Статті в журналах: "Prediction of binding affinity"

1

Wang, Debby D., Haoran Xie, and Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity." Bioinformatics 37, no. 17 (February 27, 2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.

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Abstract Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were validated on the PDBbind v2019 core set and CSAR-HiQ sets. The PrtCmm IFP Score outperformed several other models in predicting protein–ligand binding affinities. Besides, conventional ECFPs were simplified to generate new IFPs, which provided consistent but faster predictions. The relationship between the base atom properties of ECFPs and the accuracy of predictions was also investigated. Availability PrtCmm IFP has been implemented in the IFP Score Toolkit on github (https://github.com/debbydanwang/IFPscore). Supplementary information Supplementary data are available at Bioinformatics online.

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Kondabala, Rajesh, Vijay Kumar, Amjad Ali, and Manjit Kaur. "A novel astrophysics-based framework for prediction of binding affinity of glucose binder." Modern Physics Letters B 34, no. 31 (July 25, 2020): 2050346. http://dx.doi.org/10.1142/s0217984920503467.

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In this paper, a novel astrophysics-based prediction framework is developed for estimating the binding affinity of a glucose binder. The proposed framework utilizes the molecule properties for predicting the binding affinity. It also uses the astrophysics-learning strategy that incorporates the concepts of Kepler’s law during the prediction process. The proposed framework is compared with 10 regression algorithms over ZINC dataset. Experimental results reveal that the proposed framework provides 99.30% accuracy of predicting binding affinity. However, decision tree provides the prediction with 97.14% accuracy. Cross-validation results show that the proposed framework provides better accuracy than the other existing models. The developed framework enables researchers to screen glucose binder rapidly. It also reduces computational time for designing small glucose binding molecule.

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Antunes, Dinler A., Jayvee R. Abella, Didier Devaurs, Maurício M. Rigo, and Lydia E. Kavraki. "Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes." Current Topics in Medicinal Chemistry 18, no. 26 (January 24, 2019): 2239–55. http://dx.doi.org/10.2174/1568026619666181224101744.

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Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based methods have become the gold standard for peptide-MHC binding affinity prediction, but structure-based methods are expected to provide more general predictions (i.e., predictions applicable to all types of MHC receptors). In addition, structural modeling of peptide-MHC complexes has the potential to uncover yet unknown drivers of T-cell activation, thus allowing for the development of better and safer therapies. In this review, we discuss the use of computational methods for the structural modeling of peptide-MHC complexes (i.e., binding mode prediction) and for the structure-based prediction of binding affinity.

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Kwon, Yongbeom, Woong-Hee Shin, Junsu Ko, and Juyong Lee. "AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks." International Journal of Molecular Sciences 21, no. 22 (November 10, 2020): 8424. http://dx.doi.org/10.3390/ijms21228424.

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Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

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Shar, Piar Ali, Weiyang Tao, Shuo Gao, Chao Huang, Bohui Li, Wenjuan Zhang, Mohamed Shahen, Chunli Zheng, Yaofei Bai, and Yonghua Wang. "Pred-binding: large-scale protein–ligand binding affinity prediction." Journal of Enzyme Inhibition and Medicinal Chemistry 31, no. 6 (February 18, 2016): 1443–50. http://dx.doi.org/10.3109/14756366.2016.1144594.

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Nguyen, Austin, Abhinav Nellore, and Reid F. Thompson. "Discordant results among major histocompatibility complex binding affinity prediction tools." F1000Research 12 (June 7, 2023): 617. http://dx.doi.org/10.12688/f1000research.132538.1.

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Background: Human leukocyte antigen (HLA) alleles are critical components of the immune system’s ability to recognize and eliminate tumors and infections. A large number of machine learning-based major histocompatibility complex (MHC) binding affinity (BA) prediction tools have been developed and are widely used for both investigational and therapeutic applications, so it is important to explore differences in tool outputs. Methods: We examined predictions of four popular tools (netMHCpan, HLAthena, MHCflurry, and MHCnuggets) across a range of possible peptide sources (human, viral, and randomly generated) and MHC class I alleles. Results: We uncovered inconsistencies in predictions of BA, allele promiscuity and the relationship between physical properties of peptides by source and BA predictions, as well as quality of training data. We found amount of training data does not explain inconsistencies between tools and yet for all tools, predicted binding quantities are similar between human and viral proteomes. Lastly, we find peptide physical properties are associated with allele-specific binding predictions. Conclusions: Our work raises fundamental questions about the fidelity of peptide-MHC binding prediction tools and their real-world implications. The real-world use of these prediction tools for theoretical binding of peptides to alleles is worrying, as the range of allele promiscuity is substantial yet does not differentiate between potential foreign versus self-antigens. Evaluating more viruses – as well as bacteria, fungi, and other pathogens – and linking these analyses with metrics such as evolutionary distance may give greater insight into the relationship between HLA evolution and disease.

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Langham, James J., Ann E. Cleves, Russell Spitzer, Daniel Kirshner, and Ajay N. Jain. "Physical Binding Pocket Induction for Affinity Prediction." Journal of Medicinal Chemistry 52, no. 19 (October 8, 2009): 6107–25. http://dx.doi.org/10.1021/jm901096y.

