Protein structure prediction: recognition of primary, secondary, and tertiary structural features from amino acid sequence

Multiple Protein Profiler 1.0 (MPP): A Webserver for Predicting and Visualizing Physiochemical Properties of Proteins at the Proteome Level

Determining the physicochemical properties of a protein can reveal important insights in their structure, biological functions, stability, and interactions with other molecules. Although tools for computing properties of proteins already existed, we could not find a comprehensive tool that enables the calculations of multiple properties for multiple input proteins on the proteome level at once. Facing this limitation, we developed Multiple Protein Profiler (MPP) 1.0 as an integrated tool that allows the profiling of 12 individual properties of multiple proteins in a significant manner. MPP provides a tabular and graphic visualization of properties of multiple proteins. The tool is freely accessible at https://mproteinprofiler.microbiologyandimmunology.dal.ca/ .

Non-canonical amino acid bioincorporation into antimicrobial peptides and its challenges

The rise of antimicrobial resistance and multi-drug resistant pathogens has necessitated explorations for novel antibiotic agents as the discovery of conventional antibiotics is becoming economically less viable and technically more challenging for biopharma. Antimicrobial peptides (AMPs) have emerged as a promising alternative because of their particular mode of action, broad spectrum and difficulty that microbes have in becoming resistant to them. The AMPs bacitracin, gramicidin, polymyxins and daptomycin are currently used clinically. However, their susceptibility to proteolytic degradation, toxicity profile, and complexities in large-scale manufacture have hindered their development. To improve their proteolytic stability, methods such as integrating non-canonical amino acids (ncAAs) into their peptide sequence have been adopted, which also improves their potency and spectrum of action. The benefits of ncAA incorporation have been made possible by solid-phase peptide synthesis. However, this method is not always suitable for commercial production of AMPs because of poor yield, scale-up difficulties, and its non-'green' nature. Bioincorporation of ncAA as a method of integration is an emerging field geared towards tackling the challenges of solid-phase synthesis as a green, cheaper, and scalable alternative for commercialisation of AMPs. This review focusses on the bioincorporation of ncAAs; some challenges associated with the methods are outlined, and notes are given on how to overcome these challenges. The review focusses particularly on addressing two key challenges: AMP cytotoxicity towards microbial cell factories and the uptake of ncAAs that are unfavourable to them. Overcoming these challenges will draw us closer to a greater yield and an environmentally friendly and sustainable approach to make AMPs more druggable.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Imprinting of nanoparticles in thin films: Quo Vadis?

Nanomaterials, and especially nanoparticles, have been introduced to almost any aspect of our lives. This has caused increasing concern as to their toxicity and adverse effects on the environment and human health. The activity of nanoparticles, including their nanotoxicity, is not only a function of the material they are made of but also their size, shape, and surface properties. It is evident that there is an unmet need for simple approaches to the speciation of nanoparticles, namely to monitor and detect them based on their properties. An appealing method for such speciation involves the imprinting of nanoparticles in soft matrices. The principles of imprinting nanoparticles originate from the molecularly imprinted polymer (MIP) approach. This review summarizes the current status of this emerging field, which bridges between the traditional MIP approach and the imprinting of larger entities such as viruses and bacteria. The concepts of nanoparticle imprinting and the requirement of both physical and chemical matching between the nanoparticles and the matrix are discussed and demonstrated.

Shock-Induced Damage Mechanism of Perineuronal Nets

The perineuronal net (PNN) region of the brain’s extracellular matrix (ECM) surrounds the neural networks within the brain tissue. The PNN is a protective net-like structure regulating neuronal activity such as neurotransmission, charge balance, and action potential generation. Shock-induced damage of this essential component may lead to neuronal cell death and neurodegenerations. The shock generated during a vehicle accident, fall, or improvised device explosion may produce sufficient energy to damage the structure of the PNN. The goal is to investigate the mechanics of the PNN in reaction to shock loading and to understand the mechanical properties of different PNN components such as glycan, GAG, and protein. In this study, we evaluated the mechanical strength of PNN molecules and the interfacial strength between the PNN components. Afterward, we assessed the PNN molecules’ damage efficiency under various conditions such as shock speed, preexisting bubble, and boundary conditions. The secondary structure altercation of the protein molecules of the PNN was analyzed to evaluate damage intensity under varying shock speeds. At a higher shock speed, damage intensity is more elevated, and hyaluronan (glycan molecule) is most likely to break at the rigid junction. The primary structure of the protein molecules is least likely to fail. Instead, the molecules’ secondary bonds will be altered. Our study suggests that the number of hydrogen bonds during the shock wave propagation is reduced, which leads to the change in protein conformations and damage within the PNN structure. As such, we found a direct connection between shock wave intensity and PNN damage.

Protein Assembly by Design

Proteins are nature's primary building blocks for the construction of sophisticated molecular machines and dynamic materials, ranging from protein complexes such as photosystem II and nitrogenase that drive biogeochemical cycles to cytoskeletal assemblies and muscle fibers for motion. Such natural systems have inspired extensive efforts in the rational design of artificial protein assemblies in the last two decades. As molecular building blocks, proteins are highly complex, in terms of both their three-dimensional structures and chemical compositions. To enable control over the self-assembly of such complex molecules, scientists have devised many creative strategies by combining tools and principles of experimental and computational biophysics, supramolecular chemistry, inorganic chemistry, materials science, and polymer chemistry, among others. Owing to these innovative strategies, what started as a purely structure-building exercise two decades ago has, in short order, led to artificial protein assemblies with unprecedented structures and functions and protein-based materials with unusual properties. Our goal in this review is to give an overview of this exciting and highly interdisciplinary area of research, first outlining the design strategies and tools that have been devised for controlling protein self-assembly, then describing the diverse structures of artificial protein assemblies, and finally highlighting the emergent properties and functions of these assemblies.

