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Creators/Authors contains: "Kabir, Anowarul"

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  1. ; ; ; ( , Cold Spring Harbor Perspectives in Biology)
    Over the years, many computational methods have been created for the analysis of the impact of single amino acid substitutions resulting from single-nucleotide variants in genome coding regions. Historically, all methods have been supervised and thus limited by the inadequate sizes of experimentally curated data sets and by the lack of a standardized definition of variant effect. The emergence of unsupervised, deep learning (DL)-based methods raised an important question: Canmachines learn the language of life fromthe unannotated protein sequence data well enough to identify significant errors in the protein “sentences”? Our analysis suggests that some unsupervised methods perform as well or better than existing supervised methods. Unsupervised methods are also faster and can, thus, be useful in large-scale variant evaluations. For all other methods, however, their performance varies by both evaluation metrics and by the type of variant effect being predicted.We also note that the evaluation of method performance is still lacking on less-studied, nonhuman proteins where unsupervised methods hold the most promise. 
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    Free, publicly-accessible full text available July 1, 2025
  2. ; ; ; ; ( , Bioinformatics Advances)
    Abstract Motivation

    Protein language models based on the transformer architecture are increasingly improving performance on protein prediction tasks, including secondary structure, subcellular localization, and more. Despite being trained only on protein sequences, protein language models appear to implicitly learn protein structure. This paper investigates whether sequence representations learned by protein language models encode structural information and to what extent.

     Results

    We address this by evaluating protein language models on remote homology prediction, where identifying remote homologs from sequence information alone requires structural knowledge, especially in the “twilight zone” of very low sequence identity. Through rigorous testing at progressively lower sequence identities, we profile the performance of protein language models ranging from millions to billions of parameters in a zero-shot setting. Our findings indicate that while transformer-based protein language models outperform traditional sequence alignment methods, they still struggle in the twilight zone. This suggests that current protein language models have not sufficiently learned protein structure to address remote homology prediction when sequence signals are weak.

     Availability and implementation

    We believe this opens the way for further research both on remote homology prediction and on the broader goal of learning sequence- and structure-rich representations of protein molecules. All code, data, and models are made publicly available.

     
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  3. ; ; ( , LLMs4Bio)
    Recent studies exploring the abilities of transformer-based protein language models have highlighted their performance on the task of remote homology detection, but have not provided datasets or evaluation procedures geared toward properly measuring performance on this task. With the goal of obtaining more informative and reproducible results, we offer a detailed procedure for constructing datasets and evaluating remote homology detection performance in a way that allows detailed analyses to be performed that shed light on the remote homology detection performance throughout the “twilight zone” of low sequence similarity. Using the proposed procedures, we found that three stateof-the-art protein language models exhibit diminishing performance when the pairwise sequence similarity between the query sequence and other proteins is restricted to below 35% identity. 
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    Free, publicly-accessible full text available February 26, 2025
  4. ; ; ( , ACM)
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  5. ; ( , IEEE Intl Conf on Knowledge Graphs (ICKG))
  6. ; ; ( , EPiC Series in Computing)

    ResNet and, more recently, AlphaFold2 have demonstrated that deep neural networks can now predict a tertiary structure of a given protein amino-acid sequence with high accuracy. This seminal development will allow molecular biology researchers to advance various studies linking sequence, structure, and function. Many studies will undoubtedly focus on the impact of sequence mutations on stability, fold, and function. In this paper, we evaluate the ability of AlphaFold2 to predict accurate tertiary structures of wildtype and mutated sequences of protein molecules. We do so on a benchmark dataset in mutation modeling studies. Our empirical evaluation utilizes global and local structure analyses and yields several interesting observations. It shows, for instance, that AlphaFold2 performs similarly on wildtype and variant sequences. The placement of the main chain of a protein molecule is highly accurate. However, while AlphaFold2 reports similar confidence in its predictions over wildtype and variant sequences, its performance on placements of the side chains suffers in comparison to main-chain predictions. The analysis overall supports the premise that AlphaFold2-predicted structures can be utilized in further downstream tasks, but that further refinement of these structures may be necessary.

     
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  7. ; ( , Graph Neural Networks: Foundations, Frontiers, and Applications)
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