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1. Adaptive Stochastic Optimization to Improve Protein Conformation Sampling

   https://doi.org/10.1109/TCBB.2021.3134103  

   Zaman, Ahmed Bin; Inan, Toki Tahmid; De Jong, Kenneth; Shehu, Amarda (January 2021, IEEE/ACM Transactions on Computational Biology and Bioinformatics)

   We have long known that characterizing protein structures structure is key to understanding protein function. Computational approaches have largely addressed a narrow formulation of the problem, seeking to compute one native structure from an amino-acid sequence. Now AlphaFold2 promises to reveal a high-quality native structure for possibly many proteins. However, researchers over the years have argued for broadening our view to account for the multiplicity of native structures. We now know that many protein molecules switch between different structures to regulate interactions with molecular partners in the cell. Elucidating such structures de novo is exceptionally difficult, as it requires exploration of possibly a very large structure space in search of competing, near-optimal structures. Here we report on a novel stochastic optimization method capable of revealing very different structures for a given protein from knowledge of its amino-acid sequence. The method leverages evolutionary search techniques and adapts its exploration of the search space to balance between exploration and exploitation in the presence of a computational budget. In addition to demonstrating the utility of this method for identifying multiple native structures, we additionally provide a benchmark dataset for researchers to continue work on this problem. 

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2. Comparative assessment of binding residue predictions in intrinsically disordered regions

   https://doi.org/10.1002/pro.70298  

   Basu, Sushmita; Kurgan, Lukasz (October 2025, Protein Science)

   Abstract Dozens of impactful methods that predict intrinsically disordered regions (IDRs) in protein sequences that interact with proteins and/or nucleic acids were developed. Their training and assessment rely on the IDR‐level binding annotations, while the equivalent structure‐trained methods predict more granular annotations of binding amino acids (AA). We compiled a new benchmark dataset that annotates binding AA in IDRs and applied it to complete a first‐of‐its‐kind assessment of predictions of the disordered binding residues. We evaluated a representative collection of 14 methods, used several hundred low‐similarity test proteins, and focused on the challenging task of differentiating these binding residues from other disordered AA and considering ligand type‐specific predictions (protein–protein vs. protein–nucleic acid interactions). We found that current methods struggle to accurately predict binding IDRs among disordered residues; however, better‐than‐random tools predict disordered binding residues significantly better than binding IDRs. We identified at least one relatively accurate tool for predicting disordered protein‐binding and disordered nucleic acid‐binding AA. Analysis of cross‐predictions between interactions with protein and nucleic acids revealed that most methods are ligand‐type‐agnostic. Only two predictors of the nucleic acid‐binding IDRs and two predictors of the protein‐binding IDRs can be considered as ligand‐type‐specific. We also discussed several potential future directions that would move this field forward by producing more accurate methods that target the prediction of binding residues, reduce cross‐predictions, and cover a broader range of ligand types. 

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3. SSIPe: accurately estimating protein–protein binding affinity change upon mutations using evolutionary profiles in combination with an optimized physical energy function

   https://doi.org/10.1093/bioinformatics/btz926  

   Huang, Xiaoqiang; Zheng, Wei; Pearce, Robin; Zhang, Yang; Valencia, ed., Alfonso (December 2019, Bioinformatics)

   Abstract MotivationMost proteins perform their biological functions through interactions with other proteins in cells. Amino acid mutations, especially those occurring at protein interfaces, can change the stability of protein–protein interactions (PPIs) and impact their functions, which may cause various human diseases. Quantitative estimation of the binding affinity changes (ΔΔGbind) caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. ResultsWe present SSIPe, which combines protein interface profiles, collected from structural and sequence homology searches, with a physics-based energy function for accurate ΔΔGbind estimation. To offset the statistical limits of the PPI structure and sequence databases, amino acid-specific pseudocounts were introduced to enhance the profile accuracy. SSIPe was evaluated on large-scale experimental data containing 2204 mutations from 177 proteins, where training and test datasets were stringently separated with the sequence identity between proteins from the two datasets below 30%. The Pearson correlation coefficient between estimated and experimental ΔΔGbind was 0.61 with a root-mean-square-error of 1.93 kcal/mol, which was significantly better than the other methods. Detailed data analyses revealed that the major advantage of SSIPe over other traditional approaches lies in the novel combination of the physical energy function with the new knowledge-based interface profile. SSIPe also considerably outperformed a former profile-based method (BindProfX) due to the newly introduced sequence profiles and optimized pseudocount technique that allows for consideration of amino acid-specific prior mutation probabilities. Availability and implementationWeb-server/standalone program, source code and datasets are freely available at https://zhanglab.ccmb.med.umich.edu/SSIPe and https://github.com/tommyhuangthu/SSIPe. Supplementary informationSupplementary data are available at Bioinformatics online. 

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4. Nanopore sequencing of intact aminoacylated tRNAs

   https://doi.org/10.1038/s41467-025-62545-9  

   White, Laura_K; Radakovic, Aleksandar; Sajek, Marcin_P; Dobson, Kezia; Riemondy, Kent_A; del_Pozo, Samantha; Szostak, Jack_W; Hesselberth, Jay_R (August 2025, Nature Communications)

   Abstract The intricate landscape of tRNA modification presents persistent analytical challenges, which have impeded efforts to simultaneously resolve sequence, modification, and aminoacylation state at the level of individual tRNAs. To address these challenges, we introduce “aa-tRNA-seq”, an integrated method that uses chemical ligation to sandwich the amino acid of a charged tRNA in between the body of the tRNA and an adaptor oligonucleotide, followed by high throughput nanopore sequencing. Our approach reveals the identity of the amino acids attached to all tRNAs in a cellular sample, at the single molecule level. We describe machine learning models that enable the accurate identification of amino acid identities based on the unique signal distortions generated by the interactions between the amino acid in the RNA backbone and the nanopore motor protein and reader head. We apply aa-tRNA-seq to characterize the impact of the loss of specific tRNA modification enzymes, confirming the hypomodification-associated instability of specific tRNAs, and identifying additional candidate targets of modification. Our studies lay the groundwork for understanding the efficiency and fidelity of tRNA aminoacylation as a function of tRNA sequence, modification, and environmental conditions. 

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5. New amino acid substitution matrix brings sequence alignments into agreement with structure matches

   https://doi.org/10.1002/prot.26050  

   Jia, Kejue; Jernigan, Robert L. (February 2021, Proteins: Structure, Function, and Bioinformatics)

   Abstract Protein sequence matching presently fails to identify many structures that are highly similar, even when they are known to have the same function. The high packing densities in globular proteins lead to interdependent substitutions, which have not previously been considered for amino acid similarities. At present, sequence matching compares sequences based only upon the similarities of single amino acids, ignoring the fact that in densely packed protein, there are additional conservative substitutions representing exchanges between two interacting amino acids, such as a small‐large pair changing to a large‐small pair substitutions that are not individually so conservative. Here we show that including information for such pairs of substitutions yields improved sequence matches, and that these yield significant gains in the agreements between sequence alignments and structure matches of the same protein pair. The result shows sequence segments matched where structure segments are aligned. There are gains for all 2002 collected cases where the sequence alignments that were not previously congruent with the structure matches. Our results also demonstrate a significant gain in detecting homology for “twilight zone” protein sequences. The amino acid substitution metrics derived have many other potential applications, for annotations, protein design, mutagenesis design, and empirical potential derivation. 

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