Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

HPREP: a comprehensive database for human proteome repeats.

Amino acid repeats are found to play important roles in both structures and functions of the proteins. These are commonly found in all kingdoms of life, especially in eukaryotes and a larger fraction of human proteins composed of repeats. Further, the abnormal expansions of shorter repeats cause various diseases to humans. Therefore, the analysis of repeats of the entire human proteome along with functional, mutational and disease information would help to better understand their roles in proteins. To fulfill this need, we developed a web database HPREP (http://bioinfo.bdu.ac.in/hprep) for human proteome repeats using Perl and HTML programming. We identified different categories of well-characterized repeats and domain repeats that are present in the human proteome of UniProtKB/Swiss-Prot by using in-house Perl programming and novel repeats by using the repeat detection T-REKS tool as well as XSTREAM web server. Further, these proteins are annotated with functional, mutational and disease information and grouped according to specific repeat types. The developed database enables the users to search by specific repeat type in order to understand their involvement in proteins. Thus, the HPREP database is expected to be a useful resource to gain better insight regarding the different repeats in human proteome and their biological roles.

Combined QSAR Model and Chemical Similarity Search for Novel HMGCoA Reductase Inhibitors for Coronary Heart Disease.

BACKGROUND: Coronary heart disease generally occurs due to cholesterol accumulation in the walls of the heart arteries. Statins are the most widely used drugs which work by inhibiting the active site of 3-Hydroxy-3-methylglutaryl-CoA reductase (HMGCR) enzyme that is responsible for cholesterol synthesis. A series of atorvastatin analogs with HMGCR inhibition activity have been synthesized experimentally which would be expensive and time-consuming. METHODS: In the present study, we employed both the QSAR model and chemical similarity search for identifying novel HMGCR inhibitors for heart-related diseases. To implement this, a 2D QSAR model was developed by correlating the structural properties to their biological activity of a series of atorvastatin analogs reported as HMGCR inhibitors. Then, the chemical similarity search of atorvastatin analogs was performed by using PubChem database search. RESULTS AND DISCUSSION: The three-descriptor model of charge (GATS1p), connectivity (SCH-7) and distance (VE1_D) of the molecules is obtained for HMGCR inhibition with the statistical values of R2= 0.67, RMSEtr= 0.33, R2 ext= 0.64 and CCCext= 0.76. The 109 novel compounds were obtained by chemical similarity search and the inhibition activities of the compounds were predicted using QSAR model, which were close in the range of experimentally observed threshold. CONCLUSION: The present study suggests that the QSAR model and chemical similarity search could be used in combination for identification of novel compounds with activity by in silico with less computation and effort.

Identification and Analysis of Long Repeats of Proteins at the Domain Level.

Amino acid repeats play an important role in the structure and function of proteins. Analysis of long repeats in protein sequences enables one to understand their abundance, structure and function in the protein universe. In the present study, amino acid repeats of length >50 (long repeats) were identified in a non-redundant set of UniProt sequences using the RADAR program. The underlying structures and functions of these long repeats were carried out using the Gene3D for structural domains, Pfam for functional domains and enzyme and non-enzyme functional classification for catalytic and binding of the proteins. From a structural perspective, these long repeats seem to predominantly occur in certain architectures such as sandwich, bundle, barrel, and roll and within these architectures abundant in the superfolds. The lengths of the repeats within each fold are not uniform exhibiting different structures for different functions. We also observed that long repeats are in the domain regions of the family and are involved in the function of the proteins. After grouping based on enzyme and non-enzyme classes, we observed the abundant occurrence of long repeats in specific catalytic and binding of the proteins. In this study, we have analyzed the occurrence of long repeats in the protein sequence universe apart from well-characterized short tandem repeats in sequences and their structures and functions of the proteins at the domain level. The present study suggests that long repeats may play an important role in the structure and function of domains of the proteins.

QSAR Analysis of Multimodal Antidepressants Vortioxetine Analogs Using Physicochemical Descriptors and MLR Modeling.

