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Understanding protein–protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot-spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such protein–protein complexes. HS have been widely considered in research, both in case studies and in a few large-scale predictive approaches. This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field-specific methodologies. We explore recent accurate artificial intelligence-based techniques, which are progressively replacing well-established classical energy-based methodologies. This article is categorized under: Data Science > Databases and Expert Systems Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions.

Understanding protein–protein interactions (PPIs) is fundamental to describe and to characterize the formation of biomolecular assemblies, and to establish the energetic principles underlying biological networks. One key aspect of these interfaces is the existence and prevalence of hot-spots (HS) residues that, upon mutation to alanine, negatively impact the formation of such protein–protein complexes. HS have been widely considered in research, both in case studies and in a few large-scale predictive approaches. This review aims to present the current knowledge on PPIs, providing a detailed understanding of the microspecifications of the residues involved in those interactions and the characteristics of those defined as HS through a thorough assessment of related field-specific methodologies. We explore recent accurate artificial intelligence-based techniques, which are progressively replacing well-established classical energy-based methodologies. This article is categorized under: Data Science > Databases and Expert Systems Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions.

Endocrine disrupting compounds (EDCs) in the environment are considered a motif of concern, due to the widespread occurrence and potential adverse ecological and human health effects. The natural estrogen, 17 beta-estradiol (E2), is frequently detected in receiving water bodies after not being efficiently removed in conventional wastewater treatment plants (WWTPs), promoting a negative impact for both the aquatic ecosystem and human health. In this study, the biodegradation of E2 by Rhodococcus sp. ED55, a bacterial strain isolated from sediments of a discharge point of WWTP in Coloane, Macau, was investigated. Rhodococcus sp. ED55 was able to completely degrade 5 mg/L of E2 in 4 h in a synthetic medium. A similar degradation pattern was observed when the bacterial strain was used in wastewater collected from a WWTP, where a significant improvement in the degradation of the compound occurred. The detection and identification of 17 metabolites was achieved by means of UPLC/ESI/HRMS, which proposed a degradation pathway of E2. The acute test with luminescent marine bacterium Aliivibrio fischeri revealed the elimination of the toxicity of the treated effluent and the standardized yeast estrogenic (S-YES) assay with the recombinant strain of Saccharomyces cerevisiae revealed a decrease in the estrogenic activity of wastewater samples after biodegradation.

Additional file 1. Code Snippets regarding DrugTax.

Membrane proteins (MPs) are key players in a variety of different cellular processes and constitute the target of around 60% of all Food and Drug Administration–approved drugs. Despite their importance, there is still a massive lack of relevant structural, biochemical and mechanistic information mainly due to their localization within the lipid bilayer. To help fulfil this gap, we developed the MEmbrane protein dimer Novel Structure Analyser database (MENSAdb). This interactive web application summarizes the evolutionary and physicochemical properties of dimeric MPs to expand the available knowledge on the fundamental principles underlying their formation. Currently, MENSAdb contains features of 167 unique MPs (63% homo- and 37% heterodimers) and brings insights into the conservation of residues, accessible solvent area descriptors, average B-factors, intermolecular contacts at 2.5 Å and 4.0 Å distance cut-offs, hydrophobic contacts, hydrogen bonds, salt bridges, π–π stacking, T-stacking and cation–π interactions. The regular update and organization of all these data into a unique platform will allow a broad community of researchers to collect and analyse a large number of features efficiently, thus facilitating their use in the development of prediction models associated with MPs. Database URL: http://www.moreiralab.com/resources/mensadb.

Structural characterization of proteins and their complexes is a fundamental part in understanding any biological phenomena. Yet, the experimental determination of the three‐dimensional (3D) structure of proteins and their complexes remains a challenging undertaking. In order to complement the experimental approaches, computational methods have been developed based on a variety of algorithms and models to fill the gap between the amount of sequences and structures. In this article, we review the most common methodological approaches currently used in the field, highlighting the ab initio structure prediction methods and methods for the prediction and structural modeling of protein–protein interfaces (PPIs). We particularly focus on the use of evolutionary information to guide the modeling process.

