Binary combinatorial scanning reveals potent poly-alanine-substituted inhibitors of protein-protein interactions

Affinity selection-mass spectrometry with linearizable macrocyclic peptide libraries

Despite their potential, the preparation of large synthetic macrocyclic libraries for ligand discovery and development has been limited. Here, we produce 100-million-membered macrocyclic libraries containing natural and nonnatural amino acids. Near-quantitative intramolecular disulfide formation is facilitated by rapid oxidation with iodine in solution. After use in affinity selection, treatment with dithiothreitol enables near-quantitative reduction, rendering linear peptide analogs for standard tandem mass spectrometry. We use these libraries to discover macrocyclic binders to cadherin-2 and anti-hemagglutinin antibody clone 12ca5. Structure-activity relationship studies of an initial cadherin-binding peptide [CBP; apparent dissociation constant (Kd) = 53 nanomolar] reveal residues responsible for driving affinity (hotspots) and mutation-tolerant residues (coldspots). Two original macrocyclic libraries are prepared in which these hotspots and coldspots are derivatized with nonnatural amino acids. Following discovery and validation, high-affinity ligands are discovered from the coldspot library, with NCBP-4 demonstrating improved affinity (Kd = 29 nanomolar). Overall, we expect that this work will improve the use of macrocyclic libraries in therapeutic peptide development.

pyBinder: Quantitation to Advance Affinity Selection-Mass Spectrometry

Affinity selection-mass spectrometry (AS-MS) is a ligand discovery platform that relies upon mass spectrometry to identify molecules bound to a biomolecular target. When utilized with large peptide libraries (108 members), AS-MS sample complexity can surpass the sequencing capacity of modern mass spectrometers, resulting in incomplete data, identification of few target-specific ligands, and/or incomplete sequencing. To address this challenge, we introduce pyBinder to perform quantitation on AS-MS data to process primary MS1 data and develop two scores to rank the peptides from the integration of their peak area: target selectivity and concentration-dependent enrichment. We benchmark pyBinder utilizing AS-MS data developed against antihemagglutinin antibody 12ca5, revealing that peptides that contain a motif known for target-specific high-affinity binding are well characterized by these two scores. AS-MS data from a second protein target, WD Repeat Domain 5 (WDR5), is analyzed to confirm the two pyBinder scores reliably capture the target-specific motif-containing peptides. From the results delivered by pyBinder, a list of target-selective features is developed and fed back into subsequent MS experiments to facilitate expanded data generation and the targeted discovery of selective ligands. pyBinder analysis resulted in a 4-fold increase in motif-containing sequence identification for WDR5 (from 3 to 14 ligands discovered), showing the utility of the two scores. This work establishes an improved approach for AS-MS to enable discovery outcomes (i.e., more ligands identified), but also a way to compare AS-MS data across samples, protocols, and conditions broadly.

Insight into Protein Engineering: From In silico Modelling to In vitro Synthesis

Protein engineering alters the polypeptide chain to obtain a novel protein with improved functional properties. This field constantly evolves with advanced in silico tools and techniques to design novel proteins and peptides. Rational incorporating mutations, unnatural amino acids, and post-translational modifications increases the applications of engineered proteins and peptides. It aids in developing drugs with maximum efficacy and minimum side effects. Currently, the engineering of peptides is gaining attention due to their high stability, binding specificity, less immunogenic, and reduced toxicity properties. Engineered peptides are potent candidates for drug development due to their high specificity and low cost of production compared with other biologics, including proteins and antibodies. Therefore, understanding the current perception of designing and engineering peptides with the help of currently available in silico tools is crucial. This review extensively studies various in silico tools available for protein engineering in the prospect of designing peptides as therapeutics, followed by in vitro aspects. Moreover, a discussion on the chemical synthesis and purification of peptides, a case study, and challenges are also incorporated.

Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics

Drug discovery AI datasets and benchmarks have not traditionally included single-cell analysis biomarkers. While benchmarking efforts in single-cell analysis have recently released collections of single-cell tasks, they have yet to comprehensively release datasets, models, and benchmarks that integrate a broad range of therapeutic discovery tasks with cell-type-specific biomarkers. Therapeutics Commons (TDC-2) presents datasets, tools, models, and benchmarks integrating cell-type-specific contextual features with ML tasks across therapeutics. We present four tasks for contextual learning at single-cell resolution: drug-target nomination, genetic perturbation response prediction, chemical perturbation response prediction, and protein-peptide interaction prediction. We introduce datasets, models, and benchmarks for these four tasks. Finally, we detail the advancements and challenges in machine learning and biology that drove the implementation of TDC-2 and how they are reflected in its architecture, datasets and benchmarks, and foundation model tooling.

