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Motivation: Protein-protein interactions are essential for a variety of biological phenomena including mediating biochemical reactions, cell signaling, and the immune response. Proteins seek to form interfaces which reduce overall system energy. Although determination of single polypeptide chain protein structures has been revolutionized by deep learning techniques, complex prediction has still not been perfected. Additionally, experimentally determining structures is incredibly resource and time expensive. An alternative is the technique of computational docking, which takes the solved individual structures of proteins to produce candidate interfaces (decoys). Decoys are then scored using a mathematical function that assess the quality of the system, known as scoring functions. Beyond docking, scoring functions are a critical component of assessing structures produced by many protein generative models. Scoring models are also used as a final filtering in many generative deep learning models including those that generate antibody binders, and those which perform docking., Results: In this work, we present improved scoring functions for protein-protein interactions which utilizes cutting-edge Euclidean graph neural network architectures, to assess protein-protein interfaces. These Euclidean docking score models are known as EuDockScore, and EuDockScore-Ab with the latter being antibody-antigen dock specific. Finally, we provided EuDockScore-AFM a model trained on antibody-antigen outputs from AlphaFold-Multimer (AFM) which proves useful in reranking large numbers of AFM outputs., Availability and Implementation: The code for these models is available at https://gitlab.com/mcfeemat/eudockscore., (© The Author(s) 2024. Published by Oxford University Press.)

Traditional computational methods for antibody design involved random mutagenesis followed by energy function assessment for candidate selection. Recently, diffusion models have garnered considerable attention as cutting-edge generative models, lauded for their remarkable performance. However, these methods often focus solely on the backbone or sequence, resulting in the incomplete depiction of the overall structure and necessitating additional techniques to predict the missing component. This study presents Antibody-SGM, an innovative joint structure-sequence diffusion model that addresses the limitations of existing protein backbone generation models. Unlike previous models, Antibody-SGM successfully integrates sequence-specific attributes and functional properties into the generation process. Our methodology generates full-atom native-like antibody heavy chains by refining the generation to create valid pairs of sequences and structures, starting with random sequences and structural properties. The versatility of our method is demonstrated through various applications, including the design of full-atom antibodies, antigen-specific CDR design, antibody heavy chains optimization, validation with Alphafold3, and the identification of crucial antibody sequences and structural features. Antibody-SGM also optimizes protein function through active inpainting learning, allowing simultaneous sequence and structure optimization. These improvements demonstrate the promise of our strategy for protein engineering and significantly increase the power of protein design models.

Detecting nucleic acids at ultralow concentrations is critical for research and clinical applications. Particle-based assays are commonly used to detect nucleic acids. However, DNA hybridization on particle surfaces is inefficient due to the instability of tethered sequences, which negatively influences the assay's detection sensitivity. Here, we report a method to stabilize sequences on particle surfaces using a double-stranded linker at the 5' end of the tethered sequence. We termed this method Rigid Double Stranded Genomic Linkers for Improved DNA Analysis (RIGID-DNA). Our method led to a 3- and 100-fold improvement of the assays' clinical and analytical sensitivity, respectively. Our approach can enhance the hybridization efficiency of particle-based assays without altering existing assay workflows. This approach can be adapted to other platforms and surfaces to enhance the detection sensitivity.

Protein kinase C (PKC) plays a key role in modulating the activities of the innate immune cells of the central nervous system (CNS). A delicate balance between pro-inflammatory and regenerative activities by microglia and CNS-associated macrophages is necessary for the proper functioning of the CNS. Thus, a maladaptive activation of these CNS innate immune cells results in neurodegeneration and demyelination associated with various neurologic disorders, such as multiple sclerosis (MS) and Alzheimer's disease. Prior studies have demonstrated that modulation of PKC activity by bryostatin-1 (bryo-1) and its analogs (bryologs) attenuates the pro-inflammatory processes by microglia/CNS macrophages and alleviates the neurologic symptoms in experimental autoimmune encephalomyelitis (EAE), an MS animal model. Here, we demonstrate that (2S,5S)-(E,E)-8-(5-(4-(trifluoromethyl)phenyl)-2,4-pentadienoylamino)benzolactam (TPPB), a structurally distinct PKC modulator, has a similar effect to bryo-1 on CNS innate immune cells both in vitro and in vivo , attenuating neuroinflammation and resulting in CNS regeneration and repair. This study identifies a new structural class of PKC modulators, which can therapeutically target CNS innate immunity as a strategy to treat neuroinflammatory and neurodegenerative disorders., Competing Interests: The Johns Hopkins University has filed a patent for the application of PKC modulator bryo-1 and related technology, and PK and MK are co-inventors on the patent. MK has received consulting fees from Biogen, Genentech, Novartis, TG Therapeutics, and OptumRx. Stanford University has filed patents on bryostatin and other PKC modulators which have been licensed by Neurotrope BioScience for the treatment of neurological disorders and by Bryologyx Inc. for use in HIV/AIDS eradication and cancer immunotherapy. PW is an advisor to both companies and a cofounder of the latter. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shanmukha, Godfrey, Gharibani, Lee, Guo, Deng, Wender, Kornberg and Kim.)