Structural characterization of proteins and their complexes is a fundamental part in understanding any biological phenomena. Yet, the experimental determination of the three‐dimensional (3D) structure of proteins and their complexes remains a challenging undertaking. In order to complement the experimental approaches, computational methods have been developed based on a variety of algorithms and models to fill the gap between the amount of sequences and structures. In this article, we review the most common methodological approaches currently used in the field, highlighting the ab initio structure prediction methods and methods for the prediction and structural modeling of protein–protein interfaces (PPIs). We particularly focus on the use of evolutionary information to guide the modeling process.

Diclofenac (DCF) is a widely used non-steroidal anti-inflammatory pharmaceutical which is detected in the environment at concentrations which can pose a threat to living organisms. In this study, biodegradation of DCF was assessed using the bacterial strain Labrys portucalensis F11. Biotransformation of 70% of DCF (1.7-34 mu M), supplied as the sole carbon source, was achieved in 30 days. Complete degradation was reached via co-metabolism with acetate, over a period of 6 days for 1.7 mu M and 25 days for 34 mu M of DCF. The detection and identification of biodegradation intermediates was performed by UPLC-QTOF/MS/MS. The chemical structure of 12 metabolites is proposed. DCF degradation by strain F11 proceeds mainly by hydroxylation reactions; the formation of benzoquinone imine species seems to be a central step in the degradation pathway. Moreover, this is the first report that identified conjugated metabolites, resulting from sulfation reactions of DCF by bacteria. Stoichiometric liberation of chlorine and no detection of metabolites at the end of the experiments are strong indications of complete degradation of DCF by strain F11. To the best of our knowledge this is the first report that points to complete degradation of DCF by a single bacterial strain isolated from the environment.

Microbial degradation is the most important process to remove organic pollutants in Waste Water Treatment Plants. Regarding chiral compounds this process is normally enantioselective and needs the suitable analytical methodology to follow the removal of both enantiomers in an accurate way. Thus, this paper describes the development and validation of an enantioselective High Performance Liquid Chromatography with Fluorescence Detection (HPLC-FD) method for simultaneous analysis of fluoxetine (FLX) and norfluoxetine (NFLX) in wastewater effluents. Briefly, this method preconcentrated a small volume of wastewater samples (50 mL) on 500 mg Oasis MCX cartridges and used HPLC-FD with a vancomycin-based chiral stationary phase under reversed mode for analyses. The optimized mobile phase was EtOH/aqueous ammonium acetate buffer (92.5/7.5, v/v ) at pH 6.8. The effect of EtOH percentage, buffer concentration, pH, column oven temperature and flow rate on chromatographic parameters was systematically investigated. The developed method was validated within the wastewater effluent used in microcosms laboratory assays. Linearity ( R 2 > 0.99), selectivity and sensitivity were achieved in the range of 4.0–60 ng mL −1 for enantiomers of FLX and 2.0–30 ng mL −1 for enantiomers of NFLX. The limits of detection were between 0.8 and 2.0 ng mL −1 and the limits of quantification were between 2.0 and 4.0 ng mL −1 for both enantiomers of FLX and the enantiomers of its demethylated metabolite NFLX. The validated method was successfully applied and proved to be robust to follow the degradation of both enantiomers of FLX in wastewater samples, during 46 days.

Organic and metallic pollutants are ubiquitous in the environment. Many metals are reported to be toxic to microorganisms and to inhibit biodegradation. The effect of the metals iron, copper and silver on the metabolism of Labrys portucalensis F11 and on fluorobenzene (FB) biodegradation was examined. The results indicate that the addition of 1 mM of Fe(2+) to the culture medium has a positive effect on bacterial growth and has no impact in the biodegradation of 1 and 2 mM of FB. The presence of 1 mM of Cu(2+) was found to strongly inhibit the growth of F11 cultures and to reduce the biodegradation of 1 and 2 mM of FB to ca. 50 %, with 80 % of stoichiometrically expected fluoride released. In the experiments with resting cells, the FB degraded (from 2 mM supplied) was reduced ca. 20 % whereas the fluoride released was reduced to 45 % of that stoichiometrically expected. Ag(+) was the most potent inhibitor of FB degradation. In experiments with growing cells, the addition of 1 mM of Ag(+) to the culture medium containing 1 and 2 mM of FB resulted in no fluoride release, whereas FB degradation was only one third of that observed in control cultures. In the experiments with resting cells, the addition of Ag(+) resulted in 25 % reduction in substrate degradation and fluoride release was only 20 % of that stoichiometrically expected. The accumulation of catechol and 4-fluorocatechol in cultures supplemented with Cu(2+) or Ag(+) suggest inhibition of the key enzyme of FB metabolism-catechol 1,2-dioxygenase.

