Biology subjects

Jernigan, R. L.

Publications and source records attributed to Jernigan, R. L..

6 recordsLinked to original sources

bioRxiv2026

New Protein Function Characterization for Human Paralog Discovery, Scraping the Bottom of the Genomics Barrel

Increasing the number of related protein paralogs is important for fully understanding protein relationships, yet it remains challenging for sequences in the twilight zone. Here, we present an integrated homolog detection framework that combines sequence-based (BLASTp, MMseqs2), structure-based (Foldseek), and embedding-distance-based (PROST) similarity metrics to identify additional paralogs. To characterize functionally related protein pairs, we develop protein-family-specific supervised logistic regression models trained on curated functional annotations from MEROPS proteases and KinHub kinases. The resulting model successfully classifies proteins, with ROC-AUC of 0.99 and F1-score of 0.92 for test datasets. Applying this model, we initially identify 686 protease and 298 kinase new candidates. Subsequent structural validation, and previous annotation comparisons yield 7 new protease and 3 new kinase paralogs in the human proteome, mostly lacking prior functional characterization. An additional outcome is structural identification of catalytically important residues for larger numbers of proteases and kinases. Despite the small number of new paralogs for well-studied proteases and kinases, our results demonstrate that integrating orthogonal homolog approaches with family-specific regression models provides a robust, scalable strategy for discovering new functionally related proteins, which is a generalizable approach for novel protein function discovery and can be applied more broadly to under-annotated proteomes.

genomics↗

bioRxiv2025

Large Numbers of New Human Paralogs Discovered

The identification of paralogs is critical for understanding protein evolution, function, and for drug design, yet many human proteins remain unannotated and poorly classified. Sequence-based homology detection alone often fails to detect distant paralogs, especially in the "twilight zone" or beyond, regarding sequence identity. Here we present an integrated homolog detection framework that combines results from BLASTp, MMseqs2, Foldseek, and the large protein language model-based tool PROST, followed by validation based on comparison of structures and for enzymes comparison of the specific structures of the catalytic residues. Using all-versus-all exhaustive comparisons across the 20,647 human proteins, we systematically identify novel paralogs and assess their catalytic residues for two serine protease clans. We discovered 14 previously uncharacterized human serine carboxypeptidases, validated against experimentally determined PDB structures, with 11 of these displaying conserved catalytic triads. We further identify 203 new paralogs for human kinases, with 163 of these in the major clusters that represent previously uncharacterized kinase subtypes and 30 putative novel human transcription factors. Across both serine protease subtypes, structural alignments enable the prediction of the previously unknown catalytic residues for those lacking UniProt annotations of active site residues. By integrating sequence, structure, and LPLM embedding-based approaches, the framework enables the discovery of surprisingly large numbers of unknown paralogs, permitting defining catalytic residues, and expands the understanding of protein functional landscapes. These findings provide the foundation for a large number of future functional, evolutionary, and therapeutic investigations.

genomics↗

bioRxiv2025

ESMStabP: A Regression Model for Protein Thermostability Prediction

Accurately predicting protein thermostability is crucial for numerous applications in biotechnology, pharmaceuticals, and food science. Experimental methods for determining protein melting temperatures are often time-consuming and costly, driving the need for efficient computational alternatives. In this paper, we introduce ESMStabP, an enhanced regression model for predicting protein thermostability. To improve model performance and generalizability, we assembled a significantly larger dataset by combining and cleaning datasets previously utilized in other thermostability models. Building on Deep-StabP, ESMStabP incorporates significant improvements, using embeddings from the ESM2 protein language model and thermophilic classifications. The predictions from ESMStabP outperform DeepStabP and other existing predictors, achieving an R2 of 0.95 and a Pearson correlation coefficient (PCC) of 0.97. Despite these improvements, challenges such as dataset availability. This work underscores the critical role of specific layer identification for model development and outlines potential directions for future advancements in protein stability predictions

