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Autism spectrum disorder (ASD) has a complex genetic architecture involving contributions from both de novo and inherited variation. Few studies have been designed to address the role of rare inherited variation or its interaction with common polygenic risk in ASD. Here, we performed whole-genome sequencing of the largest cohort of multiplex families to date, consisting of 4,551 individuals in 1,004 families having two or more autistic children. Using this study design, we identify seven previously unrecognized ASD risk genes supported by a majority of rare inherited variants, finding support for a total of 74 genes in our cohort and a total of 152 genes after combined analysis with other studies. Autistic children from multiplex families demonstrate an increased burden of rare inherited protein-truncating variants in known ASD risk genes. We also find that ASD polygenic score (PGS) is overtransmitted from nonautistic parents to autistic children who also harbor rare inherited variants, consistent with combinatorial effects in the offspring, which may explain the reduced penetrance of these rare variants in parents. We also observe that in addition to social dysfunction, language delay is associated with ASD PGS overtransmission. These results are consistent with an additive complex genetic risk architecture of ASD involving rare and common variation and further suggest that language delay is a core biological feature of ASD.

We performed a comprehensive assessment of rare inherited variation in autism spectrum disorder (ASD) by analyzing whole-genome sequences of 2,308 individuals from families with multiple affected children. We implicate 69 genes in ASD risk, including 24 passing genome-wide Bonferroni correction and 16 new ASD risk genes, most supported by rare inherited variants, a substantial extension of previous findings. Biological pathways enriched for genes harboring inherited variants represent cytoskeletal organization and ion transport, which are distinct from pathways implicated in previous studies. Nevertheless, the de novo and inherited genes contribute to a common protein-protein interaction network. We also identified structural variants (SVs) affecting non-coding regions, implicating recurrent deletions in the promoters of DLG2 and NR3C2. Loss of nr3c2 function in zebrafish disrupts sleep and social function, overlapping with human ASD-related phenotypes. These data support the utility of studying multiplex families in ASD and are available through the Hartwell Autism Research and Technology portal.

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental disorders (NDDs) with a high unmet medical need. The diagnosis of ASD is currently based on behavior criteria, which overlooks the diversity of genetic, neurophysiological, and clinical manifestations. Failure to acknowledge such heterogeneity has hindered the development of efficient drug treatments for ASD and other NDDs. DEPI® (Databased Endophenotyping Patient Identification) is a systems biology, multi-omics, and machine learning-driven platform enabling the identification of subgroups of patients with NDDs and the development of patient-tailored treatments. In this study, we provide evidence for the validation of a first clinically and biologically defined subgroup of patients with ASD identified by DEPI, ASD Phenotype 1 (ASD-Phen1). Among 313 screened patients with idiopathic ASD, the prevalence of ASD-Phen1 was observed to be ~24% in 84 patients who qualified to be enrolled in the study. Metabolic and transcriptomic alterations differentiating patients with ASD-Phen1 were consistent with an over-activation of NF-κB and NRF2 transcription factors, as predicted by DEPI. Finally, the suitability of STP1 combination treatment to revert such observed molecular alterations in patients with ASD-Phen1 was determined. Overall, our results support the development of precision medicine-based treatments for patients diagnosed with ASD. [ABSTRACT FROM AUTHOR]

RNA-binding proteins are involved in many important regulatory processes in cells and their study is essential for a complete understanding of living organisms. They show a large variability from both structural and functional points of view. However, several recent studies performed on protein-RNA crystal structures have revealed interesting common properties. RNA-binding sites usually constitute patches of positively charged or polar residues that make most of the specific and non-specific contacts with RNA. Negatively charged or aliphatic residues are less frequent at protein-RNA interfaces, although they can also be found either forming aliphatic and positive-negative pairs in protein RNA-binding sites or contacting RNA through their main chains. Aromatic residues found within these interfaces are usually involved in specific base recognition at RNA single-strand regions. This specific recognition, in combination with structural complementarity, represents the key source for specificity in protein-RNA association. From all this knowledge, a variety of computational methods for prediction of RNA-binding sites have been developed based either on protein sequence or on protein structure. Some reported methods are really successful in the identification of RNA-binding proteins or the prediction of RNA-binding sites. Given the growing interest in the field, all these studies and prediction methods will undoubtedly contribute to the identification and comprehension of protein-RNA interactions.