In this work we report the recent progresses that have been achieved by our group in the last half decade on the field of computational proteomics. Specifically, we discuss the application of Molecular Dynamics Simulations and Electronic Structure Calculations in drug design, in the clarification of the structural and dynamic properties of proteins and enzymes and in the understanding of the catalytic and inhibition mechanism of cancer-related enzymes. A set of examples illustrate the concepts and help to introduce the reader into this important and fast moving field., {"references":["J. A. McCammon, B. R. Gelin, and M. Karplus, \"Dynamics of folded\nproteins,\" Nature, vol. 267, pp. 585-590, 1977.","M. Karplus and J. A. McCammon, \"Molecular dynamics simulations of\nbiomolecules,\" Nat. Struct. Biol., vol. 9, pp. 646-652, 2002.","A. Warshel, \"Molecular dynamics simulations of biological reactions,\"\nAcc. Chem. Res., vol. 35, pp. 385-395, 2002.","S. J. Weiner, P. A. Kollman, D. A. Case, U. C. Singh, C. Ghio, G.\nAlagona, et al., \"A new force-field for molecular mechanical simulation\nof nucleic-acids and proteins,\" J. Am. Chem. Soc., vol. 106, pp. 765-\n784, 1984.","S. J. Weiner, P. A. Kollman, D. T. Nguyen, and D. A. Case, \"An allatom\nforce field for simulations of protein and nucleic acids,\" J.\nComput. Chem., vol. 7, pp. 230-252, 1986.","W. D. Cornell, P. Cieplak, C. I. Bayly, I. R. Gould, K. M. Merz, D. M.\nFergunson, et al., \"A 2nd generation force-field for the simulation of\nproteins, nucleic-acids, and organic-molecules,\" J. Am. Chem. Soc., vol.\n117, pp. 5179-5197, 1995.","B. R. Brooks, R. E. Bruccoleri, B. D. Olafson, D. J. States, S.\nSwaminathan, and M. Karplus, \"CHARMM - a programm for\nmacromolecular energy, minimization, and dynamics calculations,\" J.\nComput. Chem., vol. 4, pp. 187-217, 1983.","A. D. MacKerell, D. Bashford, M. Bellott, R. L. Dunbrack, J. D.\nEvanseck, M. J. Field et al., \"All-atom empirical potential for molecular\nmodeling and dynamics studies of proteins,\" J. Phys. Chem. B, vol. 102,\npp. 3586-3616, 1998.","A. D. MacKerell, N. Banavali, and N. Foloppe, \"Development and\ncurrent status of the CHARMM force field for nucleic acids,\"\nBiopolymers, vol. 56, pp. 257-265, 2000.\n[10] W. L. Jorgensen and J. Tirado-Rives, \"The OPLS [optimized potentials\nfor liquid simulations] potential functions for proteins, energy\nminimizations for crystals of cyclic peptides and crambin,\" J. Am. Chem. Soc., vol. 110, pp. 1657-1666, 1988.\n[11] J. Pranata, S. G. Wierschke, and W. L. Jorgensen, \"Opls Potential\nFunctions for Nucleotide Bases - Relative Association Constants of\nHydrogen-Bonded Base-Pairs in Chloroform,\" J. Am. Chem. Soc., vol. 113, pp. 2810-2819, 1991.\n[12] W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives, \"Development\nand testing of the OPLS all-atom force field on conformational\nenergetics and properties of organic liquids,\" J. Am. Chem. Soc., vol.\n118, pp. 11225-11236, 1996.\n[13] A. T. Marques, P. A. Fernandes, and M. J. Ramos, \"Molecular dynamics\nsimulations of the amyloid-beta binding alcohol dehydrogenase\n(ABAD) enzyme,\" Bioorgan. Med. Chem., vol. 16, pp. 9511-9518, 2008.\n[14] N. F. Bras, N. M. F. S. Cerqueira, P. A. Fernandes, and M. J. Ramos,\n\"Carbohydrate-binding modules from family 11: Understanding the\nbinding mode of polysaccharides,\" Int. J. Quantum Chem., vol. 108, pp.