Transcriptome- and genome-targeted cancer immunotherapy

Precision oncology targets cancer cells using antibodies or small-molecule inhibitors directed against a mutated or overexpressed oncogenic protein. Targeted drugs are developed individually for each protein at huge costs, and only a small fraction of proteins can be targeted at all. We propose to target cancer cells through their mutated transcripts and genomes instead, leveraging their mutations to alert the immune system. Nanoparticles delivered systemically could be loaded with RNA- or DNA-directed Cas mRNA and guide RNAs targeting multiple patient-specific cancer mutations. In transcriptome-targeted immunotherapy, patient-specific guide RNAs would target CRISPR/Cas to cleave the transcripts mutated in the patient's cancer cells. The cleaved transcripts would hybridize with patient-specific 5'-triphosphate RNAs to form blunt-ended dsRNAs, triggering RIG-I and type-I interferon signaling that induces a strong systemic, long-lasting immune response against all cancer cells. This approach could potentially work for all cancers and prevent resistance by targeting many mutations jointly

Do inhibitors targeted against mutant oncogenic kinases act via kinase degradation-induced immune activation?

Targeted cancer therapies by small-molecule inhibitors of receptor tyrosine and other kinases have achieved great success in recent years. Most targeted medications specifically inhibit a protein kinase mutated in the patient's tumor. Although many possible mechanisms have been investigated, the drugs' astounding efficacies are not well understood. We propose a unifying mechanism of action. Strong binding by the inhibitor could lead to increased ubiquitination and degradation by the proteasome, boosting the presentation of kinase-associated neoantigen peptides. This would facilitate tumor cell recognition by T cells, leading to a sustained immune attack. The model suggests that the as yet inevitable failure to shrink tumors further after a few months might be caused by a transition to chronic inflammation. If true, the model has a multitude of implications for cancer and clinical research

The immune activation model for targeted inhibitors of mutated oncogenic kinases

Targeted cancer therapies by small-molecule inhibitors of receptor tyrosine and other kinases have achieved great success in recent years. Most targeted medications specifically inhibit a protein kinase mutated in the patient's tumor. Although many possible mechanisms have been investigated, the drugs' astounding efficacies are not well understood. Here we propose a unifying mechanism of action. Strong binding by the inhibitor could lead to increased ubiquitination and degradation by the proteasome, boosting the presentation of kinase-associated neoantigen peptides. This could facilitate tumor cell recognition by T cells, leading to a sustained immune attack. We discuss implications for experimental and clinical cancer research

Bayesian Markov models consistently outperform PWMs at predicting motifs in nucleotide sequences.

Position weight matrices (PWMs) are the standard model for DNA and RNA regulatory motifs. In PWMs nucleotide probabilities are independent of nucleotides at other positions. Models that account for dependencies need many parameters and are prone to overfitting. We have developed a Bayesian approach for motif discovery using Markov models in which conditional probabilities of order k - 1 act as priors for those of order k This Bayesian Markov model (BaMM) training automatically adapts model complexity to the amount of available data. We also derive an EM algorithm for de-novo discovery of enriched motifs. For transcription factor binding, BaMMs achieve significantly (P    =  1/16) higher cross-validated partial AUC than PWMs in 97% of 446 ChIP-seq ENCODE datasets and improve performance by 36% on average. BaMMs also learn complex multipartite motifs, improving predictions of transcription start sites, polyadenylation sites, bacterial pause sites, and RNA binding sites by 26-101%. BaMMs never performed worse than PWMs. These robust improvements argue in favour of generally replacing PWMs by BaMMs

Prediction of protein functional residues from sequence by probability density estimation.

Motivation: The prediction of ligand-binding residues or catalytically active residues of a protein may give important hints that can guide further genetic or biochemical studies. Existing sequence-based prediction methods mostly rank residue positions by evolutionary conservation calculated from a multiple sequence alignment of homologs. A problem hampering more wide-spread application of these methods is the low per-residue precision, which at 20% sensitivity is around 35% for ligand-binding residues and 20% for catalytic residues. Results: We combine information from the conservation at each site, its amino acid distribution, as well as its predicted secondary structure (ss) and relative solvent accessibility (rsa). First, we measure conservation by how much the amino acid distribution at each site differs from the distribution expected for the predicted ss and rsa states. Second, we include the conservation of neighboring residues in a weighted linear score by analytically optimizing the signal-to-noise ratio of the total score. Third, we use conditional probability density estimation to calculate the probability of each site to be functional given its conservation, the observed amino acid distribution, and the predicted ss and rsa states. We have constructed two large data sets, one based on the Catalytic Site Atlas and the other on PDB SITE records, to benchmark methods for predicting functional residues. The new method FRcons predicts ligand-binding and catalytic residues with higher precision than alternative methods over the entire sensitivity range, reaching 50% and 40% precision at 20% sensitivity, respectively

Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

Motivation: Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de novo discovery of RNA binding motifs do not take multivalent binding into account. Results: We present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two co-operatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yielding higher affinity and sequence specificity. We furthermore illustrate that the spatial geometry between the binding sites can be learned from bound RNA sequences. These discovered bipartite motifs are consistent with previously known motifs and binding behaviors. Our results demonstrate the importance of multivalent binding for RNA-binding proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA interactions

CCMpred-fast and precise prediction of protein residue-residue contacts from correlated mutations.

Motivation: Recent breakthroughs in protein residue-residue contact prediction have made reliable de novo prediction of protein structures possible. The key was to apply statistical methods that can distinguish direct couplings between pairs of columns in a multiple sequence alignment from merely correlated pairs, i.e. to separate direct from indirect effects. Two classes of such methods exist, either relying on regularized inversion of the covariance matrix or on pseudo-likelihood maximization (PLM). Although PLM-based methods offer clearly higher precision, available tools are not sufficiently optimized and are written in interpreted languages that introduce additional overheads. This impedes the runtime and large-scale contact prediction for larger protein families, multi-domain proteins and protein-protein interactions. Results: Here we introduce CCMpred, our performance-optimized PLM implementation in C and CUDA C. Using graphics cards in the price range of current six-core processors, CCMpred can predict contacts for typical alignments 35-113 times faster and with the same precision as the most accurate published methods. For users without a CUDA-capable graphics card, CCMpred can also run in a CPU mode that is still 4-14 times faster. Thanks to our speed-ups (http://dictionary.cambridge.org/dictionary/british/speed-up) contacts for typical protein families can be predicted in 15-60s on a consumer-grade GPU and 1-6min on a six-core CPU

MMseqs software suite for fast and deep clustering and searching of large protein sequence sets.

Sequence databases are growing fast, challenging existing analysis pipelines. Reducing the redundancy of sequence databases by similarity clustering improves speed and sensitivity of iterative searches. But existing tools cannot efficiently cluster databases of the size of UniProt to 50% maximum pairwise sequence identity or below. Furthermore, in metagenomics experiments typically large fractions of reads cannot be matched to any known sequence anymore because searching with sensitive but relatively slow tools (e.g. BLAST or HMMER3) through comprehensive databases such as UniProt is becoming too costly. RESULTS: MMseqs (Many-against-Many sequence searching) is a software suite for fast and deep clustering and searching of large datasets, such as UniProt, or 6-frame translated metagenomics sequencing reads. MMseqs contains three core modules: a fast and sensitive prefiltering module that sums up the scores of similar k-mers between query and target sequences, an SSE2- and multi-core-parallelized local alignment module, and a clustering module.In our homology detection benchmarks, MMseqs is much more sensitive and 4 to 30 times faster than UBLAST and RAPsearch, respectively, although it does not reach BLAST sensitivity yet. Using its cascaded clustering workflow, MMseqs can cluster large databases down to ~30% sequence identity at hundreds of times the speed of BLASTclust and much deeper than CD-HIT and USEARCH. MMseqs can also update a database clustering in linear instead of quadratic time. Its much improved sensitivity-speed trade-off should make MMseqs attractive for a wide range of large-scale sequence analysis tasks

Evolution of the beta-propeller fold.

β-Propellers are toroidal folds, in which repeated, four-stranded β-meanders are arranged in a circular and slightly tilted fashion, like the blades of a propeller. They are found in all domains of life, with a strong preponderance among eukaryotes. Propellers show considerable sequence diversity and are classified into six separate structural groups by the SCOP and CATH databases. Despite this diversity, they often show similarities across groups, not only in structure but also in sequence, raising the possibility of a common origin. In agreement with this hypothesis, most propellers group together in a cluster map of all-β folds generated by sequence similarity, because of numerous pairwise matches, many of which are individually nonsignificant. In total, 45 of 60 propellers in the SCOP25 database, covering four SCOP folds, are clustered in this group and analysis with sensitive sequence comparison methods shows that they are similar at a level indicative of homology. Two mechanisms appear to contribute to the evolution of β-propellers: amplification from single blades and subsequent functional differentiation. The observation of propellers with nearly identical blades in genomic sequences show that these mechanisms are still operating today