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Öztürk, Hakime, Arzucan Özgür, and Elif Ozkirimli. "DeepDTA: deep drug–target binding affinity prediction." Bioinformatics 34, no. 17 (September 1, 2018): i821—i829. http://dx.doi.org/10.1093/bioinformatics/bty593.

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Wang, Xun, Dayan Liu, Jinfu Zhu, Alfonso Rodriguez-Paton, and Tao Song. "CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction." Biomolecules 11, no. 5 (April 27, 2021): 643. http://dx.doi.org/10.3390/biom11050643.

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The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions’ prediction. The proposed method is improved by a DEEPScreen model using 2-D structural representations of compounds as input. Furthermore, a channel and spatial attention mechanism (CS) is added in feature abstractions. Data experiments conducted on ChEMBLv23 datasets show that CSConv2d performs better than the original DEEPScreen model in predicting protein-ligand binding affinity, as well as some state-of-the-art DTIs (drug-target interactions) prediction methods including DeepConv-DTI, CPI-Prediction, CPI-Prediction+CS, DeepGS and DeepGS+CS. In practice, the docking results of protein (PDB ID: 5ceo) and ligand (Chemical ID: 50D) and a series of kinase inhibitors are operated to verify the robustness.

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10

Pantsar, Tatu, and Antti Poso. "Binding Affinity via Docking: Fact and Fiction." Molecules 23, no. 8 (July 30, 2018): 1899. http://dx.doi.org/10.3390/molecules23081899.

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In 1982, Kuntz et al. published an article with the title “A Geometric Approach to Macromolecule-Ligand Interactions”, where they described a method “to explore geometrically feasible alignment of ligands and receptors of known structure”. Since then, small molecule docking has been employed as a fast way to estimate the binding pose of a given compound within a specific target protein and also to predict binding affinity. Remarkably, the first docking method suggested by Kuntz and colleagues aimed to predict binding poses but very little was specified about binding affinity. This raises the question as to whether docking is the right tool to estimate binding affinity. The short answer is no, and this has been concluded in several comprehensive analyses. However, in this opinion paper we discuss several critical aspects that need to be reconsidered before a reliable binding affinity prediction through docking is realistic. These are not the only issues that need to be considered, but they are perhaps the most critical ones. We also consider that in spite of the huge efforts to enhance scoring functions, the accuracy of binding affinity predictions is perhaps only as good as it was 10–20 years ago. There are several underlying reasons for this poor performance and these are analyzed. In particular, we focus on the role of the solvent (water), the poor description of H-bonding and the lack of the systems’ true dynamics. We hope to provide readers with potential insights and tools to overcome the challenging issues related to binding affinity prediction via docking.

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11

Kappel, Kalli, Inga Jarmoskaite, Pavanapuresan P. Vaidyanathan, William J. Greenleaf, Daniel Herschlag, and Rhiju Das. "Blind tests of RNA–protein binding affinity prediction." Proceedings of the National Academy of Sciences 116, no. 17 (April 8, 2019): 8336–41. http://dx.doi.org/10.1073/pnas.1819047116.

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Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA–protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA–protein binding. Here, we bring together several advances to complete a calculation framework for RNA–protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein–RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2–RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA–protein binding that can be tested by prospective high-throughput measurements on new systems.

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12

Kim, Ryangguk, and Jeffrey Skolnick. "Assessment of programs for ligand binding affinity prediction." Journal of Computational Chemistry 29, no. 8 (2008): 1316–31. http://dx.doi.org/10.1002/jcc.20893.

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13

Marshall, K. W., K. J. Wilson, J. Liang, A. Woods, D. Zaller, and J. B. Rothbard. "Prediction of peptide affinity to HLA DRB1*0401." Journal of Immunology 154, no. 11 (June 1, 1995): 5927–33. http://dx.doi.org/10.4049/jimmunol.154.11.5927.

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Abstract A method to predict quantitatively peptide binding to HLA DRB1*0401 has been developed using a data set of the relative contributions of each of the naturally occurring amino acids in the context of a simplified peptide back-bone. The prediction assumed that the relative role of each of the peptide side chains could be treated independently and could be measured by assaying each of the 20 naturally occurring amino acids at the central 11 positions of a 13-residue peptide previously shown to contain the minimal requirements for high-affinity binding to HLA-DR proteins. The resultant database was shown to have predictive value when tested on a set of 13 unrelated peptides known to bind DRB1*0401 with a wide range of apparent affinity. The database was tested further by analyzing myelin basic protein. All 13 amino acid peptides containing a hydrophobic amino acid at the third position were synthesized and assayed for binding purified DRB1*0401. In every case, the measured affinity correlated with the predictive values within the experimental error of the assays. Finally, the ability to predict peptide binding to MHC class II molecules was shown to help in identifying T cell determinants. The specificity of DRB1*0401-restricted T cell hybridomas against human serum albumin corresponded to two peptides, predicted and shown to bind the class II protein with high affinity.