\n2030-2040, 2008.\n[15] A. Viegas, N. F. Bras, N. M. F. S. Cerqueira, P. A. Fernandes, J. A. M.\nPrates, C. M. G. A. Fontes, et al., \"Molecular determinants of ligand specificity in family 11 carbohydrate binding modules - an NMR, X-ray\ncrystallography and computational chemistry approach,\" FEBS J., vol.\n275, pp. 2524-2535, 2008.\n[16] A. T. P. Carvalho, M. Swart, J. N. P. van Stralen, P. A. Fernandes, M. J.\nRamos, and F. M. Bickelhaupt, \"Mechanism of thioredoxin-catalyzed\ndisulfide reduction. Activation of the buried thiol and role of the variable active-site residues,\" J. Phys. Chem. B, vol. 112, pp. 2511-2523, 2008.\n[17] A. T. P. Carvalho, P. A. Fernandes, and M. J. Ramos, \"Insights on\nresistance to reverse transcriptase: The different patterns of interaction\nof the nucleoside reverse transcriptase inhibitors in the\ndeoxyribonucleotide triphosphate binding site relative to the normal\nsubstrate,\" J. Med. Chem., vol. 49, pp. 7675-7682, 2006.\n[18] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Enzyme Flexibility and\nthe Catalytic Mechanism of Farnesyltransferase: Targeting the\nRelation,\" J. Phys. Chem. B, vol. 112, pp. 8681-8691, 2008.\n[19] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Molecular Dynamics\nSimulations on the Critical States of the Farnesyltransferase Enzyme,\"\nBioorg. Med. Chem., pp. (10.1016/j.bmc.2009.03.055), 2009.\n[20] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Molecular dynamics\nanalysis of farnesyltransferase: A closer look into the amino acid\nbehavior,\" Int. J. Quant. Chem., vol. 108, pp. 1939-1950, 2008.\n[21] N. M. F. S. Cerqueira, N. F. Bras, P. A. Fernandes, and M. J. Ramos,\n\"MADAMM: A multistaged docking with an automated molecular\nmodeling protocol,\" Proteins, vol. 74, pp. 192-206, 2009.\n[22] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Unraveling the\nmechanism of the farnesyltransferase Enzyme,\" J. Biol. Inorg. Chem.,\nvol. 10, pp. 3-10, 2005.\n[23] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Farnesyltransferase\ninhibitors: A detailed chemical view on an elusive biological problem,\"\nCurr. Med. Chem., vol. 15, pp. 1478-1492, 2008.\n[24] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"The Search for the\nMechanism of the Reaction Catalyzed by Farnesyltransferase,\"\nChemistry, 2009.\n[25] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Farnesyltransferase--\nNew Insights into the Zinc-Coordination Sphere Paradigm: Evidence for\na Carboxylate-Shift Mechanism,\" Biophys. J., vol. 88, pp. 483-494,2005.\n[26] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Farnesyltransferase: \nTheoretical Studies on Peptide Substrate Entrance - Thiol or Thiolate \nCoordination?,\" J. Mol. Struct. (Theochem), vol. 729, pp. 125-129, \n2005. \n[27] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"The carboxylate-shift \nin zinc enzymes: a computational study,\" J. Am. Chem. Soc., vol. 129, \npp. 1378-1385, 2007. \n[28] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Theoretical Studies on \nFarnesyltransferase: The Distances Paradox Explained,\" Proteins, vol. \n66, pp. 205-218, 2007. \n[29] M. J. Ramos and P. A. Fernandes, \"Computational enzymatic catalysis,\" \nAcc. Chem. Res., vol. 41, pp. 689-698, 2008. \n[30] S. B. Long, P. J. Casey, and L. S. Beese, \"Reaction path of protein \nfarnesyltransferase at atomic resolution,\" Nature, vol. 419, pp. 