Structural Bioinformatics: Computational Software and Databases for the Evaluation of Protein Structure

Structural genomics provides an important approach for characterizing and understanding systems biology. As a step toward better integrating protein three-dimensional (3D) structural information in cancer systems biology, we have constructed a Human Cancer Pathway Protein Interaction Network (HCPIN) by analysis of several classical cancer-associated signaling pathways and their physical protein-protein interactions. Many well known cancer-associated proteins play central roles as "hubs" or "bottlenecks" in the HCPIN. At least half of HCPIN proteins are either directly associated with or interact with multiple signaling pathways. Although some 45% of residues in these proteins are in sequence segments that meet criteria sufficient for approximate homology modeling (Basic Local Alignment Search Tool (BLAST) E-value <10(-6)), only approximately 20% of residues in these proteins are structurally covered using high accuracy homology modeling criteria (i.e. BLAST E-value <10(-6) and at least 80% sequence identity) or by actual experimental structures. The HCPIN Website provides a comprehensive description of this biomedically important multipathway network together with experimental and homology models of HCPIN proteins useful for cancer biology research. To complement and enrich cancer systems biology, the Northeast Structural Genomics Consortium is targeting >1000 human proteins and protein domains from the HCPIN for sample production and 3D structure determination. The long range goal of this effort is to provide a comprehensive 3D structure-function database for human cancer-associated proteins and protein complexes in the context of their interaction networks. The network-based target selection (BioNet) approach described here is an example of a general strategy for targeting co-functioning proteins by structural genomics projects.

The study of protein structure is of great interest in fields like biology, pharmacology and medicine due to the great impact that structural configuration has on protein functioning and behaviour. However, fully atomistic models are inaccurate or directly unavailable for many protein conformations because of the constraints that protein crystallography, the default method to obtain atomic configurations, imposes on protein structures. In that regard, Small Angle X-Ray Scattering (SAXS) has become a widely used tool to study proteins in solution, as it can retrieve structural information from proteins in their native environment without the need for crystallography, but at the cost of significant loss of information, and the need to interpret or reconstruct structure from the measurements. SAXS measurements consist of a band-pass filtered spherically averaged Fourier transform of the protein's atomic pair-distribution function, resulting in a 1D signal with limited support and with a restricted resolution range between 50 to 10 angstroms. Determining protein configurations from SAXS measurements is, thus, a very ill-posed challenge as only 1D information is available to reconstruct the protein's 3D structure and requiring regularized and constrained optimization frameworks to successfully solve it.

This article formulates the multidimensional nuclear Overhauser effect spectroscopy (NOESY) interpretation problem using graph theory and presents a novel, bottom-up, topology-constrained distance network analysis algorithm for NOESY cross peak interpretation using assigned resonances. AutoStructure is a software suite that implements this topology-constrained distance network analysis algorithm and iteratively generates structures using the three-dimensional (3D) protein structure calculation programs XPLOR/CNS or DYANA. The minimum input for AutoStructure includes the amino acid sequence, a list of resonance assignments, and lists of 2D, 3D, and/or 4D-NOESY cross peaks. AutoStructure can also analyze homodimeric proteins when X-filtered NOESY experiments are available. The quality of input data and final 3D structures is evaluated using recall, precision, and F-measure (RPF) scores, a statistical measure of goodness of fit with the input data. AutoStructure has been tested on three protein NMR data sets for which high-quality structures have previously been solved by an expert, and yields comparable high-quality distance constraint lists and 3D protein structures in hours. We also compare several protein structures determined using AutoStructure with corresponding homologous proteins determined with other independent methods. The program has been used in more than two dozen protein structure determinations, several of which have already been published.

