GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.

Phytocompounds as potential inhibitors of mycobacterial multidrug efflux pump Rv1258c: an in silico approach

The number of infections and deaths caused by multidrug resistant (MDR) tuberculosis is increasing globally. One of the efflux pumps, that makes Mycobacterium tuberculosis resistant to a number of antibiotics and results in unfavourable treatment results is Tap or Rv1258c. In our study, we tried to utilize a rational drug design technique using in silico approach to look for an efficient and secure efflux pump inhibitor (EPI) against Rv1258c. The structure of Rv1258c was built using the homology modeling tool MODELLER 9.24. 210 phytocompounds were used for blind and site-specific ligand docking against the modelled structure of Rv1258c using AutoDock Vina software. The best docked plant compounds were further analysed for druglikeness and toxicity. In addition to having excellent docking scores, two plant compounds-ellagic acid and baicalein-also exhibited highly desirable drug-like qualities. These substances outperform more well-known EPIs like piperine and verapamil in terms of effectiveness. This data shows that these two compounds might be further investigated for their potential as Rv1258c inhibitors.

Identification of common candidate genes and pathways for Spina Bifida and Wilm's Tumor using an integrative bioinformatics analysis

Spina Bifida (SB) and Wilm's Tumor (WT) are conditions, both associated with children. Several studies have shown that WT later develops in SB patients, which led us to elucidate common key genes and linked pathways of both conditions, aimed at their concurrent therapeutic management. For this, integrated bioinformatics analysis was employed. A comprehensive manual curation of genes identified 133 and 139 genes associated with SB and WT, respectively, which were used to construct a single protein-protein interaction (PPI) network. Topological parameters analysis of the network showed its scale-free and hierarchical nature. Centrality-based analysis of the network identified 116 hubs, of which, 6 were called the key genes attributed to being common between SB and WT besides being the hubs. Gene enrichment analysis of the 5 most essential modules, identified important biological processes and pathways possibly linking SB to WT. Additionally, miRNA-key gene-transcription factor (TF) regulatory network elucidated a few important miRNAs and TFs that regulate our key genes. In closing, we put forward TP53, DICER1, NCAM1, PAX3, PTCH1, MTHFR; hsa-mir-107, hsa-mir-137, hsa-mir-122, hsa-let-7d; and YY1, SOX4, MYC, STAT3; key genes, miRNAs and TFs, respectively, as the key regulators. Further, MD simulation studies of wild and Glu429Ala forms of MTHFR proteins showed that there is a slight change in MTHFR protein structure due to Glu429Ala polymorphism. We anticipate that the interplay of these three entities will be an interesting area of research to explore the regulatory mechanism of SB and WT and may serve as candidate target molecules to diagnose, monitor, and treat SB and WT, parallelly.Communicated by Ramaswamy H. Sarma.

Computational prognostic evaluation of Alzheimer's drugs from FDA-approved database through structural conformational dynamics and drug repositioning approaches

Drug designing is high-priced and time taking process with low success rate. To overcome this obligation, computational drug repositioning technique is being promptly used to predict the possible therapeutic effects of FDA approved drugs against multiple diseases. In this computational study, protein modeling, shape-based screening, molecular docking, pharmacogenomics, and molecular dynamic simulation approaches have been utilized to retrieve the FDA approved drugs against AD. The predicted MADD protein structure was designed by homology modeling and characterized through different computational resources. Donepezil and galantamine were implanted as standard drugs and drugs were screened out based on structural similarities. Furthermore, these drugs were evaluated and based on binding energy (Kcal/mol) profiles against MADD through PyRx tool. Moreover, pharmacogenomics analysis showed good possible associations with AD mediated genes and confirmed through detail literature survey. The best 6 drug (darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar) further docked and analyzed their interaction behavior through hydrogen binding. Finally, MD simulation study were carried out on these drugs and evaluated their stability behavior by generating root mean square deviation and fluctuations (RMSD/F), radius of gyration (Rg) and soluble accessible surface area (SASA) graphs. Taken together, darifenacin, astemizole, tubocurarine, elacridar, sertindole and tariquidar displayed good lead like profile as compared with standard and can be used as possible therapeutic agent in the treatment of AD after in-vitro and in-vivo assessment.

Affinity maturation of antibody fragments: A review encompassing the development from random approaches to computational rational optimization

Routinely screened antibody fragments usually require further in vitro maturation to achieve the desired biophysical properties. Blind in vitro strategies can produce improved ligands by introducing random mutations into the original sequences and selecting the resulting clones under more and more stringent conditions. Rational approaches exploit an alternative perspective that aims first at identifying the specific residues potentially involved in the control of biophysical mechanisms, such as affinity or stability, and then to evaluate what mutations could improve those characteristics. The understanding of the antigen-antibody interactions is instrumental to develop this process the reliability of which, consequently, strongly depends on the quality and completeness of the structural information. Recently, methods based on deep learning approaches critically improved the speed and accuracy of model building and are promising tools for accelerating the docking step. Here, we review the features of the available bioinformatic instruments and analyze the reports illustrating the result obtained with their application to optimize antibody fragments, and nanobodies in particular. Finally, the emerging trends and open questions are summarized.