Characterization of ribonucleotide reductases of emerging pathogens Elizabethkingia anophelis and Elizabethkingia meningoseptica and streptonigrin as their inhibitor: a computational study

Antibiotic resistance is a global concern. Two members of the bacterial genus Elizabethkingia, namely, E. anophelis and E. meningoseptica have raised much concern in recent years because of their resistance to multiple commonly used antibiotics. Identification of multidrug resistant and pan-drug resistant bacteria has propelled the search for new antibiotics that can act on unconventional targets. Researches are going on to find out the possibility of using bacterial ribonucleotide reductases as a novel target for antibiotic development. Through in silico evaluations, this study aims for characterization and functional annotation of ribonucleotide reductase enzymes of E. anophelis and E. meningoseptica. Binding affinities with these enzymes of the compounds that have shown promising results in inhibiting Pseudomonas aeruginosa growth by acting on its ribonucleotide reductase were also assessed by molecular docking and dynamics simulations. Insights from this study will help in battling these infections in the near future. Communicated by Ramaswamy H. Sarma.

Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2 KCNH2 (hERG) Mutations and Identifying New Patients

Significant advances in our understanding of the molecular mechanisms that cause congenital long QT syndrome (LQTS) have been made. A wide variety of experimental approaches, including heterologous expression of mutant ion channel proteins and the use of inducible pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) from LQTS patients offer insights into etiology and new therapeutic strategies. This review briefly discusses the major molecular mechanisms underlying LQTS type 2 (LQT2), which is caused by loss-of-function (LOF) mutations in the KCNH2 gene (also known as the human ether-à-go-go-related gene or hERG). Almost half of suspected LQT2-causing mutations are missense mutations, and functional studies suggest that about 90% of these mutations disrupt the intracellular transport, or trafficking, of the KCNH2-encoded Kv11.1 channel protein to the cell surface membrane. In this review, we discuss emerging strategies that improve the trafficking and functional expression of trafficking-deficient LQT2 Kv11.1 channel proteins to the cell surface membrane and how new insights into the structure of the Kv11.1 channel protein will lead to computational approaches that identify which KCNH2 missense variants confer a high-risk for LQT2.

Long QT Syndrome Type 2: Emerging Strategies for Correcting Class 2 KCNH2 (hERG) Mutations and Identifying New Patients

Significant advances in our understanding of the molecular mechanisms that cause congenital long QT syndrome (LQTS) have been made. A wide variety of experimental approaches, including heterologous expression of mutant ion channel proteins and the use of inducible pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) from LQTS patients offer insights into etiology and new therapeutic strategies. This review briefly discusses the major molecular mechanisms underlying LQTS type 2 (LQT2), which is caused by loss-of-function (LOF) mutations in the KCNH2 gene (also known as the human ether-à-go-go-related gene or hERG). Almost half of suspected LQT2-causing mutations are missense mutations, and functional studies suggest that about 90% of these mutations disrupt the intracellular transport, or trafficking, of the KCNH2-encoded Kv11.1 channel protein to the cell surface membrane. In this review, we discuss emerging strategies that improve the trafficking and functional expression of trafficking-deficient LQT2 Kv11.1 channel proteins to the cell surface membrane and how new insights into the structure of the Kv11.1 channel protein will lead to computational approaches that identify which KCNH2 missense variants confer a high-risk for LQT2.

Recent advances: peptides and self-assembled peptide-nanosystems for antimicrobial therapy and diagnosis

Bacterial infections, especially the refractory treatment of drug-resistant bacteria, are one of the greatest threats to human health. Self-assembling peptide-based strategies can specifically detect the bacteria at the site of infection in the body and kill it.

Folded alkane chains and the emergence of the lamellar crystal

The competition between chain stiffness and chain collapse gives rise to complex low temperature morphologies of single polymer chains, in our case alkanes. These structures are characterized by specific sequences of dihedral angles along the chain, i.e., dihedral angle correlations extending beyond local steric effects. To describe and classify these morphologies, one can transfer concepts from protein science, where this creation of dihedral angle correlations underlies the formation of α-helices and β-sheets. We show here by means of flat-histogram Monte Carlo simulations that, although lacking in primary structure being simple homopolymers, short alkane chains fold into non-trivial ground states (tertiary structure) consisting of chain segments of defined secondary structures. The folded lamellar crystal typical for polyethylene chains requires a minimum chain length to occur as the ground state folded structure, which we identify to be around 150 repeat units.

Global computational mutagenesis provides a critical stability framework in protein structures

A protein’s amino acid sequence dictates the folds and final structure the macromolecule will form. We propose that by identifying critical residues in a protein’s atomic structure, we can select a critical stability framework within the protein structure essential to proper protein folding. We use global computational mutagenesis based on the unfolding mutation screen to test the effect of every possible missense mutation on the protein structure to identify the residues that cannot tolerate a substitution without causing protein misfolding. This method was tested using molecular dynamics to simulate the stability effects of mutating critical residues in proteins involved in inherited disease, such as myoglobin, p53, and the 15^th sushi domain of complement factor H. In addition we prove that when the critical residues are in place, other residues may be changed within the structure without a stability loss. We validate that critical residues are conserved using myoglobin to show that critical residues are the same for crystal structures of 6 different species and comparing conservation indices to critical residues in 9 eye disease-related proteins. Our studies demonstrate that by using a selection of critical elements in a protein structure we can identify a critical protein stability framework. The frame of critical residues can be used in genetic engineering to improve small molecule binding for drug studies, identify loss-of-function disease-causing missense mutations in genetics studies, and aide in identifying templates for homology modeling.

