Alexandros Orges Koci
Research Subjects
Alexandros Orges Koci has a total of 19 co-authors. Here are the top co-authors by number of shared publications:
- Anu Shivalikanjli
University of Barcelona
1 shared publication - Bryce Fukuda
School of Engineering and Technology, University of Washington, Box 358426, Tacoma, WA 98402, USA
1 shared publication - David L. Robertson
Affiliation MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
1 shared publication - Dušica Vidović
Frost Institute for Data Science and Computing, Molecular and Cellular Pharmacology, Sylvester Comprehensive Cancer Center, University of Miami, Coral Gables, FL, USA
1 shared publication - Dylan Baker
The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
1 shared publication - Francesca Young
MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
1 shared publication
Latest Publications
- Open Access
Interactive Data Integrator for Uniform Data Processing in the MorPhiC Program
We present an interactive Data Integrator to facilitate uniform data processing and integration with datasets generated by the NIH funded MorPhiC (Molecular Phenotypes of Null Alleles in Cells) program. In the dataset identification step, users can select published bulk RNA sequencing (RNA-seq) datasets generated by the MorPhiC program, specify external datasets from the Gene Expression Omnibus (GEO) repository, or upload raw RNA-seq data. Subsequently, users can select a subset of samples and indicate experimental design by assigning groups to samples. External data will then be processed using the MorPhiC approved bulk RNA-seq analytical pipeline, and differential expression will be inferred. Users also have the option to download the uniformly processed counts table.
Open AccessInteractive Data Integrator for Uniform Data Processing in the MorPhiC Program
We present an interactive Data Integrator to facilitate uniform data processing and integration with datasets generated by the NIH funded MorPhiC (Molecular Phenotypes of Null Alleles in Cells) program. In the dataset identification step, users can select published bulk RNA sequencing (RNA-seq) datasets generated by the MorPhiC program, specify external datasets from the Gene Expression Omnibus (GEO) repository, or upload raw RNA-seq data. Subsequently, users can select a subset of samples and indicate experimental design by assigning groups to samples. External data will then be processed using the MorPhiC approved bulk RNA-seq analytical pipeline, and differential expression will be inferred. Users also have the option to download the uniformly processed counts table.
- Open Access
From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences
Protein language models (PLMs) capture features of protein three-dimensional structure from amino acid sequences alone, without requiring multiple sequence alignments (MSA). The concepts of grammar and semantics from natural language have the potential to capture functional properties of proteins. Here, we investigate how these representations enable assessment of variation due to mutation. Applied to SARS-CoV-2's spike protein using in silico deep mutational scanning (DMS) we demonstrate the PLM, ESM-2, has learned the sequence context within which variation occurs, capturing evolutionary constraint. This recapitulates what conventionally requires MSA data to predict. Unlike other state-of-the-art methods which require protein structures or multiple sequences for training, we show what can be accomplished using an unmodified pretrained PLM. We demonstrate that the grammaticality and semantic scores represent novel metrics. Applied to SARS-CoV-2 variants across the pandemic we show that ESM-2 representations encode the evolutionary history between variants, as well as the distinct nature of variants of concern upon their emergence, associated with shifts in receptor binding and antigenicity. PLM likelihoods can also identify epistatic interactions among sites in the protein. Our results here affirm that PLMs are broadly useful for variant-effect prediction, including unobserved changes, and can be applied to understand novel viral pathogens with the potential to be applied to any protein sequence, pathogen or otherwise.
Open AccessFrom a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences
Protein language models (PLMs) capture features of protein three-dimensional structure from amino acid sequences alone, without requiring multiple sequence alignments (MSA). The concepts of grammar and semantics from natural language have the potential to capture functional properties of proteins. Here, we investigate how these representations enable assessment of variation due to mutation. Applied to SARS-CoV-2's spike protein using in silico deep mutational scanning (DMS) we demonstrate the PLM, ESM-2, has learned the sequence context within which variation occurs, capturing evolutionary constraint. This recapitulates what conventionally requires MSA data to predict. Unlike other state-of-the-art methods which require protein structures or multiple sequences for training, we show what can be accomplished using an unmodified pretrained PLM. We demonstrate that the grammaticality and semantic scores represent novel metrics. Applied to SARS-CoV-2 variants across the pandemic we show that ESM-2 representations encode the evolutionary history between variants, as well as the distinct nature of variants of concern upon their emergence, associated with shifts in receptor binding and antigenicity. PLM likelihoods can also identify epistatic interactions among sites in the protein. Our results here affirm that PLMs are broadly useful for variant-effect prediction, including unobserved changes, and can be applied to understand novel viral pathogens with the potential to be applied to any protein sequence, pathogen or otherwise.