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

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.

Before and after AlphaFold2: An overview of protein structure prediction

Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.

VHH Structural Modelling Approaches: A Critical Review

V_HH, i.e., VH domains of camelid single-chain antibodies, are very promising therapeutic agents due to their significant physicochemical advantages compared to classical mammalian antibodies. The number of experimentally solved V_HH structures has significantly improved recently, which is of great help, because it offers the ability to directly work on 3D structures to humanise or improve them. Unfortunately, most V_HHs do not have 3D structures. Thus, it is essential to find alternative ways to get structural information. The methods of structure prediction from the primary amino acid sequence appear essential to bypass this limitation. This review presents the most extensive overview of structure prediction methods applied for the 3D modelling of a given V_HH sequence (a total of 21). Besides the historical overview, it aims at showing how model software programs have been shaping the structural predictions of V_HHs. A brief explanation of each methodology is supplied, and pertinent examples of their usage are provided. Finally, we present a structure prediction case study of a recently solved V_HH structure. According to some recent studies and the present analysis, AlphaFold 2 and NanoNet appear to be the best tools to predict a structural model of V_HH from its sequence.

Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection

Sequence-based approaches are fundamental to guide experimental investigations in obtaining structural and/or functional insights into uncharacterized protein families. Powerful profile-based sequence search methods rely on a sequence space continuum to identify non-trivial relationships through homology detection. The computational design of protein-like sequences that serve as "artificial linkers" is useful in identifying relationships between distant members of a structural fold. Such sequences act as intermediates and guide homology searches between distantly related proteins. Here, we describe an approach that represents natural intermediate sequences and designed protein-like sequences as HMM (Hidden Markov Models) profiles, to improve the sensitivity of existing search methods. Searches made within the "Profile database" were shown to recognize the parent structural fold for 90% of the search queries at query coverage better than 60%. For 1040 protein families with no available structure, fold associations were made through searches in the database of natural and designed sequence profiles. Most of the associations were made with the Alpha-alpha superhelix, Transmembrane beta-barrels, TIM barrel, and Immunoglobulin-like beta-sandwich folds. For 11 domain families of unknown functions, we provide confident fold associations using the profiles of designed sequences and a consensus from other fold recognition methods. For two DUFs (Domain families of Unknown Functions), we performed detailed functional annotation through comparisons with characterized templates of families of known function.

A General Framework to Learn Tertiary Structure for Protein Sequence Characterization

During the past five years, deep-learning algorithms have enabled ground-breaking progress towards the prediction of tertiary structure from a protein sequence. Very recently, we developed SAdLSA, a new computational algorithm for protein sequence comparison via deep-learning of protein structural alignments. SAdLSA shows significant improvement over established sequence alignment methods. In this contribution, we show that SAdLSA provides a general machine-learning framework for structurally characterizing protein sequences. By aligning a protein sequence against itself, SAdLSA generates a fold distogram for the input sequence, including challenging cases whose structural folds were not present in the training set. About 70% of the predicted distograms are statistically significant. Although at present the accuracy of the intra-sequence distogram predicted by SAdLSA self-alignment is not as good as deep-learning algorithms specifically trained for distogram prediction, it is remarkable that the prediction of single protein structures is encoded by an algorithm that learns ensembles of pairwise structural comparisons, without being explicitly trained to recognize individual structural folds. As such, SAdLSA can not only predict protein folds for individual sequences, but also detects subtle, yet significant, structural relationships between multiple protein sequences using the same deep-learning neural network. The former reduces to a special case in this general framework for protein sequence annotation.

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.

Changes in Membrane Protein Structural Biology

Membrane proteins are essential components of many biochemical processes and are important pharmaceutical targets. Membrane protein structural biology provides the molecular rationale for these biochemical process as well as being a highly useful tool for drug discovery. Unfortunately, membrane protein structural biology is a difficult area of study due to low protein yields and high levels of instability especially when membrane proteins are removed from their native environments. Despite this instability, membrane protein structural biology has made great leaps over the last fifteen years. Today, the landscape is almost unrecognisable. The numbers of available atomic resolution structures have increased 10-fold though advances in crystallography and more recently by cryo-electron microscopy. These advances in structural biology were achieved through the efforts of many researchers around the world as well as initiatives such as the Membrane Protein Laboratory (MPL) at Diamond Light Source. The MPL has helped, provided access to and contributed to advances in protein production, sample preparation and data collection. Together, these advances have enabled higher resolution structures, from less material, at a greater rate, from a more diverse range of membrane protein targets. Despite this success, significant challenges remain. Here, we review the progress made and highlight current and future challenges that will be overcome.

