I-TASSER Models Research Articles

In silico analysis of the Val66Met mutation in BDNF protein: implications for psychological stress.

The brain-derived neurotrophic factor (BDNF) involves stress regulation and psychiatric disorders. The Val66Met polymorphism in the BDNF gene has been linked to altered protein function and susceptibility to stress-related conditions. This in silico analysis aimed to predict and analyze the consequences of the Val66Met mutation in the BDNF gene of stressed individuals. Computational techniques, including ab initio, comparative, and I-TASSER modeling, were used to evaluate the functional and stability effects of the Val66Met mutation in BDNF. The accuracy and reliability of the models were validated. Sequence alignment and secondary structure analysis compared amino acid residues and structural components. The phylogenetic analysis assessed the conservation of the mutation site. Functional and stability prediction analyses provided mixed results, suggesting potential effects on protein function and stability. Structural models revealed the importance of BDNF in key biological processes. Sequence alignment analysis showed the conservation of amino acid residues across species. Secondary structure analysis indicated minor differences between the wild-type and mutant forms. Phylogenetic analysis supported the evolutionary conservation of the mutation site. This computational study suggests that the Val66Met mutation in BDNF may have implications for protein stability, structural conformation, and function. Further experimental validation is needed to confirm these findings and elucidate the precise effects of this mutation on stress-related disorders.

Homology modeling, binding site identification, molecular docking and molecular dynamics simulation study of emerging and promising drug target of Wnt signaling – Human Porcupine enzyme

Wnt signaling is a critical pathway involved in cell proliferation, differentiation and cellular homeostasis. To address treatment need of diseases associated with the dysregulated Wnt signaling like cancer, Alzheimer’s disease, Osteoporosis, Myocardial infarction etc.; small molecules that target the very first and unique component of the Wnt pathway, Porcupine enzyme, have been proven to be effective. Till date, none of the Porcupine inhibitor has reached to market. To design the novel Porcupine inhibitors, there was a need of crystal structure of Porcupine but in 2019, it was not resolved and deposited in the protein data bank. So, with an aim to predict its 3D structure, homology modeling study was performed using two distinct platforms; I-TASSER and Molsoft ICMPro so that we could use the best validated model for the designing of NCEs. Both the generated homology models were compared through Ramachandran plot, Protein health tool of Molsoft ICM and other tools available on metaserver, SAVES v6.0. Molsoft model was found better with 84.6 % residues in most favored region and only 0.3 % residues in disallowed region in Ramachandran plot in comparison to 75.9 % and 1.7 % residues respectively in I-TASSER model. The ICM model was further refined for 50 ns under MD simulation where it got stabilized after 36 ns. Quantification of protein was done by Radius of Gyration (ROG) where model showed larger ROG value of 23.25°A which indicated closely packed structure. Binding site in the predicted structure of Porcupine was identified using ICM binding pocket identifier and used for molecular docking of known Porcupine inhibitors like IWP-2, IWP-3, IWP-L6, and LKG974. Key binding site residues; Arg124, Phe257, Leu261, Val302, Trp305, Asn306, Ser310, Leu313, His341, Phen345, Ala349, Val350 etc. involved in the interaction were identified after MD simulation which can be useful in designing of novel porcupine inhibitors in future.

In Silico Functional Characterization of a Hypothetical Protein From Pasteurella Multocida Reveals a Novel S-Adenosylmethionine-Dependent Methyltransferase Activity.

Genomes may now be sequenced in a matter of weeks, leading to an influx of "hypothetical" proteins (HP) whose activities remain a mystery in GenBank. The information included inside these genes has quickly grown in prominence. Thus, we selected to look closely at the structure and function of an HP (AFF25514.1; 246 residues) from Pasteurella multocida (PM) subsp. multocida str. HN06. Possible insights into bacterial adaptation to new environments and metabolic changes might be gained by studying the functions of this protein. The PM HN06 2293 gene encodes an alkaline cytoplasmic protein with a molecular weight of 28352.60 Da, an isoelectric point (pI) of 9.18, and an overall average hydropathicity of around -0.565. One of its functional domains, tRNA (adenine (37)-N6)-methyltransferase TrmO, is a S-adenosylmethionine (SAM)-dependent methyltransferase (MTase), suggesting that it belongs to the Class VIII SAM-dependent MTase family. The tertiary structures represented by HHpred and I-TASSER models were found to be flawless. We predicted the model's active site using the Computed Atlas of Surface Topography of Proteins (CASTp) and FTSite servers, and then displayed it in 3 dimensional (3D) using PyMOL and BIOVIA Discovery Studio. Based on molecular docking (MD) results, we know that HP interacts with SAM and S-adenosylhomocysteine (SAH), 2 crucial metabolites in the tRNA methylation process, with binding affinities of 7.4 and 7.5 kcal/mol, respectively. Molecular dynamic simulations (MDS) of the docked complex, which included only modest structural adjustments, corroborated the strong binding affinity of SAM and SAH to the HP. Evidence for HP's possible role as an SAM-dependent MTase was therefore given by the findings of Multiple sequence alignment (MSA), MD, and molecular dynamic modeling. These in silico data suggest that the investigated HP might be used as a useful adjunct in the investigation of Pasteurella infections and the development of drugs to treat zoonotic pasteurellosis.