Functional details of the Mycobacterium tuberculosis VapBC26 toxin-antitoxin system based on a structural study: insights into unique binding and antibiotic peptides

Toxin-antitoxin (TA) systems are essential for bacterial persistence under stressful conditions. In particular, Mycobacterium tuberculosis express VapBC TA genes that encode the stable VapC toxin and the labile VapB antitoxin. Under normal conditions, these proteins interact to form a non-toxic TA complex, but the toxin is activated by release from the antitoxin in response to unfavorable conditions. Here, we present the crystal structure of the M. tuberculosis VapBC26 complex and show that the VapC26 toxin contains a pilus retraction protein (PilT) N-terminal (PIN) domain that is essential for ribonuclease activity and that, the VapB26 antitoxin folds into a ribbon-helix-helix DNA-binding motif at the N-terminus. The active site of VapC26 is sterically blocked by the flexible C-terminal region of VapB26. The C-terminal region of free VapB26 adopts an unfolded conformation but forms a helix upon binding to VapC26. The results of RNase activity assays show that Mg²⁺ and Mn²⁺ are essential for the ribonuclease activity of VapC26. As shown in the nuclear magnetic resonance spectra, several residues of VapB26 participate in the specific binding to the promoter region of the VapBC26 operon. In addition, toxin-mimicking peptides were designed that inhibit TA complex formation and thereby increase toxin activity, providing a novel approach to the development of new antibiotics.

Dataset of eye disease-related proteins analyzed using the unfolding mutation screen

A number of genetic diseases are a result of missense mutations in protein structure. These mutations can lead to severe protein destabilization and misfolding. The unfolding mutation screen (UMS) is a computational method that calculates unfolding propensities for every possible missense mutation in a protein structure. The UMS validation demonstrated a good agreement with experimental and phenotypical data. 15 protein structures (a combination of homology models and crystal structures) were analyzed using UMS. The standard and clustered heat maps, and patterned protein structure from the analysis were stored in a UMS library. The library is currently composed of 15 protein structures from 14 inherited eye diseases including retina degenerations, glaucoma, and cataracts, and contains data for 181,110 mutations. The UMS protein library introduces 13 new human models of eye disease related proteins and is the first collection of the consistently calculated unfolding propensities, which could be used as a tool for the express analysis of novel mutations in clinical practice, next generation sequencing, and genotype-to-phenotype relationships in inherited eye disease.

Basic units of protein structure, folding, and function

Study of the hierarchy of domain structure with alternative sets of domains and analysis of discontinuous domains, consisting of remote segments of the polypeptide chain, raised a question about the minimal structural unit of the protein domain. The hypothesis on the decisive role of the polypeptide backbone in determining the elementary units of globular proteins have led to the discovery of closed loops. It is reviewed here how closed loops form the loop-n-lock structure of proteins, providing the foundation for stability and designability of protein folds/domain and underlying their co-translational folding. Simplified protein sequences are considered here with the aim to explore the basic principles that presumably dominated the folding and stability of proteins in the early stages of structural evolution. Elementary functional loops (EFLs), closed loops with one or few catalytic residues, are, in turn, units of the protein function. They are apparent descendants of the prebiotic ring-like peptides, which gave rise to the first functional folds/domains being fused in the beginning of the evolution of protein structure. It is also shown how evolutionary relations between protein functional superfamilies and folds delineated with the help of EFLs can contribute to establishing the rules for design of desired enzymatic functions. Generalized descriptors of the elementary functions are proposed to be used as basic units in the future computational design.

A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling

Generating tertiary structural models for a target protein from the known structure of its homologous template proteins and their pairwise sequence alignment is a key step in protein comparative modeling. Here, we developed a new stochastic point cloud sampling method, called MTMG, for multi-template protein model generation. The method first superposes the backbones of template structures, and the Cα atoms of the superposed templates form a point cloud for each position of a target protein, which are represented by a three-dimensional multivariate normal distribution. MTMG stochastically resamples the positions for Cα atoms of the residues whose positions are uncertain from the distribution, and accepts or rejects new position according to a simulated annealing protocol, which effectively removes atomic clashes commonly encountered in multi-template comparative modeling. We benchmarked MTMG on 1,033 sequence alignments generated for CASP9, CASP10 and CASP11 targets, respectively. Using multiple templates with MTMG improves the GDT-TS score and TM-score of structural models by 2.96–6.37% and 2.42–5.19% on the three datasets over using single templates. MTMG’s performance was comparable to Modeller in terms of GDT-TS score, TM-score, and GDT-HA score, while the average RMSD was improved by a new sampling approach. The MTMG software is freely available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/mtmg.html.

An Improved Integration of Template-Based and Template-Free Protein Structure Modeling Methods and its Assessment in CASP11

Most computational protein structure prediction methods are designed for either template based or template-free (ab initio) structure prediction. The approaches that integrate the prediction capabilities of both template-based modeling and template-free modeling are needed to synergistically combine the two kinds of methods to improve protein structure prediction. In this work, we develop a new method to integrate several protein structure prediction methods including our template-based MULTICOM server, our ab initio contact-based protein structure prediction method CONFOLD, our multi-template-based model generation tool MTMG, and locally installed external Rosetta, I-TASSER and RaptorX protein structure prediction tools to improve protein structure prediction of a fullspectrum difficulty ranging from easy, to medium and to hard. Our method participated in the 11(th) community-wide Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014 as MULTICOM-NOVEL server. It was ranked among top 10 methods for protein tertiary structure prediction according to the official CASP11 assessment, which demonstrates that integrating complementary modeling methods is useful for advancing protein structure prediction.

