Computational and experimental studies of plasmodium falciparum protein PfAMA1 domain-II loop dynamics: implications in PfAMA1-PfRON2 binding event

Protein-protein interactions are interesting targets for various drug discovery campaigns. One such promising and therapeutically pertinent protein-protein complex is PfAMA1-PfRON2, which is involved in malarial parasite invasion into human red blood cells. A thorough understanding of the interactions between these macromolecular binding partners is absolutely necessary to design better therapeutics to fight against the age-old disease affecting mostly under-developed nations. Although crystal structures of several PfAMA1-PfRON2 complexes have been solved to understand the molecular interactions between these two proteins, the mechanistic aspects of the domain II loop-PfRON2 association is far from clear. The current work investigates a crucial part of the recognition event; i.e., how the domain II loop of PfAMA1 exerts its effect on the alpha helix of the PfRON2, thus influencing the overall kinetics of this intricate recognition phenomenon. To this end, we have conducted thorough computational investigation of the dynamics and free energetics of domain II loop closing processes using molecular dynamics simulation. The computational results are validated by systematic alanine substitutions of the PfRON2 peptide helix. The subsequent evaluation of the binding affinity of Ala-substituted PfRON2 peptide ligands by surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) provides a rank of the relative importance of the residues in context. Our combined (computational and experimental) investigation has revealed that the domain II loop of PfAMA1 is in fact responsible for arresting the PfRON2 molecule from egress, K2027 and D2028 of PfRON2 being the determinant residues for the capturing event. Our study provides a comprehensive understanding of the molecular recognition event between PfAMA1 and PfRON2, specifically in the post binding stage, which potentially can be utilized for drug discovery against malaria.

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Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes

Current Medicinal Chemistry ◽

10.2174/0929867325666180214113704 ◽

2019 ◽

Vol 26 (21) ◽

pp. 3890-3910 ◽

Cited By ~ 6

Author(s):

Branislava Gemovic ◽

Neven Sumonja ◽

Radoslav Davidovic ◽

Vladimir Perovic ◽

Nevena Veljkovic

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Protein Complexes ◽

Experimental Studies ◽

Protein Protein Interactions ◽

Web Based ◽

Prediction Tools ◽

Physiological Processes ◽

Modern Drug ◽

Ppi Prediction

Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.

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Protein Interaction Domains and Post-Translational Modifications: Structural Features and Drug Discovery Applications

Current Medicinal Chemistry ◽

10.2174/0929867326666190620101637 ◽

2020 ◽

Vol 27 (37) ◽

pp. 6306-6355 ◽

Cited By ~ 2

Author(s):

Marian Vincenzi ◽

Flavia Anna Mercurio ◽

Marilisa Leone

Keyword(s):

Drug Discovery ◽

Protein Interaction ◽

Protein Interactions ◽

Structural Information ◽

Protein Complexes ◽

Structural Features ◽

Protein Protein Interactions ◽

Modular Architecture ◽

Post Translational Modifications ◽

Interaction Domains

Background:: Many pathways regarding healthy cells and/or linked to diseases onset and progression depend on large assemblies including multi-protein complexes. Protein-protein interactions may occur through a vast array of modules known as protein interaction domains (PIDs). Objective:: This review concerns with PIDs recognizing post-translationally modified peptide sequences and intends to provide the scientific community with state of art knowledge on their 3D structures, binding topologies and potential applications in the drug discovery field. Method:: Several databases, such as the Pfam (Protein family), the SMART (Simple Modular Architecture Research Tool) and the PDB (Protein Data Bank), were searched to look for different domain families and gain structural information on protein complexes in which particular PIDs are involved. Recent literature on PIDs and related drug discovery campaigns was retrieved through Pubmed and analyzed. Results and Conclusion:: PIDs are rather versatile as concerning their binding preferences. Many of them recognize specifically only determined amino acid stretches with post-translational modifications, a few others are able to interact with several post-translationally modified sequences or with unmodified ones. Many PIDs can be linked to different diseases including cancer. The tremendous amount of available structural data led to the structure-based design of several molecules targeting protein-protein interactions mediated by PIDs, including peptides, peptidomimetics and small compounds. More studies are needed to fully role out, among different families, PIDs that can be considered reliable therapeutic targets, however, attacking PIDs rather than catalytic domains of a particular protein may represent a route to obtain selective inhibitors.

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Prediction of Disease Comorbidity Using HeteSim Scores based on Multiple Heterogeneous Networks

Current Gene Therapy ◽

10.2174/1566523219666190917155959 ◽

2019 ◽

Vol 19 (4) ◽

pp. 232-241 ◽

Cited By ~ 5

Author(s):

Xuegong Chen ◽

Wanwan Shi ◽

Lei Deng

Keyword(s):

Protein Interactions ◽

Experimental Studies ◽

Treatment Strategies ◽

Computational Method ◽

Biological Information ◽

Support Vector ◽

Protein Protein Interactions ◽

Efficient Treatment ◽

Disease Associations ◽

Previous State

Background: Accumulating experimental studies have indicated that disease comorbidity causes additional pain to patients and leads to the failure of standard treatments compared to patients who have a single disease. Therefore, accurate prediction of potential comorbidity is essential to design more efficient treatment strategies. However, only a few disease comorbidities have been discovered in the clinic. Objective: In this work, we propose PCHS, an effective computational method for predicting disease comorbidity. Materials and Methods: We utilized the HeteSim measure to calculate the relatedness score for different disease pairs in the global heterogeneous network, which integrates six networks based on biological information, including disease-disease associations, drug-drug interactions, protein-protein interactions and associations among them. We built the prediction model using the Support Vector Machine (SVM) based on the HeteSim scores. Results and Conclusion: The results showed that PCHS performed significantly better than previous state-of-the-art approaches and achieved an AUC score of 0.90 in 10-fold cross-validation. Furthermore, some of our predictions have been verified in literatures, indicating the effectiveness of our method.

