Background and Aim: BIOINFORMATICS innovations are generally designed to collect, store, process, or analyze biological data using computer-implemented methods. Therefore, it is considered that software is the main foundation of BIOINFORMATICS and protecting BIOINFORMATICS software is a key element for growth and development of this science. Given the special features of the development and application of BIOINFORMATICS software, the study of regulations and different types of legal protection systems that can be applied to these softwares is a matter of concern. Materials and Methods: The research method is analytical-comparative. It has been prepared and compiled by library method using data collection and note-taking techniques in documents, books and articles. Results: The findings indicate that, except in a few industrialized countries, there is no specific case law in the field of BIOINFORMATICS software. Applicable laws and regulations in the selected countries are also largely restricted to traditional intellectual property laws, and no sui-generis legistation has been adopted to protect BIOINFORMATICS software. Ethical considerations: In all stages of writing this paper, the ethical principles of research, especially, the originality of the text, honesty and confidentiality have been fully observed. Conclusion: Due to different approaches and interests of the selected countries, we see many similarities and contradictions regarding the protection of BIOINFORMATICS software. Accordingly, the need to harmonize relevant regulations at international level is fully felt. However, based on global experiences, it seems that regardless of whether BIOINFORMATICS software conformed with traditional IP laws or a sui-generis law is required, it is necessary to review more carefully and in more detail legal, commercial and technical requirements and resolve any ambiguities or gaps within legal protection that BIOINFORMATICS softaware face, in order to provide an appropriate and comprehensive legal framework in this field.

Growth hormone (GH) is a protein that contains 191 amino acids with two disulfide bonds and four α-helixes. Its receptor belongs to class I cytokine receptor family. Two GH receptors bind to the hormone one after the other. Binding to the first site forms an inactive intermediate complex. The assembly becomes functionally active only when the second receptor molecule binds at the second binding site on the other side of the hormone. Any factor that disrupts this assembly can block the hormone's function. Changing a single glycine at position 120 to arginine will block receptor binding at the second site. By comparing the modified hormone with its wild type, it can be observed that the large side chain of arginine 120 cannot be accommodated when the second receptor binds. In this study, we considered the effects of mutagenesis on structure quality of the protein by BIOINFORMATICS. The 3D structure of growth hormone was obtained from PDB (http://www.rcsb.org/pdb/home). Modified growth hormone was modeled by M4T server. Then the analysis for structure was obtained with Qmean server: a server for quality estimation of protein structure models, http://swissmodel .expasy.org/qmean/cgi /index. The quality and stability of modeled structures were evaluated by Z-score value. This value for growth hormone and its mutant was -3.50 and -1.86, respectively. In the present study, we revealed that the mutant structure of growth hormone as an antagonist is a validate structure compared to GH and it would be very promising for using in cancer therapy.

Protein engineering is an important tool for overcoming the limitations of natural enzymes as biocatalysts. In this regard computational tools are becoming increasingly important in order to create improved or novel enzymes. We developed a useful collection of BIOINFORMATICS tools in protein engineering. These applications are freely available at http: //bioinf.modares.ac.ir. A short description will come in the following: PUTracer is a useful and easy to use web based application to assign number of domains and domain boundaries in a protein. Recognition of domains in proteins is a critical step in chimeric protein engineering. LinDa (Linker Design Assistant) is a database of protein fragments in 4-20 amino acid length. Linda is an effective tool to chimeric protein engineering and peptide design. MPDB (Mutation Proposer on Disulfide Bond): Disulfide bonds formation is one of the solutions to increase the thermal stability of enzymes. MPDB gives a list of amino acids pair with potential to form a disulfide bond. CFinder (Contact Finder) Accurate identification of amino acids involved in the binding of the two proteins has wide applications, including antibody optimization, analysis of docking studies, increasing protein stability and peptide design. CFinder identifies amino acids involved in binding in a protein complex and also an individual amino acid neighbors too. Mini Mutate is a user friendly application to have the structure of a mutated protein. The input of application is native protein structure in PDB format and desired mutation. It will give the mutated protein structure after replacing amino acid and optimizing the structure.