The Progress and Promise of RNA Medicine─An Arsenal of Targeted Treatments.

In the past decade, there has been a shift in research, clinical development, and commercial activity to exploit the many physiological roles of RNA for use in medicine. With the rapid success in the development of lipid-RNA nanoparticles for mRNA vaccines against COVID-19 and with several approved RNA-based drugs, RNA has catapulted to the forefront of drug research. With diverse functions beyond the role of mRNA in producing antigens or therapeutic proteins, many classes of RNA serve regulatory roles in cells and tissues. These RNAs have potential as new therapeutics, with RNA itself serving as either a drug or a target. Here, based on the CAS Content Collection, we provide a landscape view of the current state and outline trends in RNA research in medicine across time, geography, therapeutic pipelines, chemical modifications, and delivery mechanisms.

Ortholog identification in the presence of domain architecture rearrangement.

Ortholog identification is used in gene functional annotation, species phylogeny estimation, phylogenetic profile construction and many other analyses. Bioinformatics methods for ortholog identification are commonly based on pairwise protein sequence comparisons between whole genomes. Phylogenetic methods of ortholog identification have also been developed; these methods can be applied to protein data sets sharing a common domain architecture or which share a single functional domain but differ outside this region of homology. While promiscuous domains represent a challenge to all orthology prediction methods, overall structural similarity is highly correlated with proximity in a phylogenetic tree, conferring a degree of robustness to phylogenetic methods. In this article, we review the issues involved in orthology prediction when data sets include sequences with structurally heterogeneous domain architectures, with particular attention to automated methods designed for high-throughput application, and present a case study to illustrate the challenges in this area.

ModBase, a database of annotated comparative protein structure models, and associated resources.

ModBase (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by ModPipe, an automated modeling pipeline that relies primarily on Modeller for fold assignment, sequence-structure alignment, model building and model assessment (http://salilab.org/modeller/). ModBase currently contains 10,355,444 reliable models for domains in 2,421,920 unique protein sequences. ModBase allows users to update comparative models on demand, and request modeling of additional sequences through an interface to the ModWeb modeling server (http://salilab.org/modweb). ModBase models are available through the ModBase interface as well as the Protein Model Portal (http://www.proteinmodelportal.org/). Recently developed associated resources include the SALIGN server for multiple sequence and structure alignment (http://salilab.org/salign), the ModEval server for predicting the accuracy of protein structure models (http://salilab.org/modeval), the PCSS server for predicting which peptides bind to a given protein (http://salilab.org/pcss) and the FoXS server for calculating and fitting Small Angle X-ray Scattering profiles (http://salilab.org/foxs).

SATCHMO-JS: a webserver for simultaneous protein multiple sequence alignment and phylogenetic tree construction.

We present the jump-start simultaneous alignment and tree construction using hidden Markov models (SATCHMO-JS) web server for simultaneous estimation of protein multiple sequence alignments (MSAs) and phylogenetic trees. The server takes as input a set of sequences in FASTA format, and outputs a phylogenetic tree and MSA; these can be viewed online or downloaded from the website. SATCHMO-JS is an extension of the SATCHMO algorithm, and employs a divide-and-conquer strategy to jump-start SATCHMO at a higher point in the phylogenetic tree, reducing the computational complexity of the progressive all-versus-all HMM-HMM scoring and alignment. Results on a benchmark dataset of 983 structurally aligned pairs from the PREFAB benchmark dataset show that SATCHMO-JS provides a statistically significant improvement in alignment accuracy over MUSCLE, Multiple Alignment using Fast Fourier Transform (MAFFT), ClustalW and the original SATCHMO algorithm. The SATCHMO-JS webserver is available at http://phylogenomics.berkeley.edu/satchmo-js. The datasets used in these experiments are available for download at http://phylogenomics.berkeley.edu/satchmo-js/supplementary/.

Berkeley PHOG: PhyloFacts orthology group prediction web server.

Ortholog detection is essential in functional annotation of genomes, with applications to phylogenetic tree construction, prediction of protein-protein interaction and other bioinformatics tasks. We present here the PHOG web server employing a novel algorithm to identify orthologs based on phylogenetic analysis. Results on a benchmark dataset from the TreeFam-A manually curated orthology database show that PHOG provides a combination of high recall and precision competitive with both InParanoid and OrthoMCL, and allows users to target different taxonomic distances and precision levels through the use of tree-distance thresholds. For instance, OrthoMCL-DB achieved 76% recall and 66% precision on this dataset; at a slightly higher precision (68%) PHOG achieves 10% higher recall (86%). InParanoid achieved 87% recall at 24% precision on this dataset, while a PHOG variant designed for high recall achieves 88% recall at 61% precision, increasing precision by 37% over InParanoid. PHOG is based on pre-computed trees in the PhyloFacts resource, and contains over 366 K orthology groups with a minimum of three species. Predicted orthologs are linked to GO annotations, pathway information and biological literature. The PHOG web server is available at http://phylofacts.berkeley.edu/orthologs/.