Large-scale model quality assessment for improving protein tertiary structure prediction

Motivation: Sampling structural models and ranking them are the two major challenges of protein structure prediction. Traditional protein structure prediction methods generally use one or a few quality assessment (QA) methods to select the best-predicted models, which cannot consistently select relatively better models and rank a large number of models well.

Results: Here, we develop a novel large-scale model QA method in conjunction with model clustering to rank and select protein structural models. It unprecedentedly applied 14 model QA methods to generate consensus model rankings, followed by model refinement based on model combination (i.e. averaging). Our experiment demonstrates that the large-scale model QA approach is more consistent and robust in selecting models of better quality than any individual QA method. Our method was blindly tested during the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM group. It was officially ranked third out of all 143 human and server predictors according to the total scores of the first models predicted for 78 CASP11 protein domains and second according to the total scores of the best of the five models predicted for these domains. MULTICOM’s outstanding performance in the extremely competitive 2014 CASP11 experiment proves that our large-scale QA approach together with model clustering is a promising solution to one of the two major problems in protein structure modeling.

Availability and implementation: The web server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/human/.

Contact: chengji@missouri.edu

A large-scale conformation sampling and evaluation server for protein tertiary structure prediction and its assessment in CASP11

Background

With more and more protein sequences produced in the genomic era, predicting protein structures from sequences becomes very important for elucidating the molecular details and functions of these proteins for biomedical research. Traditional template-based protein structure prediction methods tend to focus on identifying the best templates, generating the best alignments, and applying the best energy function to rank models, which often cannot achieve the best performance because of the difficulty of obtaining best templates, alignments, and models.

Methods

We developed a large-scale conformation sampling and evaluation method and its servers to improve the reliability and robustness of protein structure prediction. In the first step, our method used a variety of alignment methods to sample relevant and complementary templates and to generate alternative and diverse target-template alignments, used a template and alignment combination protocol to combine alignments, and used template-based and template-free modeling methods to generate a pool of conformations for a target protein. In the second step, it used a large number of protein model quality assessment methods to evaluate and rank the models in the protein model pool, in conjunction with an exception handling strategy to deal with any additional failure in model ranking.

Results

The method was implemented as two protein structure prediction servers: MULTICOM-CONSTRUCT and MULTICOM-CLUSTER that participated in the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) in 2014. The two servers were ranked among the best 10 server predictors.

Conclusions

The good performance of our servers in CASP11 demonstrates the effectiveness and robustness of the large-scale conformation sampling and evaluation. The MULTICOM server is available at: http://sysbio.rnet.missouri.edu/multicom_cluster/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0775-x) contains supplementary material, which is available to authorized users.

Massive integration of diverse protein quality assessment methods to improve template based modeling in CASP11

Model evaluation and selection is an important step and a big challenge in template-based protein structure prediction. Individual model quality assessment methods designed for recognizing some specific properties of protein structures often fail to consistently select good models from a model pool because of their limitations. Therefore, combining multiple complimentary quality assessment methods is useful for improving model ranking and consequently tertiary structure prediction. Here, we report the performance and analysis of our human tertiary structure predictor (MULTICOM) based on the massive integration of 14 diverse complementary quality assessment methods that was successfully benchmarked in the 11th Critical Assessment of Techniques of Protein Structure prediction (CASP11). The predictions of MULTICOM for 39 template-based domains were rigorously assessed by six scoring metrics covering global topology of Cα trace, local all-atom fitness, side chain quality, and physical reasonableness of the model. The results show that the massive integration of complementary, diverse single-model and multi-model quality assessment methods can effectively leverage the strength of single-model methods in distinguishing quality variation among similar good models and the advantage of multi-model quality assessment methods of identifying reasonable average-quality models. The overall excellent performance of the MULTICOM predictor demonstrates that integrating a large number of model quality assessment methods in conjunction with model clustering is a useful approach to improve the accuracy, diversity, and consequently robustness of template-based protein structure prediction. Proteins 2016; 84(Suppl 1):247-259. © 2015 Wiley Periodicals, Inc.

Organization of the multiaminoacyl-tRNA synthetase complex and the cotranslational protein folding

Aminoacyl-tRNA synthetases (ARSs) play an essential role in the protein synthesis by catalyzing an attachment of their cognate amino acids to tRNAs. Unlike their prokaryotic counterparts, ARSs in higher eukaryotes form a multiaminoacyl-tRNA synthetase complex (MARS), consisting of the subset of ARS polypeptides and three auxiliary proteins. The intriguing feature of MARS complex is the presence of only nine out of twenty ARSs, specific for Arg, Asp, Gln, Glu, Ile, Leu, Lys, Met, and Pro, regardless of the organism, cell, or tissue types. Although existence of MARSs complex in higher eukaryotes has been already known for more than four decades, its functional significance remains elusive. We found that seven of the nine corresponding amino acids (Arg, Gln, Glu, Ile, Leu, Lys, and Met) together with Ala form a predictor of the protein α-helicity. Remarkably, all amino acids (besides Ala) in the predictor have the highest possible number of side-chain rotamers. Therefore, compositional bias of a typical α-helix can contribute to the helix's stability by increasing the entropy of the folded state. It also appears that position-specific α-helical propensity, specifically periodic alternation of charged and hydrophobic residues in the helices, may well be provided by the structural organization of the complex. Considering characteristics of MARS complex from the perspective of the α-helicity, we hypothesize that specific composition and structure of the complex represents a functional mechanism for coordination of translation with the fast and correct folding of amphiphilic α-helices.