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14

Gim, Mogan, Junseok Choe, Seungheun Baek, Jueon Park, Chaeeun Lee, Minjae Ju, Sumin Lee, and Jaewoo Kang. "ArkDTA: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction." Bioinformatics 39, Supplement_1 (June 1, 2023): i448—i457. http://dx.doi.org/10.1093/bioinformatics/btad207.

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Abstract Motivation Protein–ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein–ligand attention mechanism for more explainable deep drug–target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guided by NCIs. Results Experimental results show that ArkDTA achieves predictive performance comparable to current state-of-the-art models while significantly improving model explainability. Qualitative investigation into our novel attention mechanism reveals that ArkDTA can identify potential regions for NCIs between candidate drug compounds and target proteins, as well as guiding internal operations of the model in a more interpretable and domain-aware manner. Availability ArkDTA is available at https://github.com/dmis-lab/ArkDTA Contact kangj@korea.ac.kr

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15

Walpoth, Belinda Nazan, and Burak Erman. "Regulation of ryanodine receptor RyR2 by protein-protein interactions: prediction of a PKA binding site on the N-terminal domain of RyR2 and its relation to disease causing mutations." F1000Research 4 (January 28, 2015): 29. http://dx.doi.org/10.12688/f1000research.5858.1.

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Protein-protein interactions are the key processes responsible for signaling and function in complex networks. Determining the correct binding partners and predicting the ligand binding sites in the absence of experimental data require predictive models. Hybrid models that combine quantitative atomistic calculations with statistical thermodynamics formulations are valuable tools for bioinformatics predictions. We present a hybrid prediction and analysis model for determining putative binding partners and interpreting the resulting correlations in the yet functionally uncharacterized interactions of the ryanodine RyR2 N-terminal domain. Using extensive docking calculations and libraries of hexameric peptides generated from regulator proteins of the RyR2 channel, we show that the residues 318-323 of protein kinase A, PKA, have a very high affinity for the N-terminal of RyR2. Using a coarse grained Elastic Net Model, we show that the binding site lies at the end of a pathway of evolutionarily conserved residues in RyR2. The two disease causing mutations are also on this path. The program for the prediction of the energetically responsive residues by the Elastic Net Model is freely available on request from the corresponding author.

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16

Henrich, Stefan, Isabella Feierberg, Ting Wang, Niklas Blomberg, and Rebecca C. Wade. "Comparative binding energy analysis for binding affinity and target selectivity prediction." Proteins: Structure, Function, and Bioinformatics 78, no. 1 (August 17, 2009): 135–53. http://dx.doi.org/10.1002/prot.22579.

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Limbu, Sarita, and Sivanesan Dakshanamurthy. "A New Hybrid Neural Network Deep Learning Method for Protein–Ligand Binding Affinity Prediction and De Novo Drug Design." International Journal of Molecular Sciences 23, no. 22 (November 11, 2022): 13912. http://dx.doi.org/10.3390/ijms232213912.

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Accurately predicting ligand binding affinity in a virtual screening campaign is still challenging. Here, we developed hybrid neural network (HNN) machine deep learning methods, HNN-denovo and HNN-affinity, by combining the 3D-CNN (convolutional neural network) and the FFNN (fast forward neural network) hybrid neural network framework. The HNN-denovo uses protein pocket structure and protein–ligand interactions as input features. The HNN-affinity uses protein sequences and ligand features as input features. The HNN method combines the CNN and FCNN machine architecture for the protein structure or protein sequence and ligand descriptors. To train the model, the HNN methods used thousands of known protein–ligand binding affinity data retrieved from the PDBBind database. We also developed the Random Forest (RF), Gradient Boosting (GB), Decision Tree with AdaBoost (DT), and a consensus model. We compared the HNN results with models developed based on the RF, GB, and DT methods. We also independently compared the HNN method results with the literature reported deep learning protein–ligand binding affinity predictions made by the DLSCORE, KDEEP, and DeepAtom. The predictive performance of the HNN methods (max Pearson’s R achieved was 0.86) was consistently better than or comparable to the DLSCORE, KDEEP, and DeepAtom deep learning learning methods for both balanced and unbalanced data sets. The HNN-affinity can be applied for the protein–ligand affinity prediction even in the absence of protein structure information, as it considers the protein sequence as standalone feature in addition to the ligand descriptors. The HNN-denovo method can be efficiently implemented to the structure-based de novo drug design campaign. The HNN-affinity method can be used in conjunction with the deep learning molecular docking protocols as a standalone. Further, it can be combined with the conventional molecular docking methods as a multistep approach to rapidly screen billions of diverse compounds. The HNN method are highly scalable in the cloud ML platform.

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OUYANG, XUCHANG, STEPHANUS DANIEL HANDOKO, and CHEE KEONG KWOH. "CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN–LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE." Journal of Bioinformatics and Computational Biology 09, supp01 (December 2011): 1–14. http://dx.doi.org/10.1142/s021972001100577x.

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Protein–ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein–ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected.

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Pandey, Mohit, Mariia Radaeva, Hazem Mslati, Olivia Garland, Michael Fernandez, Martin Ester, and Artem Cherkasov. "Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks." Molecules 27, no. 16 (August 11, 2022): 5114. http://dx.doi.org/10.3390/molecules27165114.