Biocomputational characterisation of MBO_200107 protein of Mycobacterium tuberculosis variant caprae: a molecular docking and simulation study

The principal objective of this study was to delineate the potentiality of the MBO_200107 protein from the Mycobacterium tuberculosis variant caprae in cancer research. It is a cytoplasmic protein, comprised of a 354-long amino acid chain, alkaline, had a molecular weight of 39089.37 Da, an isoelectric point of 9.62 and a grand average of hydropathicity of −0.345. One of the functional domains was predicted as Gammaglutamylcyclotransferase (GGCT). Among tertiary structures, the Modeller and Phyre2 model satisfied all the quality parameters, though they are truncated; contrarily, the I-TASSER model is full length and contains the sequence for the GGCT domain, though it did not meet all the quality parameters. It also has significant sequence similarities (47.5% by EMBOSS Water and 72.4% by EMBOSS Matcher) with a human GGCT, and the conserved sequences are confined to the GGCT domain of the MBO_200107. According to molecular docking analyses, the protein has a binding affinity of −4.8 kcal/mol by Autodock Vina and −56.465 kcal/mol by HPEPDOCK to the human glutathione (GSH), an essential metabolite for GGCT metabolism. The Molecular dynamic simulation of the docked complex showed the binding efficiency of the GSH to MBO_200107 with a minimal structural alteration. The in silico findings mentioned above revealed that the protein could be used as a supplementary tool in cancer research, such as designing vaccines or drugs where the role of GGCT has been implicated. Further, we recommend fully characterising the protein and conducting essential in vitro and in vivo experiments to determine its detailed usefulness. Communicated by Ramaswamy H. Sarma

New Putative Antimicrobial Candidates: In silico Design of Fish-Derived Antibacterial Peptide-Motifs.

Antimicrobial resistance remains a great threat to global health. In response to the World Health Organizations’ global call for action, nature has been explored for novel and safe antimicrobial candidates. To date, fish have gained recognition as potential source of safe, broad spectrum and effective antimicrobial therapeutics. The use of computational methods to design antimicrobial candidates of industrial application has however, been lagging behind. To fill the gap and contribute to the current fish-derived antimicrobial peptide repertoire, this study used Support Vector Machines algorithm to fish out fish-antimicrobial peptide-motif candidates encrypted in 127 peptides submitted at the Antimicrobial Peptide Database (APD3), steered by their physico-chemical characteristics (i.e., positive net charge, hydrophobicity, stability, molecular weight and sequence length). The best two novel antimicrobial peptide-motifs (A15_B, A15_E) with the lowest instability index (−28.25, −22.49, respectively) and highest isoelectric point (pI) index (10.48 for each) were selected for further analysis. Their 3D structures were predicted using I-TASSER and PEP-FOLD servers while ProSA, PROCHECK, and ANOLEA were used to validate them. The models predicted by I-TASSER were found to be better than those predicted by PEP-FOLD upon validation. Two I-TASSER models with the lowest c-score of −0.10 and −0.30 for A15_B and A15_E peptide-motifs, respectively, were selected for docking against known bacterial-antimicrobial target-proteins retrieved from protein databank (PDB). Carbapenam-3-carboxylate synthase (PDB ID; 4oj8) yielded the lowest docking energy (−8.80 and −7.80 Kcal/mol) against motif A15_B and A15_E, respectively, using AutoDock VINA. Further, in addition to Carbapenam-3-carboxylate synthase, these peptides (A15_B and A15_E) were found to as well bind to membrane protein (PDB ID: 1by3) and Carbapenem synthetase (PDB: 1q15) when ClusPro and HPEPDOCK tools were used. The membrane protein yielded docking energy scores (DES): −290.094, −270.751; coefficient weight (CW): −763.6, 763.3 for A15_B and A15_E) whereas, Carbapenem synthetase (PDB: 1q15) had a DES of −236.802, −262.75 and a CW of −819.7, −829.7 for peptides A15_B and A15_E, respectively. Motif A15_B of amino acid positions 2–19 in Pleurocidin exhibited the strongest in silico antimicrobial potentials. This segment could be a good biological candidate of great application in pharmaceutical industries as an antimicrobial drug candidate.

