Bhageerath—Targeting the near impossible: Pushing the frontiers of atomic models for protein tertiary structure prediction#

Improved 3-D Protein Structure Predictions using Deep ResNet Model

Protein Structure Prediction (PSP) is considered to be a complicated problem in computational biology. In spite of, the remarkable progress made by the co-evolution-based method in PSP, it is still a challenging and unresolved problem. Recently, along with co-evolutionary relationships, deep learning approaches have been introduced in PSP that lead to significant progress. In this paper a novel methodology using deep ResNet architecture for predicting inter-residue distance and dihedral angles is proposed, that aims to generate 125 homologous sequences in an average from a set of customized sequence database. These sequences are used to generate input features. As an outcome of neural networks, a pool of structures is generated from which the lowest potential structure is chosen as the final predicted 3-D protein structure. The proposed method is trained using 6521 protein sequences extracted from Protein Data Bank (PDB). For testing 48 protein sequences whose residue length is less than 400 residues are chosen from the 13th Critical Assessment of protein Structure Prediction (CASP 13) dataset are used. The model is compared with Alphafold, Zhang, and RaptorX. The template modeling (TM) score is used to evaluate the accuracy of the estimated structure. The proposed method produces better performances for 52% of the target sequences while that of Alphafold, Zhang, RaptorX were 10%, 22.9%, and 6% respectively. Additionally, for 37.5% target sequences, the proposed method was able to achieve accuracy greater than or equal to 0.80. The TM score obtained for the sequences under consideration were 0.69, 0.67, 0.65, and 0.58 respectively for the proposed method, Alphafold, Zhang, and RaptorX.

De novo peptide structure prediction: an overview

Peptide structure identification is an important contribution to the further characterization of the residues involved in functional interactions. De novo structure peptide prediction has, in the past few years, made significant progresses that make reasonable, for peptides up to 50 amino acids, its use for the fast identification of their structural topologies. Here, we introduce some of the concepts underlying approaches of the field, together with their limits.

Overview of computational vaccinology: vaccine development through information technology

Pathogenic organisms, causes of various infectious diseases, possess a rich repository of antigenic proteins that engender an immune response in a host. These types of diseases are usually treated with the use of pharmaceuticals; unfortunately, many of these also have a potential to induce fatal side effects, especially allergic responses in the diseased host. In addition, many pathogens evolve (by selective survival) single or multi-drug resistance (MDR). Therefore, a means to prevent the host from becoming susceptible to the pathogen from the onset, rather than trying to devise pharmacologic protocols to treat an ongoing infection, are increasingly seen as desirable to reduce the incidence of infectious diseases altogether. To this end, cost-effective development and use of "safe" vaccines is key. This paper provides an overview on the new and expanding area of computational vaccinology and a brief background on pathogen antigenicity, identification of pathogen-specific antigens, and screening of candidate antigens using various tools and databases developed in the recent past.

Bhageerath-H: a homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins

BACKGROUND

The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequence-structure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals.

RESULTS

Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥ 0.5 in 91% of the cases and Cα RMSDs ≤ 5 Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution.

CONCLUSION

Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment.

Proceedings of the Indo-U.S. bilateral workshop on accelerating botanicals/biologics agent development research for cancer chemoprevention, treatment, and survival

With the evolving evidence of the promise of botanicals/biologics for cancer chemoprevention and treatment, an Indo-U.S. collaborative Workshop focusing on “Accelerating Botanicals Agent Development Research for Cancer Chemoprevention and Treatment” was conducted at the Moffitt Cancer Center, 29–31 May 2012. Funded by the Indo-U.S. Science and Technology Forum, a joint initiative of Governments of India and the United States of America and the Moffitt Cancer Center, the overall goals of this workshop were to enhance the knowledge (agents, molecular targets, biomarkers, approaches, target populations, regulatory standards, priorities, resources) of a multinational, multidisciplinary team of researcher's to systematically accelerate the design, to conduct a successful clinical trials to evaluate botanicals/biologics for cancer chemoprevention and treatment, and to achieve efficient translation of these discoveries into the standards for clinical practice that will ultimately impact cancer morbidity and mortality. Expert panelists were drawn from a diverse group of stakeholders, representing the leadership from the National Cancer Institute's Office of Cancer Complementary and Alternative Medicine (OCCAM), NCI Experimental Therapeutics (NExT), Food and Drug Administration, national scientific leadership from India, and a distinguished group of population, basic and clinical scientists from the two countries, including leaders in bioinformatics, social sciences, and biostatisticians. At the end of the workshop, we established four Indo-U.S. working research collaborative teams focused on identifying and prioritizing agents targeting four cancers that are of priority to both countries. Presented are some of the key proceedings and future goals discussed in the proceedings of this workshop.

