Polypeptides are amino acid chains that form the building blocks of proteins.
They consist of a sequence of amino acids linked together by peptide bonds.
Polypeptides can vary in length and complexity, playing crucial roles in biological processes such as enzyme catalysis, structural support, and cellular signaling.
Understanding the structure, function, and interactions of polypeptides is essential for advancing research in areas like protein engineering, drug development, and disease diagnosis.
PubCompare.ai is an AI-driven platform that enhances the reproducibility and accuracy of polypeptide research by helping researchers easily locate protocols from literature, pre-prints, and patents, and leverage AI-driven comparisons to identify the best protocols and products.
This tool can improve the efficency and reliabiltiy of polypeptide research, supporting advancements in this important field of study.
We implemented two protein complex prediction modes in ColabFold. One is based on AlphaFold-multimer4 and the other is based on the manipulation of residue index in the original AlphaFold2 model. Baek et al.3 (link) show that RoseTTAFold is able to model complexes despite being trained only on single chains. This is done by providing a paired alignment and modifying the residue index. The residue index is used as an input to the models to compute positional embedding. In AlphaFold2 we find the same to be true, although surprisingly the paired alignment is often not needed (Fig. 2c). AlphaFold2 uses relative positional encoding with a cap at , meaning that any pair of residues separated by 32 or more are given the same relative positional encoding. By offsetting the residue index between two proteins to be > 32, AlphaFold2 treats them as separate polypeptide chains. ColabFold integrates this for modeling complexes. For homo-oligomeric complexes (Supplementary Fig. 4a) the MSA is copied multiple times for each component. Interestingly, it was found that providing a separate MSA copy (padding by gap characters to extend to other copies) works significantly better than concatenating from left to right. For hetero-oligomeric complexes (Supplementary Fig. 4b), a separate MSA is generated for each component. The MSA is paired according to the chosen pair_mode (section 2.4.2). Given that pLDDT is useful only for assessing local structure confidence, we use the fine-tuned model parameters to return the PAE for each prediction. As illustrated in Supplementary Fig. 4c, the inter-PAE, the predicted TM-score or interface TM-score (both derived from PAE) can be used to rank and assess the confidence of the predicted protein–protein interaction.
Mirdita M., Schütze K., Moriwaki Y., Heo L., Ovchinnikov S, & Steinegger M. (2022). ColabFold: making protein folding accessible to all. Nature Methods, 19(6), 679-682.
PharmMapper requires a sufficient number of available pharmacophore models describing the binding modes of known ligands at the binding sites of protein targets. The