ZEPPI: Proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence coevolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI can be implemented on a proteome-wide scale and is applied here to millions of structural models of dimeric complexes in the Escherichia coli and human interactomes found in the PrePPI database. PrePPI's scoring function is based primarily on the evaluation of protein-protein interfaces, and ZEPPI adds a new feature to this analysis through the incorporation of evolutionary information. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. As we discuss, the standard CAPRI scores used to evaluate docking models are based on model quality and not on the ability to give yes/no answers as to whether two proteins interact. ZEPPI is able to detect weak signals from PPI models that the CAPRI scores define as incorrect and, similarly, to identify potential PPIs defined as low confidence by the current PrePPI scoring function. A number of examples that illustrate how the combination of PrePPI and ZEPPI can yield functional hypotheses are provided.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

ZEPPI: proteome-scale sequence-based evaluation of protein-protein interaction models.

We introduce ZEPPI (Z-score Evaluation of Protein-Protein Interfaces), a framework to evaluate structural models of a complex based on sequence co-evolution and conservation involving residues in protein-protein interfaces. The ZEPPI score is calculated by comparing metrics for an interface to those obtained from randomly chosen residues. Since contacting residues are defined by the structural model, this obviates the need to account for indirect interactions. Further, although ZEPPI relies on species-paired multiple sequence alignments, its focus on interfacial residues allows it to leverage quite shallow alignments. ZEPPI performance is evaluated through applications to experimentally determined complexes and to decoys from the CASP-CAPRI experiment. ZEPPI can be implemented on a proteome-wide scale as evidenced by calculations on millions of structural models of dimeric complexes in the E. coli and human interactomes found in the PrePPI database. A number of examples that illustrate how these tools can yield novel functional hypotheses are provided.

PrePPI: A structure informed proteome-wide database of protein-protein interactions.

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural clues within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) clue is derived from templatebased modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ~1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features ( https://honiglab.c2b2.columbia.edu/PrePPI ). PrePPI is a state-of- the-art resource that offers an unprecedented structure-informed view of the human interactome.

PrePPI: A Structure Informed Proteome-wide Database of Protein-Protein Interactions.

We present an updated version of the Predicting Protein-Protein Interactions (PrePPI) webserver which predicts PPIs on a proteome-wide scale. PrePPI combines structural and non-structural evidence within a Bayesian framework to compute a likelihood ratio (LR) for essentially every possible pair of proteins in a proteome; the current database is for the human interactome. The structural modeling (SM) component is derived from template-based modeling and its application on a proteome-wide scale is enabled by a unique scoring function used to evaluate a putative complex. The updated version of PrePPI leverages AlphaFold structures that are parsed into individual domains. As has been demonstrated in earlier applications, PrePPI performs extremely well as measured by receiver operating characteristic curves derived from testing on E. coli and human protein-protein interaction (PPI) databases. A PrePPI database of ∼1.3 million human PPIs can be queried with a webserver application that comprises multiple functionalities for examining query proteins, template complexes, 3D models for predicted complexes, and related features (https://honiglab.c2b2.columbia.edu/PrePPI). PrePPI is a state-of-the-art resource that offers an unprecedented structure-informed view of the human interactome.

PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.

We describe the Predicting Protein-Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between 6.8 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence- and structural similarity-based metrics are established between template proteins, T, in the Protein Data Bank that bind compounds, C, and query proteins in the model database, Q. When the metrics exceed threshold values, it is assumed that C also binds to Q with a likelihood ratio (LR) derived from machine learning. If the relationship is based on structural similarity, the LR is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto coefficient identifies other small molecules that may bind to Q. An overall LR for the binding of C to Q is obtained from Naive Bayesian statistics. The PrePCI database can be queried by entering a UniProt ID or gene name for a protein to obtain a list of compounds predicted to bind to it along with associated LRs. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database to lead discovery, elucidation of drug mechanism of action, and biological function annotation are described.

Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.

Integrating 3D structural information into systems biology.

Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus-host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.

Education and Outreach in Physical Sciences in Oncology.

Physical sciences are often overlooked in the field of cancer research. The Physical Sciences in Oncology Initiative was launched to integrate physics, mathematics, chemistry, and engineering with cancer research and clinical oncology through education, outreach, and collaboration. Here, we provide a framework for education and outreach in emerging transdisciplinary fields.

Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses.

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.

Safety and tolerability of Vitamin D3 5000 IU/day in epilepsy.

