Longitudinal Genotype-Phenotype (Vineland Questionnaire) Characterization of 15 ADNP Syndrome Cases Highlights Mutated Protein Length and Structural Characteristics Correlation with Communicative Abilities Accentuated in Males.

Activity-dependent neuroprotective protein (ADNP) is essential for neurodevelopment and de novo mutations in ADNP cause the ADNP syndrome. From brain pathologies point of view, tauopathy has been demonstrated at a young age, implying stunted development coupled with early/accelerated neurodegeneration. Given potential genotype-phenotype differences and age-dependency, we have assessed here a cohort of 15 individuals (1-27-year-old), using 1-3 longitudinal parent (caretaker) interview/s (Vineland 3 questionnaire) over several years. Our results indicated developmental delays, or even developmental arrests, coupled with potential spurts of development at early ages. Severe outcomes correlated with the truncating high impact mutation, in other words, the remaining mutated protein length as well as with the tested individual age, corroborating the hypothesis of developmental delays coupled with accelerated aging. A significant correlation was noted between mutated protein length and communication, implying a high impact of ADNP on communicative skills. Additionally, correlations were discovered between the two previously described epi-genetic signatures in ADNP emphasizing aberrant acquisition of motor behaviors, with truncating mutations around the nuclear localization signal being mostly affected. Finally, all individuals seem to acquire an age equivalent of 1-6 years, requiring disease modification treatment, such as the ADNP-derived drug candidate, NAP (davunetide), which has recently shown efficacy in women suffering from the neurodegenerative disorder, progressive supranuclear palsy (PSP), a late-onset tauopathy.

Understanding Type 2 Diabetes Mellitus Risk Parameters through Intermittent Fasting: A Machine Learning Approach.

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated blood glucose levels. Despite the availability of pharmacological treatments, dietary plans, and exercise regimens, T2DM remains a significant global cause of mortality. As a result, there is an increasing interest in exploring lifestyle interventions, such as intermittent fasting (IF). This study aims to identify underlying patterns and principles for effectively improving T2DM risk parameters through IF. By analyzing data from multiple randomized clinical trials investigating various IF interventions in humans, a machine learning algorithm was employed to develop a personalized recommendation system. This system offers guidance tailored to pre-diabetic and diabetic individuals, suggesting the most suitable IF interventions to improve T2DM risk parameters. With a success rate of 95%, this recommendation system provides highly individualized advice, optimizing the benefits of IF for diverse population subgroups. The outcomes of this study lead us to conclude that weight is a crucial feature for females, while age plays a determining role for males in reducing glucose levels in blood. By revealing patterns in diabetes risk parameters among individuals, this study not only offers practical guidance but also sheds light on the underlying mechanisms of T2DM, contributing to a deeper understanding of this complex metabolic disorder.

NAP (Davunetide): The Neuroprotective ADNP Drug Candidate Penetrates Cell Nuclei Explaining Pleiotropic Mechanisms.

(1) Background: Recently, we showed aberrant nuclear/cytoplasmic boundaries/activity-dependent neuroprotective protein (ADNP) distribution in ADNP-mutated cells. This malformation was corrected upon neuronal differentiation by the ADNP-derived fragment drug candidate NAP (davunetide). Here, we investigated the mechanism of NAP nuclear protection. (2) Methods: CRISPR/Cas9 DNA-editing established N1E-115 neuroblastoma cell lines that express two different green fluorescent proteins (GFPs)-labeled mutated ADNP variants (p.Tyr718* and p.Ser403*). Cells were exposed to NAP conjugated to Cy5, followed by live imaging. Cells were further characterized using quantitative morphology/immunocytochemistry/RNA and protein quantifications. (3) Results: NAP rapidly distributed in the cytoplasm and was also seen in the nucleus. Furthermore, reduced microtubule content was observed in the ADNP-mutated cell lines. In parallel, disrupting microtubules by zinc or nocodazole intoxication mimicked ADNP mutation phenotypes and resulted in aberrant nuclear-cytoplasmic boundaries, which were rapidly corrected by NAP treatment. No NAP effects were noted on ADNP levels. Ketamine, used as a control, was ineffective, but both NAP and ketamine exhibited direct interactions with ADNP, as observed via in silico docking. (4) Conclusions: Through a microtubule-linked mechanism, NAP rapidly localized to the cytoplasmic and nuclear compartments, ameliorating mutated ADNP-related deficiencies. These novel findings explain previously published gene expression results and broaden NAP (davunetide) utilization in research and clinical development.

