Lattices for ab initio protein structure prediction

Proline/Glycine residues of the PG-levels guide conformational changes along the transport cycle in the mitochondrial carnitine/acylcarnitine carrier (SLC25A20)

Mitochondrial carnitine/acylcarnitine carrier (CAC) is a member of the mitochondrial carrier (MC) family and imports acylcarnitine into the mitochondrial matrix in exchange for carnitine, playing a pivotal role in carnitine shuttle, crucial for fatty acid oxidation. The crystallized structure of CAC has not been solved yet, however, the availability of several in vitro/in silico studies, also based on the crystallized structures of the ADP/ATP carrier in the cytosolic-conformation and in the matrix-conformation, has made possible to confirm the hypothesis of the single-binding centered-gated pore mechanism for all the members of the MC family. In addition, our recent bioinformatics analyses allowed quantifying in silico the importance of protein residues of MC substrate binding region, of those involved in the formation of the matrix and cytosolic gates, and of those belonging to the Pro/Gly (PG) levels, proposed to be crucial for the tilting/kinking/bending of the six MC transmembrane helices, funneling the substrate translocation pathway. Here we present a combined in silico/in vitro analysis employed for investigating the role played by a group of 6 proline residues and 6 glycine residues, highly conserved in CAC, belonging to MC PG-levels. Residues of the PG-levels surround the similarly located MC common substrate binding region, and were proposed to lead conformational changes and substrate translocation, following substrate binding. For our analysis, we employed 3D molecular modeling approaches, alanine scanning site-directed mutagenesis and in vitro transport assays. Our analysis reveals that P130 (H3), G268 (H6) and G220 (H5), mutated in alanine, affect severely CAC transport activity (mutant catalytic efficiency lower than 5 % compared to the wild type CAC), most likely due to their major role in triggering CAC conformational changes, following carnitine binding. Notably, P30A (H1) and G121A (H3) CAC mutants, increase the carnitine uptake up to 217 % and 112 %, respectively, compared to the wild type CAC.

Personalized Medicine in Mitochondrial Health and Disease: Molecular Basis of Therapeutic Approaches Based on Nutritional Supplements and Their Analogs

Mitochondrial diseases (MDs) may result from mutations affecting nuclear or mitochondrial genes, encoding mitochondrial proteins, or non-protein-coding mitochondrial RNA. Despite the great variability of affected genes, in the most severe cases, a neuromuscular and neurodegenerative phenotype is observed, and no specific therapy exists for a complete recovery from the disease. The most used treatments are symptomatic and based on the administration of antioxidant cocktails combined with antiepileptic/antipsychotic drugs and supportive therapy for multiorgan involvement. Nevertheless, the real utility of antioxidant cocktail treatments for patients affected by MDs still needs to be scientifically demonstrated. Unfortunately, clinical trials for antioxidant therapies using α-tocopherol, ascorbate, glutathione, riboflavin, niacin, acetyl-carnitine and coenzyme Q have met a limited success. Indeed, it would be expected that the employed antioxidants can only be effective if they are able to target the specific mechanism, i.e., involving the central and peripheral nervous system, responsible for the clinical manifestations of the disease. Noteworthily, very often the phenotypes characterizing MD patients are associated with mutations in proteins whose function does not depend on specific cofactors. Conversely, the administration of the antioxidant cocktails might determine the suppression of endogenous oxidants resulting in deleterious effects on cell viability and/or toxicity for patients. In order to avoid toxicity effects and before administering the antioxidant therapy, it might be useful to ascertain the blood serum levels of antioxidants and cofactors to be administered in MD patients. It would be also worthwhile to check the localization of mutations affecting proteins whose function should depend (less or more directly) on the cofactors to be administered, for estimating the real need and predicting the success of the proposed cofactor/antioxidant-based therapy.

On the Number of Saturated and Optimal Extended 2-Regular Simple Stacks in the Nussinov-Jacobson Energy Model

It is known that both RNA secondary structure and protein contact map can be presented using combinatorial diagrams, the combinatorial enumeration and related problems of which have been studied extensively. Motivated by previous enumeration works on saturated RNA secondary structures and extended stack structures of protein contact maps, we are interested in the enumeration problems of saturated and optimal extended stacks in the Nussinov-Jacobson energy model, in which each base pair contributes energy -1. Then optimal structures are those with most arcs, and locally optimal structures are exactly the saturated structures, in which no more arcs can be added without violating the structure definition. For saturated extended 2-regular simple stacks, whose degree configuration is related to the protein fold in two-dimensional honeycomb lattice, we obtain generating function equation and asymptotic formula for its number. Moreover, an explicit formula for the number of optimal extended 2-regular simple stacks is also obtained.

