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Stakeholder satisfaction is the main motive of the software industry. Consumer and producer satisfaction means achieving a software-quality product. There are so many techniques, methods, and models are present for the software-quality prediction. Computational intelligence is also playing a crucial role in the prediction of quality characteristics. This paper gives a new optimal mathematical model for the prediction of the degree of stakeholder satisfaction (Q). Optimal models validate the real data using the relationship impacts of various quality attributes. It uses the equations of constraints. The optimal model gives the maximum and minimum values for Q. Constraints constitutes of software quality characteristics. In the given case study example, the idle value of Q is 30 but using optimal model it gives the maximum optimal value of Q = 22.788020075 for xLAB IT consulting services on their project In-Reg Molecule Registration using the MATLAB. It means if there is any change in the value of any software quality characteristics, then it will decrease the value of Q. It proves that the given result is an optimal solution. INDEX TERMS ISO/IEC 9126, software quality, degree of customer satisfaction, reliability, reusability.

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Testis-specific protein, Y-encoded (TSPY) binds to eukaryotic translation elongation factor 1 alpha (eEF1A) at its SET/NAP domain that is essential for the elongation during protein synthesis implicated with normal spermatogenesis. The eEF1A exists in two forms, eEF1A1 (alpha 1) and eEF1A2 (alpha 2), encoded by separate loci. Despite critical interplay of the TSPY and eEF1A proteins, literature remained silent on the residues playing significant roles during such interactions. We deduced 3D structures of TSPY and eEF1A variants by comparative modeling (Modeller 9.13) and assessed protein-protein interactions employing HADDOCK docking. Pairwise alignment using EMBOSS Needle for eEF1A1 and eEF1A2 proteins revealed high degree (~92%) of homology. Efficient binding of TSPY with eEF1A2 as compared to eEF1A1 was observed, in spite of the occurrence of significant structural similarities between the two variants. We also detected strong interactions of domain III followed by domains II and I of both eEF1A variants with TSPY. In the process, seven interacting residues of TSPY's NAP domain namely, Asp 175, Glu 176, Asp 179, Tyr 183, Asp 240, Glu 244, and Tyr 246 common to both eEF1A variants were detected. Additionally, six lysine residues observed in eEF1A2 suggest their possible role in TSPY-eEF1A2 complex formation essential for germ cell development and spermatogenesis. Thus, more efficient binding of TSPY with eEF1A2 as compared to that of eEF1A1 established autonomous functioning of these two variants. Studies on mutated protein following similar approach would uncover the causative obstruction, between the interacting partners leading to deeper understanding on the structure-function relationship.

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Molecular interactions between mesenchymal-derived Keratinocyte growth factor (KGF) and Kit ligand (KITLG) are essential for follicular development. These factors are expressed by theca and granulosa cells. We determined full length coding sequence of buffalo KGF and KITLG proteins having 194 and 274 amino acids, respectively. The recombinant KGF and KITLG proteins were solubilized in 10 mM Tris, pH 7.5 and 50 mM Tris, pH 7.4 and purified using Ni-NTA column and GST affinity chromatography, respectively. The purity and molecular weight of His-KGF (~23 kDa) and GST-KITLG (~57 kDa) proteins were confirmed by SDS-PAGE and western blotting. The co-immunoprecipitation assay accompanied with computational analysis demonstrated the interaction between KGF and KITLG proteins. We deduced 3D structures of the candidate proteins and assessed their binding based on protein docking. In the process, KGF specific residues, Lys123, Glu135, Lys140, Lys155 and Trp156 and KITLG specific ones, Ser226, Phe233, Gly234, Ala235, Phe236, Trp238 and Lys239 involved in the formation of KGF-KITLG complex were detected. The hydrophobic interactions surrounding KGF-KITLG complex affirmed their binding affinity and stability to the interacting interface. Additionally, in-silico site directed mutagenesis enabled the assessment of changes that occurred in the binding energies of mutated KGF-KITLG protein complex. Our results demonstrate that in the presence of KITLG, KGF mimics its native binding mode suggesting all the KGF residues are specific to their binding complex. This study provides an insight on the critical amino acid residues participating in buffalo ovarian folliculogenesis.

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