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This paper is available on under CC 4.0 license. Arxiv Authors: (1) Md Masud Rana, Department of Mathematics, University of Kentucky; (2) Duc Duy Nguyen, Department of Mathematics, University of Kentucky & ducnguyen@uky.edu. Table of Links Abstract & Introduction Datasets and Results Methods Conclusion, Data and Software Availability, Competing interests, Acknowledgments & References 4 Conclusion The study of protein-protein interactions (PPIs) and the prediction of mutation-induced binding free energy changes are of great importance in understanding the molecular basis of biological processes. The application of geometric graph theory and atom-level graph coloring techniques provides a powerful framework for analyzing biomolecules and capturing their intricate relationships. By utilizing the concept of geometric subgraphs and constructing multi-scale weighted colored geometric subgraphs (MWCGS), we can effectively represent the structural and functional properties of PPIs. The site-specific MWCGS features allow us to extract meaningful patterns and characteristics, shedding light on the effects of mutations and the underlying molecular interactions. In this work, we developed a mutation-induced binding free energy change predictor, called GGL-PPI, by incorporating site-specific MWCGS features for PPIs and gradient-boosting trees. Our method demonstrates superior performance compared to existing methods. The model was validated on three datasets: AB-Bind S645, SKEMPI 1.0 S1131, and SKEMPI 2.0 S4169 and S8338, showcasing its robustness and effectiveness. Furthermore, GGL-PPI was evaluated on a blind test set, the Ssym dataset. To prevent data leakage between the test and training sets, the model was trained on a homology-reduced balanced training set Q3488. This approach ensures the reliability and fairness of the evaluation process. GGL-PPI exhibits the most unbiased and superior performance in predicting binding free energy changes for both direct and reverse mutations, outperforming other existing methods, particularly for reverse mutations. Overall, the results highlight the potential of the GGL-PPI approach in accurately predicting mutation-induced binding free energy changes in protein-protein interactions, providing valuable insights into the molecular mechanisms underlying protein-protein interactions and facilitating drug design and discovery efforts. 5 Data and Software Availability The source code is available at Github: https://github.com/NguyenLabUKY/GGL-Mutation. 6 Competing interests No competing interest is declared. 7 Acknowledgments This work is supported in part by funds from the National Science Foundation (NSF: # 2053284, # 2151802, and # 2245903), and the University of Kentucky Startup Fund. References [1] Dana Chuderland and Rony Seger. Protein-protein interactions in the regulation of the extracellular signal-regulated kinase. Mol. Biotechnol., 29:57–74, 2005. [2] Harry Jubb, Tom L Blundell, and David B Ascher. Flexibility and small pockets at protein–protein interfaces: new insights into druggability. Prog. Biophys. Mol., 119(1):2–9, 2015. [3] Mileidy W Gonzalez and Maricel G Kann. Chapter 4: Protein interactions and disease. PLoS Comput. Biol., 8(12):e1002819, 2012. [4] Cunliang Geng, Li C Xue, Jorge Roel-Touris, and Alexandre MJJ Bonvin. Finding the δδg spot: are predictors of binding affinity changes upon mutations in protein–protein interactions ready for it? Wiley Interdiscip. Rev. Comput. Mol. Sci., 9(5):e1410, 2019. [5] Rocco Moretti, Sarel J Fleishman, Rudi Agius, Mieczyslaw Torchala, Paul A Bates, Panagiotis L Kastritis, Joao PGLM Rodrigues, Mika¨el Trellet, Alexandre MJJ Bonvin, Meng Cui, et al. Community-wide evaluation of methods for predicting the effect of mutations on protein–protein interactions. Proteins: Struct. Funct. Genet., 81(11):1980–1987, 2013. [6] Jeffrey R Brender and Yang Zhang. Predicting the effect of mutations on protein-protein binding interactions through structure-based interface profiles. PLoS Comput. Biol., 11(10):e1004494, 2015. [7] Ning Zhang, Yuting Chen, Haoyu Lu, Feiyang Zhao, Roberto Vera Alvarez, Alexander Goncearenco, Anna R Panchenko, and Minghui Li. Mutabind2: predicting the impacts of single and multiple mutations on protein-protein interactions. Iscience, 23(3):100939, 2020. [8] Carlos HM Rodrigues, Yoochan Myung, Douglas EV Pires, and David B Ascher. mcsm-ppi2: predicting the effects of mutations on protein–protein interactions. Nucleic Acids Res., 47(W1):W338–W344, 2019. [9] Minghui Li, Marharyta Petukh, Emil Alexov, and Anna R Panchenko. Predicting the impact of missense mutations on protein–protein binding affinity. J. Chem. Theory Comput., 10(4):1770–1780, 2014. [10] Hafumi Nishi, Manoj Tyagi, Shaolei Teng, Benjamin