According to the United Nations Educational, Scientific, and Cultural Organization (UNESCO), Open Science is the movement to make scientific research and data accessible to all. It has great potential for advancing science. At its core, it includes (but is not limited to) open access, open data, and open research. Some of the associated advantages are promoting collaboration, sharing and reproducibility in research, and preventing the reinvention of the wheel, thus saving resources. As research becomes more globalized and its output grows exponentially, especially in data, the need for open scientific research practices is more evident — the future of modern science. This has resulted in a concerted global interest in open science uptake. Even so, barriers still exist. The formal training curriculum in most, if not all, universities in Kenya does not equip students with the knowledge and tools to subsequently practice open science in their research. Therefore, to work openly and collaboratively, there is a need for awareness and training in the use of open science tools. These have been neglected, especially in most developing countries, and remain barriers to the cause. Moreover, there is scanty research on the state of affairs regarding the practice and/or adoption of open science. Thus, we developed, through the OpenScienceKE framework, a model to narrow the gap. A sensitize-train-hack-collaborate model was applied in Nairobi, the economic and administrative capital of Kenya. Using the model, we sensitized through seminars, trained on the use of tools through workshops, applied the skills learned in training through hackathons to collaboratively answer the question on the state of open science in Kenya. While the former parts of the model had 20–50 participants, the latter part mainly involved participants with a bioinformatics background, leveraging their advanced computational skills. This model resulted in an open resource that researchers can use to publish as open access cost-effectively. Moreover, we observed a growing interest in open science practices in Kenya through literature search and data mining and that lack of awareness and skills may still hinder the adoption and practice of open science. Furthermore, at the time of the analyses, we surprisingly found that out of the 20,069 papers downloaded from BioRXiv, only 18 had Kenyan authors, a majority of which are international (16) collaborations. This may suggest poor uptake of the use of preprints among Kenyan researchers. The findings in this study highlight the state of open science in Kenya and challenges facing its adoption and practice while bringing forth possible areas for primary consideration in the campaign toward open science. It also proposes a model (sensitize-train-hack-collaborate model) that may be adopted by researchers, funders and other proponents of open science to address some of the challenges faced in promoting its adoption in Kenya.

Background: The emergence of artemisinin resistance in South East Asia calls for urgent discovery of new drug compounds that have antiplasmodial activity. Unlike the classical compound screening drug discovery methods, the rational approach involving targeted drug discovery is less cumbersome and therefore key for innovation of new antiplasmodial compounds.  Plasmodium falciparum (Pf) utilizes the process of host erythrocyte remodeling using Plasmodium-helical interspersed sub-telomeric domain (PHIST) containing proteins, which are amenable drug targets. The aim of this study is to identify inhibitors of PHIST from sulfated polysaccharides as new antimalarials. Methods: 251 samples from an ongoing study of epidemiology of malaria and drug resistance sensitivity patterns in Kenya were sequenced for PHISTb/RLP1 gene using Sanger sequencing. The sequenced reads were mapped to the reference Pf3D7 protein sequence of PHISTb/RLP1 using CLC Main Workbench. Homology modeling of both reference and mutant protein structures was achieved using the LOMETs tool. The models were refined using ModRefiner for energy minimization. Ramachandran plot was generated by ProCheck to assess the conformation of amino acids in the protein model. Protein binding sites predictions were assessed using FT SITE software. We searched for prospective antimalarials from PubChem. Docking experiments were achieved using AutoDock Vina and analysis results visualized in PyMOL. Results: Sanger sequencing generated 86 complete sequences. Upon mapping of the sequences to the reference, 12 non-synonymous single nucleotide polymorphisms were considered for mutant protein structure analysis. Eleven drug compounds with antiplasmodial activity were identified. Both modelled PHISTb/RLP1 reference and mutant structures had a Ramachandran score of >90% of the amino acids in the favored region. Ten of the drug compounds interacted with amino acid residues in PHISTb and RESA domains, showing potential activity against these proteins. Conclusion: These interactions provide lead compounds for new anti-malarial molecules. Further in vivo testing is recommended.

Charge transfer reactions in proteins are fundamentally important for life, but it is currently not clear how protein structural dynamics control these electron transfer reactions. Photolyases/cryptochromes, which repair DNA and signal in all kingdoms of life, have a paradigm electron transfer cascade. Here, photoreduction of the flavin cofactor initiates charge transfer along a chain of four conserved tryptophans. We report femtosecond X-ray crystallographic snap-shots for the Drosophila melanogaster (6-4) photolyase, revealing protein structural changes while electron transfer occurs. At femto- and picosecond delays, photoreduction of the flavin by the first tryptophan causes directed structural responses at key residue asparagine 403, at a conserved salt bridge, and by rearrangements of nearby water molecules. Along the tryptophan cascade, we detect charge-induced protein structural changes close to the second tryptophan from 1 ps to 20 ps, thereby identifying methionine 408 as an active participant in the redox chain, and from 300 ps around the fourth tryptophan. The data reveal that the protein undergoes highly directed and carefully timed adaptations of its structure to facilitate electron transfer. This suggests that evolution has optimized fast protein fluctuations for optimal function.