Protein Structure Prediction: Inroads to Biology

Novel Insertion/Deletion Polymorphisms and Genetic Studies of the Shadow of Prion Protein (SPRN) in Raccoon Dogs

Prion diseases, or transmissible spongiform encephalopathies (TSEs), are a group of invariably fatal neurodegenerative disorders. One of the candidate genes involved in prion diseases is the shadow of the prion protein (SPRN) gene. Raccoon dogs, a canid, are considered to be a prion disease-resistant species. To date, the genetic polymorphisms of the SPRN gene and the predicted protein structure of the shadow of prion protein (Sho) have not been explored in raccoon dogs. SPRN was amplified using polymerase chain reaction (PCR). We also investigated the genetic polymorphisms of SPRN by analyzing the frequencies of genotypes, alleles, and haplotypes, as well as the linkage disequilibrium among the identified genetic variations. In addition, in silico analysis with MutPred-Indel was performed to predict the pathogenicity of insertion/deletion polymorphisms. Predicted 3D structures were analyzed by the Alphafold2. We found a total of two novel synonymous single nucleotide polymorphisms and three insertion/deletion polymorphisms. In addition, the 3D structure of the Sho protein in raccoon dogs was predicted to resemble that of the Sho protein in dogs. This is the first study regarding the genetic and structural characteristics of the raccoon dog SPRN gene.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.

In silico structural exploration of serine protease from a CTG-clade yeast Meyerozyma guilliermondii strain SO

In eukaryotes, serine proteases are cellular localized hydrolases reported to regulate essential biological reactions. Improved industrial applications of proteins are aided by prediction and analysis of their 3-dimensional structures (3D). A serine protease was identified from CTG-clade yeast Meyerozyma guilliermondii strain SO and its 3D structure as well as its catalytic attributes have not been fully understood yet, thus we seek to report on the catalytic mechanism of M. guilliermondii strain SO MgPRB1 using substrate PMSF via in silico docking as well as its stability by way of disulfide bonds formation. Herein, bioinformatics tools and techniques were used to predict, validate and analyze the possible changes of CUG ambiguity (if any) in strain SO using template PDB ID: 3F7O. Structural assessments confirmed the classic catalytic triad Asp305, His337, and Ser499. Superimposition of MgPRB1 and template 3F7O structures revealed the unlinked cysteine residues between Cys341, Cys440, Cys471 and Cys506 of MgPRB1 compared to template 3F7O with two disulfide bonds formation, which confers structural stability. In conclusion, serine protease structure from strain SO was successfully predicted and studies towards understanding at the molecular level may be undertaken for its potential applications in the degradation of peptide bonds.

Contact-Assisted Threading in Low-Homology Protein Modeling

The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling. As such, contact-assisted threading has garnered considerable research effort. In this chapter, we provide an overview of existing contact-assisted threading methods while highlighting the recent advances and discussing some of the current limitations and future prospects in the application of contact-assisted threading for improving the accuracy of low-homology protein modeling.

Structural analysis of SARS-CoV-2 Spike protein variants through graph embedding

Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected almost all countries. The unprecedented spreading of this virus has led to the insurgence of many variants that impact protein sequence and structure that need continuous monitoring and analysis of the sequences to understand the genetic evolution and to prevent possible dangerous outcomes. Some variants causing the modification of the structure of the proteins, such as the Spike protein S, need to be monitored. Protein contact networks (PCNs) have been recently proposed as a modelling framework for protein structures. In such a framework, the protein structure is represented as an unweighted graph whose nodes are the central atoms of the backbones (C- α ), and edges connect two atoms falling in the spatial distance between 4 and 7 Å. PCN may also be a data-rich representation since we may add to each node/atom biological and topological information. Such formalism enables the possibility of using algorithms from graph theory to analyze the graph. In particular, we refer to graph embedding methods enabling the analysis of such graphs with deep learning methods. In this work, we explore the possibility of embedding PCN using Graph Neural Networks and then analyze in the embedded space each residue to distinguish mutated residues from non-mutated ones. In particular, we analyzed the structure of the Spike protein of the coronavirus. First, we obtained the PCNs of the Spike protein for the wild-type, α , β , and δ variants. Then we used the GraphSage embedding algorithm to obtain an unsupervised embedding. Then we analyzed the point of mutation in the embedded space. Results show the characteristics of the mutation point in the embedding space.

PCN-Miner: an open-source extensible tool for the analysis of Protein Contact Networks

MOTIVATION

Protein Contact Network (PCN) is a powerful method for analysing the structure and function of proteins, with a specific focus on disclosing the molecular features of allosteric regulation through the discovery of modular substructures. The importance of PCN analysis has been shown in many contexts, such as the analysis of SARS-CoV-2 Spike protein and its complexes with the Angiotensin Converting Enzyme 2 (ACE2) human receptors. Even if there exist many software tools implementing such methods, there is a growing need for the introduction of tools integrating existing approaches.

