Natural plant sources are essential in the development of several anticancer drugs, such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, and teniposide. However, various chemotherapies fail due to adverse reactions, drug resistance, and target specificity. Researchers are now focusing on developing drugs that use natural compounds to overcome these issues. These drugs can affect multiple targets, have reduced adverse effects, and are effective against several cancer types. Developing a new drug is a highly complex, expensive, and time-consuming process. Traditional drug discovery methods take up to 15 years for a new medicine to enter the market and cost more than one billion USD. However, recent Computer Aided Drug Discovery (CADD) advancements have changed this situation. This paper aims to comprehensively describe the different CADD approaches in identifying anticancer drugs from natural products. Data from various sources, including Science Direct, Elsevier, NCBI, and Web of Science, are used in this review. In-silico techniques and optimization algorithms can provide versatile solutions in drug discovery ventures. The structure-based drug design technique is widely used to understand chemical constituents\' molecular-level interactions and identify hit leads. This review will discuss the concept of CADD, in-silico tools, virtual screening in drug discovery, and the concept of natural products as anticancer therapies. Representative examples of molecules identified will also be provided.

Heat shock proteins (HSPs) improve cross-presentation of linked tumor antigens, thus they can be exploited in therapeutic vaccine design. Herein, in silico analyses of different vaccine constructs were performed based on human papillomavirus (HPV)-16 E7 protein linked to Homo sapiens/Mus musculus Hsp27 or Hsp70 in multiepitope and whole sequence forms. Then, computational comparison between different orientations of Hsp/E7 was carried out in both forms. Finally, molecular docking was performed between the designed constructs and signaling (TLRs) or endocytic (CD14, LOX-1 and SREC-1) receptors. Our data represented the high-ranked T-cell epitopes and the potential B-cell epitopes of Homo sapiens/Mus musculus Hsp27 and Hsp70. Moreover, molecular docking showed that whole sequence of Hsp27 had better interaction with all receptors than whole sequence of Hsp70 suggesting likely stronger stimulation of innate and adaptive immunity. All designed Homo sapiens/Mus musculus Hsp27/E7 constructs had better docking scores with the endocytic receptors especially SREC-1 than all designed Homo sapiens/Mus musculus Hsp70/E7 constructs in both orientations. Generally, the multiepitope-/whole sequence-based Homo sapiens/Mus musculus Hsp27-E7 fusion constructs showed more conservancy and immunogenicity than other designed constructs. These fusion constructs were non-allergenic, non-toxic and stable suggesting them as promising vaccine candidates against HPV-related cancers.

BACKGROUND: PRKCG encodes PKC γ, which is categorized under the classical protein kinase C family. No studies have specifically established the relationship between PRKCG nsSNPs with structural and functional variations in PKC γ in the context of hepatocellular carcinoma (HCC). The present study aims to uncover this link through in-silico and experimental studies.
METHODS: The 3D structure of PKC γ was predicted. Molecular Dynamic (MD) Simulations were run and estimates were made for interactions, stability, conservation and post-translational alterations between wild and mutant structures. The association of PRKCG levels with HCC survival rate was determined. Genotyping analyses were conducted to investigate the deleterious PRKCG nsSNP association with HCC. mRNA expression of PKC γ, HIF-1 alpha, AKT, SOCS3 and VEGF in the blood of controls and HCC patients was analyzed and a genetic cascade was constructed depicting these interactions.
RESULTS: The expression level of studied oncogenes was compared to tumour suppressor genes. Through Alphafold, the 3D structure of PKC γ was explored. Fifteen SNPs were narrowed down for in-silico analyses that were identified in exons 5, 10 and 18 and the regulatory and kinase domain of PKC γ. Root mean square deviation and fluctuation along with the radius of gyration unveiled potential changes between the wild and mutated variant structures. Mutant genotype AA (homozygous) corresponding to nsSNP, rs386134171 had more frequency in patients with OR (2.446), RR (1.564) and P-values (< 0.0029) that highlights its significant association with HCC compared to controls in which the wild genotype GG was found more prevalent.
CONCLUSIONS: nsSNP rs386134171 can be a genetic marker for HCC diagnosis and therapeutic studies. This study has laid down a road map for future studies to be conducted on HCC.

Partitioning tests in water are early-stage standard experiments during the development of pharmaceutical formulations, e.g. of lipid-based drug delivery system (LBDDS). The partitioning behavior of the active pharmaceutical ingredient (API) between the fatty phase and the aqueous phase is a key property, which is supposed to be determined by those tests. In this work, we investigated the API partitioning between LBDDS and water by in-silico predictions applying the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) and validated these predictions experimentally. The API partitioning was investigated for LBDDS comprising up to four components (cinnarizine or ibuprofen with tricaprylin, caprylic acid, and ethanol). The influence of LBDDS/water mixing ratios from 1/1 up to 1/200 (w/w) as well as the influence of excipients on the API partitioning was studied. Moreover, possible API crystallization upon mixing the LBDDS with water was predicted. This work showed that PC-SAFT is a strong tool for predicting the API partitioning behavior during in-vitro tests. Thus, it allows rapidly assessing whether or not a specific LBDDS might be a promising candidate for further in-vitro tests and identifying the API load up to which API crystallization can be avoided.

Protein engineering has enabled development of novel proteins aimed at disease diagnosis, alleviation and improved health attributes. The present article provides an overview of recent approaches and techniques used to modify proteins at diverse levels, which find therapeutically relevant applications. There is immense interest among researchers to discover new and increasingly valuable solutions for various health related issues and protein engineering could be a possible venue to sort out such problems. In this mini review we have tried to decipher some of the novel aspects of protein engineering in terms of protein-based therapeutics and diagnostics, in-silico tools and related approaches. A special emphasis has been given for some innovative aspects of protein-nanoparticle conjugates; use of artificial intelligence (AI)- based tools and post-translational modifications. Utilization of such approaches in protein engineering might be ground breaking in future research endeavor of researchers across the world.