Biology subjects

Siwo, G. H.

Publications and source records attributed to Siwo, G. H..

8 recordsLinked to original sources

bioRxiv2026

Protein co-scientist aided simulations of the structural and functional impact of O-GlcNAc Transferase TPR domain deletions and amino acid substitutions associated with X-Linked Intellectual Disability

O-GlcNAc transferase (OGT) is an essential glycosyltransferase that regulates cellular processes through the addition of O-linked {beta}-N-acetyglucosamine (O-GlcNAc) to target proteins. Substrate recognition and protein-protein interactions are mediated by the tetratricopeptide repeat (TPR) domain, yet the contributions of individual TPR repeats to structural stability and function remain poorly understood. Here, we employed an integrated computational framework combining an AI protein co-scientist, Speak to a Protein, which incorporates Boltz-2 for structure prediction and rDock for ligand binding evaluation, with external tools including AlphaFold3 for more advanced structure prediction and the HDOCK server for protein-protein docking. This framework was used to investigate how deletions of TPR domains and disease-associated amino acid substitutions within the TPR domain affect OGT structure and function. Five synthetic truncation OGT isoforms, thirteen single-TPR deletion variants, and five X-linked intellectual disability (XLID)-associated amino acid substitutions were modeled and aligned to wild-type OGT for comparison. Structural perturbations were quantified using backbone root mean square deviation (RMSD), while functional effects were assessed through docking analyses with UDP-GlcNAc, Host Cell Factor 1 (HCF1), and Tet methylcytosine dioxygenase 1 (TET1). Variation in TPR length altered global protein structure and induced long-range conformational changes within the catalytic domain despite no sequence modifications in that region. Single TPR deletions showed that individual repeats contribute unequally to structural stability, with deletions in mid-to-late TPR domains producing the largest structural disruptions. Disease-associated amino acid substitutions also generated conformational changes and reduced predicted ligand binding, consistent with known reductions in enzymatic activity in XLID. Protein-protein docking analyses demonstrated substrate-specific effects, as domain deletion and amino acid substitution variants of OGT had different interactions with TET1 and HCF1. Collectively, these findings identify the TPR domain as a regulatory domain that governs structure, ligand recognition, and protein interactions. More broadly, this work demonstrates how the integration of a protein co-scientist with protein structure modeling tools can be used to predict the structural and functional consequences of domain deletions or natural amino acid variants.

bioinformatics↗

bioRxiv2025

Out-of-the-box bioinformatics capabilities of large language models (LLMs)

Large Language Models (LLMs), AI agents and co-scientists promise to accelerate scientific discovery across fields ranging from chemistry to biology. Bioinformatics- the analysis of DNA, RNA and protein sequences plays a crucial role in biological research and is especially amenable to AI-driven automation given its computational nature. Here, we assess the bioinformatics capabilities of three popular general-purpose LLMs on a set of tasks covering basic analytical questions that include code writing and multi-step reasoning in the domain. Utilizing questions from Rosalind, a bioinformatics educational platform, we compare the performance of the LLMs vs. humans on 104 questions undertaken by 110 to 68,760 individuals globally. GPT-3.5 provided correct answers for 59/104 (58%) questions, while Llama-3-70B and GPT-4o answered 49/104 (47%) correctly. GPT-3.5 was the best performing in most categories, followed by Llama-3-70B and then GPT-4o. 71% of the questions were correctly answered by at least one LLM. The best performing categories included DNA analysis, while the worst performing were sequence alignment/comparative genomics and genome assembly. Overall, LLMs performance mirrored that of humans with lower performance in tasks in which humans had low performance and vice versa. However, LLMs also failed in some instances where most humans were correct and, in a few cases, LLMs excelled where most humans failed. To the best of our knowledge, this presents the first assessment of general purpose LLMs on basic bioinformatics tasks in distinct areas relative to the performance of hundreds to thousands of humans. LLMs provide correct answers to several questions that require use of biological knowledge, reasoning, statistical analysis and computer code.

bioinformatics↗

bioRxiv2024

Pan-cancer molecular signatures connecting aspartate transaminase (AST) to cancer prognosis, metabolic and immune signatures

