Prediction Of Binding Residues Research Articles

Molecular modelling and Function Prediction of hABH7, human homologue of E. coli ALKB7

AbstractHuman homologues of ALKB protein have shown the prime role in DNA damaging drugs, used for cancer therapy. Little is known about structure and function of hABH7, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeller 9v7 to predict the 3D structure of the hABH7 protein. The tertiary structure model of hABH7, ALKBH7.B99990002.pdb was predicted and evaluated. Validation results showed 97.8% residues in favored and additional allowed regions of Ramachandran plots. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, presence of a Phe120-Gly121-Gly122 conserved pattern in the functional domain was detected. In the predicted structural model of hABH7, amino acid residues, Arginine at 57, 58, 59 and 60 along with tyrosine at 61 were predicted in RNA binding sites of the model. The predicted and validated model of human homologue hABH7 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate the research on designing appropriate inhibitors aiding in chemotherapy and cancer related diseases.

Structure and function prediction of human homologue hABH5 of E. coli ALKB5 using in silico approach

AbstractNewly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases.

Structure and function prediction of human homologue hABH5 of E. coli ALKB5 using in silico approach

Newly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases.

Structure and function prediction of human homologue hABH5 of E. coli ALKB5 using in silico approach

Newly discovered human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Little is known about the structure and function of hABH5, one of the members of this superfamily. Therefore, in the present study we intend to predict its structure and function using various bioinformatics tools. Modeling was done with modeler 9v7 to predict the 3D structure of the hABH5 protein. 3-D model of hABH5, ALKBH5.B99990005.pdb was predicted and evaluated. Validation results showed 96.8% residues in favor and an additional allowed region of the Ramachandran plot. Ligand binding residues prediction showed four ligand clusters, having 25 ligands in cluster 1. Importantly, conserved pattern of Pro158-X-Asp160-Xn-His266 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. The predicted and validated model of human homologue hABH5 resulting from this study may unveil the mechanism of DNA damage repair in humans and accelerate research on designing appropriate inhibitors, aiding in chemotherapy and cancer related diseases.

Structure-based prediction of RNA-binding domains and RNA-binding sites and application to structural genomics targets

Mechanistic understanding of many key cellular processes often involves identification of RNA binding proteins (RBPs) and RNA binding sites in two separate steps. Here, they are predicted simultaneously by structural alignment to known protein–RNA complex structures followed by binding assessment with a DFIRE-based statistical energy function. This method achieves 98% accuracy and 91% precision for predicting RBPs and 93% accuracy and 78% precision for predicting RNA-binding amino-acid residues for a large benchmark of 212 RNA binding and 6761 non-RNA binding domains (leave-one-out cross-validation). Additional tests revealed that the method makes no false positive prediction from 311 DNA binding domains but correctly detects six domains binding with both DNA and RNA. In addition, it correctly identified 31 of 75 unbound RNA-binding domains with 92% accuracy and 65% precision for predicted binding residues and achieved 86% success rate in its application to SCOP RNA binding domain superfamily (Structural Classification Of Proteins). It further predicts 25 targets as RBPs in 2076 structural genomics targets: 20 of 25 predicted ones (80%) are putatively RNA binding. The superior performance over existing methods indicates the importance of dividing structures into domains, using a Z-score to measure relative structural similarity, and a statistical energy function to measure protein–RNA binding affinity.

Theoretical Investigations on Structure and Function of Human Homologue hABH4 of E.coli ALKB4

Introduction: Recently identified human homologues of ALKB protein have shown the activity of DNA damaging drugs, used for cancer therapy. Bioinformatics study of hABH2 and hABH3 had led to the discovery of a novel DNA repair mechanism. Very little is known about structure and function of hABH4, one of the members of this superfamily. Therefore, in present study we are intended to predict its structure and function through various bioinformatics tools. Materials and Methods: Modeling was done with modeler 9v7 to predict the 3D structure of the hABH4 protein. This model was validated with the program Procheck using Ramachandran plot statistics and was submitted to PMDB with ID PM0076284. The 3d2GO server was used to predict the functions. Residues at protein ligand and protein RNA binding sites were predicted with 3dLigandSite and KYG programs respectively. Results and Discussion: 3-D model of hABH4, ALKBH4.B99990003.pdb was predicted and evaluated. Validation result showed that 96.4 % residues lies in favored and additional allowed region of Ramachandran plot. Ligand binding residues prediction showed four Ligand clusters, having 24 ligands in cluster 1. Importantly, conserved pattern of Glu196-X-Pro198- Xn-His254 in the functional domain was detected. DNA and RNA binding sites were also predicted in the model. Conclusion and Prospects: The predicted and validated model of human homologue hABH4 resulted from this study may unveil the mechanism of DNA damage repair in human and accelerate the research on designing of appropriate inhibitors aiding in chemotherapy and cancer related diseases.