RESULTS

We present PCN-Miner, a software tool implemented in the Python programming language, able to (i) import protein structures from the Protein Data Bank; (ii) generate the corresponding PCN; (iii) model, analyse and visualize PCNs and related protein structures by using a set of known algorithms and metrics. The PCN-Miner can cover a large set of applications: from clustering to embedding and subsequent analysis.

AVAILABILITY AND IMPLEMENTATION

The PCN-Miner tool is freely available at the following GitHub repository: https://github.com/hguzzi/ProteinContactNetworks. It is also available in the Python Package Index (PyPI) repository.

Computational modeling and druggability assessment of Aggregatibacter actinomycetemcomitans leukotoxin

The leukotoxin (LtxA) of Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) is a protein exotoxin belonging to the repeat-in-toxin family (RTX). Numerous studies have demonstrated that LtxA may play a critical role in the pathogenicity of A. actinomycetemcomitans since hyper-leukotoxic strains have been associated with severe disease. Accordingly, considerable effort has been made to elucidate the mechanisms by which LtxA interacts with host cells and induce their death. However, these attempts have been hampered by the unavailability of a tertiary structure of the toxin, which limits the understanding of its molecular properties and mechanisms. In this paper, we used homology and template free modeling algorithms to build the complete tertiary model of LtxA at atomic level in its calcium-bound Holo-state. The resulting model was refined by energy minimization, validated by Molprobity and ProSA tools, and subsequently subjected to a cumulative 600ns of all-atom classical molecular dynamics simulation to evaluate its structural aspects. The druggability of the proposed model was assessed using Fpocket and FTMap tools, resulting in the identification of four putative cavities and fifteen binding hotspots that could be targeted by rational drug design tools to find new ligands to inhibit LtxA activity.

Recent Advances in Protein Homology Detection Propelled by Inter-Residue Interaction Map Threading

Sequence-based protein homology detection has emerged as one of the most sensitive and accurate approaches to protein structure prediction. Despite the success, homology detection remains very challenging for weakly homologous proteins with divergent evolutionary profile. Very recently, deep neural network architectures have shown promising progress in mining the coevolutionary signal encoded in multiple sequence alignments, leading to reasonably accurate estimation of inter-residue interaction maps, which serve as a rich source of additional information for improved homology detection. Here, we summarize the latest developments in protein homology detection driven by inter-residue interaction map threading. We highlight the emerging trends in distant-homology protein threading through the alignment of predicted interaction maps at various granularities ranging from binary contact maps to finer-grained distance and orientation maps as well as their combination. We also discuss some of the current limitations and possible future avenues to further enhance the sensitivity of protein homology detection.

The fine art of preparing membrane transport proteins for biomolecular simulations: Concepts and practical considerations

Molecular dynamics (MD) simulations have developed into an invaluable tool in bimolecular research, due to the capability of the method in capturing molecular events and structural transitions that describe the function as well as the physiochemical properties of biomolecular systems. Due to the progressive development of more efficient algorithms, expansion of the available computational resources, as well as the emergence of more advanced methodologies, the scope of computational studies has increased vastly over time. We now have access to a multitude of online databases, software packages, larger molecular systems and novel ligands due to the phenomenon of emerging novel psychoactive substances (NPS). With so many advances in the field, it is understandable that novices will no doubt find it challenging setting up a protein-ligand system even before they run their first MD simulation. These initial steps, such as homology modelling, ligand docking, parameterization, protein preparation and membrane setup have become a fundamental part of the drug discovery pipeline, and many areas of biomolecular sciences benefit from the applications provided by these technologies. However, there still remains no standard on their usage. Therefore, our aim within this review is to provide a clear overview of a variety of concepts and methodologies to consider, providing a workflow for a case study of a membrane transport protein, the full-length human dopamine transporter (hDAT) in complex with different stimulants, where MD simulations have recently been applied successfully.