Molecular modeling and in vitro study on pyrocatechol as potential pharmacophore of CD151 inhibitor

CD151 has been recognized as a prognostic marker, the therapeutic target of breast cancers, but less explored for small molecule inhibitors due to lack of a validated model. The 3-D structure of CD151 large extracellular loop (LEL) was modeled using the LOMETS server and validated by the Ramachandran plot. The validated structure was employed for molecular docking and structure-based pharmacophore analysis. Druglikeness was evaluated by the ADMET description protocol. Antiproliferative activity was evaluated by MTT, BrdU incorporation, flow cytometry, and cell death ELISA^PLUS assay. This study predicted the best model for CD151-LEL with 94.1% residues in favored regions and Z score -2.79 kcal/mol using the threading method. The web-based receptor cavity method identified one functional target site, which was suitable for the binding of aromatic and heterocyclic compounds. Molecular docking study identified pyrocatechol (PCL) and 5-fluorouracil (FU) as potential leads of CD151-LEL. The pharmacophore model identified interaction points of modeled CD151-LEL with PCL and FU. Also, the analysis of ADMET properties revealed the drug-likeness of PCL and FU. The viability of MDA-MB 231 cells was significantly reduced with PCL and FU but less affected MCF-12A, normal healthy breast epithelial cell line. With 50% toxic concentration, both PCL and FU significantly inhibited 82.46 and 87.12% proliferation, respectively, of MDA-MB 231 cells by altering morphology and inducing G1 cell cycle arrest and apoptosis. In addition, PCL and FU inhibited the CD151 expression by 4.5-and 4.8-folds, respectively. This study suggests the further assessment of pyrocatechol as a potential lead of CD151 in breast cancer at the molecular level.

Evaluating the significance of contact maps in low-homology protein modeling using contact-assisted threading

The development of improved threading algorithms for remote homology modeling is a critical step forward in template-based protein structure prediction. We have recently demonstrated the utility of contact information to boost protein threading by developing a new contact-assisted threading method. However, the nature and extent to which the quality of a predicted contact map impacts the performance of contact-assisted threading remains elusive. Here, we systematically analyze and explore this interdependence by employing our newly-developed contact-assisted threading method over a large-scale benchmark dataset using predicted contact maps from four complementary methods including direct coupling analysis (mfDCA), sparse inverse covariance estimation (PSICOV), classical neural network-based meta approach (MetaPSICOV), and state-of-the-art ultra-deep learning model (RaptorX). Experimental results demonstrate that contact-assisted threading using high-quality contacts having the Matthews Correlation Coefficient (MCC) ≥ 0.5 improves threading performance in nearly 30% cases, while low-quality contacts with MCC <0.35 degrades the performance for 50% cases. This holds true even in CASP13 dataset, where threading using high-quality contacts (MCC ≥ 0.5) significantly improves the performance of 22 instances out of 29. Collectively, our study uncovers the mutual association between the quality of predicted contacts and its possible utility in boosting threading performance for improving low-homology protein modeling.

Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors

Motivation

Many Gram-negative bacteria use type VI secretion systems (T6SS) to export effector proteins into adjacent target cells. These secreted effectors (T6SEs) play vital roles in the competitive survival in bacterial populations, as well as pathogenesis of bacteria. Although various computational analyses have been previously applied to identify effectors secreted by certain bacterial species, there is no universal method available to accurately predict T6SS effector proteins from the growing tide of bacterial genome sequence data.

Results

We extracted a wide range of features from T6SE protein sequences and comprehensively analyzed the prediction performance of these features through unsupervised and supervised learning. By integrating these features, we subsequently developed a two-layer SVM-based ensemble model with fine-grain optimized parameters, to identify potential T6SEs. We further validated the predictive model using an independent dataset, which showed that the proposed model achieved an impressive performance in terms of ACC (0.943), F-value (0.946), MCC (0.892) and AUC (0.976). To demonstrate applicability, we employed this method to correctly identify two very recently validated T6SE proteins, which represent challenging prediction targets because they significantly differed from previously known T6SEs in terms of their sequence similarity and cellular function. Furthermore, a genome-wide prediction across 12 bacterial species, involving in total 54 212 protein sequences, was carried out to distinguish 94 putative T6SE candidates. We envisage both this information and our publicly accessible web server will facilitate future discoveries of novel T6SEs.

Availability and implementation

http://bastion6.erc.monash.edu/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Does inclusion of residue-residue contact information boost protein threading?