BACKGROUND: Vortioxetine is a multimodal antidepressant drug with combined effects on SERT as an inhibitor, 5-HT1A as agonist and 5-HT3A as an antagonist. Series of vortioxetine analogs have been reported as multi antidepressant compounds and they block serotonin transport into the neuronal cells, activate the postsynaptic 5-HT1A receptors and eliminate the low activity of 5-HT3A receptors. OBJECTIVE: To explore the important properties of vortioxetine analogs involved in antidepressant activity by developing 2D QSAR models. METHODS: Selections of significant descriptors were performed by Least Absolute Shrinkage and Selection Operator (LASSO) method and, the Multiple Linear Regression (MLR) method and All Subsets and GA algorithm included in QSARINS software were used for generating QSAR models. Further, the virtual screening was performed based on bioactivity and structure similarity using the PubChem database. RESULTS: The four descriptor model of complementary information content (CIC2), solubility (bcutp3), mass (bcutm8) and partial charge in van der Waals surface area (PEOEVSA7) of the molecules is obtained for SERT inhibition with the significant statistics of R2= 0.69, RMSEtr= 0.44, R2 ext= 0.62 and CCCext= 0.78. For 5-HT1A agonist, the two descriptor model of molecular shape (Kappm3) and van der Waals volume of the atoms (bcutv11) with R2= 0.78, RMSEtr= 0.33, R2 ext = 0.83, and CCCext= 0.87 is established. The three descriptor model of information content (IC3), solubility (bcutp9) and electronegativity (GATSe5) of the molecules with R2= 0.61, RMSEtr= 0.34, R2 ext= 0.69 and CCCext= 0.72 is obtained for 5-HT3A antagonist. The antidepressant activities of 16 virtual screened compounds were predicted using the developed models. CONCLUSION: The developed QSAR models may be useful to predict antidepressant activity for the newly synthesized vortioxetine analogs.

Dihedral angle preferences of amino acid residues forming various non-local interactions in proteins.

In theory, a polypeptide chain can adopt a vast number of conformations, each corresponding to a set of backbone rotation angles. Many of these conformations are excluded due to steric overlaps. Ramachandran and coworkers were the first to look into this problem by plotting backbone dihedral angles in a two-dimensional plot. The conformational space in the Ramachandran map is further refined by considering the energetic contributions of various non-bonded interactions. Alternatively, the conformation adopted by a polypeptide chain may also be examined by investigating interactions between the residues. Since the Ramachandran map essentially focuses on local interactions (residues closer in sequence), out of interest, we have analyzed the dihedral angle preferences of residues that make non-local interactions (residues far away in sequence and closer in space) in the folded structures of proteins. The non-local interactions have been grouped into different types such as hydrogen bond, van der Waals interactions between hydrophobic groups, ion pairs (salt bridges), and ππ-stacking interactions. The results show the propensity of amino acid residues in proteins forming local and non-local interactions. Our results point to the vital role of different types of non-local interactions and their effect on dihedral angles in forming secondary and tertiary structural elements to adopt their native fold.

Prediction of kinase-inhibitor binding affinity using energetic parameters.

The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict the experimental log (IC50) values of 25 different kinase-inhibitors using multiple regressions which gave a correlation coefficient of 0.93. The regression equation obtained was tested on 93 kinase-inhibitor complexes and an average deviation of 0.92 from the experimental log IC50 values was shown. The same set of descriptors was used to predict binding affinities for a test set of five individual kinase families, with correlation values > 0.9. We show that the protein-ligand interaction energies and partition coefficient values form the major deterministic factors for binding affinity of the ligand for its receptor.

Better theoretical models and protein design experiments can help to understand protein folding.

In our study, we have concluded that two proteins with 88% homology choose different energetically favorable pathways in the very early stage of the folding process to attain their native folds. Subsequent reports from other investigators by performing folding and unfolding kinetics experiments concur with our findings. We herewith discuss the key papers revealing computational and experimental analysis of two designed proteins with similar sequence distant folds. Further we suggest that the theoretical/computational analysis of protein sequences and structures along with the relevant experiments provide a better understanding of the relationship between protein sequence, folding, and structure.

Conservation of inter-residue interactions and prediction of folding rates of domain repeats.

Domains are the main structural and functional units of larger proteins. They tend to be contiguous in primary structure and can fold and function independently. It has been observed that 10-20% of all encoded proteins contain duplicated domains and the average pairwise sequence identity between them is usually low. In the present study, we have analyzed the structural similarity between domain repeats of proteins with known structures available in the Protein Data Bank using structure-based inter-residue interaction measures such as the number of long-range contacts, surrounding hydrophobicity, and pairwise interaction energy. We used RADAR program for detecting the repeats in a protein sequence which were further validated using Pfam domain assignments. The sequence identity between the repeats in domains ranges from 20 to 40% and their secondary structural elements are well conserved. The number of long-range contacts, surrounding hydrophobicity calculations and pairwise interaction energy of the domain repeats clearly reveal the conservation of 3-D structure environment in the repeats of domains. The proportions of mainchain-mainchain hydrogen bonds and hydrophobic interactions are also highly conserved between the repeats. The present study has suggested that the computation of these structure-based parameters will give better clues about the tertiary environment of the repeats in domains. The folding rates of individual domains in the repeats predicted using the long-range order parameter indicate that the predicted folding rates correlate well with most of the experimentally observed folding rates for the analyzed independently folded domains.