BackgroundSerum aspartate transaminase (sAST) level is used routinely in conjunction with other clinical assays to assess liver health and disease. Increasing evidence suggests that sAST is associated with all-cause mortality and has prognostic value in several cancers, including gastrointestinal and urothelial cancers. Here, we undertake a systems approach to unravel molecular connections between AST and cancer prognosis, metabolism, and immune signatures at the transcriptomic and proteomic levels. MethodsWe mined public gene expression data across multiple normal and cancerous tissues using the Genotype Tissue Expression (GTEX) resource and The Cancer Genome Atlas (TCGA) to assess the expression of genes encoding AST isoenzymes (GOT1 and GOT2) and their association with disease prognosis and immune infiltration signatures across multiple tumors. We examined the associations between AST and previously reported pan-cancer molecular subtypes characterized by distinct metabolic and immune signatures. We analyzed human protein-protein interaction networks for interactions between GOT1 and GOT2 with cancer-associated proteins. Using public databases and protein-protein interaction networks, we determined whether the subset of proteins that interact with AST (GOT1 and GOT2 interactomes) are enriched with proteins associated with specific diseases, miRNAs and transcription factors. ResultsWe show that AST transcript isoforms (GOT1 and GOT2) are expressed across a wide range of normal tissues. AST isoforms are upregulated in tumors of the breast, lung, uterus, and thymus relative to normal tissues but downregulated in tumors of the liver, colon, brain, kidney and skeletal sarcomas. At the proteomic level, we find that the expression of AST is associated with distinct pan-cancer molecular subtypes with an enrichment of specific metabolic and immune signatures. Based on human protein-protein interaction data, AST physically interacts with multiple proteins involved in tumor initiation, suppression, progression, and treatment. We find enrichments in the AST interactomes for proteins associated with liver and lung cancer and dermatologic diseases. At the regulatory level, the GOT1 interactome is enriched with the targets of cancer-associated miRNAs, specifically mir34a - a promising cancer therapeutic, while the GOT2 interactome is enriched with proteins that interact with cancer-associated transcription factors. ConclusionsOur findings suggest that perturbations in the levels of AST within specific tissues reflect pathophysiological changes beyond tissue damage and have implications for cancer metabolism, immune infiltration, prognosis, and treatment personalization.

genomics↗

bioRxiv2021

Genomes contain relics of a triplet code connecting the origins of primordial RNA synthesis to the origins of genetically coded protein synthesis

Life on earth relies on three types of information polymers-DNA, RNA and proteins. In all organisms and viruses, these molecules are synthesized by the copying of pre-existing templates. A triplet-based code known as the genetic code guides the synthesis of proteins by complex enzymatic machines that decode genetic information in RNA sequences. The origin of the genetic code is one of the most fundamental questions in biology. In this study, computational analysis of about 5,000 species level metagenomes using techniques for the analysis of human language suggests that the genomes of extant organisms contain relics of a distinct triplet code that potentially predates the genetic code. This code defines the relationship between adjacent triplets in DNA/RNA sequences, whereby these triplets predominantly differ by a single base. Furthermore, adjacent triplets encode amino acids that are thought to have emerged around the same period in the earths early history. The results suggest that the order of triplets in primordial RNA sequences was associated with the availability of specific amino acids, perhaps due to a coupling of a triplet-based primordial RNA synthesis mechanism to a primitive mechanism of peptide bond formation. Together, this coupling could have given rise to early nucleic acid sequences and a system for encoding amino acid sequences in RNA, i.e. the genetic code. Thus, the central role of triplets in biology potentially extends to the primordial world, contributing to both the origins of genomes and the origins of genetically coded protein synthesis. SignificanceOne of the most intriguing discoveries in biology is that the order of amino acids in each protein is determined by the order of nucleotides (commonly represented by the letters A, U, G, C) in a biological molecule known as RNA. The genetic code serves as a dictionary that maps each of the 64 triplets words in RNA to the 20 amino acids, thereby specifying how information encoded in RNA is decoded into sequences of amino acids (i.e., proteins). The deciphering of the genetic code was one of the greatest discoveries of the 20th century (1968 Nobel Prize in Medicine and Physiology) and is central to modern molecular biology. Yet, how it came to be that the order of triplets in RNA encodes the sequence of the protein synthesized remains one of the most important enigmas of biology. Paradoxically, in all life forms proteins cannot be synthesized without RNA and RNA itself cannot also be synthesized without proteins, presenting a chicken and egg dilemma. By analyzing thousands of microbial genomes using approaches drawn from the field of natural language processing, this study finds that the order of triplets across genomes contains relics of an ancient triplet code, distinct from but closely connected to the genetic code. Unlike the genetic code which specifies the relationship between information in RNA and the sequence of proteins, this ancient code describes the relationship between adjacent triplets in extant genome sequences, whereby such triplets are often different from each other by a single letter. Triplets that are closely related by this ancient code encode amino acids that are thought to have emerged around the same period in the earths early history. In other words, a fossil record of the chronological order of appearance of amino acids on early earth appears written in genome sequences. This potentially demonstrates that the process by which RNA sequences were synthesized in the primordial world relied on triplets and was coupled to amino acids available at the time. Hence, the connections between primordial RNA synthesis and a primitive mechanism for linking amino acids to form peptides could have enabled one type of molecule (RNA) to code for the other (protein), facilitating the emergence of the genetic code.

evolutionary biology↗

bioRxiv2021

Molecular relationships between SARS-CoV-2 Spike protein and LIFR, a pneumonia protective IL-6 family cytokine

