Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Discrete analysis of camelid variable domains: sequences, structures, and in-silico structure prediction.

ABSTRACT: Antigen binding by antibodies requires precise orientation of the complementarity- determining region (CDR) loops in the variable domain to establish the correct contact surface. Members of the family Camelidae have a modified form of immunoglobulin gamma (IgG) with only heavy chains, called Heavy Chain only Antibodies (HCAb). Antigen binding in HCAbs is mediated by only three CDR loops from the single variable domain (VHH) at the N-terminus of each heavy chain. This feature of the VHH, along with their other important features, e.g., easy expression, small size, thermo-stability and hydrophilicity, made them promising candidates for therapeutics and diagnostics. Thus, to design better VHH domains, it is important to thoroughly understand their sequence and structure characteristics and relationship. In this study, sequence characteristics of VHH domains have been analysed in depth, along with their structural features using innovative approaches, namely a structural alphabet. An elaborate summary of various studies proposing structural models of VHH domains showed diversity in the algorithms used. Finally, a case study to elucidate the differences in structural models from single and multiple templates is presented. In this case study, along with the above-mentioned aspects of VHH, an exciting view of various factors in structure prediction of VHH, like template framework selection, is also discussed.

SUBMITTER: Melarkode Vattekatte A

PROVIDER: S-EPMC7061911 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Structure prediction of domain insertion proteins from structures of individual domains.

Project description:Multidomain proteins continue to be a major challenge in protein structure prediction. Here we present a Monte Carlo (MC) algorithm, implemented within Rosetta, to predict the structure of proteins in which one domain is inserted into another. Three MC moves combine rigid-body and loop movements to search the constrained conformation by structure disruption and subsequent repair of chain breaks. Local searches find that the algorithm samples and recovers near-native structures consistently. Further global searches produced top-ranked structures within 5 A in 31 of 50 cases in low-resolution mode, and refinement of top-ranked low-resolution structures produced models within 2 A in 21 of 50 cases. Rigid-body orientations were often correctly recovered despite errors in linker conformation. The algorithm is broadly applicable to de novo structure prediction of both naturally occurring and engineered domain insertion proteins.

| S-EPMC2447813 | biostudies-literature

AbPredict 2: a server for accurate and unstrained structure prediction of antibody variable domains.

Project description:SummaryMethods for antibody structure prediction rely on sequence homology to experimentally determined structures. Resulting models may be accurate but are often stereochemically strained, limiting their usefulness in modeling and design workflows. We present the AbPredict 2 web-server, which instead of using sequence homology, conducts a Monte Carlo-based search for low-energy combinations of backbone conformations to yield accurate and unstrained antibody structures.Availability and implementationWe introduce several important improvements over the previous AbPredict implementation: (i) backbones and sidechains are now modeled using ideal bond lengths and angles, substantially reducing stereochemical strain, (ii) sampling of the rigid-body orientation at the light-heavy chain interface is improved, increasing model accuracy and (iii) runtime is reduced 20-fold without compromising accuracy, enabling the implementation of AbPredict 2 as a fully automated web-server (http://abpredict.weizmann.ac.il). Accurate and unstrained antibody model structures may in some cases obviate the need for experimental structures in antibody optimization workflows.

| S-EPMC6542689 | biostudies-literature

Analysis and modeling of the variable region of camelid single-domain antibodies.

Project description:Camelids have a special type of Ab, known as heavy chain Abs, which are devoid of classical Ab light chains. Relative to classical Abs, camelid heavy chain Abs (cAbs) have comparable immunogenicity, Ag recognition diversity and binding affinities, higher stability and solubility, and better manufacturability, making them promising candidates for alternate therapeutic scaffolds. Rational engineering of cAbs to improve therapeutic function requires knowledge of the differences of sequence and structural features between cAbs and classical Abs. In this study, amino acid sequences of 27 cAb variable regions (V(H)H) were aligned with the respective regions of 54 classical Abs to detect amino acid differences, enabling automatic identification of cAb V(H)H CDRs. CDR analysis revealed that the H1 often (and sometimes the H2) adopts diverse conformations not classifiable by established canonical rules. Also, although the cAb H3 is much longer than classical H3 loops, it often contains common structural motifs and sometimes a disulfide bond to the H1. Leveraging these observations, we created a Monte Carlo-based cAb V(H)H structural modeling tool, where the CDR H1 and H2 loops exhibited a median root-mean-square deviation to natives of 3.1 and 1.5 Å, respectively. The protocol generated 8-12, 14-16, and 16-24 residue H3 loops with a median root-mean-square deviation to natives of 5.7, 4.5, and 6.8 Å, respectively. The large deviation of the predicted loops underscores the challenge in modeling such long loops. cAb V(H)H homology models can provide structural insights into interaction mechanisms to enable development of novel Abs for therapeutic and biotechnological use.

| S-EPMC3098910 | biostudies-literature

Bioinformatical analysis of the sequences, structures and functions of fungal polyketide synthase product template domains.

