Antimicrobial peptides (AMPs) are known to have family-specific sequence composition, which can be mined for discovery and design of AMPs. Here, we present CAMPR3; an update to the existing CAMP database available online at www.camp3.bicnirrh.res.in. It is a database of sequences, structures and family-specific signatures of prokaryotic and eukaryotic AMPs. Family-specific sequence signatures comprising of patterns and Hidden Markov Models were generated for 45 AMP families by analysing 1386 experimentally studied AMPs. These were further used to retrieve AMPs from online sequence databases. More than 4000 AMPs could be identified using these signatures. AMP family signatures provided in CAMPR3 can thus be used to accelerate and expand the discovery of AMPs. CAMPR3 presently holds 10247 sequences, 757 structures and 114 family-specific signatures of AMPs. Users can avail the sequence optimization algorithm for rational design of AMPs. The database integrated with tools for AMP sequence and structure analysis will be a valuable resource for family-based studies on AMPs.

Antimicrobial peptides (AMPs) are gaining popularity as better substitute to antibiotics. These peptides are shown to be active against several bacteria, fungi, viruses, protozoa and cancerous cells. Understanding the role of primary structure of AMPs in their specificity and activity is essential for their rational design as drugs. Collection of Anti-Microbial Peptides (CAMP) is a free online database that has been developed for advancement of the present understanding on antimicrobial peptides. It is manually curated and currently holds 3782 antimicrobial sequences. These sequences are divided into experimentally validated (patents and non-patents: 2766) and predicted (1016) datasets based on their reference literature. Information like source organism, activity (MIC values), reference literature, target and non-target organisms of AMPs are captured in the database. The experimentally validated dataset has been further used to develop prediction tools for AMPs based on the machine learning algorithms like Random Forests (RF), Support Vector Machines (SVM) and Discriminant Analysis (DA). The prediction models gave accuracies of 93.2% (RF), 91.5% (SVM) and 87.5% (DA) on the test datasets. The prediction and sequence analysis tools, including BLAST, are integrated in the database. CAMP will be a useful database for study of sequence-activity and -specificity relationships in AMPs. CAMP is freely available at http://www.bicnirrh.res.in/antimicrobial.

Antimicrobial peptides (AMPs) are gaining importance as anti-infective agents. Here we describe the updated Collection of Antimicrobial Peptide (CAMP) database, available online at http://www.camp.bicnirrh.res.in/. The 3D structures of peptides are known to influence antimicrobial activity. Although there exists databases of AMPs, information on structures of AMPs is limited in these databases. CAMP is manually curated and currently holds 6756 sequences and 682 3D structures of AMPs. Sequence and structure analysis tools have been incorporated to enhance the usefulness of the database.

Solubility of proteins on overexpression in Escherichia coli is a manifestation of the net effect of several sequence-dependent and sequence-independent factors. This study aims to delineate the relationship between the primary structure and solubility on overexpression. The amino acid sequences of proteins reported to be soluble or to form inclusion bodies on overexpression in E. coli under normal growth conditions were analyzed. The results show a positive correlation between thermostability and solubility of proteins, and an inverse correlation between the in vivo half-life of proteins and solubility. The amino acid (Asn, Thr, Tyr) composition and the tripeptide frequency of the protein were also found to influence its solubility on overexpression. The amino acids that were seen to be present in a comparatively higher frequency in inclusion body-forming proteins have a higher sheet propensity, whereas those that are seen more in soluble proteins have a higher helix propensity; this is indicative of a possible correlation between sheet propensity and inclusion body formation. Thus, the present analysis shows that thermostability, in vivo half-life, Asn, Thr, and Tyr content, and tripeptide composition of a protein are correlated to the propensity of a protein to be soluble on overexpression in E. coli. The precise mechanism by which these properties affect the solubility status of the overexpressed protein remains to be understood.Keywords: aliphatic index; instability index; solubility index; thermostability; tripeptide frequency; inclusion body Supplemental material: see www.proteinscience.org Overexpression of recombinant proteins is needed both in industry and in research for the production of proteins with pharmaceutical, structural, and/or biochemical relevance (Clark 1998). Escherichia coli, the preferred host for overexpressing recombinant proteins, is fast and inexpensive to cultivate, easy to handle and manipulate genetically, and generally yields high levels of recombinant proteins (Rudolph 1990; Clark 1998). However, a significantly large number of proteins form insoluble inclusion bodies when overexpressed in E. coli; these have to be solubilized and refolded to obtain the functional protein (Rudolph 1990;Rudolph and Lilie 1996;Pedelacq et al. 2002;Tsumoto et al. 2003;Tresaugues et al. 2004).Several factors have been suggested to contribute to inclusion body formation in E. coli: (1) high local concentration of the overexpressed protein; (2) reducing environment in the cytoplasm due to high levels of glutathione preventing disulfide bond formation (Makrides 1996;Lilie et al. 1998); (3) lack of posttranslational modifications such as glycosylation which could improve protein's solubility (Zhang et al. 1998); (4) improper interactions with chaperones and other proteins participating in folding in vivo (Mogk et al. 2002); (5) increased aggregation of folding intermediates due to their limited solubility (King et al. 1996); (6) intermolecular cross-linking via disulfideshowever, proteins witho...