Prediction and design of transcriptional repressor domains with large-scale mutational scans and deep learning

Regulatory proteins have evolved diverse repressor domains (RDs) to enable precise context-specific repression of transcription. However, our understanding of how sequence variation impacts the functional activity of RDs is limited. To address this gap, we generated a high-throughput mutational scanning dataset measuring the repressor activity of 115,000 variant sequences spanning more than 50 RDs in human cells. We identified thousands of clinical variants with loss or gain of repressor function, including TWIST1 HLH variants associated with Saethre-Chotzen syndrome and MECP2 domain variants associated with Rett syndrome. We also leveraged these data to annotate short linear interacting motifs (SLiMs) that are critical for repression in disordered RDs. Then, we designed a deep learning model called TENet ( T ranscriptional E ffector Net work) that integrates sequence, structure and biochemical representations of sequence variants to accurately predict repressor activity. We systematically tested generalization within and across domains with varying homology using the mutational scanning dataset. Finally, we employed TENet within a directed evolution sequence editing framework to tune the activity of both structured and disordered RDs and experimentally test thousands of designs. Our work highlights critical considerations for future dataset design and model training strategies to improve functional variant prioritization and precision design of synthetic regulatory proteins.

Reversibly Reactive Affinity Selection-Mass Spectrometry Enables Identification of Covalent Peptide Binders

Covalent peptide binders have found applications as activity-based probes and as irreversible therapeutic inhibitors. Currently, there is no rapid, label-free, and tunable affinity selection platform to enrich covalent reactive peptide binders from synthetic libraries. We address this challenge by developing a reversibly reactive affinity selection platform termed ReAct-ASMS enabled by tandem high-resolution mass spectrometry (MS/MS) to identify covalent peptide binders to native protein targets. It uses mixed disulfide-containing peptides to build reversible peptide-protein conjugates that can enrich for covalent variants, which can be sequenced by MS/MS after reduction. Using this platform, we identified covalent peptide binders against two oncoproteins, human papillomavirus 16 early protein 6 (HPV16 E6) and peptidyl-prolyl cis-trans isomerase NIMA-interacting 1 protein (Pin1). The resulting peptide binders efficiently and selectively cross-link Cys58 of E6 at 37 °C and Cys113 of Pin1 at room temperature, respectively. ReAct-ASMS enables the identification of highly selective covalent peptide binders for diverse molecular targets, introducing an applicable platform to assist preclinical therapeutic development pipelines.

Development of an α-Klotho Recognizing High-Affinity Peptide Probe from In-Solution Enrichment

The kidney, parathyroid gland, and choroid plexus express the aging-related transmembrane protein α-Klotho, a coreceptor of the fibroblast growth factor 23 (FGF23) receptor complex. Reduced α-Klotho levels are correlated with chronic kidney disease and other age-related diseases, wherein they are released from membranes into circulation. Klotho's potential physiological action as a hormone is of current scientific interest. Part of the challenges associated with advancing these studies, however, has been the long-standing difficulty in detecting soluble α-Klotho in biofluids. Here, we describe the discovery of peptides that recognize α-Klotho with high affinity and selectivity by applying in-solution size-exclusion-based affinity selection-mass spectrometry (AS-MS). After two rounds of AS-MS and subsequent N-terminal modifications, the peptides improved their binding affinity to α-Klotho by approximately 2300-fold compared to the reported starting peptide Pep-10, previously designed based on the C-terminal region of FGF23. The lead peptide binders were shown to enrich α-Klotho from cell lysates and to label α-Klotho in kidney cells. Our results further support the utility of in-solution, label-free AS-MS protocols to discover peptide-based binders to target proteins of interest with high affinity and selectivity, resulting in functional probes for biological studies.