Template-based modeling is considered as one of the most successful approaches for protein structure prediction. However, reliably and accurately selecting optimal template proteins from a library of known protein structures having similar folds as the target protein and making correct alignments between the target sequence and the template structures, a template-based modeling technique known as threading, remains challenging, particularly for non- or distantly-homologous protein targets. With the recent advancement in protein residue-residue contact map prediction powered by sequence co-evolution and machine learning, here we systematically analyze the effect of inclusion of residue-residue contact information in improving the accuracy and reliability of protein threading. We develop a new threading algorithm by incorporating various sequential and structural features, and subsequently integrate residue-residue contact information as an additional scoring term for threading template selection. We show that the inclusion of contact information attains statistically significantly better threading performance compared to a baseline threading algorithm that does not utilize contact information when everything else remains the same. Experimental results demonstrate that our contact based threading approach outperforms popular threading method MUSTER, contact-assisted ab initio folding method CONFOLD2, and recent state-of-the-art contact-assisted protein threading methods EigenTHREADER and map_align on several benchmarks. Our study illustrates that the inclusion of contact maps is a promising avenue in protein threading to ultimately help to improve the accuracy of protein structure prediction.

Drug Discovery

Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.

StackSSSPred: A Stacking-Based Prediction of Supersecondary Structure from Sequence

Supersecondary structure (SSS) refers to specific geometric arrangements of several secondary structure (SS) elements that are connected by loops. The SSS can provide useful information about the spatial structure and function of a protein. As such, the SSS is a bridge between the secondary structure and tertiary structure. In this chapter, we propose a stacking-based machine learning method for the prediction of two types of SSSs, namely, β-hairpins and β-α-β, from the protein sequence based on comprehensive feature encoding. To encode protein residues, we utilize key features such as solvent accessibility, conservation profile, half surface exposure, torsion angle fluctuation, disorder probabilities, and more. The usefulness of the proposed approach is assessed using a widely used threefold cross-validation technique. The obtained empirical result shows that the proposed approach is useful and prediction can be improved further.

The application of Hadoop in structural bioinformatics

The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up.

Using the Mutation-Selection Framework to Characterize Selection on Protein Sequences

When mutational pressure is weak, the generative process of protein evolution involves explicit probabilities of mutations of different types coupled to their conditional probabilities of fixation dependent on selection. Establishing this mechanistic modeling framework for the detection of selection has been a goal in the field of molecular evolution. Building on a mathematical framework proposed more than a decade ago, numerous methods have been introduced in an attempt to detect and measure selection on protein sequences. In this review, we discuss the structure of the original model, subsequent advances, and the series of assumptions that these models operate under.

Protein structure prediction

Predicting 3D structure of protein from its amino acid sequence is one of the most important unsolved problems in biophysics and computational biology. This paper attempts to give a comprehensive introduction of the most recent effort and progress on protein structure prediction. Following the general flowchart of structure prediction, related concepts and methods are presented and discussed. Moreover, brief introductions are made to several widely-used prediction methods and the community-wide critical assessment of protein structure prediction (CASP) experiments.

Sources of Variation in Ancestral Sequence Reconstruction for HIV-1 Envelope Genes

We characterized the variation in the reconstructed ancestor of 118 HIV-1 envelope gene sequences arising from the methods used for (a) estimating and (b) rooting the phylogenetic tree, and (c) reconstructing the ancestor on that tree, from (d) the sequence format, and from (e) the number of input sequences. The method of rooting the tree was responsible for most of the sequence variation both among the reconstructed ancestral sequences and between the ancestral and observed sequences. Variation in predicted 3-D structural properties of the ancestors mirrored their sequence variation. The observed sequence consensus and ancestral sequences from center-rooted trees were most similar in all predicted attributes. Only for the predicted number of N-glycosylation sites was there a difference between MP and ML methods of reconstruction. Taxon sampling effects were observed only for outgroup-rooted trees, not center-rooted, reflecting the occurrence of several divergent basal sequences. Thus, for sequences exhibiting a radial phylogenetic tree, as does HIV-1, most of the variation in the estimated ancestor arises from the method of rooting the phylogenetic tree. Those investigating the ancestors of genes exhibiting such a radial tree should pay particular attention to alternate rooting methods in order to obtain a representative sample of ancestors.

