seminal idea that proteins are products of evolution and that their sequences contain millions of years of evolutionary information waiting to be extracted

Open Boundary Simulations of Proteins and Their Hydration Shells by Hamiltonian Adaptive Resolution Scheme

The recently proposed Hamiltonian Adaptive Resolution Scheme (H-AdResS) allows to perform molecular simulations in an open boundary framework. It allows to change on the fly the resolution of specific subset of molecules (usually the solvent), which are free to diffuse between the atomistic region and the coarse-grained reservoir. So far, the method has been successfully applied to pure liquids. Coupling the H-AdResS methodology to hybrid models of proteins, such as the Molecular Mechanics/Coarse-Grained (MM/CG) scheme, is a promising approach for rigorous calculations of ligand binding free energies in low-resolution protein models. Towards this goal, here we apply for the first time H-AdResS to two atomistic proteins in dual-resolution solvent, proving its ability to reproduce structural and dynamic properties of both the proteins and the solvent, as obtained from atomistic simulations.Comment: This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Chemical Theory and Computation, copyright \c{opyright} American Chemical Society after peer review and technical editing by the publishe

Robust principal component analysis-based prediction of protein-protein interaction hot spots.

AbstractProteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein‐protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help design protein‐protein interaction inhibitors for therapy. Unfortunately, current machine learning methods to predict hot spots, suffer from limitations caused by gross errors in the data matrices. Here, we present a novel data pre‐processing pipeline that overcomes this problem by recovering a low rank matrix with reduced noise using Robust Principal Component Analysis. Application to existing databases shows the predictive power of the method

Emergence of a recurrent insertion in the N-terminal domain of the {SARS}-{CoV}-2 spike glycoprotein

Tracking the evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) through genomic surveillance programs is undoubtedly one of the key priorities in the current pandemic situation. Although the genome of SARS-CoV-2 acquires mutations at a slower rate compared with other RNA viruses, evolutionary pressures derived from the widespread circulation of SARS-CoV-2 in the human population have progressively favored the global emergence, though natural selection, of several variants of concern that carry multiple nonsynonymous mutations in the spike glycoprotein. These are often placed in key sites within major antibody epitopes and may therefore confer resistance to neutralizing antibodies, leading to partial immune escape, or otherwise compensate infectivity deficits associated with other non-synonymous substitutions. As previously shown by other authors, several emerging variants carry recurrent deletion regions (RDRs) that display a partial overlap with antibody epitopes located in the spike N-terminal domain (NTD). Comparatively, very little attention had been directed towards spike insertion mutations prior to the emergence of the B.1.1.529 (omicron) lineage. This manuscript describes a single recurrent insertion region (RIR1) in the N-terminal domain of SARSCoV- 2 spike protein, characterized by at least 49 independent acquisitions of 1–8 additional codons between Val213 and Leu216 in different viral lineages. Even though RIR1 is unlikely to confer antibody escape, its association with two distinct formerly widespread lineages (A.2.5 and B.1.214.2), with the quickly spreading omicron and with other VOCs and VOIs warrants further investigation concerning its effects on spike structure and viral infectivity

MERMAID: dedicated web server to prepare and run coarse-grained membrane protein dynamics

Atomistic molecular dynamics simulations of membrane proteins have been shown to be extremely useful for characterizing the molecular features underlying their function, but require high computational power, limiting the understanding of complex events in membrane proteins, e.g. ion channels gating, GPCRs activation. To overcome this issue, it has been shown that coarse-grained approaches, although requiring less computational power, are still capable of correctly describing molecular events underlying big conformational changes in biological systems. Here, we present the Martini coarse-grained membrane protein dynamics (MERMAID), a publicly available web interface that allows the user to prepare and run coarse-grained molecular dynamics (CGMD) simulations and to analyse the trajectories

Coevolutionary data-based interaction networks approach highlighting key residues across protein families: The case of the G-protein coupled receptors

We present an approach that, by integrating structural data with Direct Coupling Analysis, is able to pinpoint most of the interaction hotspots (i.e. key residues for the biological activity) across very sparse protein families in a single run. An application to the Class A G-protein coupled receptors (GPCRs), both in their active and inactive states, demonstrates the predictive power of our approach. The latter can be easily extended to any other kind of protein family, where it is expected to highlight most key sites involved in their functional activity