A Shoemaker, Kosuke Hashimoto, Emil Alexov, Stefan Wuchty, and Anna R Panchenko. Cancer missense mutations alter binding properties of proteins and their interaction networks. PloS one, 8(6):e66273, 2013. [11] Mu Gao, Hongyi Zhou, and Jeffrey Skolnick. Insights into disease-associated mutations in the human proteome through protein structural analysis. Structure, 23(7):1362–1369, 2015. [12] Alessia David, Rozami Razali, Mark N Wass, and Michael JE Sternberg. Protein–protein interaction sites are hot spots for disease-associated nonsynonymous snps. Hum. Mutat., 33(2):359–363, 2012. [13] H Billur Engin, Jason F Kreisberg, and Hannah Carter. Structure-based analysis reveals cancer missense mutations target protein interaction interfaces. PLoS One, 11(4):e0152929, 2016. [14] Amelie Stein, Douglas M Fowler, Rasmus Hartmann-Petersen, and Kresten Lindorff-Larsen. Biophysical and mechanistic models for disease-causing protein variants. Trends Biochem. Sci., 44(7):575–588, 2019. [15] Stephanie Portelli, Jody E Phelan, David B Ascher, Taane G Clark, and Nicholas Furnham. Understanding molecular consequences of putative drug resistant mutations in mycobacterium tuberculosis. Sci. Rep., 8(1):1–12, 2018. [16] Sundeep Chaitanya Vedithi, Sony Malhotra, Madhusmita Das, Sheela Daniel, Nanda Kishore, Anuja George, Shantha Arumugam, Lakshmi Rajan, Mannam Ebenezer, David B Ascher, et al. Structural implications of mutations conferring rifampin resistance in mycobacterium leprae. Sci. Rep., 8(1):5016, 2018. [17] Vytautas Gapsys, Servaas Michielssens, Daniel Seeliger, and Bert L de Groot. Accurate and rigorous prediction of the changes in protein free energies in a large-scale mutation scan. Angew. Chem., Int. Ed., 55(26):7364–7368, 2016. [18] Elizabeth H Kellogg, Andrew Leaver-Fay, and David Baker. Role of conformational sampling in computing mutationinduced changes in protein structure and stability. Proteins: Structure, Function, and Bioinformatics, 79(3):830–838, 2011. [19] Brian J Bender, Alberto Cisneros III, Amanda M Duran, Jessica A Finn, Darwin Fu, Alyssa D Lokits, Benjamin K Mueller, Amandeep K Sangha, Marion F Sauer, Alexander M Sevy, et al. Protocols for molecular modeling with rosetta3 and rosettascripts. Biochemistry, 55(34):4748–4763, 2016. [20] Kurt S Thorn and Andrew A Bogan. Asedb: a database of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics, 17(3):284–285, 2001. [21] MD Shaji Kumar and M Michael Gromiha. Pint: protein–protein interactions thermodynamic database. Nucleic Acids Res., 34(suppl 1):D195–D198, 2006. [22] MD Shaji Kumar, K Abdulla Bava, M Michael Gromiha, Ponraj Prabakaran, Koji Kitajima, Hatsuho Uedaira, and Akinori Sarai. Protherm and pronit: thermodynamic databases for proteins and protein–nucleic acid interactions. Nucleic Acids Res., 34(suppl 1):D204–D206, 2006. [23] Iain H Moal and Juan Fern´andez-Recio. Skempi: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models. Bioinformatics, 28(20):2600–2607, 2012. [24] Justina Jankauskait˙e, Brian Jim´enez-Garc´ıa, Justas Dapk¯unas, Juan Fern´andez-Recio, and Iain H Moal. Skempi 2.0: an updated benchmark of changes in protein–protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics, 35(3):462–469, 2019. [25] Sarah Sirin, James R Apgar, Eric M Bennett, and Amy E Keating. Ab-bind: antibody binding mutational database for computational affinity predictions. Protein Sci., 25(2):393–409, 2016. [26] Cunliang Geng, Anna Vangone, and Alexandre MJJ Bonvin. Exploring the interplay between experimental methods and the performance of predictors of binding affinity change upon mutations in protein complexes. Protein Eng. Des. Sel., 29(8):291–299, 2016. [27] Sherlyn Jemimah, K Yugandhar, and M Michael Gromiha. Proximate: a database of mutant protein–protein complex thermodynamics and kinetics. Bioinformatics, 33(17):2787–2788, 2017. [28] Douglas EV Pires, David B Ascher, and Tom L Blundell. mcsm: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics, 30(3):335–342, 2014. [29] Cunliang Geng, Anna Vangone, Gert E Folkers, Li C Xue, and Alexandre MJJ Bonvin. isee: Interface structure, evolution, and energy-based machine learning predictor of binding affinity changes upon mutations. Proteins: Structure, Function, and Bioinformatics, 87(2):110–119, 2019. [30] Sherlyn Jemimah, Masakazu Sekijima, and M Michael Gromiha. Proaffimuseq: sequence-based method to predict the binding free energy change of protein–protein complexes upon mutation using functional classification. Bioinformatics, 36(6):1725–1730, 2020. [31] Guangyu Zhou, Muhao Chen, Chelsea JT Ju, Zheng Wang, Jyun-Yu Jiang, and Wei Wang. Mutation effect estimation on protein–protein interactions using deep contextualized representation learning. NAR genom. bioinform., 2(2):lqaa015, 2020. [32] Alexey Strokach, Tian Yu Lu, and