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.

Citrus bioflavonoid, hesperetin, as inhibitor of two thrombin-like snake venom serine proteases isolated from Crotalus simus.

Around 5.5 million people suffer from snakebites per year, with about 400,000 cases with some type of sequelae, such as amputation, and 20,000 to 125,000 cases with the fatal end. Usually, the victim outcome depends on correct, agile and many times in situ intervention based on the proper identification of the snake venom type and its potential effects, among other factors. Therefore, knowledge on the snake venom composition and a research on inhibitors of snake venom target components might ameliorate envenoming dangerous outcome. Herein, two thrombin-like serine proteases from the Crotalus simus snake venom - SVSP1 and SVSP2 - were isolated in two chromatographic steps, using gel filtration and then RP-HPLC. They showed molecular masses of around 31.3 and 24.6 kDa, respectively, and mostly ß-sheet secondary structure features. The SVSP1 and SVSP2 were sequenced using tandem mass spectrometry (Q-TOF). Using the known serine protease structure (PDB entry: 4e7n), which was evaluated as homologous to the two target proteins, in silico docking results showed that hesperetin is its excellent inhibitor. Using in vitro tests with the commercial hesperetin, kinetic parameters were obtained for SVSPs against the synthetic substrate BApNA. Obtained results pointed that hesperetin might act as an uncompetitive (SVSP1) or mixed (SVSP2) inhibitor. Also, the fluorescence quenching upon inhibition was observed, as well as, red shift in maximums of around 20 nm, which indicate that the tryptophan residues in the target enzymes suffered conformational changes caused by hesperetin binding. Thus, a naturally occurring flavone that can easily be extracted from oranges might serve as low-cost inhibitor of the investigated snake venom proteases.

Discovery of small molecule inhibitors for the snake venom metalloprotease BaP1 using in silico and in vitro tests.

Snakebites represent an important public health problem, with a great number of victims with permanent sequelae or fatal outcomes, particularly in rural, agriculturally active areas. The snake venom metalloproteases (SVMPs) are the principal proteins responsible for some clinically-relevant effects, such as local and systemic hemorrhage, dermonecrosis, and myonecrosis. Because of the difficulties in neutralizing them rapidly and locally by antivenoms, the search and design of small molecules as inhibitors of SVMPs are proposed. The Bothrops asper metalloprotease P1 (BaP1) is hereby used as a target protein and by High Throughput Virtual Screening (HTVS) approach, the free access virtual libraries: ZINC, PubChem and ChEMBL, were searched for potent small molecule inhibitors. Results from the aforementioned approaches provided strong evidences on the structural requirements for the efficient BaP1 inhibition such as the presence of the pyrimidine-2,4,6-trione moiety. The two proposed compounds have also shown excellent results in performed in vitro interaction studies against BaP1.

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.

Identification of snake bradykinin-potentiating peptides (BPPs)-simile sequences in rat brain--Potential BPP-like precursor protein?

Bradykinin-potentiating peptides (BPPs) from the South American pit viper snake venom were the first natural inhibitors of the human angiotensin I-converting enzyme (ACE) described. The pioneer characterization of the BPPs precursor from the snake venom glands by our group showed for the first time the presence of the C-type natriuretic peptide (CNP) in this same viper precursor protein. The confirmation of the BPP/CNP expression in snake brain regions correlated with neuroendocrine functions stimulated us to pursue the physiological correlates of these vasoactive peptides in mammals. Notably, several snake toxins were shown to have endogenous physiological correlates in mammals. In the present work, we expressed in bacteria the BPPs domain of the snake venom gland precursor protein, and this purified recombinant protein was used to raise specific polyclonal anti-BPPs antibodies. The correspondent single protein band immune-recognized in adult rat brain cytosol was isolated by 2D-SDS/PAGE and/or HPLC, before characterization by MS fingerprint analysis, which identified this protein as superoxide dismutase (SOD, EC 1.15.1.1), a classically known enzyme with antioxidant activity and important roles in the blood pressure modulation. In silico analysis showed the exposition of the BPP-like peptide sequences on the surface of the 3D structure of rat SOD. These peptides were chemically synthesized to show the BPP-like biological activities in ex vivo and in vivo pharmacological bioassays. Taken together, our data suggest that SOD protein have the potential to be a source for putative BPP-like bioactive peptides, which once released may contribute to the blood pressure control in mammals.

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).

Identification of new sphingomyelinases D in pathogenic fungi and other pathogenic organisms.

Sphingomyelinases D (SMases D) or dermonecrotic toxins are well characterized in Loxosceles spider venoms and have been described in some strains of pathogenic microorganisms, such as Corynebacterium sp. After spider bites, the SMase D molecules cause skin necrosis and occasional severe systemic manifestations, such as acute renal failure. In this paper, we identified new SMase D amino acid sequences from various organisms belonging to 24 distinct genera, of which, 19 are new. These SMases D share a conserved active site and a C-terminal motif. We suggest that the C-terminal tail is responsible for stabilizing the entire internal structure of the SMase D Tim barrel and that it can be considered an SMase D hallmark in combination with the amino acid residues from the active site. Most of these enzyme sequences were discovered from fungi and the SMase D activity was experimentally confirmed in the fungus Aspergillus flavus. Because most of these novel SMases D are from organisms that are endowed with pathogenic properties similar to those evoked by these enzymes alone, they might be associated with their pathogenic mechanisms.