Homology Modeling and Structural Validation of Gel C gene involved in the biosynthesis of Gellan from Sphingomonas paucimobilis ATCC 31461

Gellan gum is an anionic exopolysaccharide produced by S. Paucimobilis ATCC 31461. Gellan have wide applications in food industry, pharmaceutical and cosmetic industries. Gel cluster involved in the biosynthesis of dTDP-L-Rha, glycosyltransferases and proteins requisite for gellan polymerization and export comprises of 18 genes. This study focuses on homology modelling of gel C, a component of gellan gum gene cluster using known protein structure of PDB server by Swiss, Phyre 2 and I-TASSER. Secondary structure prediction was analyzed using I-TASSER. Appropriate template for the modeling was determined using Protein BLAST. Visualization of the protein models was done using online software tool PyMOL. Structure validation was performed using various programs such as RAMPAGE, PROSA, VERIFY3D, ERRAT and QMEAN. RAMPAGE revealed that the predicted model has 94% amino acids in the core region of Ramachandran plot. Quality of the generated 3D models was validated using ProSA which display two characteristics of the input structure: Z-score and energy plots. Protein models with 3-D sequence profiles were retreived using VERIFY3D. Amino acid environment was evaluated by ERRAT and unreliable regions were identified using QMEAN. In our study, the protein modelling using Phyre 2 was found to be the most adequate when compared to modeling by Swiss model and I-TASSER model. Structural validation confirmed the reliability of model. The results indicated that gel C plays a crucial role in the polysaccharide chain length determination and so it can be a potential candidate for multiple expressions in Sphingomonas paucimobilis ATCC 31461.

Purification, characterization, molecular modeling and docking study of fish waste protease

In the present study, the alkaline protease has been extracted from the fish processing waste using ammonium sulphate fractionation followed by ion-exchange chromatography on sephadex G-25 and on DEAE column with a 4.0 fold increase in purification of yield 7.7%. The molecular weight of the purified protease was found to be 33 kDa as determined by SDS-PAGE. The optimum temperature was found to be 30 °C at pH 8. The activation energy (Ea) for casein hydrolysis and temperature quotient (Q10) was found to be 38.25 kJ/mol and 1.65, respectively. The kinetic constants km, Vmax, kcat, and kcat/km and thermodynamic parameters ΔH*, ΔS*, ΔG*, ΔG*E–S, and ΔG*E–T revealed high affinity of the fish protease for casein. Using CD spectroscopy it was found that the fish protease has 32.7% alpha-helical, 32.8% β-turn and 34.5% random coil. 3D structure of target protein was predicted by homology modeling. Ramachandran plot revealed that the total residues in favored, allowed and outlier regions are 96.6%, 2.3%, and 1.1% residues. The biological function of the modeled fish protease was predicted by COACH based on the I-TASSER model, suggests that the fish protease may be exploited as biocatalyst in various industrial applications and processes. AutoDock 4.2.6. was used to study the protein-ligand interactions, which may lead to the discovery of novel semisynthetic enzymes from renewable biowaste.

Protein structure prediction provides comparable performance to crystallographic structures in docking-based virtual screening

Structure based virtual screening has largely been limited to protein targets for which either an experimental structure is available or a strongly homologous template exists so that a high-resolution model can be constructed. The performance of state of the art protein structure predictions in virtual screening in systems where only weakly homologous templates are available is largely untested. Using the challenging DUD database of structural decoys, we show here that even using templates with only weak sequence homology (<30% sequence identity) structural models can be constructed by I-TASSER which achieve comparable enrichment rates to using the experimental bound crystal structure in the majority of the cases studied. For 65% of the targets, the I-TASSER models, which are constructed essentially in the apo conformations, reached 70% of the virtual screening performance of using the holo-crystal structures. A correlation was observed between the success of I-TASSER in modeling the global fold and local structures in the binding pockets of the proteins versus the relative success in virtual screening. The virtual screening performance can be further improved by the recognition of chemical features of the ligand compounds. These results suggest that the combination of structure-based docking and advanced protein structure modeling methods should be a valuable approach to the large-scale drug screening and discovery studies, especially for the proteins lacking crystallographic structures.

BSP‐SLIM: A blind low‐resolution ligand‐protein docking approach using predicted protein structures

We developed BSP-SLIM, a new method for ligand-protein blind docking using low-resolution protein structures. For a given sequence, protein structures are first predicted by I-TASSER; putative ligand binding sites are transferred from holo-template structures which are analogous to the I-TASSER models; ligand-protein docking conformations are then constructed by shape and chemical match of ligand with the negative image of binding pockets. BSP-SLIM was tested on 71 ligand-protein complexes from the Astex diverse set where the protein structures were predicted by I-TASSER with an average RMSD 2.92 Å on the binding residues. Using I-TASSER models, the median ligand RMSD of BSP-SLIM docking is 3.99 Å which is 5.94 Å lower than that by AutoDock; the median binding-site error by BSP-SLIM is 1.77 Å which is 6.23 Å lower than that by AutoDock and 3.43 Å lower than that by LIGSITE(CSC) . Compared to the models using crystal protein structures, the median ligand RMSD by BSP-SLIM using I-TASSER models increases by 0.87 Å, while that by AutoDock increases by 8.41 Å; the median binding-site error by BSP-SLIM increase by 0.69Å while that by AutoDock and LIGSITE(CSC) increases by 7.31 Å and 1.41 Å, respectively. As case studies, BSP-SLIM was used in virtual screening for six target proteins, which prioritized actives of 25% and 50% in the top 9.2% and 17% of the library on average, respectively. These results demonstrate the usefulness of the template-based coarse-grained algorithms in the low-resolution ligand-protein docking and drug-screening. An on-line BSP-SLIM server is freely available at http://zhanglab.ccmb.med.umich.edu/BSP-SLIM.