Structure predictions of two Bauhinia variegata lectins reveal patterns of C-terminal properties in single chain legume lectins

Bauhinia variegata lectins (BVL-I and BVL-II) are single chain lectins isolated from the plant Bauhinia variegata. Single chain lectins undergo post-translational processing on its N-terminal and C-terminal regions, which determines their physiological targeting, carbohydrate binding activity and pattern of quaternary association. These two lectins are isoforms, BVL-I being highly glycosylated, and thus far, it has not been possible to determine their structures. The present study used prediction and validation algorithms to elucidate the likely structures of BVL-I and -II. The program Bhageerath-H was chosen from among three different structure prediction programs due to its better overall reliability. In order to predict the C-terminal region cleavage sites, other lectins known to have this modification were analysed and three rules were created: (1) the first amino acid of the excised peptide is small or hydrophobic; (2) the cleavage occurs after an acid, polar, or hydrophobic residue, but not after a basic one; and (3) the cleavage spot is located 5-8 residues after a conserved Leu amino acid. These rules predicted that BVL-I and -II would have fifteen C-terminal residues cleaved, and this was confirmed experimentally by Edman degradation sequencing of BVL-I. Furthermore, the C-terminal analyses predicted that only BVL-II underwent α-helical folding in this region, similar to that seen in SBA and DBL. Conversely, BVL-I and -II contained four conserved regions of a GS-I association, providing evidence of a previously undescribed X4+unusual oligomerisation between the truncated BVL-I and the intact BVL-II. This is the first report on the structural analysis of lectins from Bauhinia spp. and therefore is important for the characterisation C-terminal cleavage and patterns of quaternary association of single chain lectins.

A homology/ab initio hybrid algorithm for sampling near-native protein conformations

One of the major challenges for protein tertiary structure prediction strategies is the quality of conformational sampling algorithms, which can effectively and readily search the protein fold space to generate near-native conformations. In an effort to advance the field by making the best use of available homology as well as fold recognition approaches along with ab initio folding methods, we have developed Bhageerath-H Strgen, a homology/ab initio hybrid algorithm for protein conformational sampling. The methodology is tested on the benchmark CASP9 dataset of 116 targets. In 93% of the cases, a structure with TM-score ≥ 0.5 is generated in the pool of decoys. Further, the performance of Bhageerath-H Strgen was seen to be efficient in comparison with different decoy generation methods. The algorithm is web enabled as Bhageerath-H Strgen web tool which is made freely accessible for protein decoy generation (http://www.scfbio-iitd.res.in/software/Bhageerath-HStrgen1.jsp).

Novel mutations in cyclin-dependent kinase-like 5 (CDKL5) gene in Indian cases of Rett syndrome

Rett syndrome is a severe neurodevelopmental disorder, almost exclusively affecting females and characterized by a wide spectrum of clinical manifestations. Both the classic and atypical forms of Rett syndrome are primarily due to mutations in the methyl-CpG-binding protein 2 (MECP2) gene. Mutations in the X-linked cyclin-dependent kinase-like 5 (CDKL5) gene have been identified in patients with atypical Rett syndrome, X-linked infantile spasms sharing common features of generally early-onset seizures and mental retardation. CDKL5 is known as serine/threonine protein kinase 9 (STK9) and is mapped to the Xp22 region. It has a conserved serine/threonine kinase domain within its amino terminus and a large C-terminal region. Disease-causing mutations are distributed in both the amino terminal domain and in the large C-terminal domain. We have screened the CDKL5 gene in 44 patients with atypical Rett syndrome who had tested negative for MECP2 gene mutations and have identified 6 sequence variants, out of which three were novel and three known mutations. Two of these novel mutations p.V966I and p.A1011V were missense and p.H589H a silent mutation. Other known mutations identified were p.V999M, p.Q791P and p.T734A. Sequence homology for all the mutations revealed that the two mutations (p.Q791P and p.T734A) were conserved across species. This indicated the importance of these residues in structure and function of the protein. The damaging effects of these mutations were analysed in silico using PolyPhen-2 online software. The PolyPhen-2 scores of p.Q791P and p.T734A were 0.998 and 0.48, revealing that these mutations could be deleterious and might have potential functional effect. All other mutations had a low score suggesting that they might not alter the activity of CDKL5. We have also analysed the position of the mutations in the CDKL5 protein and found that all the mutations were present in the C-terminal domain of the protein. The C-terminal domain is required for cellular localization through protein-protein interaction; any mutations in this domain might alter this function of the protein. This is the first report from India showing the mutation in CDKL5 gene in Indian cases of Rett syndrome. Our study emphasizes the role of CDKL5 mutation screening in cases of atypical Rett syndrome with congenital seizure variant.