Objectives: To describe a population health-based program to support employee and dependent mental health and learn from engagement trends., Methods: Retrospective analysis of a program utilizing an assessment of mental health risk. For scoring "at risk," a Care Concierge is offered to connect users with resources., Results: Participation was offered to 56,442 employees and dependents. Eight thousand seven hundred thirty-one completed the assessment (15%). Of those, 4,644 (53%) scored moderate or higher. A total of 418 (9%) engaged the Care Concierge. Factors that negatively influenced the decision to engage care included bodily pain, financial concerns. Positive influences were younger age, high stress, anxiety, PTSD and low social support., Conclusion: Proactive assessment plus access to a Care Concierge facilitates mental healthcare utilization. Several factors influence likelihood to engage in care. A better understanding of these factors may allow for more targeted outreach and improved engagement., Competing Interests: MF was employed by Quest Diagnostics. JoH was employed by BHS. JeH was employed by emVitals. SC was employed by Johns Hopkins Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Woodard, Bailey, Esagoff, Fragala, Hayward, Hunter, Hsu, Kim, Peters and Carr.)

Here, we present HelixDiff, a score-based diffusion model for generating all-atom helical structures. We developed a hot spot-specific generation algorithm for the conditional design of α-helices targeting critical hotspot residues in bioactive peptides. HelixDiff generates α-helices with near-native geometries for most test scenarios with root-mean-square deviations (RMSDs) less than 1 Å. Significantly, HelixDiff outperformed our prior GAN-based model with regard to sequence recovery and Rosetta scores for unconditional and conditional generations. As a proof of principle, we employed HelixDiff to design an acetylated GLP-1 D-peptide agonist that activated the glucagon-like peptide-1 receptor (GLP-1R) cAMP accumulation without stimulating the glucagon-like peptide-2 receptor (GLP-2R). We predicted that this D-peptide agonist has a similar orientation to GLP-1 and is substantially more stable in MD simulations than our earlier D-GLP-1 retro-inverse design. This D-peptide analogue is highly resistant to protease degradation and induces similar levels of AKT phosphorylation in HEK293 cells expressing GLP-1R compared to the native GLP-1. We then discovered that matching crucial hotspots for the GLP-1 function is more important than the sequence orientation of the generated D-peptides when constructing D-GLP-1 agonists., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

While normal B- and T-lymphocytes require antigenic ligands to become activated via their B- and T-cell receptors (BCR and TCR, respectively), B- and T-cell lymphomas show the broad spectrum of cell activation mechanisms regarding their dependence on BCR or TCR signaling, including loss of such dependence. These mechanisms are generally better understood and characterized for B-cell than for T-cell lymphomas. While some lymphomas, particularly the indolent, low-grade ones remain antigen-driven, other retain dependence on activation of their antigen receptors seemingly in an antigen-independent manner with activating mutations of the receptors playing a role. A large group of lymphomas, however, displays complete antigen receptor independence, which can develop gradually, in a stepwise manner or abruptly, through involvement of powerful oncogenes. Whereas some of the lymphomas undergo activating mutations of genes encoding proteins involved in signaling cascades downstream of the antigen-receptors, others employ activation mechanisms capable of substituting for these BCR- or TCR-dependent signaling pathways, including reliance on signaling pathways physiologically activated by cytokines. Finally, lymphomas can develop cell-lineage infidelity and in the extreme cases drastically rewire their cell activation mechanisms and engage receptors and signaling pathways physiologically active in hematopoietic stem cells or non-lymphoid cells. Such profound reprograming may involve partial cell dedifferentiation or transdifferentiation towards histocytes, dendritic, or mesodermal cells with various degree of cell maturation along these lineages. In this review, we elaborate on these diverse pathogenic mechanisms underlying cell plasticity and signaling reprogramming as well as discuss the related diagnostic and therapeutic implications and challenges., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wasik, Kim and Nejati.)

Diffuse large B-cell lymphoma (DLBCL) is a heterogenous group of lymphoid malignancies. Based on gene expression profiling, it has been subdivided into germinal center (GC)-derived and activated B-cell (ABC) types. Advances in molecular methodologies have further refined the subclassification of DLBCL, based on recurrent genetic abnormalities. Here, we describe a distinct case of DLBCL that presented in leukemic form. DNA sequencing targeting 275 genes revealed pathogenically relevant mutations of CD79B , MyD88 , TP53 , TBL1XR1 , and PIM1 genes, indicating that this lymphoma would be best classified as MCD/C5 DLBCL, an ABC subtype. Despite an initial good clinical response to BTK inhibitor ibrutinib, anti-CD20 antibody rituxan, alkylating agent bendamustine, and hematopoietic stem-cell transplant, the lymphoma relapsed, accompanied by morphologic and molecular evidence of disease progression. Specifically, the recurrent tumor developed loss of TP53 heterozygosity (LOH) and additional chromosomal changes central to ABC DLBCL pathogenesis, such as PRDM1 loss. Acquired resistance to ibrutinib and rituxan was indicated by the emergence of BTK and FOXO1 mutations, respectively, as well as apparent activation of alternative cell-activation pathways, through copy-number alterations (CNAs), detected by high-resolution chromosomal microarrays. In vitro, studies of relapsed lymphoma cells confirmed resistance to standard BTK inhibitors but sensitivity to vecabrutinib, a noncovalent inhibitor active against both wild-type as well as mutated BTK. In summary, we provide in-depth molecular characterization of a de novo leukemic DLBCL and discuss mechanisms that may have contributed to the lymphoma establishment, progression, and development of drug resistance., (© 2023 Kim et al.; Published by Cold Spring Harbor Laboratory Press.)