The study of protein-protein interactions that are involved in essential life processes can largely benefit from the recent upraising of computational docking approaches. Predicting the structure of a protein-protein complex from their separate components is still a highly challenging task, but the field is rapidly improving. Recent advances in sampling algorithms and rigid-body scoring functions allow to produce, at least for some cases, high quality docking models that are perfectly suitable for biological and functional annotations, as it has been shown in the CAPRI blind tests. However, important challenges still remain in docking prediction. For example, in cases with significant mobility, such as multidomain proteins, fully unrestricted rigid-body docking approaches are clearly insufficient so they need to be combined with restraints derived from domain-domain linker residues, evolutionary information, or binding site predictions. Other challenging cases are weak or transient interactions, such as those between proteins involved in electron transfer, where the existence of alternative bound orientations and encounter complexes complicates the binding energy landscape. Docking methods also struggle when using in silico structural models for the interacting subunits. Bringing these challenges to a practical point of view, we have studied here the limitations of our docking and energy-based scoring approach, and have analyzed different parameters to overcome the limitations and improve the docking performance. For that, we have used the standard benchmark and some practical cases from CAPRI. Based on these results, we have devised a protocol to estimate the success of a given docking run. Proteins 2010. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Protein-RNA interactions are essential in living organisms and they are involved in very different and important cellular processes. Thus, understanding protein-RNA recognition at molecular level is a key goal not only from a basic biological point of view but also for biotechnological and therapeutic purposes. On basis of the most updated available set of nonredundant X-ray structures of protein-RNA complexes, we have computed protein-RNA interface propensities for ribonucleotides and aminoacid residues. The results show several protein residues with high tendency to bind RNA, such as arginine, lysine, and histidine. However, we could not observe any clear preferences for protein binding among the different ribonucleotides. We applied these propensity values to predict RNA-binding areas on proteins, using an ad hoc algorithm called OPRA (Optimal Protein-RNA Area). First, for each protein residue, we derived a predictive score from its corresponding protein-RNA interface propensity weighed by its accessible surface area (ASA). Then, optimal patch energy scores were computed for each residue by adding up the individual scores of the neighboring surface residues. The resulting patch scores correlate well with the known RNA-binding sites on protein surfaces. The OPRA method has been benchmarked on a test set of 30 unbound proteins involved in protein-RNA complexes of known structure, where it is able to successfully predict RNA-binding sites on protein surfaces with around 80% positive predictive value. This can be useful for identifying potential RNA-binding sites on proteins, and can help to model protein-RNA interactions of biological and therapeutic interest. Proteins 2010. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

We report the first assessment of blind predictions of water positions at protein-protein interfaces, performed as part of the critical assessment of predicted interactions (CAPRI) community-wide experiment. Groups submitting docking predictions for the complex of the DNase domain of colicin E2 and Im2 immunity protein (CAPRI Target 47), were invited to predict the positions of interfacial water molecules using the method of their choice. The predictions-20 groups submitted a total of 195 models-were assessed by measuring the recall fraction of water-mediated protein contacts. Of the 176 high- or medium-quality docking models-a very good docking performance per se-only 44% had a recall fraction above 0.3, and a mere 6% above 0.5. The actual water positions were in general predicted to an accuracy level no better than 1.5 Å, and even in good models about half of the contacts represented false positives. This notwithstanding, three hotspot interface water positions were quite well predicted, and so was one of the water positions that is believed to stabilize the loop that confers specificity in these complexes. Overall the best interface water predictions was achieved by groups that also produced high-quality docking models, indicating that accurate modelling of the protein portion is a determinant factor. The use of established molecular mechanics force fields, coupled to sampling and optimization procedures also seemed to confer an advantage. Insights gained from this analysis should help improve the prediction of protein-water interactions and their role in stabilizing protein complexes., (Copyright © 2013 Wiley Periodicals, Inc.)

Despite the importance of protein-RNA interactions in the cellular context, the number of available protein-RNA complex structures is still much lower than those of other biomolecules. As a consequence, few computational studies have been addressed towards protein-RNA complexes, and to our knowledge, no systematic benchmarking of protein-RNA docking has been reported. In this study we have extracted new pairwise residue-ribonucleotide interface propensities for protein-RNA, which can be used as statistical potentials for scoring of protein-RNA docking poses. We show here a new protein-RNA docking approach based on FTDock generation of rigid-body docking poses, which are later scored by these statistical residue-ribonucleotide potentials. The method has been successfully benchmarked in a set of 12 protein-RNA cases. The results show that FTDock is able to generate near-native solutions in more than half of the cases, and that it can rank near-native solutions significantly above random. In practically all these cases, our propensity-based scoring helps to improve the docking results, finding a near-native solution within rank 100 in 43% of them. In a remarkable case, the near-native solution was ranked 1 after the propensity-based scoring. Other previously described propensity potentials can also be used for scoring, with slightly worse performance. This new protein-RNA docking protocol permits a fast scoring of rigid-body docking poses in order to select a small number of docking orientations, which can be later evaluated with more sophisticated energy-based scoring functions.