Engineering and Design of Programmable Genome Editors.

Programmable genome editors are enzymes that can be targeted to a specific location in the genome for making site-specific alterations or deletions. The engineering, design, and development of sequence-specific editors has resulted in a dramatic increase in the precision of editing for nucleotide sequences. These editors can target specific locations in a genome, in vivo. The genome editors are being deployed for the development of genetically modified organisms for agriculture and industry, and for gene therapy of inherited human genetic disorders, cancer, and immunotherapy. Experimental and computational studies of structure, binding, activity, dynamics, and folding, reviewed here, have provided valuable insights that have the potential for increasing the functional efficiency of these gene/genome editors. Biochemical and biophysical studies of the specificities of natural and engineered genome editors reveal that increased binding affinity can be detrimental because of the increase of off-target effects and that the engineering and design of genome editors with higher specificity may require modulation and control of the conformational dynamics.

Algorithms for protein design.

Computational Protein Design has the potential to contribute to major advances in enzyme technology, vaccine design, receptor-ligand engineering, biomaterials, nanosensors, and synthetic biology. Although Protein Design is a challenging problem, proteins can be designed by experts in Protein Design, as well as by non-experts whose primary interests are in the applications of Protein Design. The increased accessibility of Protein Design technology is attributable to the accumulated knowledge and experience with Protein Design as well as to the availability of software and online resources. The objective of this review is to serve as a guide to the relevant literature with a focus on the novel methods and algorithms that have been developed or applied for Protein Design, and to assist in the selection of algorithms for Protein Design. Novel algorithms and models that have been introduced to utilize the enormous amount of experimental data and novel computational hardware have the potential for producing substantial increases in the accuracy, reliability and range of applications of designed proteins.

Molecular Docking and Virtual Screening Based Prediction of Drugs for COVID-19.

AIMS: To predict potential drugs for COVID-19 by using molecular docking for virtual screening of drugs approved for other clinical applications. BACKGROUND: SARS-CoV-2 is the betacoronavirus responsible for the COVID-19 pandemic. It was listed as a potential global health threat by the WHO due to high mortality, high basic reproduction number, and lack of clinically approved drugs and vaccines. The genome of the virus responsible for COVID-19 has been sequenced. In addition, the three-dimensional structure of the main protease has been determined experimentally. OBJECTIVE: To identify potential drugs that can be repurposed for treatment of COVID-19 by using molecular docking based virtual screening of all approved drugs. METHODS: A list of drugs approved for clinical use was obtained from the SuperDRUG2 database. The structure of the target in the apo form, as well as structures of several target-ligand complexes, were obtained from RCSB PDB. The structure of SARS-CoV-2 Mpro determined from X-ray diffraction data was used as the target. Data regarding drugs in clinical trials for COVID-19 was obtained from clinicaltrials.org. Input for molecular docking based virtual screening was prepared by using Obabel and customized python, bash, and awk scripts. Molecular docking calculations were carried out with Vina and SMINA, and the docked conformations were analyzed and visualized with PLIP, Pymol, and Rasmol. RESULTS: Among the drugs that are being tested in clinical trials for COVID-19, Danoprevir and Darunavir were predicted to have the highest binding affinity for the Main protease (Mpro) target of SARS-CoV-2. Saquinavir and Beclabuvir were identified as the best novel candidates for COVID-19 therapy by using Virtual Screening of drugs approved for other clinical indications. CONCLUSION: Protease inhibitors approved for treatment of other viral diseases have the potential to be repurposed for treatment of COVID-19.

Superparamagnetic Iron Oxide Nanoparticles (SPIONs) for Diagnosis and Treatment of Breast, Ovarian and Cervical Cancers.

BACKGROUND: The potential of Super Paramagnetic Iron Oxide Nanoparticles (SPIONs) as theranostic agents for cancer has been investigated extensively. SPIONS can be utilized for diagnostic imaging, drug delivery as well as for therapeutic applications. SPIONS are of particular interest because of their potential for non-invasive diagnosis and non-invasive therapeutic applications. This article is a review of in vivo and clinical studies of SPIONs for diagnosis and treatment of breast, ovarian and cervical cancer. The current limitations of this technology with relation to clinical therapeutic applications and the potential to overcome these limitations are also discussed. METHODS: NCBI Pubmed was searched for relevant documents by using keyword and MESH based search. The following keyword combinations were used: 'breast cancer' and SPION, 'ovarian cancer' and SPION, and 'cervical cancer' and SPION. The resulting list was manually scanned for the studies involving clinical and in vivo studies. RESULTS: The 29 most relevant publications were identified and reviewed. CONCLUSION: Although numerous in vitro and in vivo studies have demonstrated the safety and effectiveness of the use of SPIONs for both diagnostic and therapeutic applications, there is relatively little progress towards translation to clinical applications involving breast, ovarian and cervical cancer.