Targeting mitochondrial metabolite transporters in Penicillium expansum for reducing patulin production

There is an increasing need of alternative treatments to control fungal infection and consequent mycotoxin accumulation in harvested fruits and vegetables. Indeed, only few biological targets of antifungal agents have been characterized and can be used for limiting fungal spread from decayed fruits/vegetables to surrounding healthy ones during storage. On this concern, a promising target of new antifungal treatments may be represented by mitochondrial proteins due to some species-specific functions played by mitochondria in fungal morphogenesis, drug resistance and virulence. One of the most studied mycotoxins is patulin produced by several species of Penicillium and Aspergillus genera. Patulin is toxic to many biological systems including bacteria, higher plants and animalia. Although precise biochemical mechanisms of patulin toxicity in humans are not completely clarified, its high presence in fresh and processed apple fruits and other apple-based products makes necessary developing a strategy for limiting its presence/accumulation. Patulin biosynthetic pathway consists of an enzymatic cascade, whose first step is represented by the synthesis of 6-methylsalicylic acid, obtained from the condensation of one acetyl-CoA molecule with three malonyl-CoA molecules. The most abundant acetyl-CoA precursor is represented by citrate produced by mitochondria. In the present investigation we report about the possibility to control patulin production through the inhibition of mitochondrial/peroxisome transporters involved in the export of acetyl-CoA precursors from mitochondria and/or peroxisomes, with specific reference to the predicted P. expansum mitochondrial Ctp1p, DTC, Sfc1p, Oac1p and peroxisomal PXN carriers.

Targeting Penicillium expansum GMC Oxidoreductase with High Affinity Small Molecules for Reducing Patulin Production

Simple Summary

With the urgent necessity of potential treatments for limiting mycotoxin production and postharvest fungal rots, we propose a combined in silico/in vitro/in vivo strategy for the rapid and effective identification of bioactive small molecules, chosen among a chemical library hosting approved drugs and phytochemicals, to be used after harvest. The molecular target of our analysis was the GMC oxidoreductase from Penicillium expansum involved in the biosynthesis of patulin, a mycotoxin that can contaminate many foods, especially fruits and fruit-based products. The employed in silico/in vitro/in vivo assays described in our study proved the effectiveness of our strategy and in particular of two small molecules, 6-hydroxycoumarin (structurally related to umbelliferon, an already characterized patulin synthase inhibitor) and meticrane (an already approved drug) in reducing patulin accumulation. Our findings highly recommend the mentioned ligands to be subjected to further analysis for being used in the next future in place of other more toxic compounds, in postharvest treatments based on dipping or drenching methods.

Abstract

Flavine adenine dinucleotide (FAD) dependent glucose methanol choline oxidoreductase (GMC oxidoreductase) is the terminal key enzyme of the patulin biosynthetic pathway. GMC oxidoreductase catalyzes the oxidative ring closure of (E)-ascladiol to patulin. Currently, no protein involved in the patulin biosynthesis in Penicillium expansum has been experimentally characterized or solved by X-ray diffraction. Consequently, nothing is known about P. expansum GMC oxidoreductase substrate-binding site and mode of action. In the present investigation, a 3D comparative model for P. expansum GMC oxidoreductase has been described. Furthermore, a multistep computational approach was used to identify P. expansum GMC oxidoreductase residues involved in the FAD binding and in substrate recognition. Notably, the obtained 3D comparative model of P. expansum GMC oxidoreductase was used for performing a virtual screening of a chemical/drug library, which allowed to predict new GMC oxidoreductase high affinity ligands to be tested in in vitro/in vivo assays. In vitro assays performed in presence of 6-hydroxycoumarin and meticrane, among the highly affinity predicted binders, confirmed a dose-dependent inhibition (17–81%) of patulin production by 6-hydroxycoumarin (10 µM–1 mM concentration range), whereas the approved drug meticrane inhibited patulin production by 43% already at 10 µM. Furthermore, 6-hydroxycoumarin and meticrane caused a 60 and 41% reduction of patulin production, respectively, in vivo on apples at 100 µg/wound.