Here, we designed three d-GLP-2 agonists that activated the glucagon-like peptide-2 receptor (GLP-2R) cyclic adenosine monophosphate (cAMP) accumulation without stimulating the glucagon-like peptide-1 receptor (GLP-1R). All the d-GLP-2 agonists increased the protein kinase B phosphorylated (p-AKT) expression levels in a time- and concentration-dependent manner in vitro. The most effective d-GLP-2 analogue boosted the AKT phosphorylation 2.28 times more effectively compared to the native l-GLP-2. The enhancement in the p-AKT levels induced by the d-GLP-2 analogues could be explained by GLP-2R's more prolonged activation, given that the d-GLP-2 analogues induce a lower β-arrestin recruitment. The higher stability to protease degradation of our d-GLP-2 agonists helps us envision their potential applications in enhancing intestinal absorption and treating inflammatory bowel illness while lowering the high dosage required by the current treatments.

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).)

Summary: Protein complexes play vital roles in a variety of biological processes, such as mediating biochemical reactions, the immune response and cell signalling, with 3D structure specifying function. Computational docking methods provide a means to determine the interface between two complexed polypeptide chains without using time-consuming experimental techniques. The docking process requires the optimal solution to be selected with a scoring function. Here, we propose a novel graph-based deep learning model that utilizes mathematical graph representations of proteins to learn a scoring function (GDockScore). GDockScore was pre-trained on docking outputs generated with the Protein Data Bank biounits and the RosettaDock protocol, and then fine-tuned on HADDOCK decoys generated on the ZDOCK Protein Docking Benchmark. GDockScore performs similarly to the Rosetta scoring function on docking decoys generated using the RosettaDock protocol. Furthermore, state-of-the-art is achieved on the CAPRI score set, a challenging dataset for developing docking scoring functions., Availability and Implementation: The model implementation is available at https://gitlab.com/mcfeemat/gdockscore., Supplementary Information: Supplementary data are available at Bioinformatics Advances online., Competing Interests: P.M.K. is a co-founder and has been a consultant to several biotechnology ventures, including Resolute Bio, Oracle Therapeutics and Navega Therapeutics. P.M.K. also serves on the scientific advisory board of ProteinQure. M.M. is a research scientist at Oracle Therapeutics., (© The Author(s) 2023. Published by Oxford University Press.)

The generation of de novo protein structures with predefined functions and properties remains a challenging problem in protein design. Diffusion models, also known as score-based generative models (SGMs), have recently exhibited astounding empirical performance in image synthesis. Here we use image-based representations of protein structure to develop ProteinSGM, a score-based generative model that produces realistic de novo proteins. Through unconditional generation, we show that ProteinSGM can generate native-like protein structures, surpassing the performance of previously reported generative models. We experimentally validate some de novo designs and observe secondary structure compositions consistent with generated backbones. Finally, we apply conditional generation to de novo protein design by formulating it as an image inpainting problem, allowing precise and modular design of protein structure., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Accumulation of α-synuclein into toxic oligomers or fibrils is implicated in dopaminergic neurodegeneration in Parkinson's disease. Here we performed a high-throughput, proteome-wide peptide screen to identify protein-protein interaction inhibitors that reduce α-synuclein oligomer levels and their associated cytotoxicity. We find that the most potent peptide inhibitor disrupts the direct interaction between the C-terminal region of α-synuclein and CHarged Multivesicular body Protein 2B (CHMP2B), a component of the Endosomal Sorting Complex Required for Transport-III (ESCRT-III). We show that α-synuclein impedes endolysosomal activity via this interaction, thereby inhibiting its own degradation. Conversely, the peptide inhibitor restores endolysosomal function and thereby decreases α-synuclein levels in multiple models, including female and male human cells harboring disease-causing α-synuclein mutations. Furthermore, the peptide inhibitor protects dopaminergic neurons from α-synuclein-mediated degeneration in hermaphroditic C. elegans and preclinical Parkinson's disease models using female rats. Thus, the α-synuclein-CHMP2B interaction is a potential therapeutic target for neurodegenerative disorders., (© 2023. The Author(s).)

Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover's algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm's oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using [Formula: see text] iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover's algorithm over classical methods (i.e., [Formula: see text])., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: UCM is the leader of the quantum computing team at CMC Microsystems. PMK is a co-founder and consultant to multiple companies, including Oracle Therapeutics and TBG Therapeutics, and serves on the scientific advisory board of ProteinQure. MHK and NW declare no Competing Financial or Non-Financial Interests., (Copyright: © 2023 Khatami et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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.)

Objective: The aim of this study is to determine if a proactive employer-sponsored mental health program closed gaps in detection and treatment of mental health conditions., Methods: Of n = 56,442 eligible, n = 8170 (14.5%) participated in the optional screening. Participants with mental health risk were offered care concierge services including support, care planning, and connection to care. Difference in behavioral health care utilization, diagnoses, and prescriptions were evaluated postintervention through claims analysis., Results: Compared with controls (n = 2433), those receiving concierge services (n = 369) were more likely to fill mental health prescriptions (adjusted hazards ratio [HR], 1.2; 1.0-1.5; P = 0.042), use professional mental health services (adjusted HR, 1.4; 1.1-1.8; P = 0.02), and use new mental health services (adjusted HR, 1.9; 1.2-2.8; P = 0.004) in the following 6 months., Conclusions: This proactive mental health program with care concierge services identified risk, connected individuals to mental health care, and facilitated mental health treatment, among program participants., Competing Interests: Conflict of Interest: M.S.F. and C.E.B. are employed by and has stock ownership in Quest Diagnostics. J.L.H. holds stock in and serves on the Board of Directors for Emvitals Inc. Emvitals was a paid vendor of Johns Hopkins Healthcare Solutions (JHHS) and provided the Emvitals technology platform, which was white labeled by JHHS, and sold to Quest Diagnostics in support of its employee well-being programs; we were not paid for any aspect of the research analysis and research paper preparation. N.A.J. is employed by and owns stock in Emvitals Inc. M.E.P. engaged in this research as part of his faculty position at Johns Hopkins, but was previously an advisor for, and has stock ownership in, Emvitals Inc. J.I.H. and P.M.K. report no conflicts of interest., (Copyright © 2022 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Occupational and Environmental Medicine.)