Evolutionary Conserved Cysteines Function as cis-Acting Regulators of Arabidopsis PIN-FORMED 2 Distribution

Coordination of plant development requires modulation of growth responses that are under control of the phytohormone auxin. PIN-FORMED plasma membrane proteins, involved in intercellular transport of the growth regulator, are key to the transmission of such auxin signals and subject to multilevel surveillance mechanisms, including reversible post-translational modifications. Apart from well-studied PIN protein modifications, namely phosphorylation and ubiquitylation, no further post-translational modifications have been described so far. Here, we focused on root-specific Arabidopsis PIN2 and explored functional implications of two evolutionary conserved cysteines, by a combination of in silico and molecular approaches. PIN2 sequence alignments and modeling predictions indicated that both cysteines are facing the cytoplasm and therefore would be accessible to redox status-controlled modifications. Notably, mutant pin2C-A alleles retained functionality, demonstrated by their ability to almost completely rescue defects of a pin2 null allele, whereas high resolution analysis of pin2C-A localization revealed increased intracellular accumulation, and altered protein distribution within plasma membrane micro-domains. The observed effects of cysteine replacements on root growth and PIN2 localization are consistent with a model in which redox status-dependent cysteine modifications participate in the regulation of PIN2 mobility, thereby fine-tuning polar auxin transport.

Mathematical basis of improved protein subfamily classification by a HMM-based sequence filter

Informative phylogenetic analysis is dependent on the presence of curated and annotated sequences. This may be complemented by the simultaneous availability of empirical data pertaining to their in vivo function. Confounding sequences, with their similarity to more than one functional cluster, can therefore, render any categorization ambiguous, subjective, and imprecise. Here, I analyze and discuss the development of a mathematical expression that can characterize a potential confounding protein sequence. Specifically, statistical descriptors of combinatorially arranged profile HMM scores are computed and evaluated. The resultant data is then incorporated into an index of sequence suitability. The sequence may then be recommended as either suitable for inclusion or be excluded all together. The index is independent of experimental data and, can, be computed from the primary structure of the protein sequence. This can be utilized to trim previously grouped sequences and can either finalize the composition of training set or reduce the search space of sequences to be tested.

N- and C-Terminal Truncations to Enhance Protein Solubility and Crystallization: Predicting Protein Domain Boundaries with Bioinformatics Tools

Soluble protein expression is a key requirement for biochemical and structural biology approaches to study biological systems in vitro. Production of sufficient quantities may not always be achievable if proteins are poorly soluble which is frequently determined by physico-chemical parameters such as intrinsic disorder. It is well known that discrete protein domains often have a greater likelihood of high-level soluble expression and crystallizability. Determination of such protein domain boundaries can be challenging for novel proteins. Here, we outline the application of bioinformatics tools to facilitate the prediction of potential protein domain boundaries, which can then be used in designing expression construct boundaries for parallelized screening in a range of heterologous expression systems.

Prediction of Structural and Functional Aspects of Protein

To predict the structure of protein from a primary amino acid sequence is computationally difficult. An investigation of the methods and algorithms used to predict protein structure and a thorough knowledge of the function and structure of proteins are critical for the advancement of biology and the life sciences as well as the development of better drugs, higher-yield crops, and even synthetic bio-fuels. To that end, this chapter sheds light on the methods used for protein structure prediction. This chapter covers the applications of modeled protein structures and unravels the relationship between pure sequence information and three-dimensional structure, which continues to be one of the greatest challenges in molecular biology. With this resource, it presents an all-encompassing examination of the problems, methods, tools, servers, databases, and applications of protein structure prediction, giving unique insight into the future applications of the modeled protein structures. In this chapter, current protein structure prediction methods are reviewed for a milieu on structure prediction, the prediction of structural fundamentals, tertiary structure prediction, and functional imminent. The basic ideas and advances of these directions are discussed in detail.

Protein Structure Prediction Using Homology Modeling

Sequence-structure deficit marks one of the critical problems in today's scenario where high-throughput sequencing has resulted in large datasets of protein sequences but their corresponding 3D structures still needs to be determined. Homology modeling, also termed as Comparative modeling refers to modeling of 3D structure of a protein by exploiting structural information from other known protein structures with good sequence similarity. Homology models contain sufficient information about the spatial arrangement of important residues in the protein and are often used in drug design for screening of large libraries by molecular docking techniques. This chapter provides a brief description about protein tertiary structure prediction and Homology modeling. The authors provide a description of the steps involved in homology modeling protocols and provide information on the various resources available for the same.

Molecular Dynamics Simulations for Biological Systems

Molecular dynamics simulation is an important tool to capture the dynamicity of biological molecule and the atomistic insights. These insights are helpful to explore biological functions. Molecular dynamics simulation from femto seconds to milli seconds scale give a large ensemble of conformations that can reveal many biological mysteries. The main focus of the chapter is to throw light on theories, requirement of molecular dynamics for biological studies and application of molecular dynamics simulations. Molecular dynamics simulations are widely used to study protein-protein interaction, protein-ligand docking, effects of mutation on interactions, protein folding and flexibility of the biological molecules. This chapter also deals with various methods/algorithms of protein tertiary structure prediction, their strengths and weaknesses.