PathMolD-AB: Spatiotemporal pathways of protein folding using parallel molecular dynamics with a coarse-grained model

Solving the protein folding problem (PFP) is one of the grand challenges still open in computational biophysics. Globular proteins are believed to evolve from initial configurations through folding pathways connecting several thermodynamically accessible states in a free energy landscape until reaching its minimum, inhabited by the stable native structures. Despite its huge computational burden, molecular dynamics (MD) is the leading approach in the PFP studies by preserving the Newtonian temporal evolution in the canonical ensemble. Non-trivial improvements are provided by highly parallel implementations of MD in cost-effective GPUs, concomitant to multiscale descriptions of proteins by coarse-grained minimalist models. In this vein, we present the PathMolD-AB framework, a comprehensive software package for massively parallel MD simulations using the canonical ensemble, structural analysis, and visualization of the folding pathways using the minimalist AB-model. It has, also, a tool to compare the results with proteins re-scaled from the PDB. We simulate and analyze, as case studies, the folding of four proteins: 13FIBO, 2GB1, 1PLC and 5ANZ, with 13, 55, 99 and 223 amino acids, respectively. The datasets generated from simulations correspond to the MD evolution of 3500 folding pathways, encompassing 35×106 states, which contains the spatial amino acid positions, the protein free energies and radii of gyration at each time step. Results indicate that the speedup of our approach grows logarithmically with the protein length and, therefore, it is suited for most of the proteins in the PDB. The predicted structures simulated by PathMolD-AB were similar to the re-scaled biological structures, indicating that it is promising for the study of the PFP study.

Combinatorics of Contacts in Protein Contact Maps

Contacts play a fundamental role in the study of protein structure and folding problems. The contact map of a protein can be represented by arranging its amino acids on a horizontal line and drawing an arc between two residues if they form a contact. In this paper, we are mainly concerned with the combinatorial enumeration of the arcs in m-regular linear stack, an elementary structure of the protein contact map, which was introduced by Chen et al. (J Comput Biol 21(12):915-935, 2014). We modify the generating function for m-regular linear stacks by introducing a new variable y regarding to the number of arcs and obtain an equation satisfied by the generating function for m-regular linear stacks with n vertices and k arcs. Consequently, we also derive an equation satisfied by the generating function of the overall number of arcs in m-regular linear stacks with n vertices.

Enumeration of Extended m-Regular Linear Stacks

The contact map of a protein fold in the two-dimensional (2D) square lattice has arc length at least 3, and each internal vertex has degree at most 2, whereas the two terminal vertices have degree at most 3. Recently, Chen, Guo, Sun, and Wang studied the enumeration of [Formula: see text]-regular linear stacks, where each arc has length at least [Formula: see text] and the degree of each vertex is bounded by 2. Since the two terminal points in a protein fold in the 2D square lattice may form contacts with at most three adjacent lattice points, we are led to the study of extended [Formula: see text]-regular linear stacks, in which the degree of each terminal point is bounded by 3. This model is closed to real protein contact maps. Denote the generating functions of the [Formula: see text]-regular linear stacks and the extended [Formula: see text]-regular linear stacks by [Formula: see text] and [Formula: see text], respectively. We show that [Formula: see text] can be written as a rational function of [Formula: see text]. For a certain [Formula: see text], by eliminating [Formula: see text], we obtain an equation satisfied by [Formula: see text] and derive the asymptotic formula of the numbers of [Formula: see text]-regular linear stacks of length [Formula: see text].

Distance-dependent hydrophobic-hydrophobic contacts in protein folding simulations

Successful prediction of protein folding from an amino acid sequence is a challenge in computational biology. In order to reveal the geometric constraints that drive protein folding, highlight those constraints kept or missed by distinct lattices and for establishing which class of intra- and inter-secondary structure element interactions is the most relevant for the correct folding of proteins, we have calculated inter-alpha carbon distances in a set of 42 crystal structures consisting of mainly helix, sheet or mixed conformations. The inter-alpha carbon distances were also calculated in several lattice "hydrophobic-polar" models built from the same protein set. We found that helix structures are more prone to form "hydrophobic-hydrophobic" contacts than beta-sheet structures. At a distance lower than or equal to 3.8 Å (very short-range interactions), "hydrophobic-hydrophobic" contacts are almost absent in the native structures, while they are frequent in all the analyzed lattice models. At distances in-between 3.8 and 9.5 Å (short-/medium-range interactions), the best performing lattice for reproducing mainly helix structures is the body-centered-cubic lattice. If protein structures contain sheet portions, lattice performances get worse, with few exceptions observed for double-tetrahedral and body-centered-cubic lattices. Finally, we can observe that ab initio protein folding algorithms, i.e. those based on the employment of lattices and Monte Carlo simulated annealings, can be improved simply and effectively by preventing the generation of "hydrophobic-hydrophobic" contacts shorter than 3.8 Å, by monitoring the "hydrophobic-hydrophobic/polar-polar" contact ratio in short-/medium distance ranges and by using preferentially a body-centered-cubic lattice.

