Authors: Malik Mayank Kumar, Kumar Tarun, Kumar Vipin, Singh Jaspal, Singh Raj Kumar, Saini Karuna, 

Journal: Sustainable Energy Technologies and Assessments.

Publication date: 01 Oct 2022

Publisher: ELSEVIER

URL: Access Paper

Abstract:

Starch is a biodegradable biopolymer with great potential for developing novel biodegradable products. However, Starch can be derivatized to improve its functionality and uses. In current research, underutilized mandua grains were used for starch isolation by alkaline stepping method, derivatized using acetic anhydride and characterized by SEM, FTIR, XRD and TGA. Further, the highly moldable, sustainable films of esterified mandua starch were successfully prepared via combining starches with glycerol and polyvinyl alcohol using a film-forming casting technique. The films were characterized for mechanical properties and morphology. The tensile strength of the esterified mandua starch film was significantly lower than those of the alkali isolated mandua starch film with polyvinyl alcohol. Further, acute toxicity study of acetylated mandua starch showed no adverse effect on biochemical parameters of treated animals. The findings demonstrated that the mandua starch films’ flexibility was increased after acetylation. Thus, esterified mandua starch films might provide excellent substitutes for developing biodegradable products.

Authors: Sharma Ritesh, Shrivastava Sameer, Singh Sanjay Kumar, Kumar Abhinav, Saxena Sonal, Singh Raj Kumar.

Journal: Briefings in Bioinformatics.

Publication date: 17 Jan 2022

Publisher: OXFORD UNIV PRESS

URL: Access Paper

Abstract:

Fungal infections or mycosis cause a wide range of diseases in humans and animals. The incidences of community acquired; nosocomial fungal infections have increased dramatically after the emergence of COVID-19 pandemic. The increase in number of patients with immunodeficiency / immunosuppression related diseases, resistance to existing antifungal compounds and availability of limited therapeutic options has triggered the search for alternative antifungal molecules. In this direction, antifungal peptides (AFPs) have received a lot of interest as an alternative to currently available antifungal drugs. Although the AFPs are produced by diverse population of living organisms, identifying effective AFPs from natural sources is time-consuming and expensive. Therefore, there is a need to develop a robust in silico model capable of identifying novel AFPs in protein sequences. In this paper, we propose Deep-AFPpred, a deep learning classifier that can identify AFPs in protein sequences. We developed Deep-AFPpred using the concept of transfer learning with 1DCNN-BiLSTM deep learning algorithm. The findings reveal that Deep-AFPpred beats other state-of-the-art AFP classifiers by a wide margin and achieved approximately 96% and 94% precision on validation and test data, respectively. Based on the proposed approach, an online prediction server is created and made publicly available at . Using this server, one can identify novel AFPs in protein sequences and the results are provided as a report that includes predicted peptides, their physicochemical properties and motifs. By utilizing this model, we identified AFPs in different proteins, which can be chemically synthesized in lab and experimentally validated for their antifungal activity.

Authors: Sharma Ritesh, Shrivastava Sameer, Singh Sanjay Kumar, Kumar Abhinav, Saxena Sonal, Singh Raj Kumar.

Journal: Briefings in Bioinformatics.

Publication date: 01 Nov 2021

Publisher: OXFORD UNIV PRESS

URL: Access Paper

Abstract:

With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But confirming the antimicrobial function of host proteins by wet-lab experiments is expensive and time consuming. Therefore, there is a need to develop an in silico tool to identify the antimicrobial function of proteins. In the current study, we developed a model AniAMPpred by considering all the available antimicrobial peptides (AMPs) of length [10 200] from the animal kingdom. The model utilizes a support vector machine algorithm with deep learning-based features and identifies probable antimicrobial proteins (PAPs) in the genome of animals. The results show that our proposed model outperforms other state-of-the-art classifiers, has very high confidence in its predictions, is not biased and can classify both AMPs and non-AMPs for a diverse peptide length with high accuracy. By utilizing AniAMPpred, we identified 436 PAPs in the genome of Helobdella robusta. To further confirm the functional activity of PAPs, we performed BLAST analysis against known AMPs. On detailed analysis of five selected PAPs, we could observe their similarity with antimicrobial proteins of several animal species. Thus, our proposed model can help the researchers identify PAPs in the genome of animals and provide insight into the functional identity of different proteins. An online prediction server is also developed based on the proposed approach, which is freely accessible at https://aniamppred.anvil.app/.

Authors: Sharma Ritesh, Shrivastava Sameer, Singh Sanjay Kumar, Kumar Abhinav, Saxena Sonal, Singh Raj Kumar.

Journal: Briefings in Bioinformatics.

Publication date: 01 Sept 2021

Publisher: OXFORD UNIV PRESS

URL: Access Paper

Abstract:

The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-intensive and time-consuming process. Therefore, there is a need for the development of in silico models, which can identify novel ABPs in protein sequences for chemical synthesis and testing. In this study, we propose a deep learning classifier named Deep-ABPpred that can identify ABPs in protein sequences. We developed Deep-ABPpred using bidirectional long short-term memory algorithm with amino acid level features from word2vec. The results show that Deep-ABPpred outperforms other state-of-the-art ABP classifiers on both test and independent datasets. Our proposed model achieved the precision of approximately 97 and 94% on test dataset and independent dataset, respectively. The high precision suggests applicability of Deep-ABPpred in proposing novel ABPs for synthesis and experimentation. By utilizing Deep-ABPpred, we identified ABPs in the tail protein sequences of Streptococcus bacteriophages, chemically synthesized identified peptides in lab and tested their activity in vitro. These ABPs showed potent antibacterial activity against selected Gram-positive and Gram-negative bacteria, which confirms the capability of Deep-ABPpred in identifying novel ABPs in protein sequences. Based on the proposed approach, an online prediction server is also developed, which is freely accessible at https://abppred.anvil.app/. This web server takes the protein sequence as input and provides ABPs with high probability (>0.95) as output.

Authors: Varun Chandra, Kumar Singh Raj.

Journal: Journal of Orthopedics Trauma and Rehabilitation.

Publication date: 01 June 2017

Publisher: SAGE PUBLICATIONS LTD.

URL: Access Paper

Abstract:

Background/Purpose: Soft tissue necrosis after the repair of a ruptured tendo Achillis is a difficult problem commonly encountered by orthopaedic surgeons. Such wounds are difficult to manage because of the tenuous blood supply and the characteristic anatomical features of the area. Flaps such as the reverse sural flap, medial plantar flap, gastrocnemius flap and free flaps cover such wounds with a good success rate. The aim of our study was to investigate the use of a dorsalis pedis artery (DPA)-based flap to cover a wound over the tendo Achillis insertion area.