Background: The Mycobacterium tuberculosis complex (MTBC) bacteria include the slowly growing, host-associated bacteria Mycobacterium tuberculosis, Mycobacterium Bovis, Mycobacterium microti, Mycobacterium africanum, Mycobacterium pinnipedii. Aim: Comparative Functional Genomics Studies for understanding the Hypothetical Proteins in Mycobacterium tuberculosis variant microti 12. Objective: A computational genomics study was performed to understand the 247 hypothetical protein genes. Functional annotation of virtual proteins was performed on different servers to maximize confidence level. Methods: Sequence Retrieval. The whole genome sequences for the Mycobacterium tuberculosis micro variant 12 were retrieved from the KEGG database ( http://www.genome.jp/kegg/) and were used for screening 247 hypothetical proteins (Fig. 1 ). Functional Annotation and Sub-cellular localization. The Mycobacterium tuberculosis micro variant 12 hypothetical proteins were screened and sorted out from the genome and were individually analyzed for the presence of conserved functional domains by using computational biology tools like CDD-BLAST ( https://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) ;Pfam ( http://pfam.xfam.org/ncbiseq/398365647); The subcellular localization of hypothetical proteins was determined by CELLO2GO ( http://cello.life.nctu.edu.tw). These web tools can search the defined conserved domains in the sequences available in the online servers or databases and assist in the classification of proteins in the appropriate families. Protein Structure Prediction. The in-silico structure predictions of the hypothetical protein sequences showing functional properties were carried out by using the PS2 Protein Structure Prediction Server ( http://www.ps2.life.nctu.edu.tw/). The online server helps to generate the 3D structures of the hypothetical proteins. The server accepts the sequences in FASTA format as a query to generate resultant proteins 3D structures. The structure determination is completely based on the conserved template regions detected during functional annotations. Protein-protein interaction through String database: The interaction of each hypothetical protein analyzed for functional characteristics was subjected to a protein-protein interaction server for the prediction of a possible functional role in interaction amongst the available known proteins ( https://string-db.org/). This information can help us to further validated the functional role of such hypothetical proteins and their possible role in the Mycobacterium Tuberculosis micro variant. Protein secondary structure prediction through JPred4: The secondary structure prediction of all the hypothetical proteins was determined through JPred4 ( http://www.compbio.dundee.ac.uk/jpred4/index.html) and served to identify the available secondary structures in the unknown hypothetical protein sequences. These further help us to understand the available templates in the uncharacterized protein sequences for the prediction of novel functions associated with these proteins. The predictions were further characterized by the Phyre2 server for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Protein modeling, prediction, and analysis through Phyre2. The hypothetical proteins which were identified to have functional properties were further characterized by the Phyre2 server ( http://www.sbg.bio.ic.ac.uk/phyre2) for structural modeling and prediction of templates based on comparative analysis based on conserved domains. Results: A computational genomics study was performed to understand the 247 hypothetical protein genes Functional annotation of virtual proteins, and was performed on different servers to maximize confidence level. The functional prediction was performed by CDD-Blast and Pfam. The gene sequences of proteins have probably been successfully functionally annotated, characterized, and their subcellular localization and 3-D structural predictions have been predicted computationally. Online automated bioinformatics tools such as CDD-Blast, Pfam, CELLO2GO and PS2-Server were used for the structural and functional characterization of screened hypothetical proteins. The structure, function, and subcellular localization of a hypothetical protein from Mycobacterium tuberculosis variant microti 12 have been obtained and presented (Fig. 2 ). Also, the three-dimensional structure generated after using the template with the highest score was displayed as the template ID in the structure column of the respective hypothetical protein. However, as systems biology denies hypothetical protein functions, the structures of such proteins can be tested through biological processes and experiments, making them suitable for understanding their role in the life cycle, pathogenesis, and drug development. We can further explore these predictive possibilities in pharmaceuticals, and other clinically relevant studies. This study by HP helped find structure-function relationships in Mycobacterium tuberculosis variant microti 12 using a variety of bioinformatics tools. The string database made predictions about protein-protein interactions and the template helped us predict a hypothetical protein structure and even helped us find its 3D protein structure. Protein profiling can be performed on structures retrieved from these servers. This is useful for proteomics studies, including protein-protein interactions, protein expression of specific hypothetical proteins, and post-translational modifications of protein-coding genes. Further understanding of these hypothetical proteins can help us to know more about the Mycobacterium tuberculosis complex (MTBC) and may assist in Drugs and inhibitors against different pathogens within this complex. Conclusion: The all-inclusive bioinformatic study has helped to functionally elucidate 247 hypothetical proteins, which have resulted and made it easier to understand many functional proteins available in the Mycobacterium tuberculosis micro variant 12. The subcellular localization of the 247 sorted hypothetical proteins was also carried &amp;amp; which further helped us understand the localization of identified enzymes or proteins. We have successfully characterized the 247 unknown proteins of hypothetical protein sequences from Mycobacterium tuberculosis micro variant 12 to validate their structure and functions of the gene products. These predicted functions and three-dimensional structures may lead to establishing their role in the life cycle of the bacterium. This computationally generated data can also be further used for developing new protocols for new vaccines against Mycobacterium tuberculosis micro variant 12 that are essential for preventing infection, diseases, and transmission. This complete result of Hypothetical Protein is needed for further studies of the whole genomic of the Mycobacterium Tuberculosis micro variant 12 for their function interpretation which further help in the understanding of its functions as well as structure. Moreover, this interpretation would help us to study the evolution of Mycobacterium Tuberculosis micro variant 12 which further helps in the process of discovering the drugs to inhibit the causes of diseases.