Bio-Chemoinformatics-Driven Analysis of nsp7 and nsp8 Mutations and Their Effects on Viral Replication Protein Complex Stability

The nonstructural proteins 7 and 8 (nsp7 and nsp8) of SARS-CoV-2 are highly important proteins involved in the RNA-dependent polymerase (RdRp) protein replication complex. In this study, we analyzed the global mutation of nsp7 and nsp8 in 2022 and 2023 and analyzed the effects of mutation on the viral replication protein complex using bio-chemoinformatics. Frequently occurring variants are found to be single amino acid mutations for both nsp7 and nsp8. The most frequently occurring mutations for nsp7 which include L56F, L71F, S25L, M3I, D77N, V33I and T83I are predicted to cause destabilizing effects, whereas those in nsp8 are predicted to cause stabilizing effects, with the threonine to isoleucine mutation (T89I, T145I, T123I, T148I, T187I) being a frequent mutation. A conserved domain database analysis generated critical interaction residues for nsp7 (Lys-7, His-36 and Asn-37) and nsp8 (Lys-58, Pro-183 and Arg-190), which, according to thermodynamic calculations, are prone to destabilization. Trp-29, Phe-49 of nsp7 and Trp-154, Tyr-135 and Phe-15 of nsp8 cause greater destabilizing effects to the protein complex based on a computational alanine scan suggesting them as possible new target sites. This study provides an intensive analysis of the mutations of nsp7 and nsp8 and their possible implications for viral complex stability.

Discovery of reactive peptide inhibitors of human papillomavirus oncoprotein E6

Human papillomavirus (HPV) infections account for nearly all cervical cancer cases, which is the fourth most common cancer in women worldwide. High-risk variants, including HPV16, drive tumorigenesis in part by promoting the degradation of the tumor suppressor p53. This degradation is mediated by the HPV early protein 6 (E6), which recruits the E3 ubiquitin ligase E6AP and redirects its activity towards ubiquitinating p53. Targeting the protein interaction interface between HPV E6 and E6AP is a promising modality to mitigate HPV-mediated degradation of p53. In this study, we designed a covalent peptide inhibitor, termed reactide, that mimics the E6AP LXXLL binding motif by selectively targeting cysteine 58 in HPV16 E6 with quantitative conversion. This reactide provides a starting point in the development of covalent peptidomimetic inhibitors for intervention against HPV-driven cancers.

Advances in Ultrahigh Throughput Hit Discovery with Tandem Mass Spectrometry Encoded Libraries

Discovering new bioactive molecules is crucial for drug development. Finding a hit compound for a new drug target usually requires screening of millions of molecules. Affinity selection based technologies have revolutionized early hit discovery by enabling the rapid screening of libraries with millions or billions of compounds in short timeframes. In this Perspective, we describe recent technology breakthroughs that enable the screening of ultralarge synthetic peptidomimetic libraries with a barcode-free tandem mass spectrometry decoding strategy. A combination of combinatorial synthesis, affinity selection, automated de novo peptide sequencing algorithms, and advances in mass spectrometry instrumentation now enables hit discovery from synthetic libraries with over 100 million members. We provide a perspective on this powerful technology and showcase success stories featuring the discovery of high affinity binders for a number of drug targets including proteins, nucleic acids, and specific cell types. Further, we show the usage of the technology to discover synthetic peptidomimetics with specific functions and reactivity. We predict that affinity selection coupled with tandem mass spectrometry and automated de novo decoding will rapidly evolve further and become a broadly used drug discovery technology.

Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches

Protein-protein interfaces play fundamental roles in the molecular mechanisms underlying pathophysiological pathways and are important targets for the design of compounds of therapeutic interest. However, the identification of binding sites on protein surfaces and the development of modulators of protein-protein interactions still represent a major challenge due to their highly dynamic and extensive interfacial areas. Over the years, multiple strategies including structural, computational, and combinatorial approaches have been developed to characterize PPI and to date, several successful examples of small molecules, antibodies, peptides, and aptamers able to modulate these interfaces have been determined. Notably, peptides are a particularly useful tool for inhibiting PPIs due to their exquisite potency, specificity, and selectivity. Here, after an overview of PPIs and of the commonly used approaches to identify and characterize them, we describe and evaluate the impact of chemical peptide libraries in medicinal chemistry with a special focus on the results achieved through recent applications of this methodology. Finally, we also discuss the role that this methodology can have in the framework of the opportunities, and challenges that the application of new predictive approaches based on artificial intelligence is generating in structural biology.