A novel davunetide (NAPVSIPQQ to NAPVSIPQE) point mutation in activity-dependent neuroprotective protein (ADNP) causes a mild developmental syndrome.

NAP (NAPVSIPQ, drug candidate name, davunetide) is the neuroprotective fragment of activity-dependent neuroprotective protein (ADNP). Recent studies identified NAPVSIP as a Src homology 3 (SH3) domain-ligand association site, responsible for controlling signalling pathways regulating the cytoskeleton. Furthermore, the SIP motif in NAP/ADNP was identified as crucial for direct microtubule end-binding protein interaction facilitating microtubule dynamics and Tau microtubule interaction, at the microtubule end-binding protein site EB1 and EB3. Most de novo ADNP mutations reveal heterozygous STOP or frameshift STOP aberrations, driving the autistic/intellectual disability-related ADNP syndrome. Here, we report for the first time on a de novo missense mutation, resulting in ADNP containing NAPVISPQE instead of NAPVSIPQQ, in a child presenting developmental hypotonia, possibly associated with inflammation affecting food intake in early life coupled with fear of peer interactions and suggestive of a novel case of the ADNP syndrome. In silico modelling showed that the mutation Q (polar side chain) to E (negative side chain) affected the electrostatic characteristics of ADNP (reducing, while scattering the electrostatic positive patch). Comparison with the most prevalent pathogenic ADNP mutation, p.Tyr719*, indicated a further reduction in the electrostatic patch. Previously, exogenous NAP partially ameliorated deficits associated with ADNP p.Tyr719* mutations in transfected cells and in CRISPR/Cas9 genome edited cell and mouse models. These findings stress the importance of the NAP sequence in ADNP and as a future putative therapy for the ADNP syndrome.

SH3- and actin-binding domains connect ADNP and SHANK3, revealing a fundamental shared mechanism underlying autism.

De novo heterozygous mutations in activity-dependent neuroprotective protein (ADNP) cause autistic ADNP syndrome. ADNP mutations impair microtubule (MT) function, essential for synaptic activity. The ADNP MT-associating fragment NAPVSIPQ (called NAP) contains an MT end-binding protein interacting domain, SxIP (mimicking the active-peptide, SKIP). We hypothesized that not all ADNP mutations are similarly deleterious and that the NAPV portion of NAPVSIPQ is biologically active. Using the eukaryotic linear motif (ELM) resource, we identified a Src homology 3 (SH3) domain-ligand association site in NAP responsible for controlling signaling pathways regulating the cytoskeleton, namely NAPVSIP. Altogether, we mapped multiple SH3-binding sites in ADNP. Comparisons of the effects of ADNP mutations p.Glu830synfs*83, p.Lys408Valfs*31, p.Ser404* on MT dynamics and Tau interactions (live-cell fluorescence-microscopy) suggested spared toxic function in p.Lys408Valfs*31, with a regained SH3-binding motif due to the frameshift insertion. Site-directed-mutagenesis, abolishing the p.Lys408Valfs*31 SH3-binding motif, produced MT toxicity. NAP normalized MT activities in the face of all ADNP mutations, although, SKIP, missing the SH3-binding motif, showed reduced efficacy in terms of MT-Tau interactions, as compared with NAP. Lastly, SH3 and multiple ankyrin repeat domains protein 3 (SHANK3), a major autism gene product, interact with the cytoskeleton through an actin-binding motif to modify behavior. Similarly, ELM analysis identified an actin-binding site on ADNP, suggesting direct SH3 and indirect SHANK3/ADNP associations. Actin co-immunoprecipitations from mouse brain extracts showed NAP-mediated normalization of Shank3-Adnp-actin interactions. Furthermore, NAP treatment ameliorated aberrant behavior in mice homozygous for the Shank3 ASD-linked InsG3680 mutation, revealing a fundamental shared mechanism between ADNP and SHANK3.

OnTheFly: a database of Drosophila melanogaster transcription factors and their binding sites.

