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Jou, J.

Publications and source records attributed to Jou, J..

7 recordsLinked to original sources

Structure-Based Calculation of Excipient Effects on the Viscosity ofConcentrated Antibody Solutions

Computational prediction of the viscosity of therapeutic monoclonal antibodies (mAbs) at high concentration is highly desirable in the early discovery and development phases where the material needed for experimental determination is typically limited. Here, we present a unique coarse-grained (CG) simulation method that enables residue-level simulation of full-length antibodies with an elastic network, under simulated shearing force, to de novo predict viscosities of solutions of two distinct mAbs (an IgG1 and an IgG4), in the absence and presence of 6 excipients. Our results suggest the method can properly distinguish the viscosity profile of the two model mAbs, and directionally forecast viscosity change in response to added excipients. Furthermore, this CG modeling approach provides detailed protein-protein interaction mapping down to residue level contacts, including contact lifetimes and nature of interactions, illuminating microscopic insights into the underlying molecular interactions. It serves as a valuable tool for viscosity prediction, mechanistic insights, and mitigation strategies.

pharmacology and toxicology↗

Prioritizing Stability-enhancing Mutations using a Protein Language Model in conjunction with Physics-Based Predictions

Mutational protein engineering, as currently practiced in the biotechnology and pharmaceutical industries, is both tedious and expensive. Computationally driven protein design has the potential to expedite the process and generate high-quality variants at a lower cost. Using datasets of 174824 mutations for 180 proteins, we benchmark the effectiveness of a protein language model (PLM), Evolutionary Scale Modeling (ESM), alongside a physics-based method (PBM), namely molecular mechanics energies with generalized Born and surface area continuum solvation (MM/GBSA), as triaging tools for identifying and prioritizing target positions and specific mutations that improve protein thermodynamic stability. We found prediction biases in each method but also determined that these biases can be mitigated by applying the two methods in a complementary manner. We propose a hybrid mutation prioritization and selection strategy that achieves better accuracy than either method alone. Through re-ranking, the combined prioritization strategy attained a higher overall average ROC AUC of 0.744 across the dataset compared to either MM/GBSA alone (0.684) or ESM Log Odds alone (0.597).

biophysics↗

The ENCODE mouse postnatal developmental time course identifies regulatory programs of cell types and cell states

Postnatal genomic regulation significantly influences tissue and organ maturation but is under-studied relative to existing genomic catalogs of adult tissues or prenatal development in mouse. The ENCODE4 consortium generated the first comprehensive single-nucleus resource of postnatal regulatory events across a diverse set of mouse tissues. The collection spans seven postnatal time points, mirroring human development from childhood to adulthood, and encompasses five core tissues. We identified 30 cell types, further subdivided into 69 subtypes and cell states across adrenal gland, left cerebral cortex, hippocampus, heart, and gastrocnemius muscle. Our annotations cover both known and novel cell differentiation dynamics ranging from early hippocampal neurogenesis to a new sex-specific adrenal gland population during puberty. We used an ensemble Latent Dirichlet Allocation strategy with a curated vocabulary of 2,701 regulatory genes to identify regulatory "topics," each of which is a gene vector, linked to cell type differentiation, subtype specialization, and transitions between cell states. We find recurrent regulatory topics in tissue-resident macrophages, neural cell types, endothelial cells across multiple tissues, and cycling cells of the adrenal gland and heart. Cell-type-specific topics are enriched in transcription factors and microRNA host genes, while chromatin regulators dominate mitosis topics. Corresponding chromatin accessibility data reveal dynamic and sex-specific regulatory elements, with enriched motifs matching transcription factors in regulatory topics. Together, these analyses identify both tissue-specific and common regulatory programs in postnatal development across multiple tissues through the lens of the factors regulating transcription.

genomics↗

The ENCODE4 long-read RNA-seq collection reveals distinct classes of transcript structure diversity