Solvation of cholesterol in different solvents: a molecular dynamics simulation study

To the best of our knowledge, molecular dynamics simulations of an isolated cholesterol immersed in four different solvents of varying polarity, such as water, methanol, dimethyl sulfoxide and benzene, were reported for the first time to gain insights into the structural and dynamical properties. The study was mainly focused on the evaluation of solvation of cholesterol with respect to its hydrophilic and hydrophobic structural components in the form of respective functional groups interacting with the solvents. Structural evaluations suggested that both hydrophilic and hydrophobic groups of cholesterol were interacting with the solvents, in particular methanol and dimethyl sulfoxide, which presented both types of interactions that are polar and non-polar. On the other hand, the highly polar water and non-polar benzene demonstrated extreme solvation behavior, since water was involved only in hydrogen bonding to the solute hydroxyl group and non-polar benzene formed strong van der Waals interactions only. Furthermore, the hydrophobic effect of cholesterol was also analyzed mainly in polar solvents, as the effect was more pronounced in the polar environment thereby preventing the solvent mobility in the solvation layer(s). The dynamical properties in terms of lateral diffusion and hydrogen bond dynamics as well as free energies of solvation also corroborated the findings based on the structural data and the hydrophobic character of cholesterol was later quantified by the computation of the averaged solvent accessible surface area. The polarity effect of the solvents on the aggregation property of cholesterol was further investigated, which is of big concern from the clinical point of view due to its major role in cardiovascular ailments. It was another major finding of the present study that aggregation was shown to be facilitated by highly polar solvents like water.

Improvement, cloning, and expression of an in silico designed protein enriched with large neutral amino acids in Pichia pastoris for possible application in phenylketonuria

Phenylketonuria (PKU) is an inborn disease caused by defective phenylalanine hydroxylase, which consequently results in the accumulation of phenylalanine in the brain leading to further complications. One of the promising approaches in dietary treatment is the supplementation of large neutral amino acid (LNAA). The LNAA compete with phenylalanine for the common L-type LNAA transporter across the blood-brain barrier, and decrease phenylalanine levels in the brain. In this study, the earlier LNAA-enriched protein model was improved (Protein Model-66) and validated in silico. The reverse translated and codon-optimized synthetic LNAA66 gene was cloned into pPICZαC and expressed in Pichia pastoris. The expressed protein was purified by His Select affinity chromatography. SDS-PAGE and Western blotting analysis showed a band at an expected molecular weight of 12 kDa, confirming the expression of the modeled protein. To our knowledge, this is the first report showing the cloning and expression of an in silico designed LNAA-enriched protein. PRACTICAL APPLICATIONS: One of the promising dietary treatment of phenylketonuria (PKU) is the supplementation of large neutral amino acid (LNAA), wherein high levels of LNAA compete with phenylalanine for the same L-type LNAA transporter, and consequently decrease phenylalanine accumulation in the brain, thereby decreasing neurological complications. For the first time, here, we are showing that an in silico designed and validated Protein Model-66, rich in LNAA, can be successfully cloned and expressed in Pichia pastoris. The complete biochemical and structural characterization of this protein will give a clear insight into its potential application for PKU treatment. The protein can be potentially used as a supplement to treat PKU to those who are non-adherent to the restricted, non-palatable, and expensive diet. Furthermore, this novel and effective strategy of in silico designing, cloning and expression can be exploited to develop proteins for various applications of industrial, food, medical, and academic relevance.

Prediction of novel inhibitors for Crotalus adamanteus l-amino acid oxidase by repurposing FDA-approved drugs: a virtual screening and molecular dynamics simulation investigation

One of the deadliest enzymes in the snake venom is l-amino acid oxidase (LAAO) which plays an important role in the pathophysiological effects during snake envenomation. Some effects of this enzyme on the human body are apoptosis, platelet aggregation, edema, hemorrhage, and cytotoxicity. Hence, inhibiting the enzyme activity to reduce its degradation effects is of great medical and pharmacological importance. On the other hand, drug repurposing is a process to find the new existing drug for a new medical indication. Since Crotalus adamanteus LAAO has no crystal structure in the protein data bank, first, its 3D structure was constructed by homology modeling using 1REO as the template and then modeled structure was evaluated by several algorithms. We screened the FDA-approved drugs by structure-based virtual screening, molecular dynamics (MD) simulation, and Molecular Mechanics Poisson Boltzmann Surface Area (MM/PBSA) to identify new inhibitors for the snake venom LAAO. Interestingly, docking results revealed that half of the hits belong to the propionic acid derivatives drugs. In addition, MD simulation was performed to assess the interaction profile of the docked protein-hits complexes. Meanwhile, Arg88, Gln112, Lys345, Trp356 form consistent hydrogen bond interactions with Dexketoprofen, Flurbiprofen, Ketoprofen, Morphine, and Citric acid during simulation. According to the results, each of the four compounds can be an appropriate inhibitor of LAAO and since our study was based on drug repurposing could be evaluated in phase II clinical trials.