PURPOSE: Preclinical and early clinical research indicates that Vitamin D3 may reduce seizures in both animal models and open-label clinical trials. METHODS: This is an initial report of an ongoing pilot study of oral Vitamin D3 5000 IU/day in subjects with drug-resistant epilepsy. After Institutional Review Board (IRB) approval and informed consent, subjects with ;less than one focal onset or generalized tonic-clonic seizure per month were enrolled. Subjects entered a 4-week baseline, followed by a 12-week treatment period. Serum 25, OH Vitamin D3, Blood Urea Nitrogen (BUN), creatinine, and calcium levels were monitored at baseline and at 6 and 12 weeks. RESULTS: High-dose Vitamin D3 5000 IU/day was well tolerated. Serum 25, OH Vitamin D3 levels increased significantly at six and twelve weeks. Vitamin D insufficiency, defined as a 25, OH Vitamin D3 level of <20 ng/ml normalized in all subjects with insufficient vitamin D levels. Median seizure frequency declined from 5.18 seizures per month to 3.64 seizures per month at 6 weeks and to 4.2 seizures per month at 12 weeks. The median percent change in seizure frequency was -26.9% at six weeks, and -10.7% at 12 weeks (not significant, Wilcoxon Signed Rank Test, P > 0.34). CONCLUSIONS: High-dose oral Vitamin D3, 5000 IU/day was safe and well tolerated in subjects with epilepsy. Vitamin D levels increased significantly at 6 and 12 weeks but never exceeded potentially toxic levels, defined as >100 ng/ml. To reduce variability, we will now recruit subjects who only have three or more seizures per month.

A computational interactome and functional annotation for the human proteome.

We present a database, PrePPI (Predicting Protein-Protein Interactions), of more than 1.35 million predicted protein-protein interactions (PPIs). Of these at least 127,000 are expected to constitute direct physical interactions although the actual number may be much larger (~500,000). The current PrePPI, which contains predicted interactions for about 85% of the human proteome, is related to an earlier version but is based on additional sources of interaction evidence and is far larger in scope. The use of structural relationships allows PrePPI to infer numerous previously unreported interactions. PrePPI has been subjected to a series of validation tests including reproducing known interactions, recapitulating multi-protein complexes, analysis of disease associated SNPs, and identifying functional relationships between interacting proteins. We show, using Gene Set Enrichment Analysis (GSEA), that predicted interaction partners can be used to annotate a protein's function. We provide annotations for most human proteins, including many annotated as having unknown function.

A prospective long-term study of external trigeminal nerve stimulation for drug-resistant epilepsy.

BACKGROUND: External trigeminal nerve stimulation (eTNS) is an emerging noninvasive therapy for drug-resistant epilepsy (DRE). We report the long-term safety and efficacy of eTNS after completion of a phase II randomized controlled clinical trial for drug-resistant epilepsy. METHODS: This was a prospective open-label long-term study. Subjects who completed the phase II randomized controlled trial of eTNS for DRE were offered long-term follow-up for 1year. Subjects who were originally randomized to control settings were crossed over to effective device parameters (30s on, 30s off, pulse duration of 250s, frequency of 120Hz). Efficacy was assessed using last observation carried forward or parametric imputation methods for missing data points. Outcomes included change in median seizure frequency, RRATIO, and 50% responder rate. RESULTS: Thirty-five of 50 subjects from the acute double-blind randomized controlled study continued in the long-term study. External trigeminal nerve stimulation was well tolerated. No serious device-related adverse events occurred through 12months of long-term treatment. At six and twelve months, the median seizure frequency for the original treatment group decreased by -2.39 seizures per month at 6 months (-27.4%) and -3.03 seizures per month at 12 months (-34.8%), respectively, from the initial baseline (p<0.05, signed-rank test). The 50% responder rates at three, six, and twelve months were 36.8% for the treatment group and 30.6% for all subjects. CONCLUSION: The results provide long-term evidence that external trigeminal nerve stimulation is a safe and promising long-term treatment for drug-resistant epilepsy.

Randomized controlled trial of trigeminal nerve stimulation for drug-resistant epilepsy.