Design of nanostructures based on aromatic peptide amphiphiles

Aromatic peptide amphiphiles are gaining popularity as building blocks for the bottom-up fabrication of nanomaterials, including gels. These materials combine the simplicity of small molecules with the versatility of peptides, with a range of applications proposed in biomedicine, nanotechnology, food science, cosmetics, etc. Despite their simplicity, a wide range of self-assembly behaviours have been described. Due to varying conditions and protocols used, care should be taken when attempting to directly compare results from the literature. In this review, we rationalise the structural features which govern the self-assembly of aromatic peptide amphiphiles by focusing on four segments, (i) the N-terminal aromatic component, (ii) linker segment, (iii) peptide sequence, and (iv) C-terminus. It is clear that the molecular structure of these components significantly influences the self-assembly process and resultant supramolecular architectures. A number of modes of assembly have been proposed, including parallel, antiparallel, and interlocked antiparallel stacking conformations. In addition, the co-assembly arrangements of aromatic peptide amphiphiles are reviewed. Overall, this review elucidates the structural trends and design rules that underpin the field of aromatic peptide amphiphile assembly, paving the way to a more rational design of nanomaterials based on aromatic peptide amphiphiles.

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

BACKGROUND

Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models.

RESULTS

MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality.

CONCLUSIONS

Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy.

The addressing fragment of mitogaligin: first insights into functional and structural properties

Mitogaligin is a mitochondrion-targeting protein involved in cell death. The sequence of the protein is unrelated to that of any known pro- or antiapoptotic protein. Mitochondrial targeting is controlled by an internal sequence from residues 31 to 53, and although this sequence is essential and sufficient to provoke cell death, the precise mechanism of action at the mitochondrial membrane remains to be elucidated. Here, by focusing on the [31-53] fragment, we first assessed and confirmed its cell cytotoxicity by microinjection. Subsequently, with the aid of membrane models, we evaluated the impact of the membrane environment on the 3D structure of the peptide and on how the peptide is embedded and oriented within membranes. The fragment is well organized, even though it does not contain a canonical secondary structure, and adopts an interfacial location. Structural comparison with other membrane-interacting Trp-rich peptides demonstrated similarities with the antimicrobial peptide tritrpcidin.

Designing and benchmarking the MULTICOM protein structure prediction system

Background

Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor.

Results

Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction.

Conclusions

Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Characterising microbial protein test substances and establishing their equivalence with plant-produced proteins for use in risk assessments of transgenic crops

Most commercial transgenic crops are genetically engineered to produce new proteins. Studies to assess the risks to human and animal health, and to the environment, from the use of these crops require grams of the transgenic proteins. It is often extremely difficult to produce sufficient purified transgenic protein from the crop. Nevertheless, ample protein of acceptable purity may be produced by over-expressing the protein in microbes such as Escherichia coli. When using microbial proteins in a study for risk assessment, it is essential that their suitability as surrogates for the plant-produced transgenic proteins is established; that is, the proteins are equivalent for the purposes of the study. Equivalence does not imply that the plant and microbial proteins are identical, but that the microbial protein is sufficiently similar biochemically and functionally to the plant protein such that studies using the microbial protein provide reliable information for risk assessment of the transgenic crop. Equivalence is a judgement based on a weight of evidence from comparisons of relevant properties of the microbial and plant proteins, including activity, molecular weight, amino acid sequence, glycosylation and immuno-reactivity. We describe a typical set of methods used to compare proteins in regulatory risk assessments for transgenic crops, and discuss how risk assessors may use comparisons of proteins to judge equivalence.

MINING OVERREPRESENTED 3D PATTERNS OF SECONDARY STRUCTURES IN PROTEINS

We consider the problem of finding overrepresented arrangements of secondary structure elements (SSEs) in a given dataset of representative protein structures. While most papers in the literature study the distribution of geometrical properties, in particular angles and distances, between pairs of interacting SSEs, in this paper we focus on the distribution of angles of all quartets of SSEs and on the extraction of overrepresented angular patterns. We propose a variant of the Apriori method that obtains overrepresented arrangements of quartets of SSEs by combining arrangements of triplets of SSEs. This specific case will pose the basis for a natural extension of the problem to any given number of SSEs. We analyze the results of our method on a dataset of 300 nonredundant proteins. Supplementary material is available at .

The influence of the local sequence environment on RNA loop structures

RNA folding is assumed to be a hierarchical process. The secondary structure of an RNA molecule, signified by base-pairing and stacking interactions between the paired bases, is formed first. Subsequently, the RNA molecule adopts an energetically favorable three-dimensional conformation in the structural space determined mainly by the rotational degrees of freedom associated with the backbone of regions of unpaired nucleotides (loops). To what extent the backbone conformation of RNA loops also results from interactions within the local sequence context or rather follows global optimization constraints alone has not been addressed yet. Because the majority of base stacking interactions are exerted locally, a critical influence of local sequence on local structure appears plausible. Thus, local loop structure ought to be predictable, at least in part, from the local sequence context alone. To test this hypothesis, we used Random Forests on a nonredundant data set of unpaired nucleotides extracted from 97 X-ray structures from the Protein Data Bank (PDB) to predict discrete backbone angle conformations given by the discretized η/θ-pseudo-torsional space. Predictions on balanced sets with four to six conformational classes using local sequence information yielded average accuracies of up to 55%, thus significantly better than expected by chance (17%-25%). Bases close to the central nucleotide appear to be most tightly linked to its conformation. Our results suggest that RNA loop structure does not only depend on long-range base-pairing interactions; instead, it appears that local sequence context exerts a significant influence on the formation of the local loop structure.

Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison

Pathway-based information has become an important source of information for both establishing evolutionary relationships and understanding the mode of action of a chemical or pharmaceutical among species. Cross-species comparison of pathways can address two broad questions: comparison in order to inform evolutionary relationships and to extrapolate species differences used in a number of different applications including drug and toxicity testing. Cross-species comparison of metabolic pathways is complex as there are multiple features of a pathway that can be modeled and compared. Among the various methods that have been proposed, reaction alignment has emerged as the most successful at predicting phylogenetic relationships based on NCBI taxonomy. We propose an improvement of the reaction alignment method by accounting for sequence similarity in addition to reaction alignment method. Using nine species, including human and some model organisms and test species, we evaluate the standard and improved comparison methods by analyzing glycolysis and citrate cycle pathways conservation. In addition, we demonstrate how organism comparison can be conducted by accounting for the cumulative information retrieved from nine pathways in central metabolism as well as a more complete study involving 36 pathways common in all nine species. Our results indicate that reaction alignment with enzyme sequence similarity results in a more accurate representation of pathway specific cross-species similarities and differences based on NCBI taxonomy.

Exploring conformational space using a mean field technique with MOLS sampling

The computational identification of all the low energy structures of a peptide given only its sequence is not an easy task even for small peptides,due to the multiple-minima problem and combinatorial explosion. We have developed an algorithm, called the MOLS technique,that addresses this problem, and have applied it to a number of different aspects of the study of peptide and protein structure. Conformational studies of oligopeptides, including loop sequences in proteins have been carried out using this technique. In general the calculations identified all the folds determined by previous studies,and in addition picked up other energetically favorable structures. The method was also used to map the energy surface of the peptides. In another application, we have combined the MOLS technique, using it to generate a library of low energy structures of an oligopeptide, with a genetic algorithm to predict protein structures. The method has also been applied to explore the conformational space of loops in protein structures.Further, it has been applied to the problem of docking a ligand in its receptor site, with encouraging results.

HOMOLOGY MODELING AND MOLECULAR DYNAMICS STUDY OF HUMAN INOSINE TRIPHOSPHATE PYROPHOSPHATASE

With homology-modeling techniques, molecular mechanics and molecular dynamics methods, a 3D structure model of the human inosine triphosphatase (ITPase; EC 3.6.1.19) is created and refined. This model is further assessed by Profile-3D and ProStat, which confirm that the refined model is reliable. With this model, a flexible docking study is performed, and the results indicate that Arg178, Lys19 and Glu44 are three important determinant residues in substrate binding because they have prominent interaction energies with ITP and form strong hydrogen bonds with ITP. In addition, we further find that the P32T substitution alters the α-helices of ITPase but the β-sheets are almost not changed, and the mutation induces the interaction energy between ITPase and ITP to increase, which are consistent with the conclusion predicted by Sumi et al.8 The results from the mutagenesis imply that Pro32 is vital for the catalytic activity.

Computational study of human phosphomannose isomerase: Insights from homology modeling and molecular dynamics simulation of enzyme bound substrate

Phosphomannose isomerase is a zinc metalloenzyme that catalyzes the reversible isomerization of mannose-6-phosphate and fructose-6-phosphate, and the three-dimensional (3D) structure of human phosphomannose isomerase has not been reported. In order to understand the catalytic mechanism, the 3D structure of the protein is built by using homology modeling based on the known crystal structure of mannose-6-phosphate isomerase from (PDB code 1PMI). The model structure is further refined by energy minimization and molecular dynamics methods. The mannose-6-phosphate-enzyme complex is developed by molecular docking and the key residues involved in the ligand binding are determined, which will facilitate the understanding of the action mode of the ligands and guide further genetic studies. Our results suggest a hydride transfer mechanism of alpha-hydrogen between the C1 and C2 positions but do not support the cis-enediol mechanism. The detailed mechanism involves, on one side, Zn2+ mediating the movement of a proton between O1 and O2, and, on the other side, the hydrophobic environment formed in part by Tyr278 promoting transfer of a hydride ion.

The pleckstrin homology domain of phospholipase Cbeta transmits enzymatic activation through modulation of the membrane-domain orientation

Phospholipase Cbeta (PLCbeta) enzymes are activated by Galpha q and Gbetagamma subunits and catalyze the hydrolysis of the minor membrane lipid phosphatidylinositol 4,5-bisphosphate [PI(4,5)P2]. Activation of PLCbeta2 by Gbetagamma subunits has been shown to be conferred through its N-terminal pleckstrin homology (PH) domain, although the underlying mechanism is unclear. Also unclear are observations that the extent of Gbetagamma activation differs on different membrane surfaces. In this study, we have identified a unique region of the PH domain of the PLCbeta2 domain (residues 71-88) which, when added to the enzyme as a peptide, causes enzyme activation similar to that with Gbetagamma subunits. This PH domain segment interacts strongly with membranes composed of lipid mixtures but not those containing lipids with electrically neutral zwitterionic headgroups. Also, addition of this segment perturbs interaction of the catalytic domain, but not the PH domain, with membrane surfaces. We monitored the orientation of the PH and catalytic domains of PLC by intermolecular fluorescence resonance energy transfer (FRET) using the Gbetagamma activatable mutant, PLCbeta2/delta1(C193S). We find an increase in the level of FRET with binding to membranes with mixed lipids but not to those containing only lipids with electrically neutral headgroups. These results suggest that enzymatic activation can be conferred through optimal association of the PHbeta71-88 region to specific membrane surfaces. These studies allow us to understand the basis of variations of Gbetagamma activation on different membrane surfaces.