Analysis of sequence repeats of proteins in the PDB.

Internal repeats in protein sequences play a significant role in the evolution of protein structure and function. Applications of different bioinformatics tools help in the identification and characterization of these repeats. In the present study, we analyzed sequence repeats in a non-redundant set of proteins available in the Protein Data Bank (PDB). We used RADAR for detecting internal repeats in a protein, PDBeFOLD for assessing structural similarity, PDBsum for finding functional involvement and Pfam for domain assignment of the repeats in a protein. Through the analysis of sequence repeats, we found that identity of the sequence repeats falls in the range of 20-40% and, the superimposed structures of the most of the sequence repeats maintain similar overall folding. Analysis sequence repeats at the functional level reveals that most of the sequence repeats are involved in the function of the protein through functionally involved residues in the repeat regions. We also found that sequence repeats in single and two domain proteins often contained conserved sequence motifs for the function of the domain.

Performance of secondary structure prediction methods on proteins containing structurally ambivalent sequence fragments.

Several approaches for predicting secondary structures from sequences have been developed and reached a fair accuracy. One of the most rigorous tests for these prediction methods is their ability to correctly predict identical fragments of protein sequences adopting different secondary structures in unrelated proteins. In our previous work, we obtained 30 identical octapeptide sequence fragments adopting different backbone conformations. It is of interest to find whether the presence of structurally ambivalent fragments in proteins will affect the accuracy of secondary structure prediction methods or not. Hence, in this work, we have made a systematic comparative analysis on secondary structure prediction results of 30 identical octapeptide pairs and 52 identical heptapeptide pairs adopting different conformations with the aid of segment overlap measure. The results reveal the better performance of profile-based methods such as PSIpred and JPred and misprediction by classical rule-based methods such as Garnier Osguthorpe Robson Method and Double Prediction Method. The results discussed here insist that modern secondary structure prediction methods are able to better discriminate conformationally ambivalent peptide fragments.

Search and analysis of identical reverse octapeptides in unrelated proteins.

For the past few decades, intensive studies have been carried out in an attempt to understand how the amino acid sequences of proteins encode their three dimensional structures to perform their specific functions. In order to understand the sequence-structure relationship of proteins, several sub-sequence search studies in non-redundant sequence-structure databases have been undertaken which have given some fruitful clues. In our earlier work, we analyzed a set of 3124 non-redundant protein sequences from the Protein Data Bank (PDB) and retrieved 30 identical octapeptides having different secondary structures. These octapeptides were characterized by using different computational procedures. This prompted us to explore the presence of octapeptides with reverse sequences and to analyze whether these octapeptides would adopt similar structures as that of their parent octapeptides. Our identical reverse octapeptide search resulted in the finding of eight octapeptide pairs (octapeptide and reverse octapeptide) with similar secondary structure and 23 octapeptide pairs with different secondary structures. In the present work, the geometrical and biophysical characteristics of identical reverse octapeptides were explored and compared with unrelated octapeptide pairs by using various computational tools. We thus conclude that proteins containing identical reverse octapeptides are not very abundant and residues in the octapeptide pairs do not contribute to the stability of the protein. Furthermore, compared to unrelated octapeptides, identical reverse octapeptides do not show certain biophysical and geometrical properties.

An alphabetic code based atomic level molecular similarity search in databases.

Atomic level molecular similarity and diversity studies have gained considerable importance through their wide application in Bioinformatics and Chemo-informatics for drug design. The availability of large volumes of data on chemical compounds requires new methodologies for efficient and effective searching of its archives in less time with optimal computational power. We describe an alphabetic algorithm for similarity searching based on atom-atom bonding preference for ligands. We represented 170 cyclindependent kinase 2 inhibitors using strings of pre-defined alphabets for searching using known protein sequence alignment tools. Thus, a common pattern was extracted using this set of compounds for database searching to retrieve similar active compounds. Area under the receiver operating characteristic (ROC) curve was used for the discrimination of similar and dissimilar compounds in the databases. An average retrieval rate of about 60% is obtained in cross-validation using the home-grown dataset and the directory of useful decoys (DUD, formally known as the ZINC database) data. This will help in the effective retrieval of similar compounds using database search.