Although many studies have addressed the significance of interactions in the dimeric structures of proteins, there is no dedicated database of these interactions. To this end, the Molecular Interactions in Protein Dimer Structure (MIPDS) database has been developed; it is an open-access repository containing 60 298 3D structures of dimeric proteins sourced from the Protein Data Bank. This helps researchers comprehend the types of interaction, which include those mediated by water, small molecules or ligands and direct interactions, in 3D structures at the molecular level. The database is accessible through a user-friendly interface, where users can conduct searches based on PDB accession number, interaction type and geometric parameters. It can be viewed in textual and graphical formats using the plug-in JSmol. MIPDS is updated weekly using programmed scripts to incorporate newly released dimeric structures and analyses of their interaction types. The database is intended for the scientific community working in structural biology, structural bioinformatics, drug discovery and development. MIPDS is freely accessible to users worldwide at http://dicsoft1.physics.iisc.ac.in/mipds.

The 3D structure of proteins is the major factor that determines their biological activity. The synthesis of new proteins and the crystallographic analysis of their 3D structure is very slow and very expensive process. If we can predict the 3D structure of many proteins, than only proteins with expected properties have to be synthesized. The main idea, implemented in our research, is not to use lattice cube with constant size to make possible conformations in this space, but to use flexible constrain for spreading away from the center of the formed molecule, which constrain has a coefficient that can vary in the process of folding according the percentage of failing to make possible conformation, caused by lack of space. Less space allowed causes difficulties to make the conformations but the achieved forms are more compact and with lower energy. More space causes making many useless random conformations and more computational time is needed to find the best conformation and to make the same one more times in this random process in order to have bigger probability that it is the best one. This method may be used in every other model for protein folding prediction to improve the computational time and the probability of finding the accurate 3D structure – our results show that advantage.

Chapter 5 - Homology modeling: Developing 3D structures of target proteins missing in databases

Cumin (Cuminum cyminum L.) belongs to Apiaceae family, is an important seed spice and one of the earliest spices used by mankind for its aromatic and medicinal properties. Cumin seeds are very commonly used to give flavour and texture to the food. In addition, it also acts as preservative and provides nutritional and health benefits. The typical aroma of the cumin seeds is due to their volatile oil content, the principal constituent of which is cuminol (cumin aldehyde). Studies have been done on properties of the essential oil content of cumin and other aspects. Only few studies are available citing the nucleotides sequences present in cumin but no reports are available on in-silico studies of proteins structure supposed to be present in cumin. In the present study, a high quality 3D structure and function of cumin protein (GQ 33) have been predicted for the hypothetical amino acid sequence obtained from reported nucleotides of cumin. The physiochemical properties for GQ 33 cumin protein were identified using Protparam. The instability index 39.52, isoelectric point (pI) 9.97 and grand average hydropathicity (GRAVY) -0.245 determined from primary and secondary structural analysis illustrate the stability of GQ 33 protein. Stability of 3D structure of protein was confirmed using Ramachandran plot having 95.7% residues or empirically distributed data-points present in the structure lie in most favoured region. For the prediction of secondary structure of protein FASTA sequences were used and prediction was performed using PSIPRED, JPRED, Protein Predict and SOPMA. SAVES server was used for further validation of protein structure.

