A comparison between internal protein nanoenvironments of α-helices and ß-sheets.

Secondary structure elements are generally found in almost all protein structures revealed so far. In general, there are more ß-sheets than α helices found inside the protein structures. For example, considering the PDB, DSSP and Stride definitions for secondary structure elements and by using the consensus among those, we found 60,727 helices in 4,376 chains identified in all-α structures and 129,440 helices in 7,898 chains identified in all-α and α + ß structures. For ß-sheets, we identified 837,345 strands in 184,925 ß-sheets located within 50,803 chains of all-ß structures and 1,541,961 strands in 355,431 ß-sheets located within 86,939 chains in all-ß and α + ß structures (data extracted on February 1, 2019). In this paper we would first like to address a full characterization of the nanoenvironment found at beta sheet locations and then compare those characteristics with the ones we already published for alpha helical secondary structure elements. For such characterization, we use here, as in our previous work about alpha helical nanoenvironments, set of STING protein structure descriptors. As in the previous work, we assume that we will be able to prove that there is a set of protein structure parameters/attributes/descriptors, which could fully describe the nanoenvironment around beta sheets and that appropriate statistically analysis will point out to significant changes in values for those parameters when compared for loci considered inside and outside defined secondary structure element. Clearly, while the univariate analysis is straightforward and intuitively understood, it is severely limited in coverage: it could be successfully applied at best in up to 25% of studied cases. The indication of the main descriptors for the specific secondary structure element (SSE) by means of the multivariate MANOVA test is the strong statistical tool for complete discrimination among the SSEs, and it revealed itself as the one with the highest coverage. The complete description of the nanoenvironment, by analogy, might be understood in terms of describing a key lock system, where all lock mini cylinders need to combine their elevation (controlled by a matching key) to open the lock. The main idea is as follows: a set of descriptors (cylinders in the key-lock example) must precisely combine their values (elevation) to form and maintain a specific secondary structure element nanoenvironment (a required condition for a key being able to open a lock).

Study of specific nanoenvironments containing α-helices in all-α and (α+ß)+(α/ß) proteins.

Protein secondary structure elements (PSSEs) such as α-helices, ß-strands, and turns are the primary building blocks of the tertiary protein structure. Our primary interest here is to reveal the characteristics of the nanoenvironment formed by both PSSEs and their surrounding amino acid residues (AARs), which might contribute to the general understanding of how proteins fold. The characteristics of such nanoenvironments must be specific to each secondary structure element, and we have set our goal here to gather the fullest possible description of the α-helical nanoenvironment. In general, this postulate (the existence of specific nanoenvironments for specific protein substructures/neighbourhoods/regions with distinct functionality) was already successfully explored and confirmed for some protein regions, such as protein-protein interfaces and enzyme catalytic sites. Consequently, PSSEs were the obvious next choice for additional work for further evidence showing that specific nanoenvironments (having characteristics fully describable by means of structural and physical chemical descriptors) do exist for the corresponding and determined intraprotein regions. The nanoenvironment of α-helices (nEoαH) is defined as any region of the protein where this secondary structure element type is detected. The nEoαH, therefore, includes not only the α-helix amino acid residues but also the residues immediately around the α-helix. The hypothesis that motivated this work is that it might in fact be possible to detect a postulated "signal" or "signature" that distinguishes the specific location of α-helices. This "signal" must be discernible by tracking differences in the values of physical, chemical, physicochemical, structural and geometric descriptors immediately before (or after) the PSSE from those in the region along the α-helices. The search for this specific nanoenvironment "signal" was made possible by aligning previously selected α-helices of equal length. Afterward, we calculated the average value, standard deviation and mean square error at each aligned residue position for each selected descriptor. We applied Student's t-test, the Kolmogorov-Smirnov test and MANOVA statistical tests to the dataset constructed as described above, and the results confirmed that the hypothesized "signal"/"signature" is both existing/identifiable and capable of distinguishing the presence of an α-helix inside the specific nanoenvironment, contextualized as a specific region within the whole protein. However, such conclusion might rarely be reached if only one descriptor is considered at a time. A more accurate signal with broader coverage is achieved only if one applies multivariate analysis, which means that several descriptors (usually approximately 10 descriptors) should be considered at the same time. To a limited extent (up to a maximum of 15% of cases), such conclusion is also possible with only a single descriptor, and the conclusion is also possible in general for up to 50-80% of cases when no less than 5 nonlinear descriptors are selected and considered. Using all the descriptors considered in this work, provided all assumptions about data characteristics for this analysis are met, multivariate analysis regularly reached a coverage and accuracy above 90%. Understanding how secondary structure elements are formed and maintained within a protein structure could enable a more detailed understanding of how proteins reach their final 3D structure and consequently, their function. Likewise, this knowledge may also improve the tools used to determine how good a structure is by means of comparing the "signal" around a selected PSSE with the one obtained from the best (resolution and quality wise) protein structures available.

