Structured States of Disordered Proteins from Genomic Sequences.

Protein flexibility ranges from simple hinge movements to functional disorder. Around half of all human proteins contain apparently disordered regions with little 3D or functional information, and many of these proteins are associated with disease. Building on the evolutionary couplings approach previously successful in predicting 3D states of ordered proteins and RNA, we developed a method to predict the potential for ordered states for all apparently disordered proteins with sufficiently rich evolutionary information. The approach is highly accurate (79%) for residue interactions as tested in more than 60 known disordered regions captured in a bound or specific condition. Assessing the potential for structure of more than 1,000 apparently disordered regions of human proteins reveals a continuum of structural order with at least 50% with clear propensity for three- or two-dimensional states. Co-evolutionary constraints reveal hitherto unseen structures of functional importance in apparently disordered proteins.

The EVcouplings Python framework for coevolutionary sequence analysis.

SUMMARY: Coevolutionary sequence analysis has become a commonly used technique for de novo prediction of the structure and function of proteins, RNA, and protein complexes. We present the EVcouplings framework, a fully integrated open-source application and Python package for coevolutionary analysis. The framework enables generation of sequence alignments, calculation and evaluation of evolutionary couplings (ECs), and de novo prediction of structure and mutation effects. The combination of an easy to use, flexible command line interface and an underlying modular Python package makes the full power of coevolutionary analyses available to entry-level and advanced users. AVAILABILITY AND IMPLEMENTATION: https://github.com/debbiemarkslab/evcouplings.

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks.

While genes are defined by sequence, in biological systems a protein's function is largely determined by its three-dimensional structure. Evolutionary information embedded within multiple sequence alignments provides a rich source of data for inferring structural constraints on macromolecules. Still, many proteins of interest lack sufficient numbers of related sequences, leading to noisy, error-prone residue-residue contact predictions. Here we introduce DeepContact, a convolutional neural network (CNN)-based approach that discovers co-evolutionary motifs and leverages these patterns to enable accurate inference of contact probabilities, particularly when few related sequences are available. DeepContact significantly improves performance over previous methods, including in the CASP12 blind contact prediction task where we achieved top performance with another CNN-based approach. Moreover, our tool converts hard-to-interpret coupling scores into probabilities, moving the field toward a consistent metric to assess contact prediction across diverse proteins. Through substantially improving the precision-recall behavior of contact prediction, DeepContact suggests we are near a paradigm shift in template-free modeling for protein structure prediction.

Pan-cancer genetic analysis identifies PARK2 as a master regulator of G1/S cyclins.

Coordinate control of different classes of cyclins is fundamentally important for cell cycle regulation and tumor suppression, yet the underlying mechanisms are incompletely understood. Here we show that the PARK2 tumor suppressor mediates this coordination. The PARK2 E3 ubiquitin ligase coordinately controls the stability of both cyclin D and cyclin E. Analysis of approximately 5,000 tumor genomes shows that PARK2 is a very frequently deleted gene in human cancer and uncovers a striking pattern of mutual exclusivity between PARK2 deletion and amplification of CCND1, CCNE1 or CDK4-implicating these genes in a common pathway. Inactivation of PARK2 results in the accumulation of cyclin D and acceleration of cell cycle progression. Furthermore, PARK2 is a component of a new class of cullin-RING-containing ubiquitin ligases targeting both cyclin D and cyclin E for degradation. Thus, PARK2 regulates cyclin-CDK complexes, as does the CDK inhibitor p16, but acts as a master regulator of the stability of G1/S cyclins.