bioinformatics↗

bioRxiv2022

Protein Language Model Performs Efficient Homology Detection

MotivationThere are now 225 million sequences in the UniProtKB database, as of January 2022 and 451 million protein sequences in the NCBI non-redundant database. This huge sequence data is ripe for analysis and can be extremely informative about biological function when analyzed with the appropriate methods. Evolutionary information such as the relationship among protein sequences is key to performing sequence analyses. Since sequence matching is one of the primary ways that annotations are found, higher-quality sequence matches yield a larger number of identified homologs. Thus, there is an essential need for a faster and more accurate homolog detection method to process the huge amount of rapidly growing biological sequences. MethodRecently, we have seen major improvements in various predictive computational tasks such as structure prediction from the ever-improving artificial intelligence methods. One such approach has been to use language models to represent proteins numerically in a representation matrix (embeddings) while retaining context-dependent biochemical, biophysical, and evolutionary information. Computational transformer architectures that utilize attention neural networks can generate these context-aware numerical representations in an unsupervised fashion. One such use for these protein embeddings is remote homolog detection. In this work, we utilize protein language models and then apply discrete cosine transforms to extract the essential part of these embeddings, resulting in a significantly smaller fixed-size matrix for each sequence. This allows us to numerically and efficiently calculate the distance between all pairs of proteins resulting in homolog detection. ResultsOur Protein LAnguage model Search Tool (PLAST) is significantly faster, with linear runtimes in the number of sequences within the query database. With only one CPU core, it can scan a million sequences in less than a second. It essentially removes the noise in the sequence data and leads to significant improvements. PLAST is more accurate in the benchmarks tested from the PFAM, SCOP, and CATH databases than other approaches. When benchmarked with the PFAM database, the increase in the area under the receiver operating characteristic curve (AUROC) 3.1% when compared with NCBI-BLAST. The number of remote homologs that are detectable now is significantly larger and pushes sequence matches deeply into the usual twilight zone. Compared with the state-of-the-art profile-based homology search tools like CSBLAST, the increase was still 2.0%. PLAST can find remote homologs for a significant number of proteins that had been thought to be unique due to homolog detection failure. These homologs that are found usually have less than 20% sequence identity making them indistinguishable from noise with most other sequence matching methods. ConclusionPLAST is an accurate and fast homolog detection tool essential for easy and rapid progress to utilize the vast amount of data generated by next-generation sequencing methods. Quantization of sequence embeddings into highly-compressed noise-free representations with the use of direct cosine transforms allows for the efficient and accurate detection of normal homologs and remote ones that are un-detectable by other sequence similarity methods. The PLAST web server is accessible from https://mesihk.github.io/plast.

bioinformatics↗

bioRxiv2022

New Functions Identified for HIV gp120 with the New ProtSub Sequence Matching

The reduced cost of sequencing is leading to an explosive growth in the number of available sequences across diverse genomes, and for individual patients. Inferring meaningful functions of individual genes/proteins is lagging, which hinders the deeper understanding of biological function and evolution. Traditionally, protein function has been determined by time consuming experimental methods or by sequence matching that often does not agree with the experimental findings. We have significantly improved protein sequence matching, by accounting for inter-dependent amino acid substitutions observed within densely packed protein structures, which yields additional substitutions beyond those usually seen, with good matches to additional proteins, some having new functions, not identified by conventional sequence matching. In the current study, we have applied this approach to predict novel functions for the proteins from HIV. These newly found functional annotations are then manually reviewed and many are validated from the literature, here for the HIV envelope protein gp120. These new functions are both more specific as well as some being entirely novel functions. We also show statistically that on average our new functional annotations are more informative than those given by conventional substitution matrices such as BLOSUM62. These results suggest that the new ProtSub protein sequence matching that incorporates structural information generally yields better identifications of related proteins, which can have broader and often gains in identifying more specific functions

bioinformatics↗

bioRxiv2018

SeqStruct : A New Amino Acid Similarity Matrix Based on Sequence Correlations and Structural Contacts Yields Sequence-Structure Congruence

Protein sequence matching does not properly account for some well-known features of protein structures: surface residues being more variable than core residues, the high packing densities in globular proteins, and does not yield good matches of sequences of many proteins known to be close structural relatives. There are now abundant protein sequences and structures to enable major improvements to sequence matching. Here, we utilize structural frameworks to mount the observed correlated sequences to identify the most important correlated parts. The rationale is that protein structures provide the important physical framework for improving sequence matching. Combining the sequence and structure data in this way leads to a simple amino acid substitution matrix that can be readily incorporated into any sequence matching. This enables the incorporation of allosteric information into sequence matching and transforms it effectively from a 1-D to a 3-D procedure. The results from testing in over 3,000 sequence matches demonstrate a 37% gain in sequence similarity and a loss of 26% of the gaps when compared with the use of BLOSUM62. And, importantly there are major gains in the specificity of sequence matching across diverse proteins. Specifically, all known cases where protein structures match but sequences do not match well are resolved.

bioinformatics↗