The majority of mammalian genes encode multiple transcript isoforms that result from differential promoter use, changes in exonic splicing, and alternative 3 end choice. Detecting and quantifying transcript isoforms across tissues, cell types, and species has been extremely challenging because transcripts are much longer than the short reads normally used for RNA-seq. By contrast, long-read RNA-seq (LR-RNA-seq) gives the complete structure of most transcripts. We sequenced 264 LR-RNA-seq PacBio libraries totaling over 1 billion circular consensus reads (CCS) for 81 unique human and mouse samples. We detect at least one full-length transcript from 87.7% of annotated human protein coding genes and a total of 200,000 full-length transcripts, 40% of which have novel exon junction chains. To capture and compute on the three sources of transcript structure diversity, we introduce a gene and transcript annotation framework that uses triplets representing the transcript start site, exon junction chain, and transcript end site of each transcript. Using triplets in a simplex representation demonstrates how promoter selection, splice pattern, and 3 processing are deployed across human tissues, with nearly half of multitranscript protein coding genes showing a clear bias toward one of the three diversity mechanisms. Evaluated across samples, the predominantly expressed transcript changes for 74% of protein coding genes. In evolution, the human and mouse transcriptomes are globally similar in types of transcript structure diversity, yet among individual orthologous gene pairs, more than half (57.8%) show substantial differences in mechanism of diversification in matching tissues. This initial large-scale survey of human and mouse long-read transcriptomes provides a foundation for further analyses of alternative transcript usage, and is complemented by short-read and microRNA data on the same samples and by epigenome data elsewhere in the ENCODE4 collection.

genomics↗

The ENCODE Uniform Analysis Pipelines

The Encyclopedia of DNA elements (ENCODE) project is a collaborative effort to create a comprehensive catalog of functional elements in the human genome. The current database comprises more than 19000 functional genomics experiments across more than 1000 cell lines and tissues using a wide array of experimental techniques to study the chromatin structure, regulatory and transcriptional landscape of the Homo sapiens and Mus musculus genomes. All experimental data, metadata, and associated computational analyses created by the ENCODE consortium are submitted to the Data Coordination Center (DCC) for validation, tracking, storage, and distribution to community resources and the scientific community. The ENCODE project has engineered and distributed uniform processing pipelines in order to promote data provenance and reproducibility as well as allow interoperability between genomic resources and other consortia. All data files, reference genome versions, software versions, and parameters used by the pipelines are captured and available via the ENCODE Portal. The pipeline code, developed using Docker and Workflow Description Language (WDL; https://openwdl.org/) is publicly available in GitHub, with images available on Dockerhub (https://hub.docker.com), enabling access to a diverse range of biomedical researchers. ENCODE pipelines maintained and used by the DCC can be installed to run on personal computers, local HPC clusters, or in cloud computing environments via Cromwell. Access to the pipelines and data via the cloud allows small labs the ability to use the data or software without access to institutional compute clusters. Standardization of the computational methodologies for analysis and quality control leads to comparable results from different ENCODE collections - a prerequisite for successful integrative analyses. Database URL: https://www.encodeproject.org/

bioinformatics↗

Mapping and modeling human colorectal carcinoma interactions with the tumor microenvironment

The initiation and progression of cancer are inextricably linked to the tumor microenvironment (TME). Understanding the function of specific cancer-TME interactions poses a major challenge due in part to the complexity of the in vivo microenvironment. Here we predict cancer-TME interactions from single cell transcriptomic maps of both human colorectal cancers (CRCs) and mouse CRC models, ask how these interactions are altered in established, long-term human tumor organoid (tumoroid) cultures, and functionally recapitulate human myeloid-carcinoma interactions in vitro. Tumoroid cultures suppress gene expression programs involved in promoting inflammation and immune cell migration through receptor-ligand interactions, providing a reductive platform for re-establishing carcinoma-immune cell interactions in vitro. Introduction of human monocyte-derived macrophages into tumoroid cultures instructs macrophages to acquire pro-tumorigenic gene expression programs similar to those observed in vivo. This includes hallmark induction of SPP1, encoding Osteopontin, an extracellular CD44 ligand with established oncogenic effects. Taken together, these findings offer a framework for understanding CRC-TME interactions and provide a reductionist tool for modeling specific aspects of these interactions.