We present OnTheFly (http://bhapp.c2b2.columbia.edu/OnTheFly/index.php), a database comprising a systematic collection of transcription factors (TFs) of Drosophila melanogaster and their DNA-binding sites. TFs predicted in the Drosophila melanogaster genome are annotated and classified and their structures, obtained via experiment or homology models, are provided. All known preferred TF DNA-binding sites obtained from the B1H, DNase I and SELEX methodologies are presented. DNA shape parameters predicted for these sites are obtained from a high throughput server or from crystal structures of protein-DNA complexes where available. An important feature of the database is that all DNA-binding domains and their binding sites are fully annotated in a eukaryote using structural criteria and evolutionary homology. OnTheFly thus provides a comprehensive view of TFs and their binding sites that will be a valuable resource for deciphering non-coding regulatory DNA.

Predicting nucleic acid binding interfaces from structural models of proteins.

The function of DNA- and RNA-binding proteins can be inferred from the characterization and accurate prediction of their binding interfaces. However, the main pitfall of various structure-based methods for predicting nucleic acid binding function is that they are all limited to a relatively small number of proteins for which high-resolution three-dimensional structures are available. In this study, we developed a pipeline for extracting functional electrostatic patches from surfaces of protein structural models, obtained using the I-TASSER protein structure predictor. The largest positive patches are extracted from the protein surface using the patchfinder algorithm. We show that functional electrostatic patches extracted from an ensemble of structural models highly overlap the patches extracted from high-resolution structures. Furthermore, by testing our pipeline on a set of 55 known nucleic acid binding proteins for which I-TASSER produces high-quality models, we show that the method accurately identifies the nucleic acids binding interface on structural models of proteins. Employing a combined patch approach we show that patches extracted from an ensemble of models better predicts the real nucleic acid binding interfaces compared with patches extracted from independent models. Overall, these results suggest that combining information from a collection of low-resolution structural models could be a valuable approach for functional annotation. We suggest that our method will be further applicable for predicting other functional surfaces of proteins with unknown structure.

From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

Protein nucleic acid interactions play a critical role in all steps of the gene expression pathway. Nucleic acid (NA) binding proteins interact with their partners, DNA or RNA, via distinct regions on their surface that are characterized by an ensemble of chemical, physical and geometrical properties. In this study, we introduce a novel methodology based on differential geometry, commonly used in face recognition, to characterize and predict NA binding surfaces on proteins. Applying the method on experimentally solved three-dimensional structures of proteins we successfully classify double-stranded DNA (dsDNA) from single-stranded RNA (ssRNA) binding proteins, with 83% accuracy. We show that the method is insensitive to conformational changes that occur upon binding and can be applicable for de novo protein-function prediction. Remarkably, when concentrating on the zinc finger motif, we distinguish successfully between RNA and DNA binding interfaces possessing the same binding motif even within the same protein, as demonstrated for the RNA polymerase transcription-factor, TFIIIA. In conclusion, we present a novel methodology to characterize protein surfaces, which can accurately tell apart dsDNA from an ssRNA binding interfaces. The strength of our method in recognizing fine-tuned differences on NA binding interfaces make it applicable for many other molecular recognition problems, with potential implications for drug design.

Classifying RNA-binding proteins based on electrostatic properties.

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein-protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs.

Patch Finder Plus (PFplus): a web server for extracting and displaying positive electrostatic patches on protein surfaces.

Positively charged electrostatic patches on protein surfaces are usually indicative of nucleic acid binding interfaces. Interestingly, many proteins which are not involved in nucleic acid binding possess large positive patches on their surface as well. In some cases, the positive patches on the protein are related to other functional properties of the protein family. PatchFinderPlus (PFplus) http://pfp.technion.ac.il is a web-based tool for extracting and displaying continuous electrostatic positive patches on protein surfaces. The input required for PFplus is either a four letter PDB code or a protein coordinate file in PDB format, provided by the user. PFplus computes the continuum electrostatics potential and extracts the largest positive patch for each protein chain in the PDB file. The server provides an output file in PDB format including a list of the patch residues. In addition, the largest positive patch is displayed on the server by a graphical viewer (Jmol), using a simple color coding.