New Deep Learning Methods for Protein Loop Modeling

Computational protein structure prediction is a long-standing challenge in bioinformatics. In the process of predicting protein 3D structures, it is common that parts of an experimental structure are missing or parts of a predicted structure need to be remodeled. The process of predicting local protein structures of particular regions is called loop modeling. In this paper, five new loop modeling methods based on machine learning techniques, called NearLooper, ConLooper, ResLooper, HyLooper1, and HyLooper2 are proposed. NearLooper is based on the nearest neighbor technique. ConLooper applies deep convolutional neural networks to predict ${\mathrm{C}}_{{{\alpha }}}$Cα atoms distance matrix as an orientation-independent representation of protein structure. ResLooper uses residual neural networks instead of deep convolutional neural networks. HyLooper1 combines the results of NearLooper and ConLooper while HyLooper2 combines NearLooper and ResLooper. Three commonly used benchmarks for loop modeling are used to compare the performance between these methods and existing state-of-the-art methods. The experiment results show promising performance in which our best method improves existing state-of-the-art methods by 28 and 54 percent of average RMSD on two datasets while being comparable on the other one.

Improving Coarse-Grained Protein Force Fields with Small-Angle X-ray Scattering Data

Small-angle X-ray scattering (SAXS) experiments provide valuable structural data for biomolecules in solution. We develop a highly efficient maximum entropy approach to fit SAXS data by introducing minimal biases to a coarse-grained protein force field, the associative memory, water mediated, structure, and energy model (AWSEM). We demonstrate that the resulting force field, AWSEM-SAXS, succeeds in reproducing scattering profiles and models protein structures with shapes that are in much better agreement with experimental results. Quantitative metrics further reveal a modest, but consistent, improvement in the accuracy of modeled structures when SAXS data are incorporated into the force field. Additionally, when applied to a multiconformational protein, we find that AWSEM-SAXS is able to recover the population of different protein conformations from SAXS data alone. We, therefore, conclude that the maximum entropy approach is effective in fine-tuning the force field to better characterize both protein structure and conformational fluctuation.

Comparison of Peptide Ion Conformers Arising from Non-Helical and Helical Peptides Using Ion Mobility Spectrometry and Gas-Phase Hydrogen/Deuterium Exchange

The dominant gas-phase conformer of [M+3H]³⁺ ions of the model peptide acetyl-PSSSSKSSSSKSSSSKSSSSK has been examined with ion mobility spectrometry (IMS), gas-phase hydrogen deuterium exchange (HDX), and mass spectrometry (MS) techniques. The [M+3H]³⁺ peptide ions are observed predominantly as a relatively compact conformer type. Upon subjecting these ions to electron transfer dissociation (ETD), the level of protection for each amino acid residue in the peptide sequence is assessed. The overall per-residue deuterium uptake is observed to be relatively more efficient for the neutral residues than for the model peptide acetyl-PAAAAKAAAAKAAAAKAAAAK. In comparison, the N-terminal and C-terminal regions of the serine peptide show greater relative protection compared with interior residues. Molecular dynamics (MD) simulations have been used to generate candidate structures for collision cross section and HDX reactivity matching. Hydrogen accessibility scoring (HAS) for select structural candidates from MD simulations has been used to suggest conformer types that could contribute to the observed HDX patterns. The results are discussed with respect to recent studies employing extensive MD simulations of gas-phase structure establishment of a peptide system. Graphical Abstract ᅟ.

A Novel Framework for Ab Initio Coarse Protein Structure Prediction

Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fitness values; (ii) use of probability-based selection operators that limit the local convergence; (iii) use of secondary structure based mutation operator that makes the structure more closely to the laboratory determined structure; and (iv) incorporating all the above-mentioned features developed a complete two-tier framework. The developed framework builds the protein conformation on the square and triangular lattice. The test has been performed using benchmark sequences, and a comparative evaluation is done with various state-of-the-art algorithms. Moreover, in addition to hypothetical test sequences, we have tested protein sequences deposited in protein database repository. It has been observed that the proposed framework has shown superior performance regarding accuracy (fitness value) and speed (number of generations needed to attain the final conformation). The concepts used to enhance the performance are generic and can be used with any other population-based search algorithm such as genetic algorithm, ant colony optimization, and immune algorithm.

In silico Homology Modeling and Epitope Prediction of NadA as a Potential Vaccine Candidate in Neisseria meningitidis

Neisseria meningitidis is a facultative pathogen bacterium which is well founded with a number of adhesion molecules to facilitate its colonization in human nasopharynx track. Neisseria meningitidis is a major cause of mortality from severe meningococcal disease and septicemia. Neisseria meningitidis adhesion, NadA, is a trimeric autotransporter adhesion molecule which is involved in cell adhesion, invasion, and antibody induction. It is identified in approximately 50% of N. meningitidis isolates, and is established as a vaccine candidate due to its antigenic effects. In the present study, we exploited bioinformatics tools to better understand and determine the 3D structure of NadA and its functional residues to select B cell epitopes, and provide information for elucidating the biological function and vaccine efficacy of NadA. Therefore, this study provided essential data to close gaps existing in biological areas. The most appropriate model of NadA was designed by SWISS MODEL software and important residues were determined using the subsequent epitope mapping procedures. Locations of important linear and conformational epitopes were determined and conserved residues were identified to broaden our knowledge of efficient vaccine design to reduce meningococcal infectioun in population. These data now provide a theme to design more broadly cross-protective antigens.

