Aljazzar H, Leitner-Fischer F, Leue S, Simeonov D (2011) DiPro - a tool for probabilistic counter example generation. In: Groce, A, Musuvathi, M. (eds) Model Checking Software. SPIN 2011. Lecture Notes in Computer Sciencevol 6823. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22306-8_13

Almagro Armenteros JJ, Salvatore M, Emanuelsson O, Winther O, von Heijne G, Elofsson A, Nielsen H (2019a) Detecting sequence signals in targeting peptides using deep learning. Life Sci Alliance 2(5):e201900429. https://doi.org/10.26508/lsa.201900429

Article  PubMed  PubMed Central  Google Scholar 

Almagro Armenteros JJ, Tsirigos KD, Sønderby CK, Petersen TN, Winther O, Brunak S, von Heijne G, Nielsen H (2019) SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat Biotechnol 37(4):420–423. https://doi.org/10.1038/s41587-019-0036-z

Article  PubMed  CAS  Google Scholar 

Anderson I, Chertkov O, Chen A, Saunders E, Lapidus A, Nolan M, Lucas S, Hammon N, Deshpande S, Cheng JF, Han C, Tapia R, Goodwin LA, Pitluck S, Liolios K, Pagani I, Ivanova N, Mikhailova N, Pati A, Palaniappan K, Land M, Pan C, Rohde M, Pukall R, Göker M, Detter JC, Woyke T, Bristow J, Eisen JA, Markowitz V, Hugenholtz P, Kyrpides NC, Klenk HP, Mavromatis K (2012) Complete genome sequence of the moderately thermophilic mineral-sulfide-oxidizing firmicute Sulfobacillus acidophilus type strain (NAL(T)). Stand Genomic Sci 6(3):1–13. https://doi.org/10.4056/sigs.2736042

Article  PubMed  PubMed Central  CAS  Google Scholar 

Bailey TL, Johnson J, Grant CE, Noble WS (2015) The MEME suite. Nucleic Acids Res 43(W1):W39-49. https://doi.org/10.1093/nar/gkv416

Article  PubMed  PubMed Central  CAS  Google Scholar 

Banerjee P, Kemmler E, Dunkel M, Preissner R (2024) ProTox 3.0: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Res 52(W1):W513–W520. https://doi.org/10.1093/nar/gky318

Barakat MA (2011) New trends in removing heavy metals from industrial wastewater. Arab J Chem 4(4):361–377

Article  CAS  Google Scholar 

Beg MA, Shivangi TSC, Meena LS (2018) Structural prediction and mutational analysis of Rv3906c Gene of Mycobacterium tuberculosis H37Rv to determine its essentiality in survival. Adv Bioinform 2018:6152014. https://doi.org/10.1155/2018/6152014

Article  CAS  Google Scholar 

Binkowski TA, Naghibzadeh S, Liang J (2003) CASTp: computed atlas of surface topography of proteins. Nucleic Acids Res 31(13):3352–3355. https://doi.org/10.1093/nar/gkg512

Article  PubMed  PubMed Central  CAS  Google Scholar 

Brehmer D, Rüdiger S, Gässler CS, Klostermeier D, Packschies L, Reinstein J, Mayer MP, Bukau B (2001) Tuning of chaperone activity of Hsp70 proteins by modulation of nucleotide exchange. Nat Struct Biol 8(5):427–432. https://doi.org/10.1038/87588

Article  PubMed  CAS  Google Scholar 

Cao X, Hu X, Zhang X, Gao S, Ding C, Feng Y, Bao W (2017) Identification of metal ion binding sites based on amino acid sequences. PLoS ONE 12(8):e0183756. https://doi.org/10.1371/journal.pone.0183756

Article  PubMed  PubMed Central  CAS  Google Scholar 

Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, He Y, Xia R (2020) TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol Plant 13(8):1194–1202. https://doi.org/10.1016/j.molp.2020.06.009

Article  PubMed  CAS  Google Scholar 

Clerico EM, Tilitsky JM, Meng W, Gierasch LM (2015) How hsp70 molecular machines interact with their substrates to mediate diverse physiological functions. J Mol Biol 427(7):1575–1588. https://doi.org/10.1016/j.jmb.2015.02.004

Article  PubMed  PubMed Central  CAS  Google Scholar 

Colovos C, Yeates TO (1993) Verification of protein structures: patterns of nonbonded atomic interactions. Protein Sci 2(9):1511–1519. https://doi.org/10.1002/pro.5560020916

Article  PubMed  PubMed Central  CAS  Google Scholar 

Dimitrov I, Bangov I, Flower DR, Doytchinova I (2014) AllerTOP vol 2–a server for in silico prediction of allergens. J Mol Model 20(6):2278. https://doi.org/10.1007/s00894-014-2278-5

