Most approaches in predicting protein function from protein-protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that two separate forms of functional association accounts for such a phenomenon, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested to find out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction.

We made a statistical study on recent interaction data and observed that functional association between level-2 neighbours is clearly observable. A substantial number of proteins are observed to share functions with level-2 neighbours but not with level-1 neighbours. We develop an algorithm that predicts the functions of a protein in two steps: (1) assign a weight to each of its level-1 and level-2 neighbours by estimating its functional similarity with the protein using the local topology of the interaction network as well as the reliability of experimental sources; (2) scoring each function based on its weighted frequency in these neighbours. Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs well.

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Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions

Microtubules are tubulin polymers present in the eukaryotic cytoskeleton essential for structural stability and cell division that are also roadways for intracellular transport of vesicles and organelles. In the human malaria parasite Plasmodium falciparum, apart from providing structural stability and cell division, microtubules also facilitate important biological activities crucial for parasite survival in hosts, such as egression and motility. Hence, parasite structures and processes involving microtubules are among the most important drug targets for discovering much-needed novel Plasmodium inhibitors. The current study aims to construct reliable and high-quality 3D models of α-, β-, and γ-tubulins using various modeling techniques. We identified a common binding pocket specific to Plasmodium α-, β-, and γ-tubulins. Molecular dynamics simulations confirmed the stability of the Plasmodium tubulin 3D structures. The models generated in the present study may be used for protein-protein and protein-drug interaction investigations targeted toward designing malaria parasite tubulin-specific inhibitors.

Atomic Resolution Homology Models and Molecular Dynamics Simulations of Plasmodium falciparum Tubulins.

Host-pathogen interactions

G-protein-coupled receptors (GPCRs) are membrane proteins which play an important role in many cellular processes and are excellent drug targets. Despite the existence of several US Food and Drug A...

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Ligand Docking Methods to Recognize Allosteric Inhibitors for G-Protein-Coupled Receptors.

Malaria parasites have become the major health threat in increasing resistance toward common antimalarial drugs and become prime factors causing the strength of the disease. The objective of this study was investigating novel cinchona alkaloid derivatives (CADs) as potential antimalarial agents through molecular docking, pharmacopore modeling and biosynthesis design. Protein structure and cinchona alkaloid derivative structures were taken and performed for molecular interaction studies, pharmacophore modeling and mapping the binding modes of receptor-inhibitors which may increase the possibility of success rate in finding potential antimalarial candidates. Here, we report the greatest prospective inhibitor of Pf falcipain-2 is cinchonidine salicylate (-9.1 kcal/mol) through molecular docking approach. This compound exhibited distortion free of Lipinski`s rule. Hence, cinchonidine salicylate showed the most potential compound as antimalarial inhibitor over other cinchona alkaloid derivatives. Eventually, we construct biosynthesis pathways by using iron oxide nanoparticle (IONP) that could act as a coated nanoparticle to the natural bioactives to acquire optimum yield of the product by making coated nanoparticle with CADs which are powerful biosynthesis application in green environment of aqueous solution.

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Novel Cinchona Alkaloid Derivatives as Potential Antimalarial Agents through Receptor–Inhibitor Interaction Fingerprint and Biosynthesis Design

Malaria, a life-threatening blood disease, has been a major concern in the field of healthcare. One of the severe forms of malaria is caused by the parasite Plasmodium falciparum which is initiated through protein interactions of pathogen with the host proteins. It is essential to analyse the protein-protein interactions among the host and pathogen for better understanding of the process and characterizing specific molecular mechanisms involved in pathogen persistence and survival. In this study, a complete protein-protein interaction network of human host and Plasmodium falciparum has been generated by integration of the experimental data and computationally predicting interactions using the interolog method. The interacting proteins were filtered according to their biological significance and functional roles. α-tubulin was identified as a potential protein target and inhibitors were designed against it by modification of amiprophos methyl. Docking and binding affinity analysis showed two modified inhibitors exhibiting better docking scores of −10.5 kcal/mol and −10.43 kcal/mol and an improved binding affinity of −83.80 kJ/mol and −98.16 kJ/mol with the target. These inhibitors can further be tested and validated in vivo for their properties as an antimalarial drug.

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In Silico Designing and Analysis of Inhibitors against Target Protein Identified through Host-Pathogen Protein Interactions in Malaria.

Protein–DNA interactions carry out many important regulatory functions in our body which are essential for growth and survival. The molecular level understanding of these interactions helps us to decipher the mechanism of regulation. Conventionally these interactions were identified using small scale experimental techniques and high throughput technologies. But these approaches being time consuming and expensive, arouse the need of a computational approach for the prediction of interactions. To this end, a machine learning approach for predicting and classifying DNA-binding proteins has been used. Weka and LibSVM platform have been used to develop an effective classifying model. The different classifiers were applied on the attributes generated from protein sequence and tried to develop an efficient model for classifying a protein as DNA-interacting or DNA-non-interacting. Among several classifying algorithms applied to generate models, best performance was achieved using LibSVM with 87.83 % accuracy. The tool named INTERACT-O-FINDER, based on the prediction model is available at http://interacto.eurekanow.org/index.html
 .

Monika Samant

Papers

In Silico Designing and Analysis of Inhibitors against Target Protein Identified through Host-Pathogen Protein Interactions in Malaria.

INTERACT-O-FINDER: A Tool for Prediction of DNA-Binding Proteins Using Sequence Features