Lipid production and molecular dynamics simulation for regulation of accD gene in cyanobacteria under different N and P regimes

BACKGROUND

Microalgae grown under different nutrient deficient conditions present a good source of natural lipids with applications for several types of biofuels. The expression of acetyl-CoA carboxylase gene can further provide an insight to the mechanisms leading to enhanced lipid production under such stresses. In this study, two nutrients viz. nitrogen and phosphorus were modulated to see its effect on lipid productivity in selected cyanobacteria and its correlation with Accase followed by molecular dynamics simulation.

RESULTS

Selected cyanobacteria viz. Oscillatoria sp. (SP8), Anabaena sp. (SP12), Anabaena sp. (SP13), Microcoleus sp. (SP18), and Nostoc sp. (SP20) varied in their ability to accumulate lipids which ranged from a lowest of 0.13% in Anabaena sp. (SP13) to the maximum of 7.24% in Microcoleus sp. (SP18). Microcoleus sp. (SP18) also recorded highest lipid accumulation at both N (6 mM NaNO3) and P (0.20 mM K2HPO4) limiting conditions. The overall expression of accD was found to be upregulated in both Oscillatoria sp. (SP8) and Microcoleus sp. (SP18) for all nitrogen concentrations but was differentially regulated with both positive and negative induction under phosphorus stress conditions. Maximum induction was observed in Microcoleus sp. (SP18) at 0.20 mM K2HPO4. The obtained 3D structure of SP8 protein (21.8 kDa) showed six alpha helices, while SP18 protein (16.7 kDa) exhibited four alpha helices and four beta sheets. The phi (ϕ)/psi(ψ) angles of the amino acid residues observed in Ramachandran plot analysis showed that both SP8 and SP18 proteins were highly stable with more than 90% amino acids in allowed regions. The molecular dynamics simulation results also indicated the stability of ligand-bound protein complexes.

CONCLUSION

It has been demonstrated that cyanobacterial isolates are affected differently by nutrient limitation leading to variation in their lipid productivity. The same has been revealed by the behavior of accD gene expression which was regulated more by nutrients concentrations rather than the organism. However, the ligand-bound protein complexes were stable throughout MD simulations.

Homology-Based Modeling of Universal Stress Protein from Listeria innocua Up-Regulated under Acid Stress Conditions

An Universal Stress Protein (USP) expressed under acid stress condition by Listeria innocua ATCC 33090 was investigated. The USP was up-regulated not only in the stationary phase but also during the exponential growth phase. The three dimensional (3D) structure of USP was predicted using a combined proteomic and bioinformatics approach. Phylogenetic analysis showed that the USP from Listeria detected in our study was distant from the USPs of other bacteria (such as Pseudomonas spp., Escherichia coli, Salmonella spp.) and clustered in a separate and heterogeneous class including several USPs from Listeria spp. and Lactobacillus spp. An important information on the studied USP was obtained from the 3D-structure established through the homology modeling procedure. In detail, the Model_USP-691 suggested that the investigated USP had a homo-tetrameric quaternary structure. Each monomer presented an architecture analogous to the Rossmann-like α/β-fold with five parallel β-strands, and four α-helices. The analysis of monomer-monomer interfaces and quality of the structure alignments confirmed the model reliability. In fact, the structurally and sequentially conserved hydrophobic residues of the β-strand 5 (in particular the residues V¹⁴⁶ and V¹⁴⁸) were involved in the inter-chains contact. Moreover, the highly conserved residues I¹³⁹ and H¹⁴¹ in the region α4 were involved in the dimer association and functioned as hot spots into monomer–monomer interface assembly. The hypothetical assembly of dimers was also supported by the large interface area and by the negative value of solvation free energy gain upon interface interaction. Finally, the structurally conserved ATP-binding motif G-2X-G-9X-G(S/T-N) suggested for a putative role of ATP in stabilizing the tetrameric assembly of the USP. Therefore, the results obtained from a multiple approach, consisting in the application of kinetic, proteomic, phylogenetic and modeling analyses, suggest that Listeria USP could be considered a new type of ATP-binding USP involved in the response to acid stress condition during the exponential growth phase.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

Comparative Protein Structure Modeling Using MODELLER

Comparative protein structure modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and how to use the ModBase database of such models, and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. © 2016 by John Wiley & Sons, Inc.