Philip M Kim. Elaspic2 (el2): combining contextualized language models and graph neural networks to predict effects of mutations. J. Mol. Biol., 433(11):166810, 2021. [33] Xianggen Liu, Yunan Luo, Pengyong Li, Sen Song, and Jian Peng. Deep geometric representations for modeling effects of mutations on protein-protein binding affinity. PLoS Comput. Biol., 17(8):e1009284, 2021. [34] Piero Fariselli, Pier Luigi Martelli, Castrense Savojardo, and Rita Casadio. Inps: predicting the impact of nonsynonymous variations on protein stability from sequence. Bioinformatics, 31(17):2816–2821, 2015. [35] Grant Thiltgen and Richard A Goldstein. Assessing predictors of changes in protein stability upon mutation using self-consistency. PLoS One, 7(10):e46084, 2012. [36] Fabrizio Pucci, Katrien V Bernaerts, Jean Marc Kwasigroch, and Marianne Rooman. Quantification of biases in predictions of protein stability changes upon mutations. Bioinformatics, 34(21):3659–3665, 2018. [37] Dinara R Usmanova, Natalya S Bogatyreva, Joan Ari˜no Bernad, Aleksandra A Eremina, Anastasiya A Gorshkova, German M Kanevskiy, Lyubov R Lonishin, Alexander V Meister, Alisa G Yakupova, Fyodor A Kondrashov, et al. Self-consistency test reveals systematic bias in programs for prediction change of stability upon mutation. Bioinformatics, 34(21):3653–3658, 2018. [38] Duc D Nguyen, Tian Xiao, Menglun Wang, and Guo-Wei Wei. Rigidity strengthening: A mechanism for protein– ligand binding. J. Chem. Inf. Model., 57(7):1715–1721, 2017. [39] Md Masud Rana and Duc Duy Nguyen. Geometric graph learning with extended atom-types features for proteinligand binding affinity prediction. Comput. Biol. Med., 164:107250, 2023. [40] Jian Jiang, Rui Wang, and Guo-Wei Wei. Ggl-tox: geometric graph learning for toxicity prediction. J. Chem. Inf. Model., 61(4):1691–1700, 2021. [41] Menglun Wang, Zixuan Cang, and Guo-Wei Wei. A topology-based network tree for the prediction of protein–protein binding affinity changes following mutation. Nat. Mach. Intell., 2(2):116–123, 2020. [42] Duan Chen, Zhan Chen, Changjun Chen, Weihua Geng, and Guo-Wei Wei. Mibpb: a software package for electrostatic analysis. J. Comput. Chem., 32(4):756–770, 2011. [43] Xiang Liu, Huitao Feng, Jie Wu, and Kelin Xia. Hom-complex-based machine learning (hcml) for the prediction of protein–protein binding affinity changes upon mutation. J. Chem. Inf. Model., 62(17):3961–3969, 2022. [44] Douglas EV Pires and David B Ascher. mcsm-ab: a web server for predicting antibody–antigen affinity changes upon mutation with graph-based signatures. Nucleic Acids Res., 44(W1):W469–W473, 2016. [45] Peng Xiong, Chengxin Zhang, Wei Zheng, and Yang Zhang. Bindprofx: assessing mutation-induced binding affinity change by protein interface profiles with pseudo-counts. J. Mol. Biol., 429(3):426–434, 2017. [46] Quanya Liu, Peng Chen, Bing Wang, Jun Zhang, and Jinyan Li. dbmpikt: a database of kinetic and thermodynamic mutant protein interactions. BMC bioinformatics, 19(1):1–7, 2018. [47] Bian Li, Yucheng T Yang, John A Capra, and Mark B Gerstein. Predicting changes in protein thermodynamic stability upon point mutation with deep 3d convolutional neural networks. PLoS Comp. Bio., 16(11):e1008291, 2020. [48] Lijun Quan, Qiang Lv, and Yang Zhang. Strum: structure-based prediction of protein stability changes upon single-point mutation. Bioinformatics, 32(19):2936–2946, 2016. [49] Leo Breiman. Random forests. Machine learning, 45:5–32, 2001. [50] Corinna Cortes and Vladimir Vapnik. Support-vector networks. Machine learning, 20:273–297, 1995. [51] Duc Duy Nguyen, Kaifu Gao, Menglun Wang, and Guo-Wei Wei. Mathdl: mathematical deep learning for d3r grand challenge 4. J. Comput. Aided. Mol. Des., 34(2):131–147, 2020. [52] Jerome H Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232, 2001. This paper is available on under CC 4.0 license. Arxiv

PPIs and the Prediction of Mutation-Induced Binding Free Energy Changes: What It All Means

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jeffrey Ouyang-Zhang, UT Austin (2) Daniel J. Diaz, UT Austin (3) Adam R. Klivans, UT Austin (4) Philipp Krähenbühl, UT Austin Table of Links Abstract & Introduction Related Works Preliminary Methodology Results Limitations Conclusion, Acknowledgements, & References Experimental Setting & Additional Results 7 Conclusion We present a method that efficiently scales thermodynamic stability prediction from single mutations to higher-order mutations. Our key insight is that the effects of mutations on the same protein are correlated. Thus, for a target protein, it suffices to run a deep backbone once and decode the effect of all mutations simultaneously using a shallow decoder. With the AlphaFold model as our backbone, our method outperforms existing methods on a variety of single and multiple mutation benchmarks. Our method scales to millions of mutations with minimal computational overhead and runs in a fraction of the time it would take prior works. 