Prediction of Ligand Binding Using an Approach Designed to Accommodate Diversity in Protein-Ligand Interactions

Computational determination of protein-ligand interaction potential is important for many biological applications including virtual screening for therapeutic drugs. The novel internal consensus scoring strategy is an empirical approach with an extended set of 9 binding terms combined with a neural network capable of analysis of diverse complexes. Like conventional consensus methods, internal consensus is capable of maintaining multiple distinct representations of protein-ligand interactions. In a typical use the method was trained using ligand classification data (binding/no binding) for a single receptor. The internal consensus analyses successfully distinguished protein-ligand complexes from decoys (r2, 0.895 for a series of typical proteins). Results are superior to other tested empirical methods. In virtual screening experiments, internal consensus analyses provide consistent enrichment as determined by ROC-AUC and pROC metrics

Ranking strategies to support toxicity prediction: a case study on potential LXR binders

The current paradigm of toxicity testing is set within a framework of Mode-of-Action (MoA)/Adverse Outcome Pathway (AOP) investigations, where novel methodologies alternative to animal testing play a crucial role, and allow to consider causal links between molecular initiating events (MIEs), further key events and an adverse outcome. In silico (computational) models are developed to support toxicity assessment within the MoA/AOP framework. This paper focuses on the evaluation of potential binding to the Liver X Receptor (LXR), as this has been identified among the MIEs leading to liver steatosis within an AOP framework addressing repeated dose and target-organ toxicity

SInCRe—structural interactome computational resource for Mycobacterium tuberculosis

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein–protein and protein–small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein–protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host–pathogen protein–protein interactions. Together they provide prerequisites for identification of off-target binding

Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity

STRUCTURAL DETERMINATION OF L-ASPARAGINASE II OF STREPTOMYCES ALBIDOFLAVUS AND INTERACTION ANALYSIS WITH L-ASPARAGINE AND CEFOTAXIME

Objective: L-Asparaginase enzyme possesses a crucial role in the treatment of various hematologic malignancies. The current study focuses on homology modeling and interaction analysis of L-Asparaginase proteins belonging to Streptomyces albidoflavus (S. albidoflavus) with the essential ligand L-Asparagine and subsequent analysis with essential β-lactam antibiotic Cefotaxime. Methods: The process of understanding Asparaginase interactions primarily involved structure determination of WP_096097608, WP_095730301, which is achieved by GalaxyTBM, I-TASSER and SWISS-MODEL. Further, the S. albidoflavus Asparaginase proteins are subjected to GalaxySite and Autodock Vina of PyRx analysis. Results: The GalaxyTBM predicted structures of both the proteins are found promising on various validation studies. The two Asparaginase proteins exhibited high binding affinities of-6.8 and-6.5 kcal/mol with Cefotaxime and-5.1 and-4.9 kcal/mol towards Asparagine. The protein WP_096097608 residues forming hydrogen bonds with L-Asparagine are also analysed to involve in interaction with Cefotaxime on individual docking analysis. Conclusion: The current findings details the two S. albidoflavus Asparaginase proteins affinity towards L-Asparagine, hence can be assessed further for immunogenicity studies. In addition to the above findings, an attempt is made to find the L-Asparaginase binding possibilities with non-metals that identified an essential β-lactam antibiotic Cefotaxime to be an effective inhibitor. This study helps in understanding the interactions of L-Asparaginase with Cefotaxime, as intake of antibiotics between the phases of chemotherapy is observed to treat various infections and also as an antibiotic to microbes that utilize Asparaginase as a vital enzyme

Using bioinformatics tools to screen for trypanosomal cathepsin B cysteine protease inhibitors from the SANCDB as a novel therapeutic modality against Human African Trypanosomiasis (HAT)

Human African Trypanosomiasis (HAT), also known as sleeping sickness, is a fatal chronic disease that is caused by flagellated protozoans, Trypanosoma brucei gambiense and Trypanosoma brucei rhodesiense. HAT is spread by a bite from an infected tsetse fly of the Glosina genus. Up to 60 million people in 36 countries in sub-Saharan Africa are at a risk of infection from HAT with up to 30 000 deaths reported every year. Current chemotherapy for HAT is insufficient since the available drugs exhibit unacceptable side effects (toxicity) and parasite resistance. Novel treatments and approaches for development of specific and more potent drugs for HAT are therefore required. One approach is to target vital proteins that are essential to the life cycle of the parasite. The main interest of this study is to explore Trypanosoma brucei cathepsin B-like protease (TbCatB) structural and functional properties with the primary goal of discovering non peptide small molecule inhibitors of TbCatB using bioinformatics approaches. TbCatB is a papain family C1 cysteine protease which belongs to clan CA group and it has emerged as a potential HAT drug target. Papain family cysteine proteases of Clan CA group of Trypanosoma brucei (rhodesain and TbCatB) have demonstrated potential as chemotherapeutic targets using synthetic protease inhibitors like Z-Phe-Ala-CHN2 to kill the parasite in vitro and in vivo. TbCatB has been identified as the essential cysteine protease of T. brucei since mRNA silencing of TbCatB killed the parasite and resulted in a cure in mice infected with T. brucei while mRNA silencing of rhodesain only extended mice life. TbCatB is therefore a promising drug target against HAT and the discovery and development of compounds that can selectively inhibit TbCatB without posing any danger to the human host represent a great therapeutic solution for treatment of HAT. To understand protein-inhibitor interactions, useful information can be obtained from high resolution protease-inhibitor crystal structure complexes. This study aims to use bioinformatics approaches to carry out comparative sequence, structural and functional analysis of TbCatB protease and its homologs from T. congolense, T, cruzi, T. vivax and H. sapien as well as to identify non-peptide small molecule inhibitors of TbCatB cysteine proteases from natural compounds of South African origin. Sequences of TbCatB (PDB ID: 3HHI) homologs were retrieved by a BLAST search. Human cathepsin B (PDB ID: 3CBJ) was selected from a list of templates for homology modelling found by HHpred. MODELLER version 9.10 program was used to generate a hundred models for T. congolense, T, cruzi and T. vivax cathepsin B like proteases using 3HHI and 3CBJ as templates. The best models were chosen based on their low DOPE Z scores before validation using MetaMQAPII, ANOLEA, PROCHECK and QMEAN6. The DOPE Z scores and the RMSD (RMS) values of the calculated models indicate that the models are of acceptable energy (stability) and fold (conformation). Results from the different MQAPs indicate the models are of acceptable quality and they can be used for docking studies. High throughput screening of SANCDB using AutoDock Vina revealed nine compounds, SANC00 478, 479, 480, 481, 482, 488, 489, 490 and 491, having a strong affinity for Trypanosoma spp. cathepsin B proteases than HsCatB. SANC00488 has the strongest binding to Trypanosoma spp. cathepsin B proteases and the weakest binding to HsCatB protease. Molecular dynamics (MD) simulations show that the complexes between SANC00488 and TbCatB, TcCatB, TcrCatB and TvCatB are stable and do not come apart during simulation. The complex between this compound and HsCatB however is unstable and comes apart during simulation. Residues that are important for the stability of SANC00488-TbCatB complex are Gly328 of the S2 subsite, Phe208, and Ala256. In conclusion SANC00488 is a good candidate for development of a drug against HAT