Highly accurate and consistent method for prediction of helix and strand content from primary protein sequences

OBJECTIVE

One of interesting computational topics in bioinformatics is prediction of secondary structure of proteins. Over 30 years of research has been devoted to the topic but we are still far away from having reliable prediction methods. A critical piece of information for accurate prediction of secondary structure is the helix and strand content of a given protein sequence. Ability to accurately predict content of those two secondary structures has a good potential to improve accuracy of prediction of the secondary structure. Most of the existing methods use composition vector to predict the content. Their underlying assumption is that the vector can be used to provide functional mapping between primary sequence and helix/strand content. While this is true for small sets of proteins we show that for larger protein sets such mapping are inconsistent, i.e. the same composition vectors correspond to different contents. To this end, we propose a method for prediction of helix/strand content from primary protein sequences that is fundamentally different from currently available methods.

METHODS AND MATERIAL

Our method is accurate and uses a novel approach to obtain information from primary sequence based on a composition moment vector, which is a measure that includes information about both composition of a given primary sequence and the position of amino acids in the sequence. In contrast to the composition vector, we show that it provides functional mapping between primary sequence and the helix/strand content.

RESULTS

A set of benchmarks involving a large protein dataset consisting of over 11,000 protein sequences from Protein Data Bank was performed to validate the method. Prediction done by a neural network had average accuracy of 91.5% for the helix and 94.5% for the strand contents. We also show that using the new measure results in about 40% reduction of error rates when compared with the composition vector results.

CONCLUSIONS

The developed method has much better accuracy when compared with other existing methods, as shown on a large body of proteins, in contrast to other reported results that often target small sets of specific protein types, such as globular proteins.

Homology modeling and substrate binding study of Nudix hydrolase Ndx1 from Thermos thermophilus HB8

With homology modeling techniques, molecular mechanics, and molecular dynamics methods, a 3D structure model of Ndx1 is created and refined. This model is further assessed by Profile-3D and ProStat, which confirm that the refined model is reliable. With this model, a flexible docking study is performed and the result indicates that Glu46, Arg88, and Glu90 are three important determinant residues in binding, as they have strong hydrogen bonding interactions and electrostatic interactions with Ap6A. In addition, we further find that three residues, Ser38, Leu39 and Glu46, coordinate enzyme-bound Mg2+ ions in complex N-A. The Glu46 is consistent with the experimental results by Iwai et al., and the other four residues mentioned above may also play vital roles in catalysis of Ndx1.

Modeling for tertiary structure of globin chain in Hemoglobin Suan-Dok disorder

Hemoglobinopathies are an important inherited disorder with a high prevalence in Southeast Asia. Hemoglobin Suan-Dok is an example of a hemoglobinopathy that was first identified and described in Thailand. It has been identified as an unstable hemoglobin variant associated with alpha-thalassemia. The role of the hemoglobin instability in Hb Suan-Dok in the altered red cell morphology in comparison to the thalassemia-like deficit of alpha globin mRNA has not been entirely resolved and needs additional structural study for clarification. In this study the amino acid sequence of human alpha globin was extracted using ExPASY and compared with that obtained from the Hb Suan-Dok disorder. The derived sequences, alpha globin chains in both the normal and Hb Suan-Dok disorder, were used for further investigation of the tertiary structures. Modeling these proteins for the tertiary structure was performed using the CPHmodels 2.0 Server. For comparison the tertiary structure of human alpha globin chains in normal and hemoglobin Suan-Dok are calculated and presented. Based on this information, there was no significant difference between the predicted alpha globin tertiary structures of normal hemoglobin and Hb Suan-Dok. Therefore, from this study we can state that the tertiary structure of alpha globin is not significantly affected by the mutation in the Hb Suan-Dok disorder and that the effect of this hemoglobin abnormality may be silent. The data suggests that the thalassemic defect associated with the Suan-Dok mutation results from another unidentified process rather than the structural aberration and that the finding of a thalassemic picture might be due to another undetectable inherited hemoglobin disorder.

Crystal structure of human pFGE, the paralog of the Calpha-formylglycine-generating enzyme

In eukaryotes, sulfate esters are degraded by sulfatases, which possess a unique Calpha-formylglycine residue in their active site. The defect in post-translational formation of the Calpha-formylglycine residue causes a severe lysosomal storage disorder in humans. Recently, FGE (formylglycine-generating enzyme) has been identified as the protein required for this specific modification. Using sequence comparisons, a protein homologous to FGE was found and denoted pFGE (paralog of FGE). pFGE binds a sulfatase-derived peptide bearing the FGE recognition motif, but it lacks formylglycine-generating activity. Both proteins belong to a large family of pro- and eukaryotic proteins containing the DUF323 domain, a formylglycine-generating enzyme domain of unknown three-dimensional structure. We have crystallized the glycosylated human pFGE and determined its crystal structure at a resolution of 1.86 A. The structure reveals a novel fold, which we denote the FGE fold and which therefore serves as a paradigm for the DUF323 domain. It is characterized by an asymmetric partitioning of secondary structure elements and is stabilized by two calcium cations. A deep cleft on the surface of pFGE most likely represents the sulfatase polypeptide binding site. The asymmetric unit of the pFGE crystal contains a homodimer. The putative peptide binding site is buried between the monomers, indicating a biological significance of the dimer. The structure suggests the capability of pFGE to form a heterodimer with FGE.