New mutation in the myocilin gene segregates with juvenile-onset open-angle glaucoma in a Brazilian family.

Mutations in the myocilin gene (MYOC) account for most cases of autosomal dominant juvenile-onset open-angle glaucoma (JOAG), an earlier and more severe form of POAG. We accessed seven members of a Brazilian JOAG family by clinical and molecular investigation. Four out of seven family members were diagnosed with JOAG. All of these patients presented high intraocular pressure and two of them were bilaterally blind. The disease onset varied from 20 to 30years old. There was a nine-year-old family member who had not yet manifested the disease, although he was also a carrier of the mutation. Ophthalmologic examination included: evaluation of the visual field and optic disc, intraocular pressure measurement, and gonioscopy. The three exons and intron/exon junctions of the MYOC gene were screened for mutations through direct sequencing of PCR-amplified DNA fragments. Mutation screening revealed an in-frame mutation in the third exon of the MYOC gene: an insertion of six nucleotides between the cDNA positions 1187 and 1188 (c.1187_1188insCCCAGA, p.D395_E396insDP). This mutation presented an autosomal dominant pattern of inheritance, segregating with the disease in four family members for three generations, and it was absent in 60 normal controls. We also performed a computational structure modeling of olfactomedin-like domain of myocilin protein and conducted in silico analysis to predict the structural changes in the myocilin protein due to the presence of the mutation. These findings may be important for future diagnosis of other presymptomatic family members, as well as for the increase of the panel of MYOC mutations and their effects on phenotype.

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.

Protein cutoff scanning: A comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins.

In this study, we carried out a comparative analysis between two classical methodologies to prospect residue contacts in proteins: the traditional cutoff dependent (CD) approach and cutoff free Delaunay tessellation (DT). In addition, two alternative coarse-grained forms to represent residues were tested: using alpha carbon (CA) and side chain geometric center (GC). A database was built, comprising three top classes: all alpha, all beta, and alpha/beta. We found that the cutoff value at about 7.0 A emerges as an important distance parameter. Up to 7.0 A, CD and DT properties are unified, which implies that at this distance all contacts are complete and legitimate (not occluded). We also have shown that DT has an intrinsic missing edges problem when mapping the first layer of neighbors. In proteins, it may produce systematic errors affecting mainly the contact network in beta chains with CA. The almost-Delaunay (AD) approach has been proposed to solve this DT problem. We found that even AD may not be an advantageous solution. As a consequence, in the strict range up to 7.0 A, the CD approach revealed to be a simpler, more complete, and reliable technique than DT or AD. Finally, we have shown that coarse-grained residue representations may introduce bias in the analysis of neighbors in cutoffs up to 6.8 A, with CA favoring alpha proteins and GC favoring beta proteins. This provides an additional argument pointing to the value of 7.0 A as an important lower bound cutoff to be used in contact analysis of proteins.

Using linear algebra for protein structural comparison and classification.

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Using linear algebra for protein structural comparison and classification

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80 percent. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.

Crystallographic structure and substrate-binding interactions of the molybdate-binding protein of the phytopathogen Xanthomonas axonopodis pv. citri.

In Xanthomonas axonopodis pv. citri (Xac or X. citri), the modA gene codes for a periplasmic protein (ModA) that is capable of binding molybdate and tungstate as part of the ABC-type transporter required for the uptake of micronutrients. In this study, we report the crystallographic structure of the Xac ModA protein with bound molybdate. The Xac ModA structure is similar to orthologs with known three-dimensional structures and consists of two nearly symmetrical domains separated by a hinge region where the oxyanion-binding site lies. Phylogenetic analysis of different ModA orthologs based on sequence alignments revealed three groups of molybdate-binding proteins: bacterial phytopathogens, enterobacteria and soil bacteria. Even though the ModA orthologs are segregated into different groups, the ligand-binding hydrogen bonds are mostly conserved, except for Archaeglobus fulgidus ModA. A detailed discussion of hydrophobic interactions in the active site is presented and two new residues, Ala38 and Ser151, are shown to be part of the ligand-binding pocket.

Single nucleotide polymorphisms identification in expressed genes of Schistosoma mansoni.

Single nucleotide polymorphism (SNP) markers have been shown to be useful in genetic investigations of medically important parasites and their hosts. In this paper, we describe the prediction and validation of SNPs in ESTs of Schistosoma mansoni. We used 107,417 public sequences of S. mansoni and identified 15,614 high-quality candidate SNPs in 12,184 contigs. The presence of predicted SNPs was observed in well characterized antigens and vaccine candidates such as those coding for myosin; Sm14 and Sm23; cathepsin B and triosephosphate isomerase (TPI). Additionally, SNPs were experimentally validated for the cathepsin B. A comparative model of the S. mansoni cathepsin B was built for predicting the possible consequences of amino acid substitutions on the protein structure. An analysis of the substitutions indicated that the amino acids were mostly located on the surface of the molecule, and we found no evidence for a significant conformational change of the enzyme. However, at least one of the substitutions could result in a structural modification of an epitope.

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.