Therapeutic antibodies have broad indications across diverse disease states, such as oncology, autoimmune diseases, and infectious diseases. New research continues to identify antibodies with therapeutic potential as well as methods to improve upon endogenous antibodies and to design antibodies de novo. On April 27-30, 2022, experts in antibody research across academia and industry met for the Keystone symposium "Antibodies as Drugs" to present the state-of-the-art in antibody therapeutics, repertoires and deep learning, bispecific antibodies, and engineering., (© 2022 New York Academy of Sciences.)

Although there are multiple variations of accessory foramina described in the literature, to our knowledge there is only 1 report of an isolated mandibular foramen and inferior alveolar neurovascular bundle that courses through the lateral ramus, particularly in those with branchial arch syndromes. Goldenhar syndrome, of the oculo-auriculo-vertebral spectrum, is a rare congenital condition, which most characteristically presents with hemifacial microsomia. Depending on the severity of hemifacial microsomia, there are predictable treatment modalities to correct the consequent facial asymmetry. We report on a patient with Goldenhar syndrome who was found to have a unique course of the inferior alveolar nerve during orthognathic surgery work-up and treatment., Competing Interests: The authors report no conflicts of interest., (Copyright © 2022 by Mutaz B. Habal, MD.)

Motivation: Protein and peptide engineering has become an essential field in biomedicine with therapeutics, diagnostics and synthetic biology applications. Helices are both abundant structural feature in proteins and comprise a major portion of bioactive peptides. Precise design of helices for binding or biological activity is still a challenging problem., Results: Here, we present HelixGAN, the first generative adversarial network method to generate de novo left-handed and right-handed alpha-helix structures from scratch at an atomic level. We developed a gradient-based search approach in latent space to optimize the generation of novel α-helical structures by matching the exact conformations of selected hotspot residues. The designed α-helical structures can bind specific targets or activate cellular receptors. There is a significant agreement between the helix structures generated with HelixGAN and PEP-FOLD, a well-known de novo approach for predicting peptide structures from amino acid sequences. HelixGAN outperformed RosettaDesign, and our previously developed structural similarity method to generate D-peptides matching a set of given hotspots in a known L-peptide. As proof of concept, we designed a novel D-GLP1_1 analog that matches the conformations of critical hotspots for the GLP1 function. MD simulations revealed a stable binding mode of the D-GLP1_1 analog coupled to the GLP1 receptor. This novel D-peptide analog is more stable than our previous D-GLP1 design along the MD simulations. We envision HelixGAN as a critical tool for designing novel bioactive peptides with specific properties in the early stages of drug discovery., Availability and Implementation: https://github.com/xxiexuezhi/helix_gan., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2023. Published by Oxford University Press.)

Abiotrophia defectiva or nutritionally variant streptococci (NVS) was first described in 1961 as a rare cause of infectious endocarditis (IE). It is distinct to the Streptococcus genus, pleomorphic, and demonstrates variable Gram-staining characteristics due to poor growth in culture media requiring extended incubation or 16S rRNA sequencing in culture negative situations. 1 NVS has also been implicated in the pathogenesis of culture-negative endocarditis. In comparison with other causes of endocarditis, it has the ability to secrete exopolysaccharide and adheres to fibronectin in the extracellular matrix of endovascular structures. 2 NVS is associated with significant morbidity and mortality, higher rates of multisystem complications ranging from pancreatic abscess, intracranial aneurysms, osteomyelitis, and valvular destruction, despite sensitivity to antibiotics., Competing Interests: The authors declare no conflicts of interest., (Crown Copyright © 2022 Published by Elsevier B.V. on behalf of Craniofacial Research Foundation. All rights reserved.)

The COVID-19 pandemic continues to spread around the world, with several new variants emerging, particularly those of concern (VOCs). Omicron (B.1.1.529), a recent VOC with many mutations in the spike protein's receptor-binding domain (RBD), has attracted a great deal of scientific and public interest. We previously developed two D-peptide inhibitors for the infection of the original SARS-CoV-2 and its VOCs, alpha and beta, in vitro . Here, we demonstrated that Covid3 and Covid_extended_1 maintained their high-affinity binding (29.4-31.3 nM) to the omicron RBD. Both D-peptides blocked the omicron variant in vitro infection with IC50s of 3.13 and 5.56 μM, respectively. We predicted that Covid3 shares a larger overlapping binding region with the ACE2 binding motif than different classes of neutralizing monoclonal antibodies. We envisioned the design of D-peptide inhibitors targeting the receptor-binding motif as the most promising approach for inhibiting current and future VOCs of SARS-CoV-2, given that the ACE2 binding interface is more limited to tolerate mutations than most of the RBD's surface.

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).)

