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Cys 2 His 2 zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate engagement of ZF domains with DNA has made their design challenging. Here we describe the screening of 49 billion protein-DNA interactions and the development of a deep-learning model, ZFDesign, that solves ZF design for any genomic target. ZFDesign is a modern machine learning method that models global and target-specific differences induced by a range of library environments and specifically takes into account compatibility of neighboring fingers using a novel hierarchical transformer architecture. We demonstrate the versatility of designed ZFs as nucleases as well as activators and repressors by seamless reprogramming of human transcription factors. These factors could be used to upregulate an allele of haploinsufficiency, downregulate a gain-of-function mutation or test the consequence of regulation of a single gene as opposed to the many genes that a transcription factor would normally influence., (© 2023. The Author(s).)

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding., Competing Interests: Declaration of interests P.M.K. is a cofounder and consultant to multiple companies, including Oracle Therapeutics, TBG Therapeutics, and Zymedi. O.A. and M.M. consult for Oracle Therapeutics., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Protein-peptide interactions play a fundamental role in many cellular processes, but remain underexplored experimentally and difficult to model computationally. Here, we present PepNN-Struct and PepNN-Seq, structure and sequence-based approaches for the prediction of peptide binding sites on a protein. A main difficulty for the prediction of peptide-protein interactions is the flexibility of peptides and their tendency to undergo conformational changes upon binding. Motivated by this, we developed reciprocal attention to simultaneously update the encodings of peptide and protein residues while enforcing symmetry, allowing for information flow between the two inputs. PepNN integrates this module with modern graph neural network layers and a series of transfer learning steps are used during training to compensate for the scarcity of peptide-protein complex information. We show that PepNN-Struct achieves consistently high performance across different benchmark datasets. We also show that PepNN makes reasonable peptide-agnostic predictions, allowing for the identification of novel peptide binding proteins., (© 2022. The Author(s).)

Background: Pain is a common and dose-limiting side effect of many potentially curative cancer chemotherapeutic agents. This chemotherapy-induced pain (CIP) affects the quality of life of cancer patients and survivors and hampers the optimal clinical management of chemotherapy in cancer patients. The underlying mechanisms remain largely unknown, but changes in levels of cytokines/chemokines may contribute to the pathophysiology of CIP., Objective: This retrospective study was aimed at examining whether plasma levels of various cytokines change in prostate cancer patients after chemotherapy treatment and whether such changes (if any) are associated with their pain intensity., Methods: Using a Luminex assay, plasma levels of 27 cytokines/chemokines were measured in 78 men: 30 patients with metastatic prostate cancer who received chemotherapy (Docetaxel, 75 mg/m2 intravenously), 29 untreated patients with nonmetastatic prostate cancer, and 19 healthy controls. Subjective pain was assessed in chemotherapy-treated cancer patients using the 11-point numeric rating scale (NRS) pain scores., Results: Chemotherapy-treated patients with pain (NRS ≥ 3) exhibited significantly increased levels of the pro-inflammatory cytokines (IL-6, IL-8) and chemokines (Eotaxin, VEGF, and IP-10) compared with untreated cancer patients or with patients without pain (NRS = 0). Of the 27 cytokines examined, only IL-6 was positively correlated with pain intensity in the chemotherapy-treated patients with pain., Conclusions: These findings suggest that the cytokines, particularly IL-6, whose levels were elevated in the chemotherapy-treated patients may be involved in the pathophysiology of CIP, and that they might be potential new targets for pain control in cancer patients receiving chemotherapy.