Six physicochemical properties together with residue and dipeptide-compositions have been used to develop a support vector machine-based classifier to predict the overexpression status in E.coli. The prediction accuracy is approximately 72% suggesting that it performs reasonably well in predicting the propensity of a protein to be soluble or to form inclusion bodies. The algorithm could also correctly predict the change in solubility for most of the point mutations reported in literature. This algorithm can be a useful tool in screening protein libraries to identify soluble variants of proteins.

Antimicrobial peptides (AMPs) are gaining popularity as anti-infective agents. Information on sequence features that contribute to target specificity of AMPs will aid in accelerating drug discovery programs involving them. In this study, an algorithm called ClassAMP using Random Forests (RFs) and Support Vector Machines (SVMs) has been developed to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity. ClassAMP is available at http://www.bicnirrh.res.in/classamp/.

Collection of antimicrobial peptides (CAMP), CAMPSign, and ClassAMP are open‐access resources that have been developed to enhance research on antimicrobial peptides (AMPs). Comprehensive information on AMPs and machine learning‐based predictive models are made available for users through these resources. As of date, CAMPR3 has 10,247 sequences, 757 structures, and 114 family‐specific signatures of AMPs along with associated tools for AMP sequence and structure analysis. CAMPSign uses family‐specific sequence conservation, in the form of patterns and hidden Markov models for identification of AMPs. ClassAMP can be used to classify AMPs as antibacterial, antifungal, or antiviral based on sequence information. Here we describe CAMP and its derivatives and illustrate, with a few examples, the contribution of these online resources to the advancement of our current understanding of AMPs.

COVID -19 is an acute infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Human surfactant protein D (SP-D) is known to interact with spike protein of SARS-CoV, but its immune-surveillance against SARS-CoV-2 is not known.The study aimed to examine the potential of a recombinant fragment of human SP-D (rfhSP-D) as an inhibitor of replication and infection of SARS-CoV-2. The interaction of rfhSP-D with spike protein of SARS-CoV-2 and hACE-2 receptor was predicted via docking analysis.The inhibition of interaction between spike protein and ACE-2 by rfhSP-D was confirmed using direct and indirect ELISA. The effect of rfhSP-D on replication and infectivity of SARS-CoV-2 from clinical samples was studied by measuring the expression of RdRp gene of the virus using qPCR. In-silico interaction studies indicated that three amino acid residues in the RBD of spike of SARS-CoV-2 were commonly involved in interacting with rfhSP-D and ACE-2. Studies using clinical samples of SARS-CoV-2 positive cases (asymptomatic, n=7 and symptomatic, n=8 and negative controls n=15) demonstrated that treatment with 1.67 µM rfhSP-D inhibited viral replication by ~5.5 fold and was more efficient than Remdesivir (100 µM). Approximately, a 2-fold reduction in viral infectivity was also observed after treatment with 1.67 µM rfhSP-D. These results conclusively demonstrate that the calcium independent rfhSP-D mediated inhibition of binding between the receptor binding domain of the S1 subunit of the SARS-CoV-2 spike protein and human ACE-2, its host cell receptor, and a significant reduction in SARS-CoV-2 infection and replication invitro.