The recognition of PPxY viral Late domains by the third WW domain of the human HECT-E3 ubiquitin ligase NEDD4 (NEDD4-WW3) is essential for the budding of many viruses. Blocking these interactions is a promising strategy to develop broad-spectrum antivirals. As all WW domains, NEDD4-WW3 is a challenging therapeutic target due to the low binding affinity of its natural interactions, its high conformational plasticity, and its complex thermodynamic behavior. In this work, we set out to investigate whether high affinity can be achieved for monovalent ligands binding to the isolated NEDD4-WW3 domain. We show that a competitive phage-display set-up allows for the identification of high-affinity peptides showing inhibitory activity of viral budding. A detailed biophysical study combining calorimetry, nuclear magnetic resonance, and molecular dynamic simulations reveals that the improvement in binding affinity does not arise from the establishment of new interactions with the domain, but is associated to conformational restrictions imposed by a novel C-terminal -LFP motif in the ligand, unprecedented in the PPxY interactome. These results, which highlight the complexity of WW domain interactions, provide valuable insight into the key elements for high binding affinity, of interest to guide virtual screening campaigns for the identification of novel therapeutics targeting NEDD4-WW3 interactions., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Invasive apocrine carcinoma of the breast is a rare entity with an incidence of 3 or 4 cases per million women. Although apocrine breast cancers may have an aggressive clinical presentation, the 7-year cancer survival rate is the same as non-apocrine cancers. The prognosis of IAC is currently determined by conventional factors such as grade, tumor size, and nodal status. Targeted AR therapy is increasingly adopted with some positive results in clinical trials. This research may result in the development of a tailored treatment for these unusual and rare cancers., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Microglia and CNS-infiltrating macrophages play significant roles in the pathogenesis of neuroinflammatory and neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and multiple sclerosis. Prolonged and dysregulated inflammatory responses by these innate immune cells can have deleterious effects on the surrounding CNS microenvironment, which can worsen neurodegeneration and demyelination. However, although chronic activation of pro-inflammatory microglia is maladaptive, other functional microglial subtypes play beneficial roles during CNS repair and regeneration. Therefore, there is a tremendous interest in understanding the underlying mechanism of the activation of these reparative/regenerative microglia. In this review, we focus on the potential role of PKC, a downstream signaling molecule of TREM2 and PLCγ2, and PKC modulators in promoting the activation of reparative/regenerative microglial subtypes as a novel therapy for neuroinflammatory and neurodegenerative diseases., Competing Interests: Conflict of interest statement Johns Hopkins University has filed a patent on the application of bryostatin-1 and its derivatives for multiple sclerosis and other disorders, and P.M.K. and M.D.K. are co-inventors on this patent., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. Those generative models can learn protein representations that are often more informative of protein structure and function than hand-engineered features. Furthermore, they can be used to quickly propose millions of novel proteins that resemble the native counterparts in terms of expression level, stability, or other attributes. The protein design process can further be guided by discriminative oracles to select candidates with the highest probability of having the desired properties. In this review, we discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design., Competing Interests: Conflict of interest statement PMK is a cofounder of Resolute Bio Inc. and serves on the scientific advisory board of ProteinQure., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Blocking the association between the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein receptor-binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2) is an attractive therapeutic approach to prevent the virus from entering human cells. While antibodies and other modalities have been developed to this end, d-amino acid peptides offer unique advantages, including serum stability, low immunogenicity, and low cost of production. Here, we designed potent novel D-peptide inhibitors that mimic the ACE2 α1-binding helix by searching a mirror-image version of the PDB. The two best designs bound the RBD with affinities of 29 and 31 nM and blocked the infection of Vero cells by SARS-CoV-2 with IC 50 values of 5.76 and 6.56 μM, respectively. Notably, both D-peptides neutralized with a similar potency the infection of two variants of concern: B.1.1.7 and B.1.351 in vitro . These potent D-peptide inhibitors are promising lead candidates for developing SARS-CoV-2 prophylactic or therapeutic treatments.

The ELASPIC web server allows users to evaluate the effect of mutations on protein folding and protein-protein interaction on a proteome-wide scale. It uses homology models of proteins and protein-protein interactions, which have been precalculated for several proteomes, and machine learning models, which integrate structural information with sequence conservation scores, in order to make its predictions. Since the original publication of the ELASPIC web server, several advances have motivated a revisiting of the problem of mutation effect prediction. First, progress in neural network architectures and self-supervised pre-trained has resulted in models which provide more informative embeddings of protein sequence and structure than those used by the original version of ELASPIC. Second, the amount of training data has increased several-fold, largely driven by advances in deep mutation scanning and other multiplexed assays of variant effect. Here, we describe two machine learning models which leverage the recent advances in order to achieve superior accuracy in predicting the effect of mutation on protein folding and protein-protein interaction. The models incorporate features generated using pre-trained transformer- and graph convolution-based neural networks, and are trained to optimize a ranking objective function, which permits the use of heterogeneous training data. The outputs from the new models have been incorporated into the ELASPIC web server, available at http://elaspic.kimlab.org., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: PMK serves on the scientific advisory board of ProteinQure Inc and is a shareholder in Resolute Bio Inc., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Computational generation of new proteins with a predetermined three-dimensional shape and computational optimization of existing proteins while maintaining their shape are challenging problems in structural biology. Here, we present a protocol that uses ProteinSolver, a pre-trained graph convolutional neural network, to quickly generate thousands of sequences matching a specific protein topology. We describe computational approaches that can be used to evaluate the generated sequences, and we show how select sequences can be validated experimentally. For complete details on the use and execution of this protocol, please refer to Strokach et al. (2020)., Competing Interests: The authors are in the process of obtaining a patent on the described technology. P.M.K. is a cofounder of Resolute Bio Inc. and serves on the scientific advisory board of ProteinQure., (© 2021 The Authors.)