Computational methods in drug discovery

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

A Thermostable Bilirubin-Oxidizing Enzyme from Activated Sludge Isolated by a Metagenomic Approach

A gene coding for a multicopper oxidase (BopA) was identified through the screening of a metagenomic library constructed from wastewater treatment activated sludge. The recombinant BopA protein produced in Escherichia coli exhibited oxidation activity toward 2,2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonate) (ABTS) in the presence of copper, suggesting that BopA is laccase. A bioinformatic analysis of the bopA gene sequence indicated that it has a phylogenetically bacterial origin, possibly derived from a bacterium within the phylum Deinococcus-Thermus. Purified BopA exhibited maximum activity at pH 7.5 with bilirubin as its substrate and was found to be active over a markedly broad pH range from 6 to 11. It also showed notable thermostability; its activity remained intact even after a heat treatment at 90°C for 60 min. This enzyme is a thermostable-bilirubin oxidase that exhibits markedly higher thermostability than that previously reported for laccases.

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.

Natural Inhibitors of Snake Venom Metalloendopeptidases: History and Current Challenges

The research on natural snake venom metalloendopeptidase inhibitors (SVMPIs) began in the 18th century with the pioneering work of Fontana on the resistance that vipers exhibited to their own venom. During the past 40 years, SVMPIs have been isolated mainly from the sera of resistant animals, and characterized to different extents. They are acidic oligomeric glycoproteins that remain biologically active over a wide range of pH and temperature values. Based on primary structure determination, mammalian plasmatic SVMPIs are classified as members of the immunoglobulin (Ig) supergene protein family, while the one isolated from muscle belongs to the ficolin/opsonin P35 family. On the other hand, SVMPIs from snake plasma have been placed in the cystatin superfamily. These natural antitoxins constitute the first line of defense against snake venoms, inhibiting the catalytic activities of snake venom metalloendopeptidases through the establishment of high-affinity, non-covalent interactions. This review presents a historical account of the field of natural resistance, summarizing its main discoveries and current challenges, which are mostly related to the limitations that preclude three-dimensional structural determinations of these inhibitors using “gold-standard” methods; perspectives on how to circumvent such limitations are presented. Potential applications of these SVMPIs in medicine are also highlighted.

Novel proteases from the genome of the carnivorous plant Drosera capensis: Structural prediction and comparative analysis

In his 1875 monograph on insectivorous plants, Darwin described the feeding reactions of Drosera flypaper traps and predicted that their secretions contained a "ferment" similar to mammalian pepsin, an aspartic protease. Here we report a high-quality draft genome sequence for the cape sundew, Drosera capensis, the first genome of a carnivorous plant from order Caryophyllales, which also includes the Venus flytrap (Dionaea) and the tropical pitcher plants (Nepenthes). This species was selected in part for its hardiness and ease of cultivation, making it an excellent model organism for further investigations of plant carnivory. Analysis of predicted protein sequences yields genes encoding proteases homologous to those found in other plants, some of which display sequence and structural features that suggest novel functionalities. Because the sequence similarity to proteins of known structure is in most cases too low for traditional homology modeling, 3D structures of representative proteases are predicted using comparative modeling with all-atom refinement. Although the overall folds and active residues for these proteins are conserved, we find structural and sequence differences consistent with a diversity of substrate recognition patterns. Finally, we predict differences in substrate specificities using in silico experiments, providing targets for structure/function studies of novel enzymes with biological and technological significance. Proteins 2016; 84:1517-1533. © 2016 Wiley Periodicals, Inc.

Multiple Docking for Protein Structure Prediction

Protein structure prediction and protein docking prediction are two related problems in molecular biology. We suggest the use of multiple docking in the process of protein structure prediction. Once reliable structural models are predicted to disjoint fragments of the protein target sequence, a combinatorial assembly may be used to predict their native arrangement. Here, we present CombDock, a combinatorial docking algorithm for the structural units assembly problem. We have tested the algorithm on various examples using both domains and domain substructures as input. Inaccurate models of the structural units were also used, to test the robustness of the algorithm. The algorithm was able to predict a near-native arrangement of the input structural units in almost all of the cases, showing that the combinatorial approach succeeds in overcoming the inexact shape complementarity caused by the inaccuracy of the models.

Are There Rab GTPases in Archaea?