QSAR studies on HIV-1 protease inhibitors using non-linearly transformed descriptors.

Human Immunodeficiency Virus (HIV)-1 protease is one of the key targets for Acquired Immunodeficiency Syndrome (AIDS). A large number of inhibitors are being designed for this target with the focus towards interactions with backbone atoms to combat drug resistance. In the present study, we have developed QSAR models for 99 inhibitors which include P1/P1' and P2/P2' substituents with diverse scaffolds. In the present work, HIV-1 protease inhibitors dataset with tanimoto similarity of 0.7 were compiled from The Binding Database (Binding DB). Multiple linear regression analysis was performed to compute the relationship between 2D and 3D structure descriptors and binding affinity. Untransformed and non-linearly transformed descriptors were used for the QSAR model development. Transformation of descriptors resulted in better QSAR model (r²=0.77) compared to the model developed using untransformed descriptors (r²=0.74). Molecular connectivity, cosmic bond angle energy and charged based descriptor were reported as a priori properties in the prediction of binding affinity. The developed models were validated using an external test set and r² test values of 0.73 and 0.72 were obtained. Models developed in this study have potential application in the prediction of binding affinity for the newly synthesized compounds.

Role of interactions and volume variation in discriminating active and inactive forms of cyclin-dependent kinase-2 inhibitor complexes.

Molecular recognition process occurs through various non-bonded interactions in protein-ligand complexes. Analysis and visualization of interactions in a set of protein-ligand complexes provide insight for structure-based drug design. In the present study, we have made a comprehensive analysis on similarities and differences observed in hydrophobic interactions and hydrogen bond interactions of 170 X-ray crystal structures of active and inactive cyclin-dependent kinase-2 (CDK-2) ligand complexes obtained from the Protein Data Bank. We have also systematically analyzed variation of protein binding cavity volume (PCV) and ligand volume (LV) in CDK-2 ligand complexes. Hierarchical clustering of interaction patterns of CDK-2 ligands have been carried out in active and inactive forms. In PCV and LV analysis, PCV variation was observed to be high for inactive conformation and less for active conformation; the latter was found to bind ligands with higher volume. Further, correlation of interactions, PCV, and LV with the binding affinity was analyzed in active and inactive forms. Analysis of interactions and volume changes revealed the marked difference in CDK-2 active and inactive conformations. In conclusion, this study highlights the importance of considering inactive conformation in the docking and scoring methods to attain selectivity and potency.

Role of inter and intramolecular interactions in protein-DNA recognition.

Protein-DNA recognition plays an essential role in the regulation of gene expression. Regulatory proteins are known to recognize specific DNA sequences directly through atomic contacts between protein and DNA, and/or indirectly through the conformational properties of the DNA. In this work, we have analyzed the specificity of intermolecular interactions by statistical analysis of base-amino acid interactions within protein-DNA complexes as well as the computer simulations of base-amino acid interactions. The specificity of the intramolecular interactions was studied by statistical analysis of the sequence-dependent DNA conformational parameters and the elastic properties of DNA. Systematic comparison of these specificities in a large number of protein-DNA complexes revealed that both intermolecular and intramolecular interactions contribute to the specificity of protein-DNA recognition, and their relative contributions vary depending upon the protein-DNA complex. We demonstrated that combination of the intermolecular and intramolecular energies leads to enhanced specificity and the combined energy could explain experimental data on binding affinity changes caused by base mutations. These results provided new insight into the relationship between specificity and structure in the process of protein-DNA recognition, which would lead to prediction of specific protein-DNA binding sites.

Integration of bioinformatics and computational biology to understand protein-DNA recognition mechanism.

Transcription factors play essential role in the gene regulation in higher organisms, binding to multiple target sequences and regulating multiple genes in a complex manner. In order to decipher the mechanism of gene regulation, it is important to understand the molecular mechanism of protein-DNA recognition. Here we describe a strategy to approach this problem, using various methods in bioinformatics and computational biology. We have used a knowledge-based approach, utilizing rapidly increasing structural data of protein-DNA complexes, to derive empirical potential functions for the specific interactions between bases and amino acids as well as for DNA conformation, from the statistical analyses on the structural data. Then these statistical potentials are used to quantify the specificity of protein-DNA recognition. The quantification of specificity has enabled us to establish the structure-function analysis of transcription factors, such as the effects of binding cooperativity on target recognition. The method is also applied to real genome sequences, predicting potential target sites. We are also using computer simulations of protein-DNA interactions and DNA conformation in order to complement the empirical method. The integration of these approaches together will provide deeper insight into the mechanism of protein-DNA recognition and improve the target prediction of transcription factors.