Multiple structure single parameter: analysis of a single protein nano environment descriptor characterizing a shared loci on structurally aligned proteins.

MOTIVATION: A graphical representation of physicochemical and structural descriptors attributed to amino acid residues occupying the same topological position in different, structurally aligned proteins can provide a more intuitive way to associate possible functional implications to identified variations in structural characteristics. This could be achieved by observing selected characteristics of amino acids and of their corresponding nano environments, described by the numerical value of matching descriptor. For this purpose, a web-based tool called multiple structure single parameter (MSSP) was developed and here presented. RESULTS: MSSP produces a two-dimensional plot of a single protein descriptor for a number of structurally aligned protein chains. From a total of 150 protein descriptors available in MSSP, selected of >1500 parameters stored in the STING database, it is possible to create easily readable and highly informative XY-plots, where X-axis contains the amino acid position in the multiple structural alignment, and Y-axis contains the descriptor's numerical values for each aligned structure. To illustrate one of possible MSSP contributions to the investigation of changes in physicochemical and structural properties of mutants, comparing them with the cognate wild-type structure, the oncogenic mutation of M918T in RET kinase is presented. The comparative analysis of wild-type and mutant structures shows great changes in their electrostatic potential. These variations are easily depicted at the MSSP-generated XY-plot. AVAILABILITY AND IMPLEMENTATION: The web server is freely available at http://www.cbi.cnptia.embrapa.br/SMS/STINGm/MPA/index.html Web server implemented in Perl, Java and JavaScript and JMol or Protein Viewer as structure visualizers. CONTACT: goran.neshich@embrapa.br or gneshich@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational Biology Tools for Identifying Specific Ligand Binding Residues for Novel Agrochemical and Drug Design.

The term "agrochemicals" is used in its generic form to represent a spectrum of pesticides, such as insecticides, fungicides or bactericides. They contain active components designed for optimized pest management and control, therefore allowing for economically sound and labor efficient agricultural production. A "drug" on the other side is a term that is used for compounds designed for controlling human diseases. Although drugs are subjected to much more severe testing and regulation procedures before reaching the market, they might contain exactly the same active ingredient as certain agrochemicals, what is the case described in present work, showing how a small chemical compound might be used to control pathogenicity of Gram negative bacteria Xylella fastidiosa which devastates citrus plantations, as well as for control of, for example, meningitis in humans. It is also clear that so far the production of new agrochemicals is not benefiting as much from the in silico new chemical compound identification/discovery as pharmaceutical production. Rational drug design crucially depends on detailed knowledge of structural information about the receptor (target protein) and the ligand (drug/agrochemical). The interaction between the two molecules is the subject of analysis that aims to understand relationship between structure and function, mainly deciphering some fundamental elements of the nanoenvironment where the interaction occurs. In this work we will emphasize the role of understanding nanoenvironmental factors that guide recognition and interaction of target protein and its function modifier, an agrochemical or a drug. The repertoire of nanoenvironment descriptors is used for two selected and specific cases we have approached in order to offer a technological solution for some very important problems that needs special attention in agriculture: elimination of pathogenicity of a bacterium which is attacking citrus plants and formulation of a new fungicide. Finally, we also briefly describe a workflow which might be useful when research requires that model structures of target proteins are firstly generated (starting from genome sequences), followed by identification of ligand-target sites at the surface of those modeled structures, then application of procedures that adequately prepare both protein and ligand structures (the latter also involving filtration that satisfies acceptable adsorption/desorption/metabolism/excretion/toxicity [ADMET] parameters) for virtual high throughput screening (involving docking of ligands to indicated sites) and terminating by ranking of best pairs: target protein with selected ligand.

Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).

Analysis of binding properties and specificity through identification of the interface forming residues (IFR) for serine proteases in silico docked to different inhibitors.