A complex endomembrane system is one of the hallmarks of Eukaryotes. Vesicle trafficking between compartments is controlled by a diverse protein repertoire, including Rab GTPases. These small GTP-binding proteins contribute identity and specificity to the system, and by working as molecular switches, trigger multiple events in vesicle budding, transport, and fusion. A diverse collection of Rab GTPases already existed in the ancestral Eukaryote, yet, it is unclear how such elaborate repertoire emerged. A novel archaeal phylum, the Lokiarchaeota, revealed that several eukaryotic-like protein systems, including small GTPases, are present in Archaea. Here, we test the hypothesis that the Rab family of small GTPases predates the origin of Eukaryotes. Our bioinformatic pipeline detected multiple putative Rab-like proteins in several archaeal species. Our analyses revealed the presence and strict conservation of sequence features that distinguish eukaryotic Rabs from other small GTPases (Rab family motifs), mapping to the same regions in the structure as in eukaryotic Rabs. These mediate Rab-specific interactions with regulators of the REP/GDI (Rab Escort Protein/GDP dissociation Inhibitor) family. Sensitive structure-based methods further revealed the existence of REP/GDI-like genes in Archaea, involved in isoprenyl metabolism. Our analysis supports a scenario where Rabs differentiated into an independent family in Archaea, interacting with proteins involved in membrane biogenesis. These results further support the archaeal nature of the eukaryotic ancestor and provide a new insight into the intermediate stages and the evolutionary path toward the complex membrane-associated signaling circuits that characterize the Ras superfamily of small GTPases, and specifically Rab proteins.

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.

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.

Nataraja K. An Approach to Function Annotation for Proteins of Unknown Function (PUFs) in the Transcriptome of Indian Mulberry

The modern sequencing technologies are generating large volumes of information at the transcriptome and genome level. Translation of this information into a biological meaning is far behind the race due to which a significant portion of proteins discovered remain as proteins of unknown function (PUFs). Attempts to uncover the functional significance of PUFs are limited due to lack of easy and high throughput functional annotation tools. Here, we report an approach to assign putative functions to PUFs, identified in the transcriptome of mulberry, a perennial tree commonly cultivated as host of silkworm. We utilized the mulberry PUFs generated from leaf tissues exposed to drought stress at whole plant level. A sequence and structure based computational analysis predicted the probable function of the PUFs. For rapid and easy annotation of PUFs, we developed an automated pipeline by integrating diverse bioinformatics tools, designated as PUFs Annotation Server (PUFAS), which also provides a web service API (Application Programming Interface) for a large-scale analysis up to a genome. The expression analysis of three selected PUFs annotated by the pipeline revealed abiotic stress responsiveness of the genes, and hence their potential role in stress acclimation pathways. The automated pipeline developed here could be extended to assign functions to PUFs from any organism in general. PUFAS web server is available at http://caps.ncbs.res.in/pufas/ and the web service is accessible at http://capservices.ncbs.res.in/help/pufas.

Cell Wall Anchoring of the Campylobacter Antigens to Lactococcus lactis

Campylobacter jejuni is the most frequent cause of human food-borne gastroenteritis and chicken meat is the main source of infection. Recent studies showed that broiler chicken immunization against Campylobacter should be the most efficient way to lower the number of human infections by this pathogen. Induction of the mucosal immune system after oral antigen administration should provide protective immunity to chickens. In this work we tested the usefulness of Lactococcus lactis, the most extensively studied lactic acid bacterium, as a delivery vector for Campylobacter antigens. First we constructed hybrid protein - CjaA antigen presenting CjaD peptide epitopes on its surface. We showed that specific rabbit anti-rCjaAD serum reacted strongly with both CjaA and CjaD produced by a wild type C. jejuni strain. Next, rCjaAD and CjaA were fused to the C-terminus of the L. lactis YndF containing the LPTXG motif. The genes expressing these proteins were transcribed under control of the L. lactis Usp45 promoter and their products contain the Usp45 signal sequences. This strategy ensures a cell surface location of both analyzed proteins, which was confirmed by immunofluorescence assay. In order to evaluate the impact of antigen location on vaccine prototype efficacy, a L. lactis strain producing cytoplasm-located rCjaAD was also generated. Animal experiments showed a decrease of Campylobacter cecal load in vaccinated birds as compared with the control group and showed that the L. lactis harboring the surface-exposed rCjaAD antigen afforded greater protection than the L. lactis producing cytoplasm-located rCjaAD. To the best of our knowledge, this is the first attempt to employ Lactic Acid Bacteria (LAB) strains as a mucosal delivery vehicle for chicken immunization. Although the observed reduction of chicken colonization by Campylobacter resulting from vaccination was rather moderate, the experiments showed that LAB strains can be considered as an alternative vector to deliver heterologous antigens to the bird immune system. Additionally, the analysis of the structure and immunogenicity of the generated rCjaAD hybrid protein showed that the CjaA antigen can be considered as a starting point to construct multiepitope anti-Campylobacter vaccines.