8 Acknowledgements This work is supported by the NSF AI Institute for Foundations of Machine Learning (IFML). References [1] Randomization of genes by pcr mutagenesis. Genome research 2(1), 28–33 (1992) 2 [2] Adams, J.P., Brown, M.J., Diaz-Rodriguez, A., Lloyd, R.C., Roiban, G.D.: Biocatalysis: A Pharma Perspective. Advanced Synthesis and Catalysis 361(11), 2421–2432 (2019). https://doi.org/10.1002/adsc.201900424 1 [3] Ahdritz, G., Bouatta, N., Kadyan, S., Xia, Q., Gerecke, W., O’Donnell, T.J., Berenberg, D., Fisk, I., Zanichelli, N., Zhang, B., Nowaczynski, A., Wang, B., Stepniewska-Dziubinska, M.M., Zhang, S., Ojewole, A., Guney, M.E., Biderman, S., Watkins, A.M., Ra, S., Lorenzo, P.R., Nivon, L., Weitzner, B., Ban, Y.E.A., Sorger, P.K., Mostaque, E., Zhang, Z., Bonneau, R., AlQuraishi, M.: Openfold: Retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization. bioRxiv (2022). https://doi.org/10.1101/2022.11.20.517210 6 [4] Arnold, F.H.: Design by directed evolution. Accounts of chemical research 31(3), 125–131 (1998) 1, 2, 3 [5] Bell, E.L., Finnigan, W., France, S.P., Green, A.P., Hayes, M.A., Hepworth, L.J., Lovelock, S.L., Niikura, H., Osuna, S., Romero, E., Ryan, K.S., Turner, N.J., Flitsch, S.L.: Biocatalysis. Nature Reviews Methods Primers 1(1), 1–21 (2021). https://doi.org/10.1038/s43586-021-00044-z 1 [6] Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G., Sanavia, T.: An antisymmetric neural network to predict free energy changes in protein variants. Journal of Physics D: Applied Physics 54(24), 245403 [7] Benevenuta, S., Pancotti, C., Fariselli, P., Birolo, G., Sanavia, T.: An antisymmetric neural network to predict free energy changes in protein variants. Journal of Physics D: Applied Physics 54(24), 245403 (2021) 8 [8] Benevenuta, S., Birolo, G., Sanavia, T., Capriotti, E., Fariselli, P.: Challenges in predicting stabilizing variations: An exploration. Frontiers in Molecular Biosciences 9, 1075570 (2023) 5, 6, 17 [9] Brückner, A., Polge, C., Lentze, N., Auerbach, D., Schlattner, U.: Yeast two-hybrid, a powerful tool for systems biology. International journal of molecular sciences 10(6), 2763–2788 (2009) 1 [10] Cadet, F., Saavedra, E., Syren, P.O., Gontero, B.: Machine learning, epistasis, and protein engineering: From sequence-structure-function relationships to regulation of metabolic pathways. Frontiers in Molecular Biosciences 9, 1098289 (2022) 1 [11] Capriotti, E., Fariselli, P., Casadio, R.: I-mutant2. 0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic acids research 33(suppl_2), W306–W310 (2005) 3, 8 [12] Chen, K., Arnold, F.H.: Tuning the activity of an enzyme for unusual environments: sequential random mutagenesis of subtilisin e for catalysis in dimethylformamide. Proceedings of the National Academy of Sciences 90(12), 5618–5622 (1993) 1, 3 [13] Chen, T., Gong, C., Diaz, D.J., Chen, X., Wells, J.T., Wang, Z., Ellington, A., Dimakis, A., Klivans, A., et al.: Hotprotein: A novel framework for protein thermostability prediction and editing. In: The Eleventh International Conference on Learning Representations (2022) 3 [14] Chen, Y., Lu, H., Zhang, N., Zhu, Z., Wang, S., Li, M.: Premps: Predicting the impact of missense mutations on protein stability. PLoS computational biology 16(12), e1008543 (2020) 3, 8 [15] Cheng, J., Randall, A., Baldi, P.: Prediction of protein stability changes for single-site mutations using support vector machines. Proteins: Structure, Function, and Bioinformatics 62(4), 1125–1132 (2006) 3, 8 [16] Dehghanpoor, R., Ricks, E., Hursh, K., Gunderson, S., Farhoodi, R., Haspel, N., Hutchinson, B., Jagodzinski, F.: Predicting the effect of single and multiple mutations on protein structural stability. Molecules 23(2), 251 (2018) 4, 7 [17] Dehouck, Y., Kwasigroch, J.M., Gilis, D., Rooman, M.: Popmusic 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC bioinformatics 12(1), 1–12 (2011) 3, 8 [18] Diaz, D.J., Gong, C., Ouyang-Zhang, J., Loy, J.M., Wells, J.T., Yang, D., Ellington, A.J., Dimakis, A., Klivans, A.R.: Stability oracle: A structure-based graph-transformer for identifying stabilizing mutations (2023). https://doi.org/10.1101/2023.05.15.540857 3, 4, 7, 8, 16, 17, 18, 19 [19] Diaz, D.J., Kulikova, A.V., Ellington, A.D., Wilke, C.O.: Using machine learning to predict the effects and consequences of mutations in proteins. Current Opinion in Structural Biology 78, 102518 (2023) 3 [20] d’Oelsnitz, S., Diaz, D.J., Acosta, D.J., Schechter, M.W., Minus, M.B., Howard, J.R., Do, H., Loy, J., Alper, H., Ellington, A.D.: Synthetic microbial sensing and biosynthesis of amaryllidaceae alkaloids. bioRxiv pp. 2023–04 (2023) 3 [21] d’Oelsnitz, S., Kim, W., Burkholder, N.T., Javanmardi, K., Thyer, R., Zhang, Y., Alper, H.S., Ellington, A.D.: Using fungible biosensors to evolve improved alkaloid biosyntheses. Nature Chemical Biology 18(9), 981–989 (2022) 1 [22] Ellefson, J.W., Gollihar, J., Shroff, R., Shivram, H., Iyer, V.R., Ellington, A.D.: Synthetic evolutionary origin of a proofreading reverse transcriptase. Science 352(6293), 1590–1593 (2016) 1 [23] Feng, X., Sanchis, J., Reetz, M.T., Rabitz, H.: Enhancing the efficiency of directed evolution in focused enzyme libraries by the adaptive substituent reordering algorithm. Chemistry–A European Journal 18(18), 5646–5654 (2012) 3 [24] Frazer, J., Notin, P., Dias, M., Gomez, A., Min, J.K., Brock, K., Gal, Y., Marks, D.S.: Disease variant prediction with deep generative models of evolutionary data. Nature 599(7883), 91–95 (2021) 9 [25] Gebauer, M., Skerra, A.: Engineered protein scaffolds as next-generation therapeutics. Annual Review of Pharmacology and Toxicology 60, 391–415 (2020). https://doi.org/10.1146/annurev-pharmtox-010818- 021118 1 [26] Gerasimavicius, L., Livesey, B.J., Marsh, J.A.: Correspondence between functional scores from deep mutational scans and predicted effects on protein stability. bioRxiv pp. 2023–02 (2023) 9 [27] Giver, L., Gershenson, A., Freskgard, P.O., Arnold, F.H.: Directed evolution of a thermostable esterase. Proceedings of the National Academy of Sciences 95(22), 12809–12813 (1998) 2, 3 [28] Hie, B.L., Shanker, V.R., Xu, D., Bruun, T.U., Weidenbacher, P.A., Tang, S., Wu, W., Pak, J.E., Kim, P.S.: Efficient evolution of human antibodies from general protein language models. Nature Biotechnology (2023) 3 [29] Jaroszewicz, W., Morcinek-Orłowska, J., Pierzynowska, K., Gaffke, L., W˛egrzyn, G.: Phage display and other peptide display technologies. FEMS Microbiology Reviews 46(2), fuab052 (2022) 1 [30] Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Transactions on Information Systems (TOIS) 20(4), 422–446 (2002) 6, 17 [31] Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al.: Highly accurate protein structure prediction with alphafold. Nature 596(7873), 583–589 (2021) 2, 3, 4, 6, 9, 10 [32] Kellogg, E.H., Leaver-Fay, A., Baker, D.: Role of conformational sampling in computing mutation-induced changes in protein structure and stability. Proteins: Structure, Function, and Bioinformatics 79(3), 830–838 (2011) 3 [33] Klabunde, T., Petrassi, H.M., Oza, V.B., Raman, P., Kelly, J.W., Sacchettini, J.C.: Rational design of potent human transthyretin amyloid disease inhibitors. Nature structural biology 7(4), 312–321 (2000) 1, 2 [34] Kouba, P., Kohout, P., Haddadi, F., Bushuiev, A., Samusevich, R., Sedlar, J., Damborsky, J., Pluskal, T., Sivic, J., Mazurenko, S.: Machine learning-guided protein engineering. ACS Catalysis 13, 13863–13895 (2023) 3 [35] Laimer, J., Hofer, H., Fritz, M., Wegenkittl, S., Lackner, P.: Maestro-multi agent stability prediction upon point mutations. BMC bioinformatics 16(1), 1–13 (2015) 3, 4, 7, 8 [36] Li, B., Yang, Y.T., Capra, J.A., Gerstein, M.B.: Predicting changes in protein thermodynamic stability upon point mutation with deep 3d convolutional neural networks. PLoS computational biology 16(11), e1008291 (2020) 3, 4, 8 [37] Li, G., Panday, S.K., Alexov, E.: Saafec-seq: a sequence-based method for predicting the effect of single point mutations on protein thermodynamic stability. International journal of molecular sciences 22(2), 606 (2021) 3 [38] Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al.: Language models of protein sequences at the scale of evolution enable accurate structure prediction. bioRxiv (2022) 3, 4, 6, 9, 10, 18 [39] Loell, K., Nanda, V.: Marginal protein stability drives subcellular proteome isoelectric point. Proceedings of the National Academy of Sciences 115(46), 11778–11783 (2018) 1 [40] Lu, H., Diaz, D.J., Czarnecki, N.J., Zhu, C., Kim, W., Shroff, R., Acosta, D.J., Alexander, B.R., Cole, H.O., Zhang, Y., et al.: Machine learning-aided engineering of hydrolases for pet depolymerization. Nature 604(7907), 662–667 (2022) 3 [41] Meghwanshi, G.K., Kaur, N., Verma, S., Dabi, N.K., Vashishtha, A., Charan, P.D., Purohit, P., Bhandari, H.S., Bhojak, N., Kumar, R.: Enzymes for pharmaceutical and therapeutic applications. Biotechnology and Applied Biochemistry 67(4), 586–601 (2020). https://doi.org/10.1002/bab.1919 1 [42] Meier, J., Rao, R., Verkuil, R., Liu, J., Sercu, T., Rives, A.: Language models enable zero-shot prediction of the effects of mutations on protein function. Advances in Neural Information Processing Systems 34, 29287–29303 (2021) 9, 18 [43] Mirdita, M., Schütze, K., Moriwaki, Y., Heo, L., Ovchinnikov, S., Steinegger, M.: Colabfold: making protein folding accessible to all. Nature methods 19(6), 679–682 (2022) 6 [44] Montanucci, L., Capriotti, E., Frank, Y., Ben-Tal, N., Fariselli, P.: Ddgun: an untrained method for the prediction of protein stability changes upon single and multiple point variations. BMC bioinformatics 20, 1–10 (2019) 3, 4, 7, 8, 16, 18 [45] Montanucci, L., Savojardo, C., Martelli, P.L., Casadio, R., Fariselli, P.: On the biases in predictions of protein stability changes upon variations: the inps test case. Bioinformatics 35(14), 2525–2527 (2019) 16 [46] Musdal, Y., Govindarajan, S., Mannervik, B.: Exploring sequence-function space of a poplar glutathione transferase using designed information-rich gene variants. Protein Engineering, Design and Selection 30(8), 543–549 (2017) 3 [47] Nijkamp, E., Ruffolo, J., Weinstein, E.N., Naik, N., Madani, A.: Progen2: exploring the boundaries of protein language models. arXiv preprint arXiv:2206.13517 (2022) 9 [48] Nikam, R., Kulandaisamy, A., Harini, K., Sharma, D., Gromiha, M.M.: Prothermdb: thermodynamic database for proteins and mutants revisited after 15 years. Nucleic acids research 49(D1), D420–D424 (2021) 2, 16 [49] Notin, P., Dias, M., Frazer, J., Hurtado, J.M., Gomez, A.N., Marks, D., Gal, Y.: Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. In: International Conference on Machine Learning. pp. 16990–17017. PMLR (2022) 2, 3, 9, 16, 18 [50] Notin, P.M., Van Niekerk, L., Kollasch, A.W., Ritter, D., Gal, Y., Marks, D.: Trancepteve: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction. bioRxiv pp. 2022–12 (2022) 9 [51] Nutschel, C., Fulton, A., Zimmermann, O., Schwaneberg, U., Jaeger, K.E., Gohlke, H.: Systematically scrutinizing the impact of substitution sites on thermostability and detergent tolerance for bacillus subtilis lipase a. Journal of chemical information and modeling 60(3), 1568–1584 (2020) 9 [52] Paik, I., Ngo, P.H., Shroff, R., Diaz, D.J., Maranhao, A.C., Walker, D.J., Bhadra, S., Ellington, A.D.: Improved bst dna polymerase variants derived via a machine learning approach. Biochemistry (2021) 3 [53] Pak, M.A., Markhieva, K.A., Novikova, M.S., Petrov, D.S., Vorobyev, I.S., Maksimova, E.S., Kondrashov, F.A., Ivankov, D.N.: Using alphafold to predict the impact of single mutations on protein stability and function. Plos one 18(3), e0282689 (2023) 3 [54] Pancotti, C., Benevenuta, S., Birolo, G., Alberini, V., Repetto, V., Sanavia, T., Capriotti, E., Fariselli, P.: Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset. Briefings in Bioinformatics 23(2), bbab555 (2022) 2, 8, 16, 17 [55] Pancotti, C., Benevenuta, S., Repetto, V., Birolo, G., Capriotti, E., Sanavia, T., Fariselli, P.: A deep-learning sequence-based method to predict protein stability changes upon genetic variations. Genes 12(6), 911 (2021) 8 [56] Pires, D.E., Ascher, D.B., Blundell, T.L.: Duet: a server for predicting effects of mutations on protein stability using an integrated computational approach. Nucleic acids research 42(W1), W314–W319 (2014) 3, 8 [57] Pires, D.E., Ascher, D.B., Blundell, T.L.: mcsm: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 30(3), 335–342 (2014) 8 [58] Pucci, F., Schwersensky, M., Rooman, M.: Artificial intelligence challenges for predicting the impact of mutations on protein stability. Current opinion in structural biology 72, 161–168 (2022) 17 [59] Qiu, Y., Wei, G.W.: Persistent spectral theory-guided protein engineering. Nature Computational Science pp. 1–15 (2023) 3, 6, 17 [60] Rao, R.M., Liu, J., Verkuil, R., Meier, J., Canny, J., Abbeel, P., Sercu, T., Rives, A.: Msa transformer. In: International Conference on Machine Learning. pp. 8844–8856. PMLR (2021) 3, 9, 10 [61] Riesselman, A.J., Ingraham, J.B., Marks, D.S.: Deep generative models of genetic variation capture the effects of mutations. Nature methods 15(10), 816–822 (2018) 9 [62] Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C.L., Ma, J., Fergus, R.: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. PNAS (2019). https://doi.org/10.1101/622803 3, 7 [63] Rodrigues, C.H., Pires, D.E., Ascher, D.B.: Dynamut: predicting the impact of mutations on protein conformation, flexibility and stability. Nucleic acids research 46(W1), W350–W355 (2018) 3, 8 [64] Rodrigues, C.H., Pires, D.E., Ascher, D.B.: Dynamut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Science 30(1), 60–69 (2021) 3, 4, 7 [65] Sanavia, T., Birolo, G., Montanucci, L., Turina, P., Capriotti, E., Fariselli, P.: Limitations and challenges in protein stability prediction upon genome variations: towards future applications in precision medicine. Computational and Structural Biotechnology Journal 18, 1968–1979 (2020). https://doi.org/10.1016/j.csbj.2020.07.011, https://doi.org/10.1016/j.csbj.2020.07.011 6, 17 [66] Savojardo, C., Fariselli, P., Martelli, P.L., Casadio, R.: Inps-md: a web server to predict stability of protein variants from sequence and