protein function

Genomic Context

Bacterial protein-function prediction that exploits operon and genome-neighbourhood structure in addition to sequence and homology. Get it GitHub: https://github.com/bio-ontology-research-group/Genomic_context ★ 3 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

PhenoGoCon

Predicts gene–phenotype associations from predicted Gene Ontology functions; bridges GO function prediction and HPO/MPO phenotype prediction. Get it GitHub: https://github.com/bio-ontology-research-group/phenogocon ★ 3 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

GO-Agent

LLM-agent framework that decomposes protein-function prediction into tool-calling sub-tasks (sequence search, structure lookup, domain reasoning) and stitches the evidence into a final GO annotation. Get it GitHub: https://github.com/bio-ontology-research-group/go-agent ★ 7 Developed in projects KAUST Center of Excellence for Generative AI (Health and Wellness, BCB theme) Category: Protein Function Prediction

DeepPheno

Predicts loss-of-function organism-level phenotypes (HPO/MPO) directly from a gene's annotated functions, using a hierarchical neural classifier over phenotype ontologies. Get it GitHub: https://github.com/bio-ontology-research-group/deeppheno ★ 6 Developed in projects CompleX: Variant Prioritization in Complex Disease Category: Protein Function Prediction

PU-GO

Positive-unlabeled ranking of protein functions with ontology-based priors; directly addresses the partial-annotation problem in CAFA benchmarks. Get it GitHub: https://github.com/bio-ontology-research-group/PU-GO ★ 4 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

DeepGOMeta

DeepGO trained specifically for metagenomic communities; predicts functional roles of proteins recovered from environmental samples and links them to biogeochemical processes. Get it GitHub: https://github.com/bio-ontology-research-group/deepgometa ★ 8 Developed in projects Enabling desert revegetation by AI-tailored soil microbiome fortification Enabling mangrove restoration by AI-tailored microbiome fortification Category: Protein Function Prediction

DeepGOZero

Zero-shot extension of DeepGO using model-theoretic ELEmbeddings to predict GO classes that have never been observed during training. Get it GitHub: https://github.com/bio-ontology-research-group/deepgozero ★ 34 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

DeepGO2

Next-generation DeepGO model with transformer protein embeddings and improved hierarchical multi-label prediction. Get it GitHub: https://github.com/bio-ontology-research-group/deepgo2 ★ 57 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Category: Protein Function Prediction

DeepGO

Original sequence-based, ontology-aware deep classifier for predicting Gene Ontology functional annotations; basis of the entire DeepGO family of tools. Get it GitHub: https://github.com/bio-ontology-research-group/deepgo ★ 87 Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Bio2Vec: Smart analytics infrastructure for the life sciences Category: Protein Function Prediction

DeepGOPlus

CNN-ensemble protein-function predictor that augments sequence-based scoring with k-nearest-neighbour homology and GO axioms; one of the strongest CAFA-evaluated open models. Get it GitHub: https://github.com/bio-ontology-research-group/deepgoplus ★ 103 Homepage: https://deepgo.cbrc.kaust.edu.sa/deepgo/ Developed in projects Computational methods for functional metagenomics: from protein functions to multi-scale interactions Bio2Vec: Smart analytics infrastructure for the life sciences Category: Protein Function Prediction

Melissa Rios Zertuche

Melissa Maria Rios Zertuche completed her MSc in Bioscience at KAUST in 2024 under the supervision of Robert Hoehndorf. Her thesis project was carried out in collaboration with the Eppinger lab and developed a computational pipeline for the rational design and optimisation of nanobodies. Her thesis, Establishment and Evaluation of a Computational Workflow for the Design and Optimization of Nanobodies, addresses the lack of comprehensive end-to-end computational tools tailored to the design of single-domain antibodies. Nanobodies (VHHs) are roughly one-tenth the size of conventional antibodies

Md Nurul Muttakin

Md Nurul Muttakin completed his MSc in Computer Science at KAUST in 2023 under the supervision of Robert Hoehndorf. His research focused on machine learning methods for predicting protein function from three-dimensional structural information. His thesis, 3D conformation-based protein function prediction, investigated how structural information should be exploited by deep learning models in light of the availability of near-experimental-accuracy structures from AlphaFold. Almost all existing structure-aware protein function predictors combine graph neural networks (GNNs) operating on 2D

Amal Alhelal

Amal Alhelal completed her MSc in Bioengineering at KAUST in 2024 under the supervision of Robert Hoehndorf. She was a student in the Bioinformatics and Machine Learning track of the Bioengineering programme, based at the Computational Bioscience Research Center (CBRC), and defended her thesis in May 2024. Her thesis, titled Protein functional domain identification methodology (defended under the working title Exploring the association between protein functional domains and pathogenicity information derived from AI tools), investigated computational approaches to delineate functional domains

Application of AI in Bioinformatics (2025)

Year: 2025 Code: CS 321 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Neuro-symbolic AI, Protein function

Algorithms in Bioinformatics (2020)

Year: 2020 Code: CS 290 Program: Computer Science / Bioengineering Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function

Algorithms in Bioinformatics (2022)

Year: 2022 Code: CS 249 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function

Algorithms in Bioinformatics (2025)

Year: 2025 Code: CS 249 Program: Computer Science Institution: KAUST Role: Instructor Topics: Biomedical informatics, Genomics, Protein function

Computational methods for functional metagenomics: from protein functions to multi-scale interactions

Metagenomic sequencing has made it routine to read the DNA of an entire microbial community, but most analysis pipelines stop at taxonomic composition or at the level of individual protein families. The really biologically informative questions, which proteins do what, which proteins interact, which metabolic pathways are reconstructible, and how the community as a whole interacts with its environment or host, remain largely out of reach computationally. Even associations that are very robust empirically, for example between gut microbiome composition and colorectal cancer or inflammatory

Evolutionary potential of corals to adapt to climate warming

Reef-building corals in the Red Sea are exposed to among the highest sea-surface temperatures of any reef system on Earth, yet survive and reproduce at conditions that bleach corals elsewhere. Understanding the molecular basis of this resilience, and the limits of acclimatization and adaptation as the Red Sea continues to warm, is one of the central questions in marine climate biology. The project combines reciprocal transplant experiments along the north-south Red Sea thermal gradient with genome-resolved transcriptomics, epigenomics, and host-symbiont metabarcoding to dissect how the coral

Enabling desert revegetation by AI-tailored soil microbiome fortification

Roughly twelve million hectares of dryland are lost every year worldwide, and the megaprojects launched to reverse desertification — the Sahel's Great Green Wall is the canonical example — typically fail because most planted trees die when irrigation stops. The biological reason is that desert soils are low in nutrients and high in salinity, and the microbial communities that mediate nitrogen, phosphate, and mineral acquisition for plants are missing or unbalanced. This project, led by Heribert Hirt's plant-microbiome group with our group and the Modelling and Simulation group of Gabriel

Meet KAUST prospective student: Juexiao Zhou

3 min read ·
Juexiao Zhou is a 21-year-old graduate from Shenzhen, China, who will join KAUST in the fall of 2020 as an M.S./Ph.D. candidate and member of the KAUST Structural and Functional Bioinformatics research group under the supervision of Professor Xin Gao.