N-(7-Chloroquinolinyl-4-aminoalkyl)arylsulfonamides as antimalarial agents: rationale for the activity with reference to inhibition of hemozoin formation

New chloroquinolinyl arylsulfonamides with potential antimalarial activity inhibited hemozoin formation exceedingly well.

Functional demonstrations of starch binding domains present in Ostreococcus tauri starch synthases isoforms

BACKGROUND

Starch-binding domains are key modules present in several enzymes involved in polysaccharide metabolism. These non-catalytic modules have already been described as essential for starch-binding and the catalytic activity of starch synthase III from the higher plant Arabidopsis thaliana. In Ostreococcus tauri, a unicellular green alga of the Prasinophyceae family, there are three SSIII isoforms, known as Ostta SSIII-A, SSIII-B and SSIII-C.

RESULTS

In this work, using in silico and in vitro characterization techniques, we have demonstrated that Ostta SSIII-A, SSIII-B and SSIII-C contain two, three and no starch-binding domains, respectively. Additionally, our phylogenetic analysis has indicated that OsttaSSIII-B, presenting three N-terminal SBDs, is the isoform more closely related to higher plant SSIII. Furthermore, the sequence alignment and homology modeling data gathered showed that both the main 3-D structures of all the modeled domains obtained and the main amino acid residues implicated in starch binding are well conserved in O. tauri SSIII starch-binding domains. In addition, adsorption assays showed that OsttaSSIII-A D2 and SSIII-B D2 domains are the two that make the greatest contribution to amylose and amylopectin binding, while OsttaSSIII-B D1 is also important for starch binding.

CONCLUSIONS

The results presented here suggest that differences between OsttaSSIII-A and SSIII-B SBDs in the number of and binding of amino acid residues may produce differential affinities for each isoform to polysaccharides. Increasing the knowledge about SBDs may lead to their employment in biomedical and industrial applications.

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.

TMFoldWeb: a web server for predicting transmembrane protein fold class

Background

Here we present TMFoldWeb, the web server implementation of TMFoldRec, a transmembrane protein fold recognition algorithm. TMFoldRec uses statistical potentials and utilizes topology filtering and a gapless threading algorithm. It ranks template structures and selects the most likely candidates and estimates the reliability of the obtained lowest energy model. The statistical potential was developed in a maximum likelihood framework on a representative set of the PDBTM database. According to the benchmark test the performance of TMFoldRec is about 77 % in correctly predicting fold class for a given transmembrane protein sequence.

Results

An intuitive web interface has been developed for the recently published TMFoldRec algorithm. The query sequence goes through a pipeline of topology prediction and a systematic sequence to structure alignment (threading). Resulting templates are ordered by energy and reliability values and are colored according to their significance level. Besides the graphical interface, a programmatic access is available as well, via a direct interface for developers or for submitting genome-wide data sets.

Conclusions

The TMFoldWeb web server is unique and currently the only web server that is able to predict the fold class of transmembrane proteins while assigning reliability scores for the prediction. This method is prepared for genome-wide analysis with its easy-to-use interface, informative result page and programmatic access.

Considering the info-communication evolution in the last few years, the developed web server, as well as the molecule viewer, is responsive and fully compatible with the prevalent tablets and mobile devices.

Reviewers

This article was reviewed by Dr. Michael Gromiha, Dr. Sandor Pongor and Dr. Frank Eisenhaber with Wing-Cheong Wong.

Discovery of an essential nucleotidylating activity associated with a newly delineated conserved domain in the RNA polymerase-containing protein of all nidoviruses

RNA viruses encode an RNA-dependent RNA polymerase (RdRp) that catalyzes the synthesis of their RNA(s). In the case of positive-stranded RNA viruses belonging to the order Nidovirales, the RdRp resides in a replicase subunit that is unusually large. Bioinformatics analysis of this non-structural protein has now revealed a nidoviral signature domain (genetic marker) that is N-terminally adjacent to the RdRp and has no apparent homologs elsewhere. Based on its conservation profile, this domain is proposed to have nucleotidylation activity. We used recombinant non-structural protein 9 of the arterivirus equine arteritis virus (EAV) and different biochemical assays, including irreversible labeling with a GTP analog followed by a proteomics analysis, to demonstrate the manganese-dependent covalent binding of guanosine and uridine phosphates to a lysine/histidine residue. Most likely this was the invariant lysine of the newly identified domain, named nidovirus RdRp-associated nucleotidyltransferase (NiRAN), whose substitution with alanine severely diminished the described binding. Furthermore, this mutation crippled EAV and prevented the replication of severe acute respiratory syndrome coronavirus (SARS-CoV) in cell culture, indicating that NiRAN is essential for nidoviruses. Potential functions supported by NiRAN may include nucleic acid ligation, mRNA capping and protein-primed RNA synthesis, possibilities that remain to be explored in future studies.