Neuroinflammation characterizes multiple neurologic diseases, including primary inflammatory conditions such as multiple sclerosis and classical neurodegenerative diseases. Aberrant activation of the innate immune system contributes to disease progression, but drugs modulating innate immunity, particularly within the central nervous system (CNS), are lacking. The CNS-penetrant natural product bryostatin-1 attenuates neuroinflammation by targeting innate myeloid cells. Supplies of natural bryostatin-1 are limited, but a recent scalable good manufacturing practice (GMP) synthesis has enabled access to it and its analogs (bryologs), the latter providing a path to more efficacious, better tolerated, and more accessible agents. Here, we show that multiple synthetically accessible bryologs replicate the anti-inflammatory effects of bryostatin-1 on innate immune cells in vitro, and a lead bryolog attenuates neuroinflammation in vivo, actions mechanistically dependent on protein kinase C (PKC) binding. Our findings identify bryologs as promising drug candidates for targeting innate immunity in neuroinflammation and create a platform for evaluation of synthetic PKC modulators in neuroinflammatory diseases., Competing Interests: Declaration of interests Johns Hopkins University and Stanford University have filed patent applications on this and related technology. Stanford University patent applications have been licensed by Neurotrope BioScience for the treatment of neurological disorders and by Bryologyx Inc. for use in HIV/AIDS eradication and cancer immunotherapy. P.A.W. is an advisor to both companies and a cofounder of the latter. M.D.K. has received consulting fees from OptumRx and Biogen Idec. The remaining authors declare no competing interests., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Objective: To evaluate a proactive, voluntary screening program designed to identify employees with emerging mental health risk and engage them in care., Methods: Risk was proactively identified through online screening of 344 participants. At-risk participants were offered a mental health care concierge to provide support, develop a care plan, and connect to care., Results: Risk for common mental health conditions was identified in 244 (71%) participants, of whom 66 (27%) connected with a care concierge. Compared with participants who did not connect to a care concierge, those who did were more likely to report a financial crisis (68.2% vs 50.8%) and less likely to report verbal abuse (9.1% vs 19.6%) and difficulty meeting daily needs (12.1% vs 25.1%)., Conclusion: Implementation of this screening program identified employees at risk for mental health conditions and facilitated connection to care., Competing Interests: Conflict of Interest: M.S.F. is employed by and has stock ownership in Quest Diagnostics. J.L.H. holds stock in and serves on the Board of Directors for Emvitals Inc. A.S. was employed by Quest Diagnostics during the course of this work and received a salary and has stock ownership from Quest Diagnostics. A.S. is currently a full-time student at UC San Diego School of Medicine and does not receive a salary. N.A.J. is employed by and owns stock in Emvitals Inc. M.S. and J.I.H. are employed by Guide + Thrive powered by BHS and no funding was received from their employer's partners (Quest and emVitals) for their participation in this research project. M.E.P. engaged in this research as part of his faculty position at Johns Hopkins, but was previously an advisor for, and has stock ownership in, Emvitals Inc. SC, AMB, and PMK report no conflicts of interest., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Occupational and Environmental Medicine.)

Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large-scale peptide-phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM-ligand complexes, indicating that a large majority of the phage-derived peptides are likely to target natural peptide-binding sites and could thus act as inhibitors of natural protein-protein interactions. The complete dataset has been assembled in an online database (http://www.prm-db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs., (© 2020 The Authors. Published under the terms of the CC BY 4.0 license.)

Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction problem (CSP), akin to Sudoku puzzles. We trained ProteinSolver on over 70,000,000 real protein sequences corresponding to over 80,000 structures. We show that our method rapidly designs new protein sequences and benchmark them in silico using energy-based scores, molecular dynamics, and structure prediction methods. As a proof-of-principle validation, we use ProteinSolver to generate sequences that match the structure of serum albumin, then synthesize the top-scoring design and validate it in vitro using circular dichroism. ProteinSolver is freely available at http://design.proteinsolver.org and https://gitlab.com/ostrokach/proteinsolver. A record of this paper's transparent peer review process is included in the Supplemental Information., Competing Interests: Declaration of Interests The authors are in the process of obtaining a patent on the described technology. P.M.K. is a cofounder of Resolute Bio Inc. and serves on the scientific advisory board of ProteinQure., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Microbial eukaryotes, critical links in aquatic food webs, are unicellular, but some, such as choanoflagellates, form multicellular colonies. Are there consequences to predator avoidance of being unicellular vs. forming larger colonies? Choanoflagellates share a common ancestor with animals and are used as model organisms to study the evolution of multicellularity. Escape in size from protozoan predators is suggested as a selective factor favoring evolution of multicellularity. Heterotrophic protozoans are categorized as suspension feeders, motile raptors, or passive predators that eat swimming prey which bump into them. We focused on passive predation and measured the mechanisms responsible for the susceptibility of unicellular vs. multicellular choanoflagellates, Salpingoeca helianthica, to capture by passive heliozoan predators, Actinosphaerium nucleofilum, which trap prey on axopodia radiating from the cell body. Microvideography showed that unicellular and colonial choanoflagellates entered the predator's capture zone at similar frequencies, but a greater proportion of colonies contacted axopodia. However, more colonies than single cells were lost during transport by axopodia to the cell body. Thus, feeding efficiency (proportion of prey entering the capture zone that were engulfed in phagosomes) was the same for unicellular and multicellular prey, suggesting that colony formation is not an effective defense against such passive predators., (© 2020 International Society of Protistologists.)