Homology modeling and molecular dynamics studies of a novel C3-like ADP-ribosyltransferase

The novel C3-like ADP-ribosyltransferase is produced by a Staphylococcus aureus strain that especially ADP-ribosylates RhoE/Rnd3 subtype proteins, and its three-dimensional (3D) structure has not known. In order to understand the catalytic mechanism, the 3D structure of the protein is built by using homology modeling based on the known crystal structure of exoenzyme C3 from Clostridium botulinum (1G24). Then the model structure is further refined by energy minimization and molecular dynamics methods. The putative nicotinamide adenine dinucleotide (NAD(+))-binding pocket of exoenzyme C3(Stau) is determined by Binding-Site Search module. The NAD(+)-enzyme complex is developed by molecular dynamics simulation and the key residues involved in the combination of enzyme binding to the ligand-NAD(+) are determined, which is helpful to guide the experimental realization and the new mutant designs as well. Our results indicated that the key binding-site residues of Arg48, Glu180, Ser138, Asn134, Arg85, and Gln179 play an important role in the catalysis of exoenzyme C3(Stau), which is in consistent with experimental observation.

Homology modeling and molecular dynamics study of GSK3/SHAGGY-like kinase

Although the GSK3/SHAGGY-like kinase is a highly conserved serine/threonine kinase implicated in many signaling pathways in eukaryotes, the lack of knowledge of its three-dimensional (3D) structure has hindered efforts to understand the binding specificities of substrate and catalytic mechanism. To understand the structure-activity relationships, the protein 3D structure was built by using homology modeling based on the known X-ray diffraction structure of Glycogen synthase kinase-3beta (Gsk3beta) and the model structure was further refined using unrestrained molecular dynamics simulations. The research indicates that the general 3D organization of the GSK3/SHAGGY-like kinase is a typical kinase family and comprises an N-terminal domain of beta-sheet and a larger C-terminal domain mainly constituted by alpha-helix. In order to understand the molecular interactions between the natural substrate-ATP and GSK3/SHAGGY-like kinase, a 3D model of the complex ATP-GSK3/SHAGGY-like kinase is developed by molecular docking program, which is helpful to guide the experimental realization and the new mutant designs as well. One important finding is that the identification of the key binding-site residue of Lys69 which plays an important role in the catalysis of GSK3/SHAGGY-like kinase and this is in consistent with experimental observation.

Amino acid propensities are position-dependent throughout the length of alpha-helices

The 20 commonly occurring amino acids have been shown to have distinct position-dependent, helix-forming propensities near the ends of alpha-helices. Here, we show that the amino acids also have very strong position-dependent propensities throughout the length of a helix. Most helices are amphiphilic, and they have a strong tendency to both begin and end on the solvent-inaccessible face of the helix. These position-specific propensities should provide valuable parameters to guide de novo protein design, and should allow more precise prediction of helical topology in natural proteins.

A pentapeptide-based method for protein secondary structure prediction

We present a new method for protein secondary structure prediction, based on the recognition of well-defined pentapeptides, in a large databank. Using a databank of 635 protein chains, we obtained a success rate of 68.6%. We show that progress is achieved when the databank is enlarged, when the 20 amino acids are adequately grouped in 10 sets and when more pentapeptides are attributed one of the defined conformations, alpha-helices or beta-strands. The analysis of the model indicates that the essential variable is the number of pentapeptides of well-defined structure in the database. Our model is simple, does not rely on arbitrary parameters and allows the analysis in detail of the results of each chosen hypothesis.

On filtering false positive transmembrane protein predictions

While helical transmembrane (TM) region prediction tools achieve high (>90%) success rates for real integral membrane proteins, they produce a considerable number of false positive hits in sequences of known nontransmembrane queries. We propose a modification of the dense alignment surface (DAS) method that achieves a substantial decrease in the false positive error rate. Essentially, a sequence that includes possible transmembrane regions is compared in a second step with TM segments in a sequence library of documented transmembrane proteins. If the performance of the query sequence against the library of documented TM segment-containing sequences in this test is lower than an empirical threshold, it is classified as a non-transmembrane protein. The probability of false positive prediction for trusted TM region hits is expressed in terms of E-values. The modified DAS method, the DAS-TMfilter algorithm, has an unchanged high sensitivity for TM segments ( approximately 95% detected in a learning set of 128 documented transmembrane proteins). At the same time, the selectivity measured over a non-redundant set of 526 soluble proteins with known 3D structure is approximately 99%, mainly because a large number of falsely predicted single membrane-pass proteins are eliminated by the DAS-TMfilter algorithm.

Secondary structural wobble: the limits of protein prediction accuracy

At present, accuracies of secondary structural prediction scarcely go beyond 70-75%. Secondary structural comparison is carried out among sequence-identified proteins. The results show natural wobble between different secondary structural types is possible in homologous families, and the best prediction accuracy will rarely be 100%. Besides shortcoming of the prediction approaches, secondary structural wobble is found to be responsible for nearly all secondary structural prediction limits. Only average 73.2% of amino acid residue is conserved in secondary structural types. The wobble allows alpha-class/coil and beta-class/coil transitions but not direct alpha-class/beta-class transition. Propensity values representing the statistical occurrence of 20 amino acid residues in secondary structural wobbles are given.

The prediction of the structural class of protein: application of the measure of diversity

Based on the concept that the structural class of a protein is mainly determined by its secondary structure sequence, a new algorithm for prediction of the structural class of a protein is proposed. By use of the number of alpha -helices, beta -strands, and betaalphabeta fragments, the structural class of a protein can be predicted by an algorithm based on the increment of diversity (ID), in which the sole prediction parameter-the increment of diversity is used as the index of prediction of structural class of a protein. The results indicate that the high rates of correct prediction are obtained for complete set (standard set) from Brookhaven Protein Data Bank-CD ROM (PDB) published in October 1995 and the test set newly released from Brookhaven Protein Data Bank-CD ROM (PDB) before July 1998, respectively.