BACKGROUND: Enzymes belonging to the same super family of proteins in general operate on variety of substrates and are inhibited by wide selection of inhibitors. In this work our main objective was to expand the scope of studies that consider only the catalytic and binding pocket amino acids while analyzing enzyme specificity and instead, include a wider category which we have named the Interface Forming Residues (IFR). We were motivated to identify those amino acids with decreased accessibility to solvent after docking of different types of inhibitors to sub classes of serine proteases and then create a table (matrix) of all amino acid positions at the interface as well as their respective occupancies. Our goal is to establish a platform for analysis of the relationship between IFR characteristics and binding properties/specificity for bi-molecular complexes. RESULTS: We propose a novel method for describing binding properties and delineating serine proteases specificity by compiling an exhaustive table of interface forming residues (IFR) for serine proteases and their inhibitors. Currently, the Protein Data Bank (PDB) does not contain all the data that our analysis would require. Therefore, an in silico approach was designed for building corresponding complexes. The IFRs are obtained by "rigid body docking" among 70 structurally aligned, sequence wise non-redundant, serine protease structures with 3 inhibitors: bovine pancreatic trypsin inhibitor (BPTI), ecotine and ovomucoid third domain inhibitor. The table (matrix) of all amino acid positions at the interface and their respective occupancy is created. We also developed a new computational protocol for predicting IFRs for those complexes which were not deciphered experimentally so far, achieving accuracy of at least 0.97. CONCLUSIONS: The serine proteases interfaces prefer polar (including glycine) residues (with some exceptions). Charged residues were found to be uniquely prevalent at the interfaces between the "miscellaneous-virus" subfamily and the three inhibitors. This prompts speculation about how important this difference in IFR characteristics is for maintaining virulence of those organisms.Our work here provides a unique tool for both structure/function relationship analysis as well as a compilation of indicators detailing how the specificity of various serine proteases may have been achieved and/or could be altered. It also indicates that the interface forming residues which also determine specificity of serine protease subfamily can not be presented in a canonical way but rather as a matrix of alternative populations of amino acids occupying variety of IFR positions.

PDB-Metrics: a web tool for exploring the PDB contents.

PDB-Metrics (http://sms.cbi.cnptia.embrapa.br/SMS/pdb_metrics/index.html) is a component of the Diamond STING suite of programs for the analysis of protein sequence, structure and function. It summarizes the characteristics of the collection of protein structure descriptions deposited in the Protein Data Bank (PDB) and provides a Web interface to search and browse the PDB, using a variety of alternative criteria. PDB-Metrics is a powerful tool for bioinformaticians to examine the data span in the PDB from several perspectives. Although other Web sites offer some similar resources to explore the PDB contents, PDB-Metrics is among those with the most complete set of such facilities, integrated into a single Web site. This program has been developed using SQLite, a C library that provides all the query facilities of a database management system.

Building multiple sequence alignments with a flavor of HSSP alignments.

Homology-derived secondary structure of proteins (HSSP) is a well-known database of multiple sequence alignments (MSAs) which merges information of protein sequences and their three-dimensional structures. It is available for all proteins whose structure is deposited in the PDB. It is also used by STING and (Java)Protein Dossier to calculate and present relative entropy as a measure of the degree of conservation for each residue of proteins whose structure has been solved and deposited in the PDB. However, if the STING and (Java)Protein Dossier are to provide support for analysis of protein structures modeled in computers or being experimentally solved but not yet deposited in the PDB, then we need a new method for building alignments having a flavor of HSSP alignments (myMSAr). The present study describes a new method and its corresponding databank (SH2QS--database of sequences homologue to the query [structure-having] sequence). Our main interest in making myMSAr was to measure the degree of residue conservation for a given query sequence, regardless of whether it has a corresponding structure deposited in the PDB. In this study, we compare the measurement of residue conservation provided by corresponding alignments produced by HSSP and SH2QS. As a case study, we also present two biologically relevant examples, the first one highlighting the equivalence of analysis of the degree of residue conservation by using HSSP or SH2QS alignments, and the second one presenting the degree of residue conservation for a structure modeled in a computer, which , as a consequence, does not have an alignment reported by HSSP.

Predicting enzyme class from protein structure using Bayesian classification.

Predicting enzyme class from protein structure parameters is a challenging problem in protein analysis. We developed a method to predict enzyme class that combines the strengths of statistical and data-mining methods. This method has a strong mathematical foundation and is simple to implement, achieving an accuracy of 45%. A comparison with the methods found in the literature designed to predict enzyme class showed that our method outperforms the existing methods.