645-650, \n2002. \n[31] H. W. Park, S. R. Boduluri, J. F. Moomaw, P. J. Casey, and L. S. Beese, \n\"Crystal Structure of Protein Farnesyltransferase at 2.25 Angstrom \nResolution,\" Science, vol. 275, pp. 1800-1804, 1997. \n[32] D. A. Tobin, J. S. Pickett, H. L. Hartman, C. A. Fierke, and J. E. \nPenner-Hahn, \"Structural characterization of the zinc site in protein \nfarnesyltransferase,\" J. Am. Chem. Soc., vol. 125, pp. 9962-9969,2003. \n[33] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Theoretical Studies on \nFarnesyltransferase: Evidence for Thioether Product Coordination to the \nActive-Site Zinc Sphere,\" J. Comput. Chem., vol. 28, pp. 1160-1168, \n2007. \n[34] N. M. F. S. A. Cerqueira, S. Pereira, P. A. Fernandes, and M. J. Ramos, \n\"Overview of ribonucleotide reductase inhibitors: an appealing target in \nanti-tumour therapy,\" Curr. Med. Chem., vol. 12, pp. 1283-1294, 2005. \n[35] N. M. F. S. A. Cerqueira, P. A. Fernandes, L. A. Eriksson, and M. J. \nRamos, \"New insights into a critical biological control step of the \nmechanism of ribonucleotide reductase,\" J. Mol. Struct. (Theochem), \nvol. 709, pp. 53-65, 2004. \n[36] N. M. F. S. A. Cerqueira, P. A. Fernandes, L. A. Eriksson, and M. J. \nRamos, \"Ribonucleotide activation by enzyme ribonucleotide reductase: \nunderstanding the role of the enzyme,\" J. Comput. Chem., vol. 25, pp. \n2031-2037, 2004. \n[37] N. M. F. S. A. Cerqueira, P. A. Fernandes, L. A. Eriksson, and M. J. \nRamos, \"Dehydration of ribonucleotides catalysed by ribonucleotide \nreductase: the role of the enzyme,\" Biophys. J., vol. 90, pp. 2109-2119, \n2006. \n[38] P. A. Fernandes, L. A. Eriksson, and M. J. Ramos, \"The reduction of \nribonucleotides catalyzed by the enzyme ribonucleotide reductase,\" \nTheor. Chem. Acc., vol. 108, pp. 352-364, 2002. \n[39] S. Pereira, N. M. F. S. A. Cerqueira, P. A. Fernandes, and M. J. Ramos, \n\"Computational studies on class I ribonucleotide reductase: \nUnderstanding the mechanism of action and inhibition of a cornerstone \nenzyme for the treatment of cancer,\" Eur. Biophys. J., vol. 35, pp. 125-\n135, 2006. \n[40] P. A. Fernandes and M. J. Ramos, \"Theoretical studies on the mode of \ninhibition of ribonucleotide reductase by 2'-substituted substrate \nanalogues,\" Chem. Eur. J., vol. 9, pp. 5916-5925, 2003. \n[41] N. M. F. S. Cerqueira, P. A. Fernandes, and M. J. Ramos, \"Enzyme \nribonucleotide reductase: Unraveling an enigmatic paradigm of enzyme \ninhibition by furanone derivatives,\" J. Phys. Chem. B, vol. 110, pp. \n21272-21281, 2006. \n[42] S. Pereira, P. A. Fernandes, and M. J. Ramos, \"Theoretical study of \nribonucleotide reductase mechanism-based inhibition by 2'-azido-2'-\ndeoxyribonucleoside 5'-diphosphates,\" J. Comput. Chem., vol. 25, pp. \n227-237, 2004. \n[43] S. Pereira, P. A. Fernandes, and M. J. 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Rev. \nB, vol. 136, pp. B864, 1964. \n[49] Y. Zhao and D. G. Truhlar, \"Density functionals with broad \napplicability in chemistry,\" Acc. Chem. Res., vol. 41, pp. 157-167, \n2008. \n[50] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"General Performance of \nDensity Functionals,\" J. Phys. Chem A, vol. 111, pp. 10439-10452, \n2007. \n[51] S. F. Sousa, P. A. Fernandes, and M. J. Ramos, \"Comparative \nassessment of theoretical methods for the determination of geometrical \nproperties in biological zinc complexes,\" J. Phys. Chem. B, vol. 