Advanced Computational Methods in Molecular Medicine

The dauntingly complex functioning of human cells is often the outcome of several molecular processes. Understanding such processes is crucial for modern drug discovery, defining interaction cascades, assessing the effects of mutations changes in local concentrations of ligands, and so forth. Computational methods, from systems biology to bioinformatics and molecular simulation, allow to access features difficult or impossible to be measured. Models (if properly validated against experimental data) help understand the intricate molecular mechanisms of life processes. Bolstering the predictive power of these models calls upon the computational biologist to improve algorithms and methods. This issue reports on procedures and on applications facing current challenges in computational biology.Modern biological sciences are becoming more and more multidisciplinary. At the same time, theoretical and computational approaches gain in reliability and their field of application widens. O. Fisette at al. discuss recent advances in the areas of solution nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations that were made possible by the synergistic combination of both methods.Interaction of proteins is of vital importance for many cellular processes and when altered may cause significant health problems, thus the availability of reliable tools to predict and study the determinants of protein-protein interactions is needed. In this regard, X. -Y. Meng et al. present a newly adapted, computationally efficient Brownian Dynamics- (BD-) based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. A shell-based grid force field represents the receptor protein and solvation effects, partially considering protein flexibility.Hybrid quantum mechanics/molecular mechanics (QM/MM) calculations are routinely used to study quantum mechanical processes in biological systems. J. Kang et al. present a review paper describing an UNIX shell-based interface program connecting two widely used QM and MM calculation engines, GAMESS and AMBER. The tool was used to investigate a metalloenzyme, azurin, and PU.1-DNA complex and mechanisms of hydrolysis (editing reaction) in leucyl-tRNA synthetase complexed with the mis-aminoacylated tRNALeu. The authors investigate the influence of environmental effects on the electronic structure.Electron transfer in proteins constitutes key steps in several biological processes, ranging from photosynthesis to aerobic respiration. T. Hayashi and A. Stuchebrukhov investigate electron tunneling in NADH : ubiquinone oxidoreductase (Complex I), a key enzyme in cellular respiration as an entry point of the electron transport chain of mitochondria and bacteria, by evaluating the transition flux between donor and acceptor at atomistic resolution. The study suggests that the diffusion of internal water molecules dynamically controls tunneling efficiency

Robust Principal Component Analysis-based Prediction of Protein-Protein Interaction Hot spots ( {RBHS} )

Proteins often exert their function by binding to other cellular partners. The hot spots are key residues for protein-protein binding. Their identification may shed light on the impact of disease associated mutations on protein complexes and help design protein-protein interaction inhibitors for therapy. Unfortunately, current machine learning methods to predict hot spots, suffer from limitations caused by gross errors in the data matrices. Here, we present a novel data pre-processing pipeline that overcomes this problem by recovering a low rank matrix with reduced noise using Robust Principal Component Analysis. Application to existing databases shows the predictive power of the method

Residues in the Distal Heme Pocket of Arabidopsis Non-Symbiotic Hemoglobins: Implication for Nitrite Reductase Activity

It is well-established that plant hemoglobins (Hbs) are involved in nitric oxide (NO) metabolism via NO dioxygenase and/or nitrite reductase activity. The ferrous-deoxy Arabidopsis Hb1 and Hb2 (AHb1 and AHb2) have been shown to reduce nitrite to NO under hypoxia. Here, to test the hypothesis that a six- to five-coordinate heme iron transition might mediate the control of the nitrite reduction rate, we examined distal pocket mutants of AHb1 and AHb2 for nitrite reductase activity, NO production and spectroscopic features. Absorption spectra of AHbs distal histidine mutants showed that AHb1 mutant (H69L) is a stable pentacoordinate high-spin species in both ferrous and ferric states, whereas heme iron in AHb2 mutant (H66L) is hexacoordinated low-spin with Lys69 as the sixth ligand. The bimolecular rate constants for nitrite reduction to NO were 13.3 ± 0.40, 7.3 ± 0.5, 10.6 ± 0.8 and 171.90 ± 9.00 M(-1)·s(-1) for AHb1, AHb2, AHb1 H69L and AHb2 H66L, respectively, at pH 7.4 and 25 °C. Consistent with the reductase activity, the amount of NO detected by chemiluminescence was significantly higher in the AHb2 H66L mutant. Our data indicate that nitrite reductase activity is determined not only by heme coordination, but also by a unique distal heme pocket in each AHb

Characterization of C-S Lyase from C. diphtheriae

The emergence of antibiotic resistance in microbial pathogens requires the identification of new antibacterial drugs. The biosynthesis of methionine is an attractive target because of its central importance in cellular metabolism. Moreover, most of the steps in methionine biosynthesis pathway are absent in mammals, lowering the probability of unwanted side effects. Herein, detailed biochemical characterization of one enzyme required for methionine biosynthesis, a pyridoxal-5′-phosphate (PLP-) dependent C-S lyase from Corynebacterium diphtheriae, a pathogenic bacterium that causes diphtheria, has been performed. We overexpressed the protein in E. coli and analyzed substrate specificity, pH dependence of steady state kinetic parameters, and ligand-induced spectral transitions of the protein. Structural comparison of the enzyme with cystalysin from Treponema denticola indicates a similarity in overall folding. We used site-directed mutagenesis to highlight the importance of active site residues Tyr55, Tyr114, and Arg351, analyzing the effects of amino acid replacement on catalytic properties of enzyme. Better understanding of the active site of C. diphtheriae C-S lyase and the determinants of substrate and reaction specificity from this work will facilitate the design of novel inhibitors as antibacterial therapeutics