HYPK: A marginally disordered protein sensitive to charge decoration.

Intrinsically disordered proteins (IDPs) that lie close to the empirical boundary separating IDPs and folded proteins in Uversky's charge-hydropathy plot may behave as "marginal IDPs" and sensitively switch conformation upon changes in environment (temperature, crowding, and charge screening), sequence, or both. In our search for such a marginal IDP, we selected Huntingtin-interacting protein K (HYPK) near that boundary as a candidate; PKIα, also near that boundary, has lower secondary structure propensity; and Crk1, just across the boundary on the folded side, has higher secondary structure propensity. We used a qualitative Förster resonance energy transfer-based assay together with circular dichroism to simultaneously probe global and local conformation. HYPK shows several unique features indicating marginality: a cooperative transition in end-to-end distance with temperature, like Crk1 and folded proteins, but unlike PKIα; enhanced secondary structure upon crowding, in contrast to Crk1 and PKIα; and a cross-over from salt-induced expansion to compaction at high temperature, likely due to a structure-to-disorder transition not seen in Crk1 and PKIα. We then tested HYPK's sensitivity to charge patterning by designing charge-flipped variants including two specific sequences with identical amino acid composition that markedly differ in their predicted size and response to salt. The experimentally observed trends, also including mutants of PKIα, verify the predictions from sequence charge decoration metrics. Marginal proteins like HYPK show features of both folded and disordered proteins that make them sensitive to physicochemical perturbations and structural control by charge patterning.

Threading single proteins through pores to compare their energy landscapes.

Translocation of proteins is correlated with structural fluctuations that access conformational states higher in free energy than the folded state. We use electric fields at the solid-state nanopore to control the relative free energy and occupancy of different protein conformational states at the single-molecule level. The change in occupancy of different protein conformations as a function of electric field gives rise to shifts in the measured distributions of ionic current blockades and residence times. We probe the statistics of the ionic current blockades and residence times for three mutants of the [Formula: see text]-repressor family in order to determine the number of accessible conformational states of each mutant and evaluate the ruggedness of their free energy landscapes. Translocation becomes faster at higher electric fields when additional flexible conformations are available for threading through the pore. At the same time, folding rates are not correlated with ease of translocation; a slow-folding mutant with a low-lying intermediate state translocates faster than a faster-folding two-state mutant. Such behavior allows us to distinguish among protein mutants by selecting for the degree of current blockade and residence time at the pore. Based on these findings, we present a simple free energy model that explains the complementary relationship between folding equilibrium constants and translocation rates.

Direct Observation of Single-Protein Transition State Passage by Nanopore Ionic Current Jumps.

Conformational transitions of proteins are governed by chemical kinetics, often toggled by passage through an activated state separating two conformational ensembles. The passage time of a protein through the activated state can be too fast to be detected by single-molecule experiments without the aid of viscogenic agents. Here, we use high-bandwidth nanopore measurements to resolve microsecond-duration transitions that occur between conformational states of individual protein molecules partly blocking pore current. We measure the transition state passage time between folded and unfolded states of a two-state λ6-85 mutant and between metastable intermediates and the unfolded state of the multistate folder cytochrome c. Consistent with the principle of microscopic reversibility, the transition state passage time is the same for the forward and backward reactions. A passage time distribution whose tail is broader than a single exponential observed in cytochrome c suggests a multidimensional energy landscape for this protein.

MolData, a molecular benchmark for disease and target based machine learning.

Deep learning's automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge are necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChEMBL offer the screening data for millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological descriptions which can hinder their usage by the machine learning community. In this work, a comprehensive disease and target-based dataset is collected from PubChem in order to facilitate and accelerate molecular machine learning for better drug discovery. MolData is one the largest efforts to date for democratizing the molecular machine learning, with roughly 170 million drug screening results from 1.4 million unique molecules assigned to specific diseases and targets. It also provides 30 unique categories of targets and diseases. Correlation analysis of the MolData bioassays unveils valuable information for drug repurposing for multiple diseases including cancer, metabolic disorders, and infectious diseases. Finally, we provide a benchmark of more than 30 models trained on each category using multitask learning. MolData aims to pave the way for computational drug discovery and accelerate the advancement of molecular artificial intelligence in a practical manner. The MolData benchmark data is available at https://GitHub.com/Transilico/MolData as well as within the additional files.

Enzymatic Reconstitution and Biosynthetic Investigation of the Lasso Peptide Fusilassin.

Lasso peptides are a class of ribosomally synthesized and post-translationally modified natural product which possess a unique lariat knot conformation. The low entropy "threaded" conformation endows lasso peptides with considerable resistance to heat and proteolytic degradation, which are attractive properties for the development of peptide-based therapeutics. Despite their discovery nearly 30 years ago, the molecular mechanism underlying lasso peptide biosynthesis remains poorly characterized due to the low stability of the purified biosynthetic enzymes. Here, we report the biosynthetic reconstitution of a lasso peptide derived from Thermobifida fusca, termed fusilassin. Beyond robust catalytic activity, the fusilassin enzymes demonstrate extraordinary substrate tolerance during heterologous expression in E. coli and upon purification in cell-free biosynthetic reconstitution reactions. We provide evidence that the fusilassin biosynthetic enzymes are not capable of forming branched-cyclic products but can produce entirely unrelated lasso peptides. Finally, we leveraged our bioinformatic survey of all lasso peptides identified in GenBank to perform coevolutionary analysis of two requisite biosynthetic proteins. This effort correctly identified residues governing an important protein-protein interaction, illustrating how genomic insight can accelerate the characterization of natural product biosynthetic pathways. The fusilassin enzymes described within represent a model system for both designing future lasso peptides of biomedical importance and also for elucidating the molecular mechanisms that govern lasso peptide biosynthesis.