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Background With the rapid increase in the amount of Protein-Protein Interaction (PPI) data, the establishment of an event-centered PPI ontology that contains temporal and spatial vocabularies is urgently needed to clarify PPI biological annotations. In this paper, we propose a precisely designed schema - PPIO (PPI Ontology) for representing the biological context of PPIs. Results Inspired by the event model and the distinct characteristics of PPI events, PPIO consists of six core aspects of the information required for reporting a PPI event, including the interactor (who), the biological process (when), the subcellular location (where), the interaction type (how), the biological function (what) and the detection method (which). PPIO is implemented through the integration of appropriate terms from the corresponding vocabularies/ontologies, e.g., Gene Ontology, Protein Ontology, PSI-MI/MOD, etc. To assess PPIO, an approach based on PPIO in developed to extract PPI biological annotations from an open standard corpus “BioCreAtIvE-PPI”. The experiment results demonstrate PPIO’s high performance, a precision of 0.69, a recall of 0.72 and an F-score of 0.70. Conclusions PPIO is a well-constructed essential ontology in the interpretation of PPI biological context. The results of the experiments conducted on the BioCreAtIvE corpus demonstrate that PPIO is able to facilitate PPI annotation extraction from biomedical literature effectively and enrich essential annotation for PPIs.

Interactions between proteins lie at the heart of numerous biological processes and are essential for the proper functioning of the cell. Although the importance of hydrophobic residues in driving protein interactions is universally accepted, a characterization of protein hydrophobicity, which informs its interactions, has remained elusive. The challenge lies in capturing the collective response of the protein hydration waters to the nanoscale chemical and topographical protein patterns, which determine protein hydrophobicity. To address this challenge, here, we employ specialized molecular simulations wherein water molecules are systematically displaced from the protein hydration shell; by identifying protein regions that relinquish their waters more readily than others, we are then able to uncover the most hydrophobic protein patches. Surprisingly, such patches contain a large fraction of polar/charged atoms and have chemical compositions that are similar to the more hydrophilic protein patches. Importantly, we also find a striking correspondence between the most hydrophobic protein patches and regions that mediate protein interactions. Our work thus establishes a computational framework for characterizing the emergent hydrophobicity of amphiphilic solutes, such as proteins, which display nanoscale heterogeneity, and for uncovering their interaction interfaces.

Background Proteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory. Results We present SPRINT (Scoring PRotein INTeractions), a new sequence-based algorithm and tool for predicting protein-protein interactions. We comprehensively compare SPRINT with state-of-the-art programs on seven most reliable human PPI datasets and show that it is more accurate while running orders of magnitude faster and using very little memory. Conclusion SPRINT is the only sequence-based program that can effectively predict the entire human interactome: it requires between 15 and 100 min, depending on the dataset. Our goal is to transform the very challenging problem of predicting the entire human interactome into a routine task. Availability The source code of SPRINT is freely available from https://github.com/lucian-ilie/SPRINT/ and the datasets and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/ .

Drug-likeness quantification is useful for screening drug candidates. Quantitative estimates of drug-likeness (QED) are commonly used to assess quantitative drug efficacy but are not suitable for screening compounds targeting protein-protein interactions (PPIs), which have recently gained attention. Therefore, we developed a quantitative estimate index for compounds targeting PPIs (QEPPI), specifically for early-stage screening of PPI-targeting compounds. QEPPI is an extension of the QED method for PPI-targeting drugs that models physicochemical properties based on the information available for drugs/compounds, specifically those reported to act on PPIs. FDA-approved drugs and compounds in iPPI-DB, which comprise PPI inhibitors and stabilizers, were evaluated using QEPPI. The results showed that QEPPI is more suitable than QED for early screening of PPI-targeting compounds. QEPPI was also considered an extended concept of the “Rule-of-Four” (RO4), a PPI inhibitor index. We evaluated the discriminatory performance of QEPPI and RO4 for datasets of PPI-target compounds and FDA-approved drugs using F-score and other indices. The F-scores of RO4 and QEPPI were 0.451 and 0.501, respectively. QEPPI showed better performance and enabled quantification of drug-likeness for early-stage PPI drug discovery. Hence, it can be used as an initial filter to efficiently screen PPI-targeting compounds.