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Computational prediction of ligand–target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein–ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)—the hallmark target of SARS-CoV-2 coronavirus.

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20

Kalemati, Mahmood, Mojtaba Zamani Emani, and Somayyeh Koohi. "BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach." PLOS Computational Biology 19, no. 3 (March 31, 2023): e1011036. http://dx.doi.org/10.1371/journal.pcbi.1011036.

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Drug-target binding affinity prediction plays a key role in the early stage of drug discovery. Numerous experimental and data-driven approaches have been developed for predicting drug-target binding affinity. However, experimental methods highly rely on the limited structural-related information from drug-target pairs, domain knowledge, and time-consuming assays. On the other hand, learning-based methods have shown an acceptable prediction performance. However, most of them utilize several simple and complex types of proteins and drug compounds data, ranging from the protein sequences to the topology of a graph representation of drug compounds, employing multiple deep neural networks for encoding and feature extraction, and so, leads to the computational overheads. In this study, we propose a unified measure for protein sequence encoding, named BiComp, which provides compression-based and evolutionary-related features from the protein sequences. Specifically, we employ Normalized Compression Distance and Smith-Waterman measures for capturing complementary information from the algorithmic information theory and biological domains, respectively. We utilize the proposed measure to encode the input proteins feeding a new deep neural network-based method for drug-target binding affinity prediction, named BiComp-DTA. BiComp-DTA is evaluated utilizing four benchmark datasets for drug-target binding affinity prediction. Compared to the state-of-the-art methods, which employ complex models for protein encoding and feature extraction, BiComp-DTA provides superior efficiency in terms of accuracy, runtime, and the number of trainable parameters. The latter achievement facilitates execution of BiComp-DTA on a normal desktop computer in a fast fashion. As a comparative study, we evaluate BiComp’s efficiency against its components for drug-target binding affinity prediction. The results have shown superior accuracy of BiComp due to the orthogonality and complementary nature of Smith-Waterman and Normalized Compression Distance measures for protein sequences. Such a protein sequence encoding provides efficient representation with no need for multiple sources of information, deep domain knowledge, and complex neural networks.

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Usha, Singaravelu, and Samuel Selvaraj. "Prediction of kinase-inhibitor binding affinity using energetic parameters." Bioinformation 12, no. 3 (June 15, 2016): 172–81. http://dx.doi.org/10.6026/97320630012172.

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Das, Sourav, Michael P. Krein, and Curt M. Breneman. "Binding Affinity Prediction with Property-Encoded Shape Distribution Signatures." Journal of Chemical Information and Modeling 50, no. 2 (January 22, 2010): 298–308. http://dx.doi.org/10.1021/ci9004139.

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Yugandhar, K., and M. Michael Gromiha. "Protein–protein binding affinity prediction from amino acid sequence." Bioinformatics 30, no. 24 (August 28, 2014): 3583–89. http://dx.doi.org/10.1093/bioinformatics/btu580.

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O'Donnell, Timothy J., Alex Rubinsteyn, Maria Bonsack, Angelika B. Riemer, Uri Laserson, and Jeff Hammerbacher. "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction." Cell Systems 7, no. 1 (July 2018): 129–32. http://dx.doi.org/10.1016/j.cels.2018.05.014.

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Fan, Cong, Ping-pui Wong, and Huiying Zhao. "DStruBTarget: Integrating Binding Affinity with Structure Similarity for Ligand-Binding Protein Prediction." Journal of Chemical Information and Modeling 60, no. 1 (December 13, 2019): 400–409. http://dx.doi.org/10.1021/acs.jcim.9b00717.

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Chen, Zihao, Long Hu, Bao-Ting Zhang, Aiping Lu, Yaofeng Wang, Yuanyuan Yu, and Ge Zhang. "Artificial Intelligence in Aptamer–Target Binding Prediction." International Journal of Molecular Sciences 22, no. 7 (March 30, 2021): 3605. http://dx.doi.org/10.3390/ijms22073605.

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Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.

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Zeng, Haoyang, and David K. Gifford. "DeepLigand: accurate prediction of MHC class I ligands using peptide embedding." Bioinformatics 35, no. 14 (July 2019): i278—i283. http://dx.doi.org/10.1093/bioinformatics/btz330.

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Abstract Motivation The computational modeling of peptide display by class I major histocompatibility complexes (MHCs) is essential for peptide-based therapeutics design. Existing computational methods for peptide-display focus on modeling the peptide-MHC-binding affinity. However, such models are not able to characterize the sequence features for the other cellular processes in the peptide display pathway that determines MHC ligand selection. Results We introduce a semi-supervised model, DeepLigand that outperforms the state-of-the-art models in MHC Class I ligand prediction. DeepLigand combines a peptide language model and peptide binding affinity prediction to score MHC class I peptide presentation. The peptide language model characterizes sequence features that correspond to secondary factors in MHC ligand selection other than binding affinity. The peptide embedding is learned by pre-training on natural ligands, and can discriminate between ligands and non-ligands in the absence of binding affinity prediction. Although conventional affinity-based models fail to classify peptides with moderate affinities, DeepLigand discriminates ligands from non-ligands with consistently high accuracy. Availability and implementation We make DeepLigand available at https://github.com/gifford-lab/DeepLigand. Supplementary information Supplementary data are available at Bioinformatics online.