structure. Bioinformatics 32(16), 2542–2544 (2016) 3, 8 [67] Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., Serrano, L.: The foldx web server: an online force field. Nucleic acids research 33(suppl_2), W382–W388 (2005) 3, 7, 8, 18 [68] Shroff, R., Cole, A.W., Diaz, D.J., Morrow, B.R., Donnell, I., Annapareddy, A., Gollihar, J., Ellington, A.D., Thyer, R.: Discovery of novel gain-of-function mutations guided by structure-based deep learning. ACS synthetic biology 9(11), 2927–2935 (2020) 3 [69] Starr, T.N., Thornton, J.W.: Epistasis in protein evolution. Protein science 25(7), 1204–1218 (2016) 1 [70] Steinegger, M., Soding, J.: Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nature biotechnology 35(11), 1026–1028 (2017) 6 [71] Stemmer, W.P.: Rapid evolution of a protein in vitro by dna shuffling. Nature 370(6488), 389–391 (1994) 2, 3 [72] Tsuboyama, K., Dauparas, J., Chen, J., Laine, E., Mohseni Behbahani, Y., Weinstein, J.J., Mangan, N.M., Ovchinnikov, S., Rocklin, G.J.: Mega-scale experimental analysis of protein folding stability in biology and protein design. bioRxiv pp. 2022–12 (2022) 1, 2, 6, 16 [73] Tung, J.W., Heydari, K., Tirouvanziam, R., Sahaf, B., Parks, D.R., Herzenberg, L.A., Herzenberg, L.A.: Modern flow cytometry: a practical approach. Clinics in laboratory medicine 27(3), 453–468 (2007) 1 [74] Umerenkov, D., Shashkova, T.I., Strashnov, P.V., Nikolaev, F., Sindeeva, M., Ivanisenko, N.V., Kardymon, O.L.: Prostata: Protein stability assessment using transformers. bioRxiv pp. 2022–12 (2022) 3, 4, 6, 7, 8, 18, 19 [75] Wittmann, B.J., Johnston, K.E., Wu, Z., Arnold, F.H.: Advances in machine learning for directed evolution. Current opinion in structural biology 69, 11–18 (2021) 3 [76] Wittmann, B.J., Yue, Y., Arnold, F.H.: Informed training set design enables efficient machine learningassisted directed protein evolution. Cell Systems 12(11), 1026–1045 (2021) 3 [77] Worth, C.L., Preissner, R., Blundell, T.L.: Sdm—a server for predicting effects of mutations on protein stability and malfunction. Nucleic acids research 39(suppl_2), W215–W222 (2011) 3, 8 [78] Wu, S., Snajdrova, R., Moore, J.C., Baldenius, K., Bornscheuer, U.T.: Biocatalysis: Enzymatic Synthesis for Industrial Applications. Angewandte Chemie - International Edition 60(1), 88–119 (2021). https://doi.org/10.1002/anie.202006648 1 [79] Yeung, N., Lin, Y.W., Gao, Y.G., Zhao, X., Russell, B.S., Lei, L., Miner, K.D., Robinson, H., Lu, Y.: Rational design of a structural and functional nitric oxide reductase. Nature 462(7276), 1079–1082 (2009) 1, 2 [80] Zhou, B., Lv, O., Yi, K., Xiong, X., Tan, P., Hong, L., Wang, Y.G.: Accurate and definite mutational effect prediction with lightweight equivariant graph neural networks. arXiv preprint arXiv:2304.08299 (2023) 3 [81] Zhou, Y., Pan, Q., Pires, D.E., Rodrigues, C.H., Ascher, D.B.: Ddmut: predicting effects of mutations on protein stability using deep learning. Nucleic Acids Research p. gkad472 (2023) 3

Predicting a Protein’s Stability
under a Million Mutations: Conclusion, Acknowledgement & References

THE PINE PROCESSIONARY: THE PROCESSION

THE DORTHESIA

THE LOCUSTS: THE LAST MOULT

THE LOCUSTS: THEIR FUNCTION; THEIR ORGAN OF SOUND

THE PINE-PROCESSIONARY

So far in 2022, the entire crypto market has been impacted by wider economic and political uncertainties, led by Bitcoin, with prices down more than 50% over the past six months and the price heading for the ninth consecutive red weekly candle. And while Terraform Labs successfully airdropped new Luna tokens to previous holders, the market sentiment continues to be bearish, with the remaining in the doldrums. Despite the pessimism, Banking giant recently that despite the crypto crash, its estimate of Bitcoin's fair value is $38,000. “The past month's crypto market correction looks more like capitulation relative to last January/February and going forward we see upside for bitcoin and crypto markets more generally," the bank’s strategists said. “Historically, buying into extreme fear levels in bitcoin has been a profitable strategy,” Sam Kopelman, a manager at the cryptocurrency wallet Luno, reported in . “That said, bitcoin has also seen continued sell-offs following a fearful market sentiment. Investors must remember that past performance is not indicative of future price movements. Therefore, as investors begin to re-enter the market, they must remain wary.” Fear and Greed Index JPMorgan said Yahoo Finance One way to avoid panic selling when the price of cryptocurrencies drop significantly is to remind yourself of the fundamentals.  