Molecular dynamics of protein A and a WW domain with a united-residue model including hydrodynamic interaction

The folding of the N-terminal part of the B-domain of staphylococcal protein A (PDB ID: 1BDD, a 46-residue three-α-helix bundle) and the formin-binding protein 28 WW domain (PDB ID: 1E0L, a 37-residue three-stranded anti-parallel β protein) was studied by means of Langevin dynamics with the coarse-grained UNRES force field to assess the influence of hydrodynamic interactions on protein-folding pathways and kinetics. The unfolded, intermediate, and native-like structures were identified by cluster analysis, and multi-exponential functions were fitted to the time dependence of the fractions of native and intermediate structures, respectively, to determine bulk kinetics. It was found that introducing hydrodynamic interactions slows down both the formation of an intermediate state and the transition from the collapsed structures to the final native-like structures by creating multiple kinetic traps. Therefore, introducing hydrodynamic interactions considerably slows the folding, as opposed to the results obtained from earlier studies with the use of Gō-like models.

Conformational thermodynamics guided structural reconstruction of biomolecular fragments

Conformational thermodynamics compares the modeling protocols to identify the conformation of the missing region leading to a suitable model for metal ion free (apo) skeletal muscle Troponin C.

General overview on structure prediction of twilight-zone proteins

Protein structure prediction from amino acid sequence has been one of the most challenging aspects in computational structural biology despite significant progress in recent years showed by critical assessment of protein structure prediction (CASP) experiments. When experimentally determined structures are unavailable, the predictive structures may serve as starting points to study a protein. If the target protein consists of homologous region, high-resolution (typically <1.5 Å) model can be built via comparative modelling. However, when confronted with low sequence similarity of the target protein (also known as twilight-zone protein, sequence identity with available templates is less than 30%), the protein structure prediction has to be initiated from scratch. Traditionally, twilight-zone proteins can be predicted via threading or ab initio method. Based on the current trend, combination of different methods brings an improved success in the prediction of twilight-zone proteins. In this mini review, the methods, progresses and challenges for the prediction of twilight-zone proteins were discussed.

Computational tools for epitope vaccine design and evaluation

Rational approaches will be required to develop universal vaccines for viral pathogens such as human immunodeficiency virus, hepatitis C virus, and influenza, for which empirical approaches have failed. The main objective of a rational vaccine strategy is to design novel immunogens that are capable of inducing long-term protective immunity. In practice, this requires structure-based engineering of the target neutralizing epitopes and a quantitative readout of vaccine-induced immune responses. Therefore, computational tools that can facilitate these two areas have played increasingly important roles in rational vaccine design in recent years. Here we review the computational techniques developed for protein structure prediction and antibody repertoire analysis, and demonstrate how they can be applied to the design and evaluation of epitope vaccines.

Template-based prediction of protein function

We discuss recent approaches for structure-based protein function annotation. We focus on template-based methods where the function of a query protein is deduced from that of a template for which both the structure and function are known. We describe the different ways of identifying a template. These are typically based on sequence analysis but new methods based on purely structural similarity are also being developed that allow function annotation based on structural relationships that cannot be recognized by sequence. The growing number of available structures of known function, improved homology modeling techniques and new developments in the use of structure allow template-based methods to be applied on a proteome-wide scale and in many different biological contexts. This progress significantly expands the range of applicability of structural information in function annotation to a level that previously was only achievable by sequence comparison.

Comparative Protein Structure Modeling Using MODELLER

Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described.

3D representations of amino acids-applications to protein sequence comparison and classification

The amino acid sequence of a protein is the key to understanding its structure and ultimately its function in the cell. This paper addresses the fundamental issue of encoding amino acids in ways that the representation of such a protein sequence facilitates the decoding of its information content. We show that a feature-based representation in a three-dimensional (3D) space derived from amino acid substitution matrices provides an adequate representation that can be used for direct comparison of protein sequences based on geometry. We measure the performance of such a representation in the context of the protein structural fold prediction problem. We compare the results of classifying different sets of proteins belonging to distinct structural folds against classifications of the same proteins obtained from sequence alone or directly from structural information. We find that sequence alone performs poorly as a structure classifier. We show in contrast that the use of the three dimensional representation of the sequences significantly improves the classification accuracy. We conclude with a discussion of the current limitations of such a representation and with a description of potential improvements.