The Cys2His2 zinc finger is the most common DNA-binding domain expanding in metazoans since the fungi human split. A proposed catalyst for this expansion is an arms race to silence transposable elements yet it remains poorly understood how this domain is able to evolve the required specificities. Likewise, models of its DNA binding specificity remain error prone due to a lack of understanding of how adjacent fingers influence each other's binding specificity. Here, we use a synthetic approach to exhaustively investigate binding geometry, one of the dominant influences on adjacent finger function. By screening over 28 billion protein-DNA interactions in various geometric contexts we find the plasticity of the most common natural geometry enables more functional amino acid combinations across all targets. Further, residues that define this geometry are enriched in genomes where zinc fingers are prevalent and specificity transitions would be limited in alternative geometries. Finally, these results demonstrate an exhaustive synthetic screen can produce an accurate model of domain function while providing mechanistic insight that may have assisted in the domains expansion., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Hydrocarbon-stapled peptides are a class of bioactive α-helical ligands developed to target protein-protein interactions. Peptide stapling has benefited from the development of several chemical reactions to modulate their membrane permeability and binding affinity. However, in most current programs, choosing the best stapling positions is usually a trial-and-error process. Here, we develop a protocol to obtain optimal stapling positions computationally. Our method is based on molecular dynamics simulations and free energy calculations with nonequilibrium approaches; here, we predict the binding poses, hot-spot residues, and binding affinity differences of a set of perfluoroarene stapled α-helical peptides of the BIM BH3 peptide to the BCL XL receptor. The prediction of the hot-spot residues within the target peptide through computational alanine scanning anticipates not only the key residues for the receptor-peptide complex formation but also which positions should be avoided when applying the stapling groups. The staple moieties introduce local conformational changes not only in the replaced positions but also on their neighbor residues of the template peptide further affecting their binding behavior. Our approach is successful at rank-ordering the binding affinities of these stapled peptides with respect to the BIM BH3 peptide.

With aging populations around the world, frailty is becoming more prevalent increasing the need for health systems and social systems to deliver optimal evidence based care. However, in spite of the growing number of frailty publications, high-quality evidence for decision making is often lacking. Inadequate descriptions of the populations enrolled including frailty severity and frailty conceptualization, lack of use of validated frailty assessment tools, utilization of different frailty instruments between studies, and variation in reported outcomes impairs the ability to interpret, generalize and implement the research findings. The utilization of common data elements (CDEs) and core outcome measures (COMs) in clinical trials is increasingly being adopted to address such concerns. To catalyze the development and use of CDEs and COMs for future frailty studies, the Canadian Frailty Network (www.cfn-nce.ca; CFN), a not-for-profit pan-Canadian nationally-funded research network, convened an international group of experts to examine the issue and plan the path forward. The meeting was structured to allow for an examination of current frailty evidence, ability to learn from other COMs and CDEs initiatives, discussions about specific considerations for frailty COMs and CDEs and finally the identification of the necessary steps for a COMs and CDEs consensus initiative going forward. It was agreed at the onset of the meeting that a statement based on the meeting would be published and herein we report the statement., Competing Interests: Dr. Muscedere reports that he is the Scientific Director of the Canadian Frailty Network which is funded by the Government of Canada. Dr. Kim reports that he is the Assistant Scientific Director of the Canadian Frailty Network which is funded by the Government of Canada. Dr. Rockwood reports personal fees from Clinical Cardio Day-Cape Breton University, personal fees from CRUIGM -Montreal, personal fees from Speaker at Jackson Lab, Bar Harbor, MA, personal fees from Speaker at MouseAge, Rome Italy, personal fees from Lundbeck, personal fees from Frontemporal Dementia Study-Group, personal fees from SunLife Insurance, Japan, outside the submitted work; and Kenneth Rockwood is President and Chief Science Officer of DGI Clinical, which in the last five years has contracts with pharma and device manufacturers (Baxter, Baxalta, Shire, Hollister, Nutricia, Roche, Otsuka) on individualized outcome measurement. In 2017 he attended an advisory board meeting with Lundbeck. Otherwise any personal fees are for invited guest lectures and academic symposia, received directly from event organizers, chiefly for presentations on frailty. He is Associate Director of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes of Health Research, and with additional funding from the Alzheimer Society of Canada and several other charities, as well as, in its first phase (2013-2018), from Pfizer Canada and Sanofi Canada. He receives career support from the Dalhousie Medical Research Foundation as the Kathryn Allen Weldon Professor of Alzheimer Research, and research support from the Canadian Institutes of Health Research, the QEII Health Science Centre Foundation, the Capital Health Research Fund and the Fountain Family Innovation Fund of the QEII Health Science Centre Foundation. All remaining authors have nothing to disclose.