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Protein-Protein Interactions in Drug Discovery

Drug Design Reviews - Online ◽

10.2174/1567269053828837 ◽

2005 ◽

Vol 2 (3) ◽

pp. 179-207 ◽

Cited By ~ 19

Author(s):

P. Fischer

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Protein Protein Interactions

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Decoding Protein-protein Interactions: An Overview

Current Topics in Medicinal Chemistry ◽

10.2174/1568026620666200226105312 ◽

2020 ◽

Vol 20 (10) ◽

pp. 855-882

Author(s):

Olivia Slater ◽

Bethany Miller ◽

Maria Kontoyianni

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Drug Repurposing ◽

Protein Docking ◽

Target Space ◽

Protein Protein Interactions ◽

X Ray Crystallography ◽

Protein Protein Interaction ◽

Interaction Sites ◽

Long Time

Drug discovery has focused on the paradigm “one drug, one target” for a long time. However, small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing. In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with other biomolecules and it is this intricate network of interactions that determines the behavior of the system and its biological processes. In this review, we briefly discuss networks in disease, followed by computational methods for protein-protein complex prediction. Computational methodologies and techniques employed towards objectives such as protein-protein docking, protein-protein interactions, and interface predictions are described extensively. Docking aims at producing a complex between proteins, while interface predictions identify a subset of residues on one protein that could interact with a partner, and protein-protein interaction sites address whether two proteins interact. In addition, approaches to predict hot spots and binding sites are presented along with a representative example of our internal project on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.

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An Integrative Computational Approach for the Prediction of Human-Plasmodium Protein-Protein Interactions

BioMed Research International ◽

10.1155/2020/2082540 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Kais Ghedira ◽

Yosr Hamdi ◽

Abir El Béji ◽

Houcemeddine Othman

Keyword(s):

Protein Interactions ◽

Computational Approach ◽

Protein Protein Interactions ◽

Human Red Blood Cells ◽

Pharmacological Targets ◽

Host Pathogen ◽

Human Proteins ◽

Life Threatening ◽

Similarity Method ◽

Human Plasmodium

Host-pathogen molecular cross-talks are critical in determining the pathophysiology of a specific infection. Most of these cross-talks are mediated via protein-protein interactions between the host and the pathogen (HP-PPI). Thus, it is essential to know how some pathogens interact with their hosts to understand the mechanism of infections. Malaria is a life-threatening disease caused by an obligate intracellular parasite belonging to the Plasmodium genus, of which P. falciparum is the most prevalent. Several previous studies predicted human-plasmodium protein-protein interactions using computational methods have demonstrated their utility, accuracy, and efficiency to identify the interacting partners and therefore complementing experimental efforts to characterize host-pathogen interaction networks. To predict potential putative HP-PPIs, we use an integrative computational approach based on the combination of multiple OMICS-based methods including human red blood cells (RBC) and Plasmodium falciparum 3D7 strain expressed proteins, domain-domain based PPI, similarity of gene ontology terms, structure similarity method homology identification, and machine learning prediction. Our results reported a set of 716 protein interactions involving 302 human proteins and 130 Plasmodium proteins. This work provides a list of potential human-Plasmodium interacting proteins. These findings will contribute to better understand the mechanisms underlying the molecular determinism of malaria disease and potentially to identify candidate pharmacological targets.

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Protein-Protein Interactions in Drug Discovery. (Band 56 der Reihe “Methods and Principles in Medicinal Chemistry”) Herausgegeben von Alexander Dömling.

Angewandte Chemie ◽

10.1002/ange.201307711 ◽

2013 ◽

Vol 125 (50) ◽

pp. 13381-13382

Author(s):

Joachim Rudolph

Keyword(s):

Drug Discovery ◽

Protein Interactions ◽

Medicinal Chemistry ◽

Protein Protein Interactions

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Predicting virus-receptor mutant binding by molecular dynamics simulation

10.7287/peerj.preprints.138v1 ◽

2013 ◽

Author(s):

Austin G Meyer ◽

Sara L Sawyer ◽

Andrew D Ellington ◽

Claus O Wilke

Keyword(s):

Protein Interactions ◽

Tight Binding ◽

Dynamics Simulation ◽

Virus Protein ◽

Protein Protein Interactions ◽

Distance Curve ◽

Protein Protein Interaction ◽

Human Transferrin Receptor ◽

Machupo Virus ◽

Receptor Mutant

Existing computational methods to predict protein–protein interaction affinity often perform poorly in important test cases. In particular, the effects of multiple mutations, non-alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here a new method to interrogate affinity differences resulting from mutations in a host-virus protein–protein interface. Our method is based on extensive non-equilibrium all atom simulations: We computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1) and estimate affinity using the max imum applied force during a pulling simulation and the area under the force-versus-distance curve. We find that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our method provides an elegant framework to compare the effects of multi ple mutations, individually and jointly, on protein–protein interactions.

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