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Li, Min, Zhangli Lu, Yifan Wu, and YaoHang Li. "BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction." Bioinformatics 38, no. 7 (January 19, 2022): 1995–2002. http://dx.doi.org/10.1093/bioinformatics/btac035.

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Abstract Motivation The identification of compound–protein interactions (CPIs) is an essential step in the process of drug discovery. The experimental determination of CPIs is known for a large amount of funds and time it consumes. Computational model has therefore become a promising and efficient alternative for predicting novel interactions between compounds and proteins on a large scale. Most supervised machine learning prediction models are approached as a binary classification problem, which aim to predict whether there is an interaction between the compound and the protein or not. However, CPI is not a simple binary on–off relationship, but a continuous value reflects how tightly the compound binds to a particular target protein, also called binding affinity. Results In this study, we propose an end-to-end neural network model, called BACPI, to predict CPI and binding affinity. We employ graph attention network and convolutional neural network (CNN) to learn the representations of compounds and proteins and develop a bi-directional attention neural network model to integrate the representations. To evaluate the performance of BACPI, we use three CPI datasets and four binding affinity datasets in our experiments. The results show that, when predicting CPIs, BACPI significantly outperforms other available machine learning methods on both balanced and unbalanced datasets. This suggests that the end-to-end neural network model that predicts CPIs directly from low-level representations is more robust than traditional machine learning-based methods. And when predicting binding affinities, BACPI achieves higher performance on large datasets compared to other state-of-the-art deep learning methods. This comparison result suggests that the proposed method with bi-directional attention neural network can capture the important regions of compounds and proteins for binding affinity prediction. Availability and implementation Data and source codes are available at https://github.com/CSUBioGroup/BACPI

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Bae, Haelee, and Hojung Nam. "GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity." Biomedicines 11, no. 1 (December 27, 2022): 67. http://dx.doi.org/10.3390/biomedicines11010067.

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Drug-target binding affinity (DTA) prediction is an essential step in drug discovery. Drug-target protein binding occurs at specific regions between the protein and drug, rather than the entire protein and drug. However, existing deep-learning DTA prediction methods do not consider the interactions between drug substructures and protein sub-sequences. This work proposes GraphATT-DTA, a DTA prediction model that constructs the essential regions for determining interaction affinity between compounds and proteins, modeled with an attention mechanism for interpretability. We make the model consider the local-to-global interactions with the attention mechanism between compound and protein. As a result, GraphATT-DTA shows an improved prediction of DTA performance and interpretability compared with state-of-the-art models. The model is trained and evaluated with the Davis dataset, the human kinase dataset; an external evaluation is achieved with the independently proposed human kinase dataset from the BindingDB dataset.

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Zhang, Xianfeng, Yanhui Gu, Guandong Xu, Yafei Li, Jinlan Wang, and Zhenglu Yang. "HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 16384–85. http://dx.doi.org/10.1609/aaai.v37i13.27052.

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Gathering information from multi-perspective graphs is an essential issue for many applications especially for proteinligand binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein-ligand complexes with better interpretability while achieving excellent predictive performance. In addition, we specially analyze the protein-ligand binding affinity problem, taking into account the heterogeneity of proteins and ligands. Experimental evaluations demonstrate the effectiveness of our data representation strategy on public datasets by fusing information from different perspectives.

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Annala, Matti, Kirsti Laurila, Harri Lähdesmäki, and Matti Nykter. "A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays." PLoS ONE 6, no. 5 (May 26, 2011): e20059. http://dx.doi.org/10.1371/journal.pone.0020059.

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Zhao, Huiying, Yuedong Yang, Mark von Itzstein, and Yaoqi Zhou. "Carbohydrate-binding protein identification by coupling structural similarity searching with binding affinity prediction." Journal of Computational Chemistry 35, no. 30 (September 15, 2014): 2177–83. http://dx.doi.org/10.1002/jcc.23730.

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Strack, Rita. "Predicting RNA–protein binding affinity." Nature Methods 16, no. 6 (May 30, 2019): 460. http://dx.doi.org/10.1038/s41592-019-0445-4.

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Ghimire, Ashutosh, Hilal Tayara, Zhenyu Xuan, and Kil To Chong. "CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention." International Journal of Molecular Sciences 23, no. 15 (July 30, 2022): 8453. http://dx.doi.org/10.3390/ijms23158453.

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Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.

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35

Wang, Debby D., Moon-Tong Chan, and Hong Yan. "Structure-based protein–ligand interaction fingerprints for binding affinity prediction." Computational and Structural Biotechnology Journal 19 (2021): 6291–300. http://dx.doi.org/10.1016/j.csbj.2021.11.018.