The MAT1-1-1 and MAT1-2-1 proteins are essential for the sexual reproduction of Ophiocordyceps sinensis. Although Hirsutella sinensis has been postulated to be the sole anamorph of O. sinensis and to undergo self-fertilization under homothallism or pseudohomothallism, little is known about the three-dimensional (3D) structures of the mating proteins in the natural Cordyceps sinensis insect-fungal complex, which is a valuable therapeutic agent in traditional Chinese medicine. However, the alternative splicing and differential occurrence and translation of the MAT1-1-1 and MAT1-2-1 genes have been revealed in H. sinensis, negating the self-fertilization hypothesis but rather suggesting the occurrence of self-sterility under heterothallic or hybrid outcrossing. In this study, the MAT1-1-1 and MAT1-2-1 proteins in 173 H. sinensis strains and wild-type C. sinensis isolates were clustered into six and five clades in the Bayesian clustering trees and belonged to 24 and 21 diverse AlphaFold-predicted 3D structural morphs, respectively. Over three-quarters of the strains/isolates contained either MAT1-1-1 or MAT1-2-1 proteins but not both. The diversity of the heteromorphic 3D structures of the mating proteins suggested functional alterations of the proteins and provided additional evidence supporting the self-sterility hypothesis under heterothallism and hybridization for H. sinensis, Genotype #1 of the 17 genome-independent O. sinensis genotypes. The heteromorphic stereostructures and mutations of the MAT1-1-1 and MAT1-2-1 proteins in the wild-type C. sinensis isolates and natural C. sinensis insect-fungi complex suggest that there are various sources of the mating proteins produced by two or more cooccurring heterospecific fungal species in natural C. sinensis that have been discovered in mycobiotic, molecular, metagenomic, and metatranscriptomic studies, which may inspire future studies on the biochemistry of mating and pheromone receptor proteins and the reproductive physiology of O. sinensis.

Computational function prediction is one of the most important problems in bioinformatics as elucidating the function of genes is a central task in molecular biology and genomics. Most of the existing function prediction methods use protein sequences as the primary source of input information because the sequence is the most available information for query proteins. There are attempts to consider other attributes of query proteins. Among these attributes, the three-dimensional (3D) structure of proteins is known to be very useful in identifying the evolutionary relationship of proteins, from which functional similarity can be inferred. Here, we report a novel protein function prediction method, ContactPFP, which uses predicted residue-residue contact maps as input structural features of query proteins. Although 3D structure information is known to be useful, it has not been routinely used in function prediction because the 3D structure is not experimentally determined for many proteins. In ContactPFP, we overcome this limitation by using residue-residue contact prediction, which has become increasingly accurate due to rapid development in the protein structure prediction field. ContactPFP takes a query protein sequence as input and uses predicted residue-residue contact as a proxy for the 3D protein structure. To characterize how predicted contacts contribute to function prediction accuracy, we compared the performance of ContactPFP with several well-established sequence-based function prediction methods. The comparative study revealed the advantages and weaknesses of ContactPFP compared to contemporary sequence-based methods. There were many cases where it showed higher prediction accuracy. We examined factors that affected the accuracy of ContactPFP using several illustrative cases that highlight the strength of our method.

Knowledge regarding the 3D structure of a protein provides useful information about the protein's functional properties. Particularly, structural similarity between proteins can be used as a good predictor of functional similarity. One method that uses the 3D geometrical structure of proteins in order to compare them is the similarity value (SV). In this paper, we introduce a new definition of the SV measure for comparing two proteins. To this end, we consider the mass of the protein's atoms and concentrate on the number of protein's atoms to be compared. This defines a new measure, called the weighted similarity value (WSV), adding physical properties to geometrical properties. We also show that our results are in good agreement with the results obtained by TM-SCORE and DALILITE. WSV can be of use in protein classification and in drug discovery.