Functional analysis of the Na+,K+/H+ antiporter PeNHX3 from the tree halophyte Populus euphratica in yeast by model-guided mutagenesis

Na+,K+/H+ antiporters are H+-coupled cotransporters that are crucial for cellular homeostasis. Populus euphratica, a well-known tree halophyte, contains six Na+/H+ antiporter genes (PeNHX1-6) that have been shown to function in salt tolerance. However, the catalytic mechanisms governing their ion transport remain largely unknown. Using the crystal structure of the Na+/H+ antiporter from the Escherichia coli (EcNhaA) as a template, we built the three-dimensional structure of PeNHX3 from P. euphratica. The PeNHX3 model displays the typical TM4-TM11 assembly that is critical for ion binding and translocation. The PeNHX3 structure follows the 'positive-inside' rule and exhibits a typical physicochemical property of the transporter proteins. Four conserved residues, including Tyr149, Asn187, Asp188, and Arg356, are indentified in the TM4-TM11 assembly region of PeNHX3. Mutagenesis analysis showed that these reserved residues were essential for the function of PeNHX3: Asn187 and Asp188 (forming a ND motif) controlled ion binding and translocation, and Tyr149 and Arg356 compensated helix dipoles in the TM4-TM11 assembly. PeNHX3 mediated Na+, K+ and Li+ transport in a yeast growth assay. Domain-switch analysis shows that TM11 is crucial to Li+ transport. The novel features of PeNHX3 in ion binding and translocation are discussed.

Molecular modeling and ligand docking for solute carrier (SLC) transporters

Solute Carrier (SLC) transporters are membrane proteins that transport solutes, such as ions, metabolites, peptides, and drugs, across biological membranes, using diverse energy coupling mechanisms. In human, there are 386 SLC transporters, many of which contribute to the absorption, distribution, metabolism, and excretion of drugs and/or can be targeted directly by therapeutics. Recent atomic structures of SLC transporters determined by X-ray crystallography and NMR spectroscopy have significantly expanded the applicability of structure-based prediction of SLC transporter ligands, by enabling both comparative modeling of additional SLC transporters and virtual screening of small molecules libraries against experimental structures as well as comparative models. In this review, we begin by describing computational tools, including sequence analysis, comparative modeling, and virtual screening, that are used to predict the structures and functions of membrane proteins such as SLC transporters. We then illustrate the applications of these tools to predicting ligand specificities of select SLC transporters, followed by experimental validation using uptake kinetic measurements and other assays. We conclude by discussing future directions in the discovery of the SLC transporter ligands.

From local structure to a global framework: recognition of protein folds

Protein folding has been a major area of research for many years. Nonetheless, the mechanisms leading to the formation of an active biological fold are still not fully apprehended. The huge amount of available sequence and structural information provides hints to identify the putative fold for a given sequence. Indeed, protein structures prefer a limited number of local backbone conformations, some being characterized by preferences for certain amino acids. These preferences largely depend on the local structural environment. The prediction of local backbone conformations has become an important factor to correctly identifying the global protein fold. Here, we review the developments in the field of local structure prediction and especially their implication in protein fold recognition.

Prediction of multi-type membrane proteins in human by an integrated approach

Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.

1,4-Dihydropyridine Calcium Channel Blockers: Homology Modeling of the Receptor and Assessment of Structure Activity Relationship

1,4-Dihydropyridine (DHP), an important class of calcium antagonist, inhibits the influx of extracellular Ca+2 through L-type voltage-dependent calcium channels. Three-dimensional (3D) structure of calcium channel as a receptor for 1,4-dihydropyridine is a step in understanding its mode of action. Protein structure prediction and modeling tools are becoming integral parts of the standard toolkit in biological and biomedical research. So, homology modeling (HM) of calcium channel alpha-1C subunit as DHP receptor model was achieved. The 3D structure of potassium channel was used as template for HM process. The resulted dihydropyridine receptor model was checked by different means to assure stereochemical quality and structural integrity of the model. This model was achieved in an attempt to understand the mode of action of DHP calcium channel antagonist and in further computer-aided drug design (CADD) analysis. Also the structure-activity relationship (SAR) of DHPs as antihypertensive and antianginal agents was reviewed, summarized, and discussed.

Recognition of Errors in the Refinement and Validation of Three-Dimensional Structures of AC1 Proteins of Begomovirus Strains by Using ProSA-Web

The structural model of begomovirus AC1 protein is useful for understanding biological function at molecular level and docking study. For this study we have used the ProSA program (Protein Structure Analysis) tool to establish the structure prediction and modeling of protein. This tool was used for refinement and validation of experimental protein structures. Potential problems of protein structures based on energy plots are easily seen by ProSA and are displayed in a three-dimensional manner. In the present study we have selected different AC1 proteins of begomovirus strains (YP_003288785, YP_002004579, and YP_003288773) for structural analysis and display of energy plots that highlight potential problems spotted in protein structures. The 3D models of Rep proteins with recognized errors can be effectively used for in silico docking study for development of potential ligand molecules against begomovirus infection.