The fine-tuned control of immune responses is attained by pairs of activating and inhibitory signaling receptors which modulate the quality and magnitude of immune responses. Some viruses exploit these pathways to enter host cells as well as interfere with immune responses. Here, we report that the SARS-CoV-1/2 Spike proteins (S) contain a potential inhibitory tyrosine- based immunoreceptor motif (ITIM) with the D614G variant occurring within this motif. Through an in silico screen of ITIM-containing human proteins, we find that the S-located ITIM is closely related to a previously reported ITIM in the cytoplasmic tail of the human Leukemia Inhibitory Factor Receptor (LIFR), a pneumonia protective IL-6 family cytokine receptor. To infer potential functional interactions between SARS-CoV-2 infection and LIFR expression, we performed single-cell transcriptome analysis of public datasets of lung tissues from healthy individuals and COVID-19 patients. We show that transcripts of LIFR and its ligand LIF are highly expressed in SARS-CoV-2 susceptible lung cells from mild and severe COVID-19 patients but not in healthy individuals. In addition, the human endogenous retroviral envelope gene (ERVW-1) encoding a fusogenic protein of the same functional class as the S protein, is induced in SARS-CoV-2 susceptible cell subpopulations in COVID-19 patients with no detectable expression in healthy individuals. We also report that pulmonary epithelial cells express transcripts of several immunoreceptors including the ITIM-containing antibody receptor FCGR2B which is detectable in healthy and severe COVID-19 cases but not in mild cases. These results suggest that molecular dysregulation of ITIM-mediated inhibitory signaling by the SARS-CoV-2 S protein may play a role in COVID-19 immunopathology.

genomics↗

bioRxiv2018

“Global Transcriptional Response to CRISPR/CAS9-AAV6 Based Genome Editing” Matches Transcriptional Response to Specific Small Molecule Perturbations

Cromer et al. [1] recently reported global transcriptional changes occuring in cells in response to CRISPR/Cas9 gene editing. Using a CD34+ hematopoietic and progenitor stem cell model, they observed differentially expressed genes enriched for immune, stress and apoptotic processes following treatment with a CRISPR/Cas9-AAV6 genome-editing system. Following treatment with Cas9s mRNA they observed transcriptional changes enriched for viral response as well as a downregulation of metabolic and cell cycle processes. Similarly, they observed a downregulation of metabolic processes in response to electroporation. Surprisingly, no enrichment for viral response genes was observed following treatment with AAV6 while minor transcriptional changes enriched for DNA damage signature occurred in response to Cas9/sgRNA ribonucleoprotein.

molecular biology↗

bioRxiv2018

The Global State of Genome Editing

Genome editing technologies hold great promise in fundamental biomedical research, development of treatments for animal and plant diseases, and engineering biological organisms for food and industrial applications. Therefore, a global understanding of the growth of the field is needed to identify challenges, opportunities and biases that could shape the impact of the technology. To address this, this work applies automated literature mining of scientific publications on genome editing in the past year to infer research trends in 2 key genome editing technologies-CRISPR/Cas systems and TALENs. The study finds that genome editing research is disproportionately distributed between and within countries, with researchers in the US and China accounting for 50% of authors in the field whereas countries across Africa are underrepresented. Furthermore, genome editing research is also disproportionately being explored on diseases such as cancer, Duchene Muscular Dystrophy, sickle cell disease and malaria. Gender biases are also evident in genome editing research with considerably fewer women as principal investigators. The results of this study suggest that automated mining of scientific literature could help identify biases in genome editing research as a means to mitigate future inequalities and tap the full potential of the technology.

genomics↗

bioRxiv2017

Prediction of Molecular Mechanisms of Breast Cancer Metastasis

Metastasis -the spread of cancer to other parts of the body- causes 90% of cancer deaths, underlies major health complications in cancer patients and renders most cancers incurable. Unfortunately, the molecular mechanisms underlying the process are poorly understood and therapeutics to block it remain elusive. Here, we present a computational technique for scanning genome-scale regulatory networks for potential genes associated with metastasis. First, we demonstrate that in the breast cancer cell line MCF7, the commonly dysregulated cancer biomarkers TP53, ERBB2, ESR1 and PGR are closely connected to known metastasis genes with a significant proportion being 2nd degree neighbors of a given biomarker. Next, we identify genes whose 2nd degree neighbors are connected in a similar manner to these biomarkers. Consequently, these are referred to as metastasis associated genes or MAGs. We identify 190 genes that are TP53-MAGs, 22 ERBB2-MAGs, 240 ESR1-MAGs and 84 PGR-MAGs (FDR adjusted P <0.001). Analysis of the MAGs reveals statistically significant enrichment with biological functions previously associated with metastasis including the extracellular matrix (ECM) receptor interaction, focal adhesion, cytokine-cytokine receptor interaction and chemokine signaling. The biological significance of MAGs is further supported by their enrichment with experimentally validated binding sites for transcription factors that regulate metastasis, for example BACH1- a master regulator of breast cancer metastasis to bone. The predicted MAGs are also clinically relevant as therapeutic targets for metastasis blocking agents. Specifically, genes that are perturbed by drugs and miRNAs that influence metastasis are enriched with MAGs. Furthermore, some MAGs are associated with patient survival and provide insights into the proclivity for breast cancer subtypes to preferentially spread to specific organs. The results of this study imply that aberrations in primary tumors may constrict metastasis trajectories. This could enable the prediction of organ specific metastases based on aberrations in the primary tumor and lay a foundation for future studies on individualized or personalized models of metastasis. The approach is potentially scalable across other cancers and has clinical implications.

cancer biology↗