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Hanai, Toshihiko, A. Koseki, R. Yoshikawa, M. Ueno, T. Kinoshita, and H. Homma. "Prediction of human serum albumin–drug binding affinity without albumin." Analytica Chimica Acta 454, no. 1 (March 2002): 101–8. http://dx.doi.org/10.1016/s0003-2670(01)01515-x.

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37

Zhu, Fangqiang, Xiaohua Zhang, Jonathan E. Allen, Derek Jones, and Felice C. Lightstone. "Binding Affinity Prediction by Pairwise Function Based on Neural Network." Journal of Chemical Information and Modeling 60, no. 6 (April 27, 2020): 2766–72. http://dx.doi.org/10.1021/acs.jcim.0c00026.

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38

Rizzi, Andrea, Steven Murkli, John N. McNeill, Wei Yao, Matthew Sullivan, Michael K. Gilson, Michael W. Chiu, et al. "Overview of the SAMPL6 host–guest binding affinity prediction challenge." Journal of Computer-Aided Molecular Design 32, no. 10 (October 2018): 937–63. http://dx.doi.org/10.1007/s10822-018-0170-6.

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Suri, Sadhana, and Sivanesan Dakshanamurthy. "IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method." Vaccines 10, no. 10 (October 8, 2022): 1678. http://dx.doi.org/10.3390/vaccines10101678.

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In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, ‘IntegralVac,’ by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing deep learning tools served as a positive control to ensure successful prediction was possible. As evidenced by increased accuracy and AUC, IntegralVac improved the prediction rate of top-ranked epitopes. We also examined the compatibility between other servers’ clinical checkpoint filters and IntegralVac. This was to ensure that the other servers had a means for predicting additional checkpoint filters that we wanted to implement in IntegralVac. The clinical checkpoint filters, including allergenicity, antigenicity, and toxicity, were used as additional predictors to improve IntegralVac’s prediction accuracy. We generated immunogenicity scores by cross-comparing sequence inputs with each other and determining the overlap between each individual peptide sequence. The IntegralVac increased the immunogenicity prediction accuracy to 90.1% AUC and the binding affinity accuracy to 95.4% compared to the control NetMHCPan server. The IntegralVac opens new avenues for future in silico methods, by building upon established models for continued prediction accuracy improvement.

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Sharabi, Oz, Jason Shirian, and Julia M. Shifman. "Predicting affinity- and specificity-enhancing mutations at protein–protein interfaces." Biochemical Society Transactions 41, no. 5 (September 23, 2013): 1166–69. http://dx.doi.org/10.1042/bst20130121.

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Manipulations of PPIs (protein–protein interactions) are important for many biological applications such as synthetic biology and drug design. Combinatorial methods have been traditionally used for such manipulations, failing, however, to explain the effects achieved. We developed a computational method for prediction of changes in free energy of binding due to mutation that bring about deeper understanding of the molecular forces underlying binding interactions. Our method could be used for computational scanning of binding interfaces and subsequent analysis of the interfacial sequence optimality. The computational method was validated in two biological systems. Computational saturated mutagenesis of a high-affinity complex between an enzyme AChE (acetylcholinesterase) and a snake toxin Fas (fasciculin) revealed the optimal nature of this interface with only a few predicted affinity-enhancing mutations. Binding measurements confirmed high optimality of this interface and identified a few mutations that could further improve interaction fitness. Computational interface scanning of a medium-affinity complex between TIMP-2 (tissue inhibitor of metalloproteinases-2) and MMP (matrix metalloproteinase) 14 revealed a non-optimal nature of the binding interface with multiple mutations predicted to stabilize the complex. Experimental results corroborated our computational predictions, identifying a large number of mutations that improve the binding affinity for this interaction and some mutations that enhance binding specificity. Overall, our computational protocol greatly facilitates the discovery of affinity- and specificity-enhancing mutations and thus could be applied for design of potent and highly specific inhibitors of any PPI.

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41

Liang, Yigao, Shaohua Jiang, Min Gao, Fengjiao Jia, Zaoyang Wu, and Zhijian Lyu. "GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM." Journal of Physics: Conference Series 2219, no. 1 (April 1, 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2219/1/012008.

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Abstract Most prediction models of drug-target binding affinity (DTA) treated drugs and targets as sequences, and feature extraction networks could not sufficiently extract features. Inspired by DeepDTA and GraphDTA, we proposed an improved model named GLSTM-DTA for DTA prediction, which combined Graph Neural Network (GNN) and Long Short-Term Memory Network (LSTM). The feature extraction block consists of two parts: GNN block and LSTM block, which extract drug features and protein features respectively. The novelty of our work is using LSTM, instead of Convolutional neural network (CNN) to extract protein sequence features, which is facilitating to capture long-term dependencies in sequence. In addition, we also converted drugs into graph structures and use GNN for feature extraction. The improved model performs better than DeepDTA and GraphDTA. The comprehensive results prove the advantages of our model in accurately predicting the binding affinity of drug-targets.

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42

Zhao, Huiying, Yuedong Yang, and Yaoqi Zhou. "Highly accurate and high-resolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction." RNA Biology 8, no. 6 (November 2011): 988–96. http://dx.doi.org/10.4161/rna.8.6.17813.

