Discovery of a new class of cell-penetrating peptides by novel phage display platform.

The primary hurdles for small interference RNA (siRNA) in clinical use are targeted and cytosolic delivery. To overcome both challenges, we have established a novel platform based on phage display, called NNJA. In this approach, a lysosomal cathepsin substrate is engineered within the flexible loops of PIII, that is displaying a unique random sequence at its N-terminus. NNJA library selection targeting cell-expressed targets should yield specific peptides localized in the cytoplasm. That is because phage internalization and subsequent localization to lysosome, upon peptide binding to the cell expressed target, will result in cleavage of PIII, rendering phage non-infective. Such phage will be eliminated from the selected pool and only peptide-phage that escapes lysosomes will advance to the next round. Proof of concept studies with the NNJA library demonstrated cytosolic localization of selected peptide-phage and peptide-siRNA, confirmed through confocal microscopy. More importantly, conjugation of siHPRT to monomeric or multimeric NNJA peptides resulted in significant reduction in HPRT mRNA in various cell types without significant cytotoxicity. Sequence similarity and clustering analysis from NGS dataset provide insights into sequence composition facilitating cell penetration. NNJA platform offers a highly efficient peptide discovery engine for targeted delivery of oligonucleotides to cytosol.

Structure-free antibody paratope similarity prediction for in silico epitope binning via protein language models.

Antibodies are an important group of biological molecules that are used as therapeutics and diagnostic tools. Although millions of antibody sequences are available, identifying their structural and functional similarity and their antigen binding sites remains a challenge at large scale. Here, we present a fast, sequence-based computational method for antibody paratope prediction based on protein language models. The paratope information is then used to measure similarity among antibodies via protein language models. Our computational method enables binning of antibody discovery hits into groups as the function of epitope engagement. We further demonstrate the utility of the method by identifying antibodies targeting highly similar epitopes of the same antigens from a large pool of antibody sequences, using two case studies: SARS CoV2 Receptor Binding Domain (RBD) and Epidermal Growth Factor Receptor (EGFR). Our approach highlights the potential in accelerating antibody discovery by enhancing hit prioritization and diversity selection.

Instantaneous generation of protein hydration properties from static structures.

Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes.

Optimization of a 1,3,4-oxadiazole series for inhibition of Ca2+/calmodulin-stimulated activity of adenylyl cyclases 1 and 8 for the treatment of chronic pain.

Adenylyl cyclases type 1 (AC1) and 8 (AC8) are group 1 transmembrane adenylyl cyclases (AC) that are stimulated by Ca2+/calmodulin. Studies have shown that mice depleted of AC1 have attenuated inflammatory pain response, while AC1/AC8 double-knockout mice display both attenuated pain response and opioid dependence. Thus, AC1 has emerged as a promising new target for treating chronic pain and opioid abuse. We discovered that the 1,3,4-oxadiazole scaffold inhibits Ca2+/calmodulin-stimulated cyclic adenosine 3',5'-monophosphate (cAMP) production in cells stably expressing either AC1 or AC8. We then carried out structure-activity relationship studies, in which we designed and synthesized 65 analogs, to modulate potency and selectivity versus each AC isoform in cells. Furthermore, molecular docking of the analogs into an AC1 homology model suggests the molecules may bind at the ATP binding site. Finally, a prioritized analog was tested in a mouse model of inflammatory pain and exhibited modest analgesic properties. In summary, our data indicate the 1,3,4-oxadiazoles represent a novel scaffold for the cellular inhibition of Ca2+/calmodulin-stimulated AC1- and AC8 cAMP and warrant further exploration as potential lead compounds for the treatment of chronic inflammatory pain.