OBJECTIVE: To explore the safety and efficacy of external trigeminal nerve stimulation (eTNS) in patients with drug-resistant epilepsy (DRE) using a double-blind randomized controlled trial design, and to test the suitability of treatment and control parameters in preparation for a phase III multicenter clinical trial. METHODS: This is a double-blind randomized active-control trial in DRE. Fifty subjects with 2 or more partial onset seizures per month (complex partial or tonic-clonic) entered a 6-week baseline period, and then were evaluated at 6, 12, and 18 weeks during the acute treatment period. Subjects were randomized to treatment (eTNS 120 Hz) or control (eTNS 2 Hz) parameters. RESULTS: At entry, subjects were highly drug-resistant, averaging 8.7 seizures per month (treatment group) and 4.8 seizures per month (active controls). On average, subjects failed 3.35 antiepileptic drugs prior to enrollment, with an average duration of epilepsy of 21.5 years (treatment group) and 23.7 years (active control group), respectively. eTNS was well-tolerated. Side effects included anxiety (4%), headache (4%), and skin irritation (14%). The responder rate, defined as >50% reduction in seizure frequency, was 30.2% for the treatment group vs 21.1% for the active control group for the 18-week treatment period (not significant, p = 0.31, generalized estimating equation [GEE] model). The treatment group experienced a significant within-group improvement in responder rate over the 18-week treatment period (from 17.8% at 6 weeks to 40.5% at 18 weeks, p = 0.01, GEE). Subjects in the treatment group were more likely to respond than patients randomized to control (odds ratio 1.73, confidence interval 0.59-0.51). eTNS was associated with reductions in seizure frequency as measured by the response ratio (p = 0.04, analysis of variance [ANOVA]), and improvements in mood on the Beck Depression Inventory (p = 0.02, ANOVA). CONCLUSIONS: This study provides preliminary evidence that eTNS is safe and may be effective in subjects with DRE. Side effects were primarily limited to anxiety, headache, and skin irritation. These results will serve as a basis to inform and power a larger multicenter phase III clinical trial. CLASSIFICATION OF EVIDENCE: This phase II study provides Class II evidence that trigeminal nerve stimulation may be safe and effective in reducing seizures in people with DRE.

Protein kinase Cθ C2 domain is a phosphotyrosine binding module that plays a key role in its activation.

Protein kinase Cθ (PKCθ) is a novel PKC that plays a key role in T lymphocyte activation. To understand how PKCθ is regulated in T cells, we investigated the properties of its N-terminal C2 domain that functions as an autoinhibitory domain. Our measurements show that a Tyr(P)-containing peptide derived from CDCP1 binds the C2 domain of PKCθ with high affinity and activates the enzyme activity of the intact protein. The Tyr(P) peptide also binds the C2 domain of PKCδ tightly, but no enzyme activation was observed with PKCδ. Mutations of PKCθ-C2 residues involved in Tyr(P) binding abrogated the enzyme activation and association of PKCθ with Tyr-phosphorylated full-length CDCP1 and severely inhibited the T cell receptor/CD28-mediated activation of a PKCθ-dependent reporter gene in T cells. Collectively, these studies establish the C2 domain of PKCθ as a Tyr(P)-binding domain and suggest that the domain may play a major role in PKCθ activation via its Tyr(P) binding.

Acute and long-term safety of external trigeminal nerve stimulation for drug-resistant epilepsy.

Trigeminal nerve stimulation (TNS) is a novel therapy for drug-resistant epilepsy. We report in detail the safety of external TNS (eTNS), focusing on acute and long-term heart rate and systolic and diastolic blood pressure in response to TNS from the pilot feasibility study. The data indicate that eTNS of the infraorbital and supraorbital branches of the trigeminal nerve is safe and well tolerated.

Genome-wide structural analysis reveals novel membrane binding properties of AP180 N-terminal homology (ANTH) domains.

An increasing number of cytosolic proteins are shown to interact with membrane lipids during diverse cellular processes, but computational prediction of these proteins and their membrane binding behaviors remains challenging. Here, we introduce a new combinatorial computation protocol for systematic and robust functional prediction of membrane-binding proteins through high throughput homology modeling and in-depth calculation of biophysical properties. The approach was applied to the genomic scale identification of the AP180 N-terminal homology (ANTH) domain, one of the modular lipid binding domains, and prediction of their membrane binding properties. Our analysis yielded comprehensive coverage of the ANTH domain family and allowed classification and functional annotation of proteins based on the differences in local structural and biophysical features. Our analysis also identified a group of plant ANTH domains with unique structural features that may confer novel functionalities. Experimental characterization of a representative member of this subfamily confirmed its unique membrane binding mechanism and unprecedented membrane deforming activity. Collectively, these studies suggest that our new computational approach can be applied to genome-wide functional prediction of other lipid binding domains.

Enhancing medical students' conceptions of the CanMEDS Health Advocate Role through international service-learning and critical reflection: a phenomenological study.