Abstracts Prepulse inhibition (PPI) of startle is an operational measure of sensorimotor gating that is often impaired in patients with schizophrenia. Despite the large number of studies, there is considerable variation in PPI outcomes reported. We conducted a systematic review and meta-analysis investigating PPI impairment in patients with schizophrenia compared with healthy control subjects, and examined possible explanations for the variation in results between studies. Major databases were screened for observational studies comparing healthy subjects and patients with schizophrenia for the prepulse and pulse intervals of 60 and 120 ms as primary outcomes, ie, PPI-60 and PPI-120. Standardized mean difference (SMD) and 95% confidence intervals (CI) were extracted and pooled using random effects models. We then estimated the mean effect size of these measures with random effects meta-analyses and evaluated potential PPI heterogeneity moderators, using sensitivity analysis and meta-regressions. Sixty-seven primary studies were identified, with 3685 healthy and 4290 patients with schizophrenia. The schizophrenia group showed reduction in sensorimotor gating for both PPI-60 (SMD = −0.50, 95% CI = [−0.61, −0.39]) and PPI-120 (SMD = −0.44, 95% CI = [−0.54, −0.33]). The sensitivity and meta-regression analysis showed that sample size, gender proportion, imbalance for gender, source of control group, and study continent were sources of heterogeneity (P < .05) for both PPI-60 and PPI-120 outcomes. Our findings confirm a global sensorimotor gating deficit in schizophrenia patients, with overall moderate effect size for PPI-60 and PPI-120. Methodological consistency should decrease the high level of heterogeneity of PPI results between studies.

Global concern about problematic usage of the internet (PUI), and its public health and societal costs, continues to grow, sharpened in focus under the privations of the COVID-19 pandemic. This narrative review reports the expert opinions of members of the largest international network of researchers on PUI in the framework of the European Cooperation in Science and Technology (COST) Action (CA 16207), on the scientific progress made and the critical knowledge gaps remaining to be filled as the term of the Action reaches its conclusion. A key advance has been achieving consensus on the clinical definition of various forms of PUI. Based on the overarching public health principles of protecting individuals and the public from harm and promoting the highest attainable standard of health, the World Health Organisation has introduced several new structured diagnoses into the ICD-11, including gambling disorder, gaming disorder, compulsive sexual behaviour disorder, and other unspecified or specified disorders due to addictive behaviours, alongside naming online activity as a diagnostic specifier. These definitions provide for the first time a sound platform for developing systematic networked research into various forms of PUI at global scale. Progress has also been made in areas such as refining and simplifying some of the available assessment instruments, clarifying the underpinning brain-based and social determinants, and building more empirically based etiological models, as a basis for therapeutic intervention, alongside public engagement initiatives. However, important gaps in our knowledge remain to be tackled. Principal among these include a better understanding of the course and evolution of the PUI-related problems, across different age groups, genders and other specific vulnerable groups, reliable methods for early identification of individuals at risk (before PUI becomes disordered), efficacious preventative and therapeutic interventions and ethical health and social policy changes that adequately safeguard human digital rights. The paper concludes with recommendations for achievable research goals, based on longitudinal analysis of a large multinational cohort co-designed with public stakeholders. •Research advances in Problematic Use of the Internet (PUI) and key evidence gaps.•Narrative review by experts and public stakeholders from the European COST Action.•PUI is growing; Vulnerable groups include children and young people.•Reliable methods for early detection, recognition and prevention are needed.•Longitudinal study of PUI, health and wellbeing will inform effective policy change.

Introduction Estrogen receptor (ER) positive patients compromise about 70% of breast cancers. Tamoxifen, an antagonist of ERα66 (the classic ER), is the most effective and the standard first-line drug. However, its efficacy is limited by the development of acquired resistance. Methods A specific inhibitor of Hsp70-Bim protein–protein interaction (PPI), S1g-2, together with an inhibitor of Hsp70-Bag3 PPI, MKT-077 and an ATP-competitive inhibitor VER155008, were used as chemical tools. Cell viability assays, co-immunoprecipitation and gene knockdown were used to investigate the role of Hsp70 in tamoxifen resistance. A xenograft model was established in which tamoxifen-resistant breast cancer (MCF-7/TAM-R) cells maintained in the presence of 5 μM tamoxifen were subcutaneously inoculated. The anti-tumor efficiency of S1g-2 was measured after a daily injection of 0.8 mg/kg for 14 days. Results It was revealed that Hsp70-Bim PPI protects ERα-positive breast cancer from tamoxifen-induced apoptosis through binding and stabilizing ERα36, rather than ERα66, resulting in sustained EGFR mRNA and protein expression. Disruption of Hsp70-Bim PPI and downregulation of ERα36 expression in tumor samples are consistent with the in vitro functions of S1g-2, resulting in about a three-fold reduction in tumor volume. Conclusions The in vivo activity and safety of S1g-2 illustrated that it is a potential strategy for Hsp70-Bim disruption to overcome tamoxifen-resistant ER-positive breast cancer.