Defining the Functional Domain of Programmed Cell Death 10 through Its Interactions with Phosphatidylinositol-3,4,5-Trisphosphate

Cerebral cavernous malformations (CCM) are vascular abnormalities of the central nervous system predisposing blood vessels to leakage, leading to hemorrhagic stroke. Three genes, Krit1 (CCM1), OSM (CCM2), and PDCD10 (CCM3) are involved in CCM development. PDCD10 binds specifically to PtdIns(3,4,5)P3 and OSM. Using threading analysis and multi-template modeling, we constructed a three-dimensional model of PDCD10. PDCD10 appears to be a six-helical-bundle protein formed by two heptad-repeat-hairpin structures (α1–3 and α4–6) sharing the closest 3D homology with the bacterial phosphate transporter, PhoU. We identified a stretch of five lysines forming an amphipathic helix, a potential PtdIns(3,4,5)P3 binding site, in the α5 helix. We generated a recombinant wild-type (WT) and three PDCD10 mutants that have two (Δ2KA), three (Δ3KA), and five (Δ5KA) K to A mutations. Δ2KA and Δ3KA mutants hypothetically lack binding residues to PtdIns(3,4,5)P3 at the beginning and the end of predicted helix, while Δ5KA completely lacks all predicted binding residues. The WT, Δ2KA, and Δ3KA mutants maintain their binding to PtdIns(3,4,5)P3. Only the Δ5KA abolishes binding to PtdIns(3,4,5)P3. Both Δ5KA and WT show similar secondary and tertiary structures; however, Δ5KA does not bind to OSM. When WT and Δ5KA are co-expressed with membrane-bound constitutively-active PI3 kinase (p110-CAAX), the majority of the WT is co-localized with p110-CAAX at the plasma membrane where PtdIns(3,4,5)P3 is presumably abundant. In contrast, the Δ5KA remains in the cytoplasm and is not present in the plasma membrane. Combining computational modeling and biological data, we propose that the CCM protein complex functions in the PI3K signaling pathway through the interaction between PDCD10 and PtdIns(3,4,5)P3.

A protein sequence meta-functional signature for calcium binding residue prediction

The diversity of characterized protein functions found amongst experimentally interrogated proteins suggests that a vast array of unknown functions remains undiscovered. These protein functions are imparted by specific geometric distributions of amino acid residue chemical moieties, each contributing a functional interaction. We hypothesize that individual residue function contributions are predictable through sequence analytic knowledge based algorithms, and that they can be recombined to understand composite protein function by predicting spatial relation in tertiary structure. We assess the former by training a meta-functional signature algorithm to specifically predict calcium ion binding residues from protein sequence. We estimate the latter by testing for match between predictive contribution of positions in predicted secondary structures and patterns of side chain proximity forced by secondary structure moieties. Specific training for calcium binding results in 83% area under the receiver operator characteristic curve added value over random (AUCoR) and p < 10 −300 significance as measured by Kendall’s τ in 10-fold cross validation for parallel sets of 811 residues in 336 proteins and 696 residues in 299 proteins. Training for generalized function results in 63% AUCoR and p ≅ 10 −221 for the same tests. Including inference of side chain proximity improves predictive ability by 2% AUCoR consistently. The results demonstrate that protein meta-functional signatures can be trained to predict specific protein functions by considering amino acid identity and structural features accessible from sequence, laying the groundwork for composite sequence based function site prediction.

BSFINDER: Finding Binding Sites of HCV Proteins Using a Support Vector Machine

Hepatitis C virus (HCV) infection is a major cause of liver disease and a dangerous threat to public health. Hence, the problem of finding interactions between HCV and human proteins has received much attention. In this paper, we present an approach to predicting binding residues in HCV proteins using a support vector machine (SVM) classifier. Based on six biochemical properties of amino acids (sequence profile, accessible surface area, residue binding propensity, sequence entropy, hydrophobicity and conservation weight), the SVM classifier achieved an average accuracy of 93%. Contiguous residues in the sequence act together to determine a binding site, and a window of 11 residues (the target residue and 5 adjacent residues on each side) gave the best result in our study. Our approach has been implemented in a program called BSFinder (Binding Site Finder), which is available at http://wilab.inha.ac.kr/bsfinder. BSFinder will be of considerable help in predicting binding residues and potential interacting partners of a protein.