Christine Anne Orengo

Interrogating contrastive learning embeddings for structure-based virtual screening: a case study on DrugCLIP

This paper evaluates DrugCLIP's contrastive learning embeddings for virtual screening, showing strong performance but limitations under realistic conditions.

ContrasTED: contrastive domain embeddings for scalable remote homology classification

ContrasTED improves homology classification by using contrastive learning to assign domains to superfamilies with high accuracy, outperforming existing methods.

TrioNsight: Building a meta-predictor to evaluate the clinical impact of TrioN-like Dbl-homology domain variants

TrioNsight is a meta-predictor that evaluates the clinical impact of TrioN-like Dbl-homology domain variants using structural, evolutionary, and physiochemical features.

Expanding all-α-helical protein space through rational computational design

The paper designs de novo all-α-helical proteins using computational methods and validates their stability and structure.

Advances in protein function prediction from the fifth CAFA challenge

This work analyzes CAFA5 submissions, showing improved accuracy in protein function prediction and highlights the role of protein language models and 3D structures in performance gains.

Systematic prediction and functional analysis of amino acid residues determining product specificity in the plant oxidosqualene cyclase superfamily

The paper predicts and validates amino acid residues determining product specificity in plant oxidosqualene cyclases using structural and computational methods.

TEDLH: Domain HMMs for sensitive detection of remote homologues

The paper introduces TEDLH, a library of domain HMMs derived from TED annotations, enabling sensitive detection of remote homologues and expanding coverage of sequence space and CATH superfamily diversity.

ProFam: Open-Source Protein Family Language Modelling for Fitness Prediction and Design

ProFam is an open-source protein family language model that improves fitness prediction and design by leveraging evolutionary context through homologous sequences.

Assessing structure-function impacts on Vitellogenin by leveraging allelic variant occurring in honey bee subspecies Apis mellifera meliffera

The paper identifies a 9-nucleotide deletion in honey bee Vg alleles and shows it maintains protein structure and stability using structural bioinformatics and a transformer-based model.

Metagenomic-scale analysis of the predicted protein structure universe

The paper combines AlphaFold and ESMfold predictions to create a large structural dataset, identifies novel protein folds and domain combinations, and highlights the value of metagenomic data for structural diversity.

Leveraging protein language models and scoring function for Indel characterisation and transfer learning

This paper introduces IndeLLM, a method for indel pathogenicity prediction using protein language models and transfer learning, achieving high performance across species.

Exploring structural diversity across the protein universe with The Encyclopedia of Domains

The paper introduces TED, a resource that segments and classifies over 370 million domains from the AlphaFold Protein Structure Database, expanding known structural domains and uncovering new structural interactions.

BEAN and HABAS: Polyphyletic insertions in RNAP that point to deep-time evolutionary divergence of bacteria

The paper identifies BEAN and HABAS domains in bacterial RNA polymerase subunits, showing their evolutionary divergence and phylogenetic distribution.

Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics

The paper predicts human and viral protein variants that enhance SARS-CoV-2 binding affinity and may increase COVID-19 susceptibility.

Chainsaw: protein domain segmentation with fullyconvolutional neural networks

Chainsaw uses a fully convolutional neural network to predict protein domain boundaries, achieving higher accuracy than existing methods in domain segmentation.

VariPred: Enhancing Pathogenicity Prediction of Missense Variants Using Protein Language Models

VariPred uses a protein language model to predict missense variant pathogenicity, outperforming existing methods on the ClinVar dataset.

Understanding structural and functional diversity of ATP-PPases using protein domains and functional families in CATH database

The paper characterizes structural and functional motifs of ATP-PPase substrate-specificity groups using structural data to inform antibacterial drug design.

Broad functional profiling of fission yeast proteins using phenomics and machine learning

The paper uses phenomics and machine learning to predict protein functions in fission yeast, identifying novel roles and validating predictions through experimental validation.

ModelCIF: An extension of PDBx/mmCIF data representation for computed structure models

ModelCIF extends the PDBx/mmCIF standard to represent predicted 3D protein models, enabling FAIR data sharing for computational structural biology.

AlphaFold2 reveals commonalities and novelties in protein structure space for 21 model organisms

The paper uses AlphaFold2 to analyze protein structures from 21 organisms, identifying 26 novel superfamilies and expanding structural diversity insights.

CATHe: Detection of remote homologues for CATH superfamilies using embeddings from protein language models

The paper introduces CATHe, a method using protein language model embeddings to detect remote homologues for CATH superfamilies, achieving high accuracy and validating predictions with AlphaFold structures.

Dissecting peripheral protein-membrane interfaces

The paper analyzes amino acid composition and structural patterns in peripheral membrane protein interfaces using experimental and AlphaFold models to identify key residues and functional motifs.

Mapping the Constrained Coding Regions in the human genome to their corresponding proteins

The paper maps constrained coding regions in the human genome to their corresponding proteins and identifies protein features associated with high constraint for variation.

Transmission of SARS-CoV-2 from humans to animals and potential host adaptation

The paper analyzes SARS-CoV-2 genomic mutations in mink and deer to identify potential host-specific adaptations and assess spillover risks.

Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues

The paper predicts structures of disease-associated proteins without known structures, identifies functional sites, and explains 80% of mutations based on their proximity to these sites or structural impact.

Contrastive learning on protein embeddings enlightens midnight zone

This paper introduces ProtTucker, a contrastive learning method using protein embeddings from pLMs to improve recognition of distant homologous relationships in the midnight zone of protein similarity.

3D-Beacons: Decreasing the gap between protein sequences and structures through a federated network of protein structure data resources

3D-Beacons provides a federated network to unify access to protein structure data from multiple sources, enabling comparative analysis of models.

Insertions in the SARS-CoV-2 Spike N-Terminal Domain May Aid COVID-19 Transmission

The paper investigates how insertions in the SARS-CoV-2 Spike N-terminal domain may enhance sialic acid binding and contribute to increased transmissibility.

Clustering FunFams using sequence embeddings improves EC purity

The paper improves the purity of protein functional families by clustering them using sequence embeddings, doubling the number of pure clusters per family.

CATH functional families predict protein functional sites

The paper introduces FunSite, a machine learning method for predicting functional sites in proteins using sequence, structure, and evolutionary data from CATH functional families, showing superior performance over existing methods.