In silico determination and validation of baumannii acinetobactin utilization a structure and ligand binding site

Acinetobacter baumannii is a deadly nosocomial pathogen. Iron is an essential element for the pathogen. Under iron-restricted conditions, the bacterium expresses iron-regulated outer membrane proteins (IROMPs). Baumannii acinetobactin utilization (BauA) is the most important member of IROMPs in A. baumannii. Determination of its tertiary structure could help deduction of its functions and its interactions with ligands. The present study unveils BauA 3D structure via in silico approaches. Apart from ab initio, other rational methods such as homology modeling and threading were invoked to achieve the purpose. For homology modeling, BLAST was run on the sequence in order to find the best template. The template was then served to model the 3D structure. All the models built were evaluated qualitatively. The best model predicted by LOMETS was selected for analyses. Refinement of 3D structure as well as determination of its clefts and ligand binding sites was carried out on the structure. In contrast to the typical trimeric arrangement found in porins, BauA is monomeric. The barrel is formed by 22 antiparallel transmembrane β -strands. There are short periplasmic turns and longer surface-located loops. An N-terminal domain referred to either as the cork, the plug, or the hatch domain occludes the β -barrel.

Protein structural model selection by combining consensus and single scoring methods

Quality assessment (QA) for predicted protein structural models is an important and challenging research problem in protein structure prediction. Consensus Global Distance Test (CGDT) methods assess each decoy (predicted structural model) based on its structural similarity to all others in a decoy set and has been proved to work well when good decoys are in a majority cluster. Scoring functions evaluate each single decoy based on its structural properties. Both methods have their merits and limitations. In this paper, we present a novel method called PWCom, which consists of two neural networks sequentially to combine CGDT and single model scoring methods such as RW, DDFire and OPUS-Ca. Specifically, for every pair of decoys, the difference of the corresponding feature vectors is input to the first neural network which enables one to predict whether the decoy-pair are significantly different in terms of their GDT scores to the native. If yes, the second neural network is used to decide which one of the two is closer to the native structure. The quality score for each decoy in the pool is based on the number of winning times during the pairwise comparisons. Test results on three benchmark datasets from different model generation methods showed that PWCom significantly improves over consensus GDT and single scoring methods. The QA server (MUFOLD-Server) applying this method in CASP 10 QA category was ranked the second place in terms of Pearson and Spearman correlation performance.

New design strategies for antidepressant drugs

INTRODUCTION

In spite of research efforts spanning six decades, the most prominent antidepressant drugs to date still carry several adverse effects, often serious enough to warrant discontinuation of the drug. Molecular mechanisms of depression are now better understood such that some of the specific receptors responsible can be targeted for activation or inhibition. This advance, coupled with the recent availability of crystal structures of relevant drug targets or their homologs, has opened the door for new antidepressant therapeutic compounds.

AREAS COVERED

The authors review the evolution of monoamine-based antidepressant drugs, up to the selective serotonin reuptake inhibitors (SSRIs). The authors discuss classic and contemporary antidepressant drug design strategies, with a focus on virtual screening and fragment-based drug design methods. Furthermore, they discuss the recent advancements in the understanding of the serotonin transporter (SERT) structure/function relationship in the context of recognition of SSRIs and outline a strategy for the use of computational approaches in producing new SSRI lead compounds.

EXPERT OPINION

The authors suggest that given the long-awaited availability of credible three-dimensional structures for the SERT and related monoamine transporter proteins, cutting-edge computational methods should be the linchpin of future drug discovery efforts regarding monoamine-based antidepressant lead compounds. Because these transporter inhibitors cause a ubiquitous increase in extraneuronal neurotransmitter levels leading to side and adverse therapeutic effects, the drug discovery should extend to appropriate manipulation of the 'downstream' receptors affected by the neurotransmitter boost. Efficient use of new computational strategies will accelerate the drug discovery process and reduce its economic burden.

Insights from the molecular characterization of mercury stress proteins identified by proteomics in E.coli nissle 1917

Differently expressed proteins in probiotic Escherichia coli nissle 1917 under mercury stress identified by using a proteomic approach. We applied to separate proteins by using two-dimensional gel electrophoresis and proteins were identified using MALDI-TOF-MS using PMF, by mascot database search using the NCBI database. we identified six proteins after exposure to mercury stress with respect to different functional classes. It is useful to understand the molecular insights into mercury stress in probiotic E. coli. Next we describe a structure generated by homology modelling and functional domain identification; it is interesting to study the impact of stress on protein structures. MS characterization and computational methods together provide the opportunity to examine the impact of stress arising from mercury. The role of these proteins in metal tolerance and structure relation is discussed. To the best of our knowledge, proteomics of E. coli nissle 1917 overview of mercury stress has been reported for the first time.