It has been noticed that the efficiency of drug development has been decreasing in the past few decades. To overcome the situation, protein-protein interactions (PPIs) have been identified as new drug targets as early as 2000. PPIs are more abundant in human cells than single proteins and play numerous important roles in cellular processes including diseases. However, PPIs have very different physicochemical features from the conventional drug targets, which make targeting PPIs challenging. Therefore, as of now, only a small number of PPI inhibitors have been approved or progressed to a stage of clinical trial. In this article, we first overview previous works that analyzed differences between PPIs with PPI targeting ligands and conventional drugs with their binding pockets. Then, we constructed an up-to-date list of PPI targeting drugs that have been approved or are currently under clinical trial and have bound drug-target structures available. Using the dataset, we analyzed the PPIs and their ligands using several scores of druggability. Druggability scores showed that PPI sites and their drugs targeting PPIs are less druggable than conventional binding pockets and drugs, which also indicates that PPI drugs do not follow the conventional rules for drug design, such as Lipinski's rule of five. Our analyses suggest that developing a new rule would be beneficial for guiding PPI-drug discovery.

Dendrimers are highly branched polymers with easily modifiable surfaces. This makes them promising structures for functionalization and also for conjugation with drugs and DNA/RNA. Their architecture, which can be controlled by different synthesis processes, allows the control of characteristics such as shape, size, charge, and solubility. Dendrimers have the ability to increase the solubility and bioavailability of hydrophobic drugs. The drugs can be entrapped in the intramolecular cavity of the dendrimers or conjugated to their functional groups at their surface. Nucleic acids usually form complexes with the positively charged surface of most cationic dendrimers and this approach has been extensively employed. The presence of functional groups in the dendrimer’s exterior also permits the addition of other moieties that can actively target certain diseases and improve delivery, for instance, with folate and antibodies, now widely used as tumor targeting strategies. Dendrimers have been investigated extensively in the medical field, and cancer treatment is one of the greatest areas where they have been most used. This review will consider the main types of dendrimer currently being explored and how they can be utilized as drug and gene carriers and functionalized to improve the delivery of cancer therapy.

Functional MRI (fMRI) allows one to study task-related regional responses and task-dependent connectivity analysis using psychophysiological interaction (PPI) methods. The latter affords the additional opportunity to understand how brain regions interact in a task-dependent manner. The current implementation of PPI in Statistical Parametric Mapping (SPM8) is configured primarily to assess connectivity differences between two task conditions, when in practice fMRI tasks frequently employ more than two conditions. Here we evaluate how a generalized form of context-dependent PPI (gPPI; http://www.nitrc.org/projects/gppi), which is configured to automatically accommodate more than two task conditions in the same PPI model by spanning the entire experimental space, compares to the standard implementation in SPM8. These comparisons are made using both simulations and an empirical dataset. In the simulated dataset, we compare the interaction beta estimates to their expected values and model fit using the Akaike information criterion (AIC). We found that interaction beta estimates in gPPI were robust to different simulated data models, were not different from the expected beta value, and had better model fits than when using standard PPI (sPPI) methods. In the empirical dataset, we compare the model fit of the gPPI approach to sPPI. We found that the gPPI approach improved model fit compared to sPPI. There were several regions that became non-significant with gPPI. These regions all showed significantly better model fits with gPPI. Also, there were several regions where task-dependent connectivity was only detected using gPPI methods, also with improved model fit. Regions that were detected with all methods had more similar model fits. These results suggest that gPPI may have greater sensitivity and specificity than standard implementation in SPM. This notion is tempered slightly as there is no gold standard; however, data simulations with a known outcome support our conclusions about gPPI. In sum, the generalized form of context-dependent PPI approach has increased flexibility of statistical modeling, and potentially improves model fit, specificity to true negative findings, and sensitivity to true positive findings.