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43

Feng, Peiyuan, Jianyang Zeng, and Jianzhu Ma. "Predicting MHC-peptide binding affinity by differential boundary tree." Bioinformatics 37, Supplement_1 (July 1, 2021): i254—i261. http://dx.doi.org/10.1093/bioinformatics/btab312.

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Abstract Motivation The prediction of the binding between peptides and major histocompatibility complex (MHC) molecules plays an important role in neoantigen identification. Although a large number of computational methods have been developed to address this problem, they produce high false-positive rates in practical applications, since in most cases, a single residue mutation may largely alter the binding affinity of a peptide binding to MHC which cannot be identified by conventional deep learning methods. Results We developed a differential boundary tree-based model, named DBTpred, to address this problem. We demonstrated that DBTpred can accurately predict MHC class I binding affinity compared to the state-of-art deep learning methods. We also presented a parallel training algorithm to accelerate the training and inference process which enables DBTpred to be applied to large datasets. By investigating the statistical properties of differential boundary trees and the prediction paths to test samples, we revealed that DBTpred can provide an intuitive interpretation and possible hints in detecting important residue mutations that can largely influence binding affinity. Availability and implementation The DBTpred package is implemented in Python and freely available at: https://github.com/fpy94/DBT. Supplementary information Supplementary data are available at Bioinformatics online.

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44

Fedyushkina, I. V., V. S. Skvortsov, I. V. Romero Reyes, and I. S. Levina. "Molecular docking and 3D-QSAR on 16a,17a-cycloalkanoprogesterone analogues as progesterone receptor ligands." Biomeditsinskaya Khimiya 59, no. 6 (2013): 622–35. http://dx.doi.org/10.18097/pbmc20135906622.

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A series of 42 steroid ligands was used to predict a binding affinity to progesterone receptor. The molecules were the derivatives of 16a,17a-cycloalkanoprogesterones. Different methods of prediction were used and analyzed such as CoMFA and artificial neural networks. The best result (Q2=0.91) was obtained for a combination of molecular docking, molecular dynamics simulation and artificial neural networks. A predictive power of the model was validated by a group of 8 pentarans synthesized separately and tested in vitro (R2test=0.77). This model can be used to determine the affinity level of the ligand to progesterone receptor and accurate ranking of binding compounds.

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45

Moshari, Mahshad, Qian Wang, Marek Michalak, Mariusz Klobukowski, and Jack Adam Tuszynski. "Computational Prediction and Experimental Validation of the Unique Molecular Mode of Action of Scoulerine." Molecules 27, no. 13 (June 21, 2022): 3991. http://dx.doi.org/10.3390/molecules27133991.

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Scoulerine is a natural compound that is known to bind to tubulin and has anti-mitotic properties demonstrated in various cancer cells. Its molecular mode of action has not been precisely known. In this work, we perform computational prediction and experimental validation of the mode of action of scoulerine. Based on the existing data in the Protein Data Bank (PDB) and using homology modeling, we create human tubulin structures corresponding to both free tubulin dimers and tubulin in a microtubule. We then perform docking of the optimized structure of scoulerine and find the highest affinity binding sites located in both the free tubulin and in a microtubule. We conclude that binding in the vicinity of the colchicine binding site and near the laulimalide binding site are the most likely locations for scoulerine interacting with tubulin. Thermophoresis assays using scoulerine and tubulin in both free and polymerized form confirm these computational predictions. We conclude that scoulerine exhibits a unique property of a dual mode of action with both microtubule stabilization and tubulin polymerization inhibition, both of which have similar affinity values.

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46

Liu, Yang, Xia-hui Ouyang, Zhi-Xiong Xiao, Le Zhang, and Yang Cao. "A Review on the Methods of Peptide-MHC Binding Prediction." Current Bioinformatics 15, no. 8 (January 1, 2021): 878–88. http://dx.doi.org/10.2174/1574893615999200429122801.

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Background: T lymphocyte achieves an immune response by recognizing antigen peptides (also known as T cell epitopes) through major histocompatibility complex (MHC) molecules. The immunogenicity of T cell epitopes depends on their source and stability in combination with MHC molecules. The binding of the peptide to MHC is the most selective step, so predicting the binding affinity of the peptide to MHC is the principal step in predicting T cell epitopes. The identification of epitopes is of great significance in the research of vaccine design and T cell immune response. Objective: The traditional method for identifying epitopes is to synthesize and test the binding activity of peptide by experimental methods, which is not only time-consuming, but also expensive. In silico methods for predicting peptide-MHC binding emerge to pre-select candidate peptides for experimental testing, which greatly saves time and costs. By summarizing and analyzing these methods, we hope to have a better insight and provide guidance for future directions. Methods: Up to now, a number of methods have been developed to predict the binding ability of peptides to MHC based on various principles. Some of them employ matrix models or machine learning models based on the sequence characteristic embedded in peptides or MHC to predict the binding ability of peptides to MHC. Some others utilize the three-dimensional structural information of peptides or MHC, for example, by extracting three-dimensional structural information to construct a feature matrix or machine learning model, or directly using protein structure prediction, molecular docking to predict the binding mode of peptides and MHC. Results: Although the methods in predicting peptide-MHC binding based on the feature matrix or machine learning model can achieve high-throughput prediction, the accuracy of which depends heavily on the sequence characteristic of confirmed binding peptides. In addition, it cannot provide insights into the mechanism of antigen specificity. Therefore, such methods have certain limitations in practical applications. Methods in predicting peptide-MHC binding based on structural prediction or molecular docking are computationally intensive compared to the methods based on feature matrix or machine learning model and the challenge is how to predict a reliable structural model. Conclusion: This paper reviews the principles, advantages and disadvantages of the methods of peptide-MHC binding prediction and discussed the future directions to achieve more accurate predictions.