111, pp. \n9146-9152, 2007. \n[52] P. Jurecka, J. Cerny, P. Hobza, and D. R. Salahub, \"Density functional \ntheory augmented with an empirical dispersion term. Interaction \nenergies and geometries of 80 noncovalent complexes compared with ab \ninitio quantum mechanics calculations,\" J. Comput. Chem., vol. 28, pp. \n555-569, 2007. \n[53] A. Karton, A. Tarnopolsky, J. F. Lamere, G. C. Schatz, and J. M. L. \nMartin, \"Highly Accurate First-Principles Benchmark Data Sets for the \nParametrization and Validation of Density Functional and Other \nApproximate Methods. Derivation of a Robust, Generally Applicable, \nDouble-Hybrid Functional for Thermochemistry and Thermochemical \nKinetics,\" J. Phys. Chem. A, vol. 112, pp. 12868-12886, 2008. \n[54] Y. Zhao and D. G. Truhlar, \"Density functionals for noncovalent \ninteraction energies of biological importance,\" J. Chem. Theor. \nComput., vol. 3, pp. 289-300, 2007. \n[55] T. van Mourik, \"Assessment of Density Functionals for Intramolecular \nDispersion-Rich Interactions,\" J. Chem. Theor. Comput., vol. 4, pp. \n1610-1619, 2008. \n[56] R. Peverati and K. K. Baldridge, \"Implementation and Performance of \nDFT-D with Respect to Basis Set and Functional for Study of \nDispersion Interactions in Nanoscale Aromatic Hydrocarbons,\" J. Chem. \nTheor. Comput., vol. 4, pp. 2030-2048, 2008. \n[57] N. F. Bras, S. A. Moura-Tamames, P. A. Fernandes, and M. J. Ramos, \n\"Mechanistic Studies on the Formation of Glycosidase-Substrate and \nGlycosidase-Inhibitor Covalent Intermediates,\" J. Comput. Chem., vol. \n29, pp. 2565-2574, 2008. \n[58] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Hot spots-A review of \nthe protein-protein interface determinant amino-acid residues,\" Proteins, \nvol. 68, pp. 803-812, 007. \n[59] S. Jones and J. M. Thornton, \"Principles of protein-protein interactions,\" \nProc. Natl. Acad. Sci. USA, vol. 93, pp. 13-20, 1996. \n[60] A. A. Bogan and K. S. Thorn, \"Anatomy of hot spots in protein \ninterfaces,\" J. Mol. Biol., vol. 280, pp. 1-9, 1998. \n[61] T. Clackson and J. A. Wells, \"A Hot-Spot of Binding-Energy in A \nHormone-Receptor Interface,\" Science, vol. 267, pp. 383-386, 1995. \n[62] W. L. Delano, \"Unraveling hot spots in binding interfaces: progress and \nchallenges,\" Curr. Opin. Struct. Biol., vol. 12, pp. 14-20, 2002. \n[63] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Detailed microscopic \nstudy of the full ZipA : FtsZ interface,\" Proteins, vol. 63, pp. 811-821, \n2006. \n[64] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Unraveling the \nimportance of protein-protein interaction: Application of a \ncomputational alanine-scanning mutagenesis to the study of the IgG1 \nstreptococcal protein G (C2 fragment) complex,\" J. Phys. Chem. B, vol. \n110, pp. 10962-10969, 2006. \n[65] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Hot spot \ncomputational identification: Application to the complex formed \nbetween the hen egg white lysozyme (HEL) and the antibody HyHEL-\n10,\" Int. J. Quant. Chem., vol. 107, pp. 299-310, 2007. \n[66] I. S. Moreira, P. A. Fernandes, and M. J. Ramos, \"Hot spot occlusion \nfrom bulk water: A comprehensive study of the complex between the \nlysozyme HEL and the antibody FVD1.3,\" J. Phys. Chem. B, vol. 111, \npp. 2697-2706, 2007. \n[67] I. S. Moreira, P. A. Fernandes, and M. J. 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