Article  PubMed  CAS  Google Scholar 

Doytchinova IA, Flower DR (2007) VaxiJen: a server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinformatics 8:4. https://doi.org/10.1186/1471-2105-8-4

Article  PubMed  PubMed Central  CAS  Google Scholar 

Feige MJ, Hendershot LM (2011) Disulfide bonds in ER protein folding and homeostasis. Curr Opin Cell Biol 23(2):167–175. https://doi.org/10.1016/j.ceb.2010.10.012

Article  PubMed  CAS  Google Scholar 

Finn RD, Clements J, Eddy SR (2011) HMMER web server: interactive sequence similarity searching. Nucleic Acids Res 39(Web Server issue):W29–37. https://doi.org/10.1093/nar/gkr367

Gbotsyo YA, Rowarth NM, Weir LK, MacRae TH (2020) Short-term cold stress and heat shock proteins in the crustacean Artemia franciscana. Cell Stress Chaperones 25(6):1083–1097. https://doi.org/10.1007/s12192-020-01147-4

Article  PubMed  PubMed Central  CAS  Google Scholar 

Geertz-Hansen HM, Blom N, Feist AM, Brunak S, Petersen TN (2014) Cofactory: sequence-based prediction of cofactor specificity of Rossmann folds. Proteins 82(9):1819–28. https://doi.org/10.1002/prot.24536

Article  PubMed  CAS  Google Scholar 

Geourjon C, Deléage G (1995) SOPMA: significant improvements in protein secondary structure prediction by consensus prediction from multiple alignments. Bioinformatics 11(6):681–684

Article  CAS  Google Scholar 

Guo W, Zhang H, Zhou W, Wang Y, Zhou H, Chen X (2016) Sulfur metabolism pathways in Sulfobacillus acidophilus TPY, a gram-positive moderate thermoacidophile from a hydrothermal vent. Front Microbiol 7:1861. https://doi.org/10.3389/fmicb.2016.01861

Article  PubMed  PubMed Central  Google Scholar 

Hartl FU, Hayer-Hartl M (2002) Molecular chaperones in the cytosol: from nascent chain to folded protein. Science 295(5561):1852–1858. https://doi.org/10.1126/science.1068408

Article  PubMed  CAS  Google Scholar 

Hasan R, Rony MNH, Ahmed R (2021) In silico characterization and structural modeling of bacterial metalloprotease of family M4. J Genet EngBiotechnol 19(1):25. https://doi.org/10.1186/s43141-020-00105-y

Article  Google Scholar 

Hirokawa T, Boonćchieng S, Mitaku S (1998) SOSUI: classification and secondary structure prediction system for membrane proteins. Bioinformatics 14(4):378–379. https://doi.org/10.1093/bioinformatics/14.4.378

Article  PubMed  CAS  Google Scholar 

Hyone-Myong Eun (1996) Nucleases. Hyone-Myong Eun, (eds) Enzymology primer for recombinant DNA technology. Academic Press pp.145–232. https://doi.org/10.1016/B978-012243740-3/50006-5

Islam MM, Kobayashi K, Kidokoro SI, Kuroda Y (2019) Hydrophobic surface residues can stabilize a protein through improved water-protein interactions. FEBS J 286(20):4122–4134. https://doi.org/10.1111/febs.14941

Article  PubMed  CAS  Google Scholar 

Jendele L, Krivak R, Skoda P, Novotny M, Hoksza D (2019) PrankWeb: a web server for ligand binding site prediction and visualization. Nucleic Acids Res 47(W1):W345–W349. https://doi.org/10.1093/nar/gkz424

Article  PubMed  PubMed Central  CAS  Google Scholar 

Kelley L, Mezulis S, Yates CM, Wass MN, Sternberg MJ (2015) The Phyre2 web portal for protein modeling, prediction and analysis. Nat Protoc 10(8):845–858. https://doi.org/10.1038/nprot.2015.053

Article  PubMed  PubMed Central  CAS  Google Scholar 

Khengare NJ, Labade SN, Lalge KM, Patil VS, Khilare CJ, Sawant SS (2022) Effective removal of chromium from aqueous solution by adsorption on powdered wool: in-silico studies of adsorption mechanism. Chem Data Collect 41:100935. https://doi.org/10.1016/j.cdc.2022.100935

Article  CAS  Google Scholar 

Krasowska A, Sigler K (2014) How microorganisms use hydrophobicity and what does this mean for human needs? Front Cell Infect Microbiol 4:112. https://doi.org/10.3389/fcimb.2014.00112