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Li, Zhongyan, Qingqing Miao, Fugang Yan, Yang Meng, and Peng Zhou. "Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design." Current Drug Metabolism 20, no. 3 (May 22, 2019): 170–76. http://dx.doi.org/10.2174/1389200219666181012151944.

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Background:Protein–peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recently been recognized as a new and attractive druggable target for drug development and disease intervention.Methods:We present a systematic review on the application of machine learning techniques in the quantitative modeling and prediction of protein–peptide binding affinity, particularly focusing on its implications for therapeutic peptide design. We also briefly introduce the physical quantities used to characterize protein–peptide affinity and attempt to extend the content of generalized machine learning methods.Results:Existing issues and future perspective on the statistical modeling and regression prediction of protein– peptide binding affinity are discussed.Conclusion:There is still a long way to go before establishment of general, reliable and efficient machine leaningbased protein–peptide affinity predictors.

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Agostino, Mark, and Sebastian Öther-Gee Pohl. "Wnt Binding Affinity Prediction for Putative Frizzled-Type Cysteine-Rich Domains." International Journal of Molecular Sciences 20, no. 17 (August 26, 2019): 4168. http://dx.doi.org/10.3390/ijms20174168.

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Several proteins other than the frizzled receptors (Fzd) and the secreted Frizzled-related proteins (sFRP) contain Fzd-type cysteine-rich domains (CRD). We have termed these domains “putative Fzd-type CRDs”, as the relevance of Wnt signalling in the majority of these is unknown; the RORs, an exception to this, are well known for mediating non-canonical Wnt signalling. In this study, we have predicted the likely binding affinity of all Wnts for all putative Fzd-type CRDs. We applied both our previously determined Wnt‒Fzd CRD binding affinity prediction model, as well as a newly devised model wherein the lipid term was forced to contribute favourably to the predicted binding energy. The results obtained from our new model indicate that certain putative Fzd CRDs are much more likely to bind Wnts, in some cases exhibiting selectivity for specific Wnts. The results of this study inform the investigation of Wnt signalling modulation beyond Fzds and sFRPs.

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Yuan, Hong, Jing Huang, and Jin Li. "Protein-ligand binding affinity prediction model based on graph attention network." Mathematical Biosciences and Engineering 18, no. 6 (2021): 9148–62. http://dx.doi.org/10.3934/mbe.2021451.

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<abstract> <p>Estimating the binding affinity between proteins and drugs is very important in the application of structure-based drug design. Currently, applying machine learning to build the protein-ligand binding affinity prediction model, which is helpful to improve the performance of classical scoring functions, has attracted many scientists' attention. In this paper, we have developed an affinity prediction model called GAT-Score based on graph attention network (GAT). The protein-ligand complex is represented by a graph structure, and the atoms of protein and ligand are treated in the same manner. Two improvements are made to the original graph attention network. Firstly, a dynamic feature mechanism is designed to enable the model to deal with bond features. Secondly, a virtual super node is introduced to aggregate node-level features into graph-level features, so that the model can be used in the graph-level regression problems. PDBbind database v.2018 is used to train the model. Finally, the performance of GAT-Score was tested by the scheme $C_s$ (Core set as the test set) and <italic>CV</italic> (Cross-Validation). It has been found that our results are better than most methods from machine learning models with traditional molecular descriptors.</p> </abstract>

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Agrawal, Piyush, Pawan Kumar Raghav, Sherry Bhalla, Neelam Sharma, and Gajendra P. S. Raghava. "Overview of Free Software Developed for Designing Drugs Based on Protein-Small Molecules Interaction." Current Topics in Medicinal Chemistry 18, no. 13 (October 4, 2018): 1146–67. http://dx.doi.org/10.2174/1568026618666180816155131.

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One of the fundamental challenges in designing drug molecule against a disease target or protein is to predict binding affinity between target and drug or small molecule. In this review, our focus will be on advancement in the field of protein-small molecule interaction. This review has been divided into four major sections. In the first section, we will cover software developed for protein structure prediction. This will include prediction of binding pockets and post-translation modifications in proteins. In the second section, we will discuss software packages developed for predicting small-molecule interacting residues in a protein. Advances in the field of docking particularly advancement in the knowledgebased force fields will be discussed in the third part of the review. This section will also cover the method developed for predicting affinity between protein and drug molecules. The fourth section of the review will describe miscellaneous techniques used for designing drug molecules, like pharmacophore modelling. Our major emphasis in this review will be on computational tools that are available free for academic use.

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