Predicting mutant outcome by combining deep mutational scanning and machine learning.

Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens. Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted "targets": (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b-d) three stability related properties of IGBPG (immunoglobulin G-binding ß1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment-the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations.

Mapping protein selectivity landscapes using multi-target selective screening and next-generation sequencing of combinatorial libraries.

Characterizing the binding selectivity landscape of interacting proteins is crucial both for elucidating the underlying mechanisms of their interaction and for developing selective inhibitors. However, current mapping methods are laborious and cannot provide a sufficiently comprehensive description of the landscape. Here, we introduce a novel and efficient strategy for comprehensively mapping the binding landscape of proteins using a combination of experimental multi-target selective library screening and in silico next-generation sequencing analysis. We map the binding landscape of a non-selective trypsin inhibitor, the amyloid protein precursor inhibitor (APPI), to each of the four human serine proteases (kallikrein-6, mesotrypsin, and anionic and cationic trypsins). We then use this map to dissect and improve the affinity and selectivity of APPI variants toward each of the four proteases. Our strategy can be used as a platform for the development of a new generation of target-selective probes and therapeutic agents based on selective protein-protein interactions.

Pre-equilibrium competitive library screening for tuning inhibitor association rate and specificity toward serine proteases.

High structural and sequence similarity within protein families can pose significant challenges to the development of selective inhibitors, especially toward proteolytic enzymes. Such enzymes usually belong to large families of closely similar proteases and may also hydrolyze, with different rates, protein- or peptide-based inhibitors. To address this challenge, we employed a combinatorial yeast surface display library approach complemented with a novel pre-equilibrium, competitive screening strategy for facile assessment of the effects of multiple mutations on inhibitor association rates and binding specificity. As a proof of principle for this combined approach, we utilized this strategy to alter inhibitor/protease association rates and to tailor the selectivity of the amyloid ß-protein precursor Kunitz protease inhibitor domain (APPI) for inhibition of the oncogenic protease mesotrypsin, in the presence of three competing serine proteases, anionic trypsin, cationic trypsin and kallikrein-6. We generated a variant, designated APPIP13W/M17G/I18F/F34V, with up to 30-fold greater specificity relative to the parental APPIM17G/I18F/F34V protein, and 6500- to 230 000-fold improved specificity relative to the wild-type APPI protein in the presence of the other proteases tested. A series of molecular docking simulations suggested a mechanism of interaction that supported the biochemical results. These simulations predicted that the selectivity and specificity are affected by the interaction of the mutated APPI residues with nonconserved enzyme residues located in or near the binding site. Our strategy will facilitate a better understanding of the binding landscape of multispecific proteins and will pave the way for design of new drugs and diagnostic tools targeting proteases and other proteins.