cancer biology↗

Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface

The K* algorithm provably approximates partition functions for a set of states (e.g., protein, ligand, and protein-ligand complex) to a user-specified accuracy{varepsilon} . Often, reaching an{varepsilon} -approximation for a particular set of partition functions takes a prohibitive amount of time and space. To alleviate some of this cost, we introduce two algorithms into the osprey suite for protein design: O_SCPLOWFRIESC_SCPLOW, a Fast Removal of Inadequately Energied Sequences, and EWAK*, an Energy Window Approximation to K*. In combination, these algorithms provably retain calculational accuracy while limiting the input sequence space and the conformations included in each partition function calculation to only the most energetically favorable. This combined approach leads to significant speed-ups compared to the previous state-of-the-art multi-sequence algorithm, BBK*. As a proof of concept, we used these new algorithms to redesign the protein-protein interface (PPI) of the c-Raf-RBD:KRas complex. The Ras-binding domain of the protein kinase c-Raf (c-Raf-RBD) is the tightest known binder of KRas, a historically \"undruggable\" protein implicated in difficult-to-treat cancers including pancreatic ductal adenocarcinoma (PDAC). O_SCPLOWFRIESC_SCPLOW/EWAK* accurately retrospectively predicted the effect of 38 out of 41 different sets of mutations in the PPI of the c-Raf-RBD:KRas complex. Notably, these mutations include mutations whose effect had previously been incorrectly predicted using other computational methods. Next, we used O_SCPLOWFRIESC_SCPLOW/EWAK* for prospective design and discovered a novel point mutation that improves binding of c-Raf-RBD to KRas in its active, GTP-bound state (KRasGTP). We combined this new mutation with two previously reported mutations (which were also highly-ranked by O_SCPLOWOSPREYC_SCPLOW) to create a new variant of c-Raf-RBD, c-Raf-RBD(RKY). O_SCPLOWFRIESC_SCPLOW/EWAK* in O_SCPLOWOSPREYC_SCPLOW computationally predicted that this new variant would bind even more tightly than the previous best-binding variant, c-Raf-RBD(RK). We measured the binding affinity of c-Raf-RBD(RKY) using a bio-layer interferometry (BLI) assay and found that this new variant exhibits single-digit nanomolar affinity for KRasGTP, confirming the computational predictions made with O_SCPLOWFRIESC_SCPLOW/EWAK*. This study steps through the advancement and development of computational protein design by presenting theory, new algorithms, accurate retrospective designs, new prospective designs, and biochemical validation.\n\nAuthor summaryComputational structure-based protein design is an innovative tool for redesigning proteins to introduce a particular or novel function. One such possible function is improving the binding of one protein to another, which can increase our understanding of biomedically important protein systems toward the improvement or development of novel therapeutics. Herein we introduce two novel, provable algorithms, O_SCPLOWFRIESC_SCPLOW and EWAK*, for more efficient computational structure-based protein design as well as their application to the redesign of the c-Raf-RBD:KRas protein-protein interface. These new algorithms speed up computational structure-based protein design while maintaining accurate calculations, allowing for larger, previously infeasible protein designs. Using O_SCPLOWFRIESC_SCPLOW and EWAK* within the O_SCPLOWOSPREYC_SCPLOW suite, we designed the tightest known binder of KRas, an \"undruggable\" cancer target. This new variant of a KRas-binding domain, c-Raf-RBD, should serve as an important tool to probe the protein-protein interface between KRas and its effectors as work continues toward an effective therapeutic targeting KRas.

biochemistry↗