Frataxin (FXN) is a highly conserved protein found in prokaryotes and eukaryotes that is required for efficient regulation of cellular iron homeostasis. Experimental evidence associates amino acid substitutions of the FXN to Friedreich Ataxia, a neurodegenerative disorder. Recently, new thermodynamic experiments have been performed to study the impact of somatic variations identified in cancer tissues on protein stability. The Critical Assessment of Genome Interpretation (CAGI) data provider at the University of Rome measured the unfolding free energy of a set of variants (FXN challenge data set) with far-UV circular dichroism and intrinsic fluorescence spectra. These values have been used to calculate the change in unfolding free energy between the variant and wild-type proteins at zero concentration of denaturant ( Δ Δ G H 2 O ) . The FXN challenge data set, composed of eight amino acid substitutions, was used to evaluate the performance of the current computational methods for predicting the Δ Δ G H 2 O value associated with the variants and to classify them as destabilizing and not destabilizing. For the fifth edition of CAGI, six independent research groups from Asia, Australia, Europe, and North America submitted 12 sets of predictions from different approaches. In this paper, we report the results of our assessment and discuss the limitations of the tested algorithms., (© 2019 Wiley Periodicals, Inc.)

Predicting the impact of mutations on proteins remains an important problem. As part of the CAGI5 frataxin challenge, we evaluate the accuracy with which Provean, FoldX, and ELASPIC can predict changes in the Gibbs free energy of a protein using a limited data set of eight mutations. We find that different methods have distinct strengths and limitations, with no method being strictly superior to other methods on all metrics. ELASPIC achieves the highest accuracy while also providing a web interface which simplifies the evaluation and analysis of mutations. FoldX is slightly less accurate than ELASPIC but is easier to run locally, as it does not depend on external tools or datasets. Provean achieves reasonable results while being computational less expensive than the other methods and not requiring a structure of the protein. In addition to methods submitted to the CAGI5 community experiment, and with the aim to inform about other methods with high accuracy, we also evaluate predictions made by Rosetta's ddg_monomer protocol, Rosetta's cartesian_ddg protocol, and thermodynamic integration calculations using Amber package. ELASPIC still achieves the highest accuracy, while Rosetta's catesian_ddg protocol appears to perform best in capturing the overall trend in the data., (© 2019 Wiley Periodicals, Inc.)

The accurate determination of protein-protein interactions has been an important focus of molecular biology toward which much progress has been made due to the continuous development of existing and new technologies. However, current methods can have limitations, including scale and restriction to high affinity interactions, limiting our understanding of a large subset of these interactions. Here, we describe a modified bacterial-hybrid assay that employs combined selectable and scalable reporters that enable the sensitive screening of large peptide libraries followed by the sorting of positive interactions by the level of reporter output. We have applied this tool to characterize a set of human and E. coli PDZ domains. Our results are consistent with prior characterization of these proteins, and the improved sensitivity increases our ability to predict known and novel in vivo binding partners. This approach allows for the recovery of a wide range of affinities with a high throughput method that does not sacrifice the scale of the screen.

The lymphatic system plays crucial roles in tissue homeostasis, lipid absorption, and immune cell trafficking. Although lymphatic valves ensure unidirectional lymph flows, the flow itself controls lymphatic valve formation. Here, we demonstrate that a mechanically activated ion channel Piezo1 senses oscillating shear stress (OSS) and incorporates the signal into the genetic program controlling lymphatic valve development and maintenance. Time-controlled deletion of Piezo1 using a pan-endothelial Cre driver (Cdh5[PAC]-CreERT2) or lymphatic-specific Cre driver (Prox1-CreERT2) equally inhibited lymphatic valve formation in newborn mice. Furthermore, Piezo1 deletion in adult lymphatics caused substantial lymphatic valve degeneration. Piezo1 knockdown in cultured lymphatic endothelial cells (LECs) largely abrogated the OSS-induced upregulation of the lymphatic valve signature genes. Conversely, ectopic Piezo1 overexpression upregulated the lymphatic valve genes in the absence of OSS. Remarkably, activation of Piezo1 using chemical agonist Yoda1 not only accelerated lymphatic valve formation in animals, but also triggered upregulation of some lymphatic valve genes in cultured LECs without exposure to OSS. In summary, our studies together demonstrate that Piezo1 is the force sensor in the mechanotransduction pathway controlling lymphatic valve development and maintenance, and Piezo1 activation is a potentially novel therapeutic strategy for congenital and surgery-associated lymphedema.

Injuries involving the first metatarsophalangeal joint and its associated structures are common, especially in athletes. However, injuries to the hallucal sesamoid complex constituted only 3% of all podiatric sports medicine injuries reported by Agosta. This case study reports a female ballet dancer with an isolated fibular sesamoid retraction injury that presented with a history of chronic microtrauma secondary to overuse. When consulting epidemiologic studies of forefoot injuries involving the hallucal sesamoid complex, we were unable to find a single instance of an isolated retraction of the fibular sesamoid resulting from chronic use, demonstrating the unusual nature of this case.

Peptide-based therapeutics are an alternative to small molecule drugs as they offer superior specificity, lower toxicity, and easy synthesis. Here we present an approach that leverages the dramatic performance increase afforded by the recent arrival of GPU accelerated thermodynamic integration (TI). GPU TI facilitates very fast, highly accurate binding affinity optimization of peptides against therapeutic targets. We benchmarked TI predictions using published peptide binding optimization studies. Prediction of mutations involving charged side-chains was found to be less accurate than for non-charged, and use of a more complex 3-step TI protocol was found to boost accuracy in these cases. Using the 3-step protocol for non-charged side-chains either had no effect or was detrimental. We use the benchmarked pipeline to optimize a peptide binding to our recently discovered cancer target: EME1. TI calculations predict beneficial mutations using both canonical and non-canonical amino acids. We validate these predictions using fluorescence polarization and confirm that binding affinity is increased. We further demonstrate that this increase translates to a significant reduction in pancreatic cancer cell viability., (© 2018 Wiley Periodicals, Inc.)