Of course, that’s not so easy to say when you’ve lost money in Luna and seen your Bitcoin investments go down in value. “When the market is topping out, the stories are about internet-age Horatio Algers. When the market is bottoming, it’s about how the repo man took Mr. Alger’s Lambo so he could pay back loans he took out to run a carry trade on the Anchor protocol,” quipped Arthur Hayes, Co-Founder of 100x in a recent . blog It’s only relatively recently that Bitcoin has become correlated to the wider financial markets as more institutional money came flooding in with the easy money doled out by the Fed to alleviate COVID. “With the Fed in a difficult place, at least here in the United States, it's very difficult for a lot of these institutional shops not to treat it like a risk asset,” said top crypto influencer Anthony Pompliano. “That's exactly what they're doing right there. It's trading downward with stocks, it's trading down with other risk assets are selling across,” . he added The Mass Adoption of Crypto Assets Cryptocurrencies led by Bitcoin reached a tipping point in 2021 as they evolved from what many consider a niche investment to be a global, established asset class as venture capitalists poured money into the cryptocurrency market.  Overall, VCs invested more than $30 billion in crypto-assets and blockchain startups, with more than $10.5 billion invested in the fourth quarter of 2021 alone. With an estimated $10 globally in the first quarter of 2022, reportedly the largest amount to date, double the level for the same quarter in 2021. Looking at 2021 Galaxy Digital’s Head of Firmwide Research Alex Thorn said: “This was mainstreaming of crypto VC investing in a way we haven’t seen before. You also have a real diversification in the space. Yes, a lot went to trading and investing in the space, but you have other big categories that didn’t even exist in 2020 or 2019. That added a lot to the total capital invested,” he noted. Investment levels have continued despite Bitcoin price decline - as well as during another decline last summer. "This decoupling is demonstrative of investors' disbelief that a prolonged bear market in digital assets is forthcoming, as well as the significant amount of dry powder held by funds seeking to allocate to the sector," . he concluded Bitcoin is the Cryptocurrency of the Future One of the appeals of Bitcoin and other cryptocurrencies is that it removes friction in terms of costs and transaction speeds from payments, especially international transfers.  Indeed, according to Ark Invest, cumulative Bitcoin transfers have grown by more than 463% in the last year.  ARK analyst wrote in the report ‘Big Ideas 2022’ that Bitcoin will settle $13.1 trillion in 2021, a figure that even exceeds Visa’s payment volume. Ark Invest’s research also highlighted several areas where Bitcoin could take market share from traditional activities. These include international remittances, emerging market currencies, institutional investment and acting as a form of digital gold. Yassine Elmandjra ’ Source: ARK Invest s Yassine Elmandjra , Jan 25, 2022 tweet Innovation drives Bitcoin Although Bitcoin is not run by a centralized organization, it continues to grow along decentralized lines.  There is a small core group of developers working on improving the network, fixing bugs and security issues, and improving functionality. For example, last year Bitcoin implemented a major upgrade called Taproot to improve privacy, scalability, and security.  Another potentially significant move is the development of the Lightning Network, a layer 2 solution to Bitcoin that reduces costs and increases speed.  As reported in on May 30, “Bitcoin Lightning Network capacity attained an all-time high of 3915.776 BTC, as evidenced by data from Bitcoin Visuals, displaying a commitment to the cause of improving BTC transaction speeds and reducing fees over the layer-2 protocol.” This follows from the CEO of Strike, Jack Mallers, at the Bitcoin 2022 conference, that the company plans to collaborate with point-of-sale behemoths Shopify, NCR, and Blackhawk Network to revolutionize the payments industry. Cointelegraph news While there are good reasons to remain optimistic about Bitcoin, there are also still many things that investors and traders need to be careful about when investing in Bitcoin and cryptocurrencies. Cryptocurrencies are still a relatively new sector compared to traditional investments like stocks and funds, and while we don’t have certainty exactly how it will develop in the long term the potential is clear to see. “I believe Bitcoin is a viable long-term investment as a store of value looking to the future, especially with the price trending significantly upwards after each halving event,” said BigONE Chairman Anndy Lian. “It’s important for investors to recognize the fundamentals that it remains a hedge against inflation in the short term and a long-term asset that will continue to beat traditional financial assets.”

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