Project description:The product template (PT) domains, specifically in fungal non-reducing polyketide synthases (NR-PKSs), mediate the regioselective cyclization of polyketides dominating the final structures. However, up to date, the systematic knowledge about PT domains has been insufficient. In present study, the relationships between sequences, structures and functions of the PT domains were analyzed with 661 NR-PKS sequences. Based on the phylogenetic analysis, the PT domains were classified into prominent eight groups (I-VIII) corresponding with the representative compounds and cyclization regioselectivity (C2-C7, C4-C9, and C6-C11). Most of the cavity lining residue (CLR) sites in all groups were common, while the regional CLR site mutations resulted in the appearance of finger-like regions with different orientation. The cavity volumes and shapes, even the catalytic dyad positions of PT domains in different groups were corresponding with characteristic cyclization regioselectivity and compound sizes. The conservative residues in PT sequences were responsible for the cyclization functions and the evolution of the key residues resulted in the differentiations of cyclization functions. The above findings may help to better understand the cyclization mechanisms of PT domains and even predict the structural types of the aromatic polyketide products.

| S-EPMC5386248 | biostudies-literature

Prediction of structures of multidomain proteins from structures of the individual domains.

Project description:We describe the development of a method for assembling structures of multidomain proteins from structures of isolated domains. The method consists of an initial low-resolution search in which the conformational space of the domain linker is explored using the Rosetta de novo structure prediction method, followed by a high-resolution search in which all atoms are treated explicitly and backbone and side chain degrees of freedom are simultaneously optimized. The method recapitulates, often with very high accuracy, the structures of existing multidomain proteins.

| S-EPMC2203296 | biostudies-literature

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences.

Project description:Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

| S-EPMC10123967 | biostudies-literature

Analysis of the Sequences, Structures, and Functions of Product-Releasing Enzyme Domains in Fungal Polyketide Synthases.

Project description:Product-releasing enzyme (PRE) domains in fungal non-reducing polyketide synthases (NR-PKSs) play a crucial role in catalysis and editing during polyketide biosynthesis, especially accelerating final biosynthetic reactions accompanied with product offloading. However, up to date, the systematic knowledge about PRE domains is deficient. In the present study, the relationships between sequences, structures, and functions of PRE domains were analyzed with 574 NR-PKSs of eight groups (I-VIII). It was found that the PRE domains in NR-PKSs could be mainly classified into three types, thioesterase (TE), reductase (R), and metallo-β-lactamase-type TE (MβL-TE). The widely distributed TE or TE-like domains were involved in NR-PKSs of groups I-IV, VI, and VIII. The R domains appeared in NR-PKSs of groups IV and VII, while the physically discrete MβL-TE domains were employed by most NR-PKSs of group V. The changes of catalytic sites and structural characteristics resulted in PRE functional differentiations. The phylogeny revealed that the evolution of TE domains was accompanied by complex functional divergence. The diverse sequence lengths of TE lid-loops affected substrate specificity with different chain lengths. The volume diversification of TE catalytic pockets contributed to catalytic mechanisms with functional differentiations. The above findings may help to understand the crucial catalysis of fungal aromatic polyketide biosyntheses and govern recombination of NR-PKSs to obtain unnatural target products.

| S-EPMC5591372 | biostudies-literature

A comprehensive comparison between camelid nanobodies and single chain variable fragments.

Project description:By the emergence of recombinant DNA technology, many antibody fragments have been developed devoid of undesired properties of natural immunoglobulins. Among them, camelid heavy-chain variable domains (VHHs) and single-chain variable fragments (scFvs) are the most favored ones. While scFv is used widely in various applications, camelid antibodies (VHHs) can serve as an alternative because of their superior chemical and physical properties such as higher solubility, stability, smaller size, and lower production cost. Here, these two counterparts are compared in structure and properties to identify which one is more suitable for each of their various therapeutic, diagnosis, and research applications.

| S-EPMC8642758 | biostudies-literature

Prediction of protein functional domains from sequences using artificial neural networks.

Project description:An artificial neural network (ANN) solution is described for the recognition of domains in protein sequences. A query sequence is first compared to a reference database of domain sequences by use of and the output data, encoded in the form of six parameters, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal transfer function. The recognition is based on the distribution of scores precomputed for the known domain groups in a database versus database comparison. Applications to the prediction of function are discussed.

| S-EPMC311121 | biostudies-literature

Structure prediction of partial-length protein sequences.

Project description:Protein structure information is essential to understand protein function. Computational methods to accurately predict protein structure from the sequence have primarily been evaluated on protein sequences representing full-length native proteins. Here, we demonstrate that top-performing structure prediction methods can accurately predict the partial structures of proteins encoded by sequences that contain approximately 50% or more of the full-length protein sequence. We hypothesize that structure prediction may be useful for predicting functions of proteins whose corresponding genes are mapped expressed sequence tags (ESTs) that encode partial-length amino acid sequences. Additionally, we identify a confidence score representing the quality of a predicted structure as a useful means of predicting the likelihood that an arbitrary polypeptide sequence represents a portion of a foldable protein sequence ("foldability"). This work has ramifications for the prediction of protein structure with limited or noisy sequence information, as well as genome annotation.

| S-EPMC3742278 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data