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1. Enhancing Protein Side Chain Packing Using Rotamer Clustering and Machine Learning

   Alamri, Mohammed; Al_Sallal, Mohammad; Al_Nasr, Kamal; Akbar, Muhammad; Jad_Allah, Ahmad (January 2025, Lecture notes in computer science)

   One of the challenges and a significant part of a protein structure’s prediction in three-dimensional space is a side chain prediction/packing. This area of research has a large importance, due to its various applications in protein design. In recent years, many methodologies and techniques have been crafted for side chain prediction such as DLPacker, FASPR, SCWRL4 and OPUS-Rota4. In this research, we address the problem from a different perspective. We employed a machine learning model to predict the side chain packing of protein molecules given only the Cα trace. We analyzed 32,000 protein molecules to extract important geometrical features that can distinguish between different orientations of side chain rotamers. We designed and implemented a Random Forest model to tackle this problem. Given the accuracy of existing state-of-the-art approaches, our model represents an improvement from among other models. The results of our experiment show that Random Forest is highly effective, achieving a total average accuracy of 73.7% for proteins and 73.3% for individual amino acids. 

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2. DLPacker : Deep learning for prediction of amino acid side chain conformations in proteins

   https://doi.org/10.1002/prot.26311  

   Misiura, Mikita; Shroff, Raghav; Thyer, Ross; Kolomeisky, Anatoly B. (April 2022, Proteins: Structure, Function, and Bioinformatics)

   EDITOR-IN-CHIEF Dokholyan, Nikolay V.; ASSOCIATE EDITORS: Bahar, Ivet; Feig, Michael; Varadarajan, Raghavan; Wodak, Shoshana; Moult, John Center (Ed.)

   Prediction of side chain conformations of amino acids in proteins (also termed “packing”) is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have been developed but more accurate ones are still needed. Machine learning (ML) methods have recently become a powerful tool for solving various problems in diverse areas of science, including structural biology. In this study, we evaluate the potential of deep neural networks (DNNs) for prediction of amino acid side chain conformations. We formulate the problem as image-to-image transformation and train a U-net style DNN to solve the problem. We show that our method outperforms other physics-based methods by a significant margin: reconstruction RMSDs for most amino acids are about 20% smaller compared to SCWRL4 and Rosetta Packer with RMSDs for bulky hydrophobic amino acids Phe, Tyr, and Trp being up to 50% smaller. 

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3. To pack or not to pack: revisiting protein side-chain packing in the post-AlphaFold era

   https://doi.org/10.1093/bib/bbaf297  

   Vangaru, Sriniketh; Bhattacharya, Debswapna (May 2025, Briefings in Bioinformatics)

   Abstract Protein side-chain packing (PSCP), the problem of predicting side-chain conformations given a fixed backbone structure, has important implications in the modeling of structures and interactions. However, despite the groundbreaking progress in protein structure prediction pioneered by AlphaFold, the existing PSCP methods still rely on experimental inputs, and do not leverage AlphaFold-predicted backbone coordinates to enable PSCP at scale. Here, we perform a large-scale benchmarking of the predictive performance of various PSCP methods on public datasets from multiple rounds of the Critical Assessment of Structure Prediction challenges using a diverse set of evaluation metrics. Empirical results demonstrate that the PSCP methods perform well in packing the side-chains with experimental inputs, but they fail to generalize in repacking AlphaFold-generated structures. We additionally explore the effectiveness of leveraging the self-assessment confidence scores from AlphaFold by implementing a backbone confidence-aware integrative approach. While such a protocol often leads to performance improvement by attaining modest yet statistically significant accuracy gains over the AlphaFold baseline, it does not yield consistent and pronounced improvements. Our study highlights the recent advances and remaining challenges in PSCP in the post-AlphaFold era. 

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4. Side-chain Packing Using SE(3)-Transformer

   https://doi.org/10.1142/9789811250477_0005  

   Jindal, Akhil; Kotelnikov, Sergei; Padhorny, Dzmitry; Kozakov, Dima; Vajda, Sandor (January 2022, Pacific symposium on biocomputing)

   Predicting protein side-chains is important for both protein structure prediction and protein design. Modeling approaches to predict side-chains such as SCWRL4 have become one of the most widely used tools of its type due to fast and highly accurate predictions. Motivated by the recent success of AlphaFold2 in CASP14, our group adapted a 3D equivariant neural network architecture to predict protein side-chain conformations, specifically within a protein-protein interface, a problem that has not been fully addressed by AlphaFold2. 

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5. PIQLE: protein–protein interface quality estimation by deep graph learning of multimeric interaction geometries

   https://doi.org/10.1093/bioadv/vbad070  

   Shuvo, Md Hossain; Karim, Mohimenul; Roche, Rahmatullah; Bhattacharya, Debswapna (January 2023, Bioinformatics Advances)

   Gromiha, Michael (Ed.)

   Abstract Motivation Accurate modeling of protein–protein interaction interface is essential for high-quality protein complex structure prediction. Existing approaches for estimating the quality of a predicted protein complex structural model utilize only the physicochemical properties or energetic contributions of the interacting atoms, ignoring evolutionarily information or inter-atomic multimeric geometries, including interaction distance and orientations. Results Here, we present PIQLE, a deep graph learning method for protein–protein interface quality estimation. PIQLE leverages multimeric interaction geometries and evolutionarily information along with sequence- and structure-derived features to estimate the quality of individual interactions between the interfacial residues using a multi-head graph attention network and then probabilistically combines the estimated quality for scoring the overall interface. Experimental results show that PIQLE consistently outperforms existing state-of-the-art methods including DProQA, TRScore, GNN-DOVE and DOVE on multiple independent test datasets across a wide range of evaluation metrics. Our ablation study and comparison with the self-assessment module of AlphaFold-Multimer repurposed for protein complex scoring reveal that the performance gains are connected to the effectiveness of the multi-head graph attention network in leveraging multimeric interaction geometries and evolutionary information along with other sequence- and structure-derived features adopted in PIQLE. Availability and implementation An open-source software implementation of PIQLE is freely available at https://github.com/Bhattacharya-Lab/PIQLE. Supplementary information Supplementary data are available at Bioinformatics Advances online. 

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