Structure of a protein largely determines its functional properties. Hence, the knowledge of the protein's 3D structure is an important aspect in determining solutions to fundamental biological problems. Structure prediction algorithms generally employ clustering algorithm to select the optimal model for a target from a large number of predicted confirmations (a.k.a. decoy). Despite significant advancement in clustering-based optimal decoy selection methods, these approaches often cannot deliver high performance in terms of the time taken to cluster large number of protein structures owing to the computational cost associated with pairwise structural superpositions. Here, we propose a superposition-free approach to protein decoy clustering, called clustQ, based on weighted internal distance comparisons. Experimental results suggest that the novel weighing scheme is helpful in both reproducing the decoy-native similarity score and estimating pairwise clustering based predicted quality score in a computationally efficient manner. clustQ attains performance comparable to the state-of-the-art multi-model decoy quality estimation methods participating in the latest Critical Assessment of protein Structure Prediction (CASP) experiments irrespective of target difficulty. Moreover, clustQ predicted score offers a unique way to reliably estimate target difficulty without the knowledge of the experimental structure. clustQ is freely available at http://watson.cse.eng.auburn.edu/clustQ/.

2 - The Physiological Structure and Function of Proteins

Protein structure prediction aims to model the three‐dimensional (3D) structure of so far structurally uncharacterised proteins from their amino acid sequence. Motivated by the observation that homologous proteins with related amino acid sequences have similar 3D structures, protein homology modelling uses comparative methods to generate models for a target protein based on one or more related proteins with known 3D structure. The coordinates of the model are generated based on alignments between the target's and template's amino acid sequences, which define the correspondence between residues in both proteins. Ultimately, the quality of a computational model determines its usefulness for specific biomedical applications. Therefore, model quality estimation methods are used to identify unreliable or erroneous regions in the resulting models, and to estimate the overall accuracy of a model. Homology modelling (or comparative modelling) is currently the most accurate computational method available to routinely generate models of sufficient quality for various applications in life science research. Comparative protein modelling methods have been completely automated in recent years, and several Internet servers offer protein modelling services which are reliable and easy to use – also for the nonexpert in computational biology. Key Concepts: Protein structure prediction aims to model the three‐dimensional structure of so far structurally uncharacterised proteins (‘target’) based on their amino acid sequence. Homologous proteins with related amino acid sequences have similar three‐dimensional structures. Protein homology modelling uses information from one or more related proteins with known three‐dimensional structure (‘template’) to generate models for the target protein. Sensitive sequence searching methods are applied to identify template proteins with known structures in large databases. An alignment between the target's and template's amino acid sequences describes the correspondence between residues in both proteins. The coordinates of the model are constructed by extracting positional information from the corresponding structural template. Segments of the target protein not covered by template information (e.g. insertions/deletion in the alignment) have to be constructed using de novo modelling methods. Model quality estimation methods are used to identify unreliable or erroneous regions in the resulting models. Ultimately, the quality of a structural model determines its usefulness for specific biomedical applications.

BackgroundProtein phosphorylation catalyzed by kinases plays crucial regulatory roles in cellular processes. Given the high-throughput mass spectrometry-based experiments, the desire to annotate the catalytic kinases for in vivo phosphorylation sites has motivated. Thus, a variety of computational methods have been developed for performing a large-scale prediction of kinase-specific phosphorylation sites. However, most of the proposed methods solely rely on the local amino acid sequences surrounding the phosphorylation sites. An increasing number of three-dimensional structures make it possible to physically investigate the structural environment of phosphorylation sites.ResultsIn this work, all of the experimental phosphorylation sites are mapped to the protein entries of Protein Data Bank by sequence identity. It resulted in a total of 4508 phosphorylation sites containing the protein three-dimensional (3D) structures. To identify phosphorylation sites on protein 3D structures, this work incorporates support vector machines (SVMs) with the information of linear motifs and spatial amino acid composition, which is determined for each kinase group by calculating the relative frequencies of 20 amino acid types within a specific radial distance from central phosphorylated amino acid residue. After the cross-validation evaluation, most of the kinase-specific models trained with the consideration of structural information outperform the models considering only the sequence information. Furthermore, the independent testing set which is not included in training set has demonstrated that the proposed method could provide a comparable performance to other popular tools.ConclusionThe proposed method is shown to be capable of predicting kinase-specific phosphorylation sites on 3D structures and has been implemented as a web server which is freely accessible at http://csb.cse.yzu.edu.tw/PhosK3D/. Due to the difficulty of identifying the kinase-specific phosphorylation sites with similar sequenced motifs, this work also integrates the 3D structural information to improve the cross classifying specificity.