BACKGROUND: Medical students are expressing increasing interest in international experiences in low-income countries where there are pronounced inequities in health and socio-economic development. AIM: We carried out a detailed exploration of the international service-learning (ISL) experience of three medical students and the value of critical reflection as a pedagogical approach to enhance medical students' conceptions of the Canadian Medical Education Directions for Specialists (CanMEDS) Health Advocate Role. METHOD: A phenomenological approach enabled us to study in considerable depth the students' experience from their perspective. Students kept reflective journals and wrote essays including detailed accounts of their experiences. The content of the students' journals and essays was analyzed using the critical incident technique. RESULTS: Students noted an increasingly meaningful sense of what it means to be vulnerable and marginalized, a heightened level of awareness of the social determinants of health and the related importance of community engagement, and a deeper appreciation of the health advocate role and key concepts embedded within it. CONCLUSION: This in-depth phenomenological study focused on the detailed experiences of three students from whom we learned that social justice-oriented approaches to service-learning, coupled with critical reflection, provide potentially viable pedagogical approaches for learning the health advocate role. How this experience will affect the students' future medical practice is yet unknown.

High-throughput computational structure-based characterization of protein families: START domains and implications for structural genomics.

SkyLine, a high-throughput homology modeling pipeline tool, detects and models true sequence homologs to a given protein structure. Structures and models are stored in SkyBase with links to computational function annotation, as calculated by MarkUs. The SkyLine/SkyBase/MarkUs technology represents a novel structure-based approach that is more objective and versatile than other protein classification resources. This structure-centric strategy provides a multi-dimensional organization and coverage of protein space at the levels of family, function, and genome. The concept of "modelability", the ability to model sequences on related structures, provides a reliable criterion for membership in a protein family ("leverage") and underlies the unique success of this approach. The overall procedure is illustrated by its application to START domains, which comprise a Biomedical Theme for the Northeast Structural Genomics Consortium as part of the Protein Structure Initiative. START domains are typically involved in the non-vesicular transport of lipids. While 19 experimentally determined structures are available, the family, whose evolutionary hierarchy is not well determined, is highly sequence diverse, and the ligand-binding potential of many family members is unknown. The SkyLine/SkyBase/MarkUs approach provides significant insights and predicts: (1) many more family members (approximately 4,000) than any other resource; (2) the function for a large number of unannotated proteins; (3) instances of START domains in genomes from which they were thought to be absent; and (4) the existence of two types of novel proteins, those containing dual START domain and those containing N-terminal START domains.

Molecular dynamics simulations of PIP2 and PIP3 in lipid bilayers: determination of ring orientation, and the effects of surface roughness on a Poisson-Boltzmann description.

Molecular dynamics (MD) simulations of phosphatidylinositol (4,5)-bisphosphate (PIP2) and phosphatidylinositol (3,4,5)-trisphosphate (PIP3) in 1-palmitoyl 2-oleoyl phosphatidylcholine (POPC) bilayers indicate that the inositol rings are tilted approximately 40 degrees with respect to the bilayer surface, as compared with 17 degrees for the P-N vector of POPC. Multiple minima were obtained for the ring twist (analogous to roll for an airplane). The phosphates at position 1 of PIP2 and PIP3 are within an Angström of the plane formed by the phosphates of POPC; lipids in the surrounding shell are depressed by 0.5-0.8 A, but otherwise the phosphoinositides do not substantially perturb the bilayer. Finite size artifacts for ion distributions are apparent for systems of approximately 26 waters/lipid, but, based on simulations with a fourfold increase of the aqueous phase, the phosphoinositide positions and orientations do not show significant size effects. Electrostatic potentials evaluated from Poisson-Boltzmann (PB) calculations show a strong dependence of potential height and ring orientation, with the maxima on the -25 mV surfaces (17.1 +/- 0.1 A for PIP2 and 19.4 +/- 0.3 A for PIP3) occurring near the most populated orientations from MD. These surfaces are well above the background height of 10 A estimated for negatively charged cell membranes, as would be expected for lipids involved in cellular signaling. PB calculations on microscopically flat bilayers yield similar maxima as the MD-based (microscopically rough) systems, but show less fine structure and do not clearly indicate the most probable regions. Electrostatic free energies of interaction with pentalysine are also similar for the rough and flat systems. These results support the utility of a rigid/flat bilayer model for PB-based studies of PIP2 and PIP3 as long as the orientations are judiciously chosen.