Locating the stabilizing residues in (alpha/beta)8 barrel proteins based on hydrophobicity, long-range interactions, and sequence conservation.

In nature, 1 out of every 10 proteins has an (alpha/beta)(8) (TIM)-barrel fold, and in most cases, pairwise comparisons show no sequence similarity between them. Hence, delineating the key residues that induce very different sequences to share a common fold is important for understanding the folding and stability of TIM-barrel domains. In this work, we propose a new consensus approach for locating these stabilizing residues based on long-range interactions, hydrophobicity, and conservation of amino acid residues. We have identified 957 stabilizing residues in 63 proteins from a nonredundant set of 71 TIM-barrel domains. Most of these residues are located in the 8-stranded beta-sheet, with nearly one half of them oriented toward the interior of the barrel and the other half oriented toward the surrounding alpha-helices. Several stabilizing residues are found in the N- and C-terminal loops, whereas very few appear in the alpha-helices that surround the internal beta-sheet. Further, these 957 residues are placed in 434 stabilizing segments of various sizes, and each domain contains 1-10 of these segments. We found that 8 segments per domain is the most abundant one, and two thirds of the proteins have 7-9 stabilizing segments. Finally, we verified the identified residues with experimental temperature factors and found that these residues are among the ones with less mobility in the considered proteins. We suggest that our new protocol serves as a powerful tool to identify the stabilizing residues in TIM-barrel domains, which can be used as potential candidates for studying protein folding and stability by means of protein engineering experiments.

Intermolecular and intramolecular readout mechanisms in protein-DNA recognition.

Protein-DNA recognition plays an essential role in the regulation of gene expression. Regulatory proteins are known to recognize specific DNA sequences directly through atomic contacts (intermolecular readout) and/or indirectly through the conformational properties of the DNA (intramolecular readout). However, little is known about the respective contributions made by these so-called direct and indirect readout mechanisms. We addressed this question by making use of information extracted from a structural database containing many protein-DNA complexes. We quantified the specificity of intermolecular (direct) readout by statistical analysis of base-amino acid interactions within protein-DNA complexes. The specificity of the intramolecular (indirect) readout due to DNA was quantified by statistical analysis of the sequence-dependent DNA conformation. Systematic comparison of these specificities in a large number of protein-DNA complexes revealed that both intermolecular and intramolecular readouts contribute to the specificity of protein-DNA recognition, and that their relative contributions vary depending upon the protein-DNA complexes. We demonstrated that combination of the intermolecular and intramolecular energies derived from the statistical analyses lead to enhanced specificity, and that the combined energy could explain experimental data on binding affinity changes caused by base mutations. These results provided new insight into the relationship between specificity and structure in the process of protein-DNA recognition, which would lead to prediction of specific protein-DNA binding sites.

Specificity of protein-DNA recognition revealed by structure-based potentials: symmetric/asymmetric and cognate/non-cognate binding.

Asymmetric binding of protein homodimers to DNA, which has been observed in a number of protein-DNA complexes, leads to subtle structural differences between the two subunits. Such structural differences are frequently observed when the subunits form cognate and non-cognate protein-DNA complexes, respectively. Analysis of these structural effects on binding specificity should provide insight into the mechanism of protein-DNA recognition. We previously derived empirical potential functions for specific nucleotide base-amino acid interactions from statistical analyses of the structures of many protein-DNA complexes and used a combinatorial threading procedure to evaluate the fitness of the DNA sequences involved. We then introduced Z-scores to measure the specificity with which proteins bind to DNA within complexes, as compared to random DNA sequences. Here, we examined in detail the structural effects of asymmetric and cognate/non-cognate binding on specificity. Marked differences in the specificity of DNA binding were observed for the two subunits of lambda repressor, the glucocorticoid receptor, and for transcription factors containing a Zn(2)Cys(6) binuclear cluster domain, which are known to bind asymmetrically to DNA. Moreover, the differences in the specificity with which BamH1 and EcoRV endonucleases bind to their cognate and non-cognate DNA sequences were clearly detected using this approach; indeed, analysis of EcoRV binding enabled us to show the cooperative effect of sequence and structure on binding specificity. The present results demonstrate the utility of this approach when examining the structure-specificity relationship in protein-DNA recognition, as subtle structural differences in symmetric/asymmetric and cognate/non-cognate binding were clearly shown to cause marked differences in specificity. This method can also be used as a tool for checking new structures of protein-DNA complexes for their specificity.