Zigzag stacks and m-regular linear stacks

The contact map of a protein fold is a graph that represents the patterns of contacts in the fold. It is known that the contact map can be decomposed into stacks and queues. RNA secondary structures are special stacks in which the degree of each vertex is at most one and each arc has length of at least two. Waterman and Smith derived a formula for the number of RNA secondary structures of length n with exactly k arcs. Höner zu Siederdissen et al. developed a folding algorithm for extended RNA secondary structures in which each vertex has maximum degree two. An equation for the generating function of extended RNA secondary structures was obtained by Müller and Nebel by using a context-free grammar approach, which leads to an asymptotic formula. In this article, we consider m-regular linear stacks, where each arc has length at least m and the degree of each vertex is bounded by two. Extended RNA secondary structures are exactly 2-regular linear stacks. For any m ≥ 2, we obtain an equation for the generating function of the m-regular linear stacks. For given m, we deduce a recurrence relation and an asymptotic formula for the number of m-regular linear stacks on n vertices. To establish the equation, we use the reduction operation of Chen, Deng, and Du to transform an m-regular linear stack to an m-reduced zigzag (or alternating) stack. Then we find an equation for m-reduced zigzag stacks leading to an equation for m-regular linear stacks.

Producing high-accuracy lattice models from protein atomic coordinates including side chains

Lattice models are a common abstraction used in the study of protein structure, folding, and refinement. They are advantageous because the discretisation of space can make extensive protein evaluations computationally feasible. Various approaches to the protein chain lattice fitting problem have been suggested but only a single backbone-only tool is available currently. We introduce LatFit, a new tool to produce high-accuracy lattice protein models. It generates both backbone-only and backbone-side-chain models in any user defined lattice. LatFit implements a new distance RMSD-optimisation fitting procedure in addition to the known coordinate RMSD method. We tested LatFit's accuracy and speed using a large nonredundant set of high resolution proteins (SCOP database) on three commonly used lattices: 3D cubic, face-centred cubic, and knight's walk. Fitting speed compared favourably to other methods and both backbone-only and backbone-side-chain models show low deviation from the original data (~1.5 Å RMSD in the FCC lattice). To our knowledge this represents the first comprehensive study of lattice quality for on-lattice protein models including side chains while LatFit is the only available tool for such models.

Fractal symmetry of protein interior: what have we learned?

The application of fractal dimension-based constructs to probe the protein interior dates back to the development of the concept of fractal dimension itself. Numerous approaches have been tried and tested over a course of (almost) 30 years with the aim of elucidating the various facets of symmetry of self-similarity prevalent in the protein interior. In the last 5 years especially, there has been a startling upsurge of research that innovatively stretches the limits of fractal-based studies to present an array of unexpected results on the biophysical properties of protein interior. In this article, we introduce readers to the fundamentals of fractals, reviewing the commonality (and the lack of it) between these approaches before exploring the patterns in the results that they produced. Clustering the approaches in major schools of protein self-similarity studies, we describe the evolution of fractal dimension-based methodologies. The genealogy of approaches (and results) presented here portrays a clear picture of the contemporary state of fractal-based studies in the context of the protein interior. To underline the utility of fractal dimension-based measures further, we have performed a correlation dimension analysis on all of the available non-redundant protein structures, both at the level of an individual protein and at the level of structural domains. In this investigation, we were able to separately quantify the self-similar symmetries in spatial correlation patterns amongst peptide-dipole units, charged amino acids, residues with the π-electron cloud and hydrophobic amino acids. The results revealed that electrostatic environments in the interiors of proteins belonging to 'α/α toroid' (all-α class) and 'PLP-dependent transferase-like' domains (α/β class) are highly conducive. In contrast, the interiors of 'zinc finger design' ('designed proteins') and 'knottins' ('small proteins') were identified as folds with the least conducive electrostatic environments. The fold 'conotoxins' (peptides) could be unambiguously identified as one type with the least stability. The same analyses revealed that peptide-dipoles in the α/β class of proteins, in general, are more correlated to each other than are the peptide-dipoles in proteins belonging to the all-α class. Highly favorable electrostatic milieu in the interiors of TIM-barrel, α/β-hydrolase structures could explain their remarkably conserved (evolutionary) stability from a new light. Finally, we point out certain inherent limitations of fractal constructs before attempting to identify the areas and problems where the implementation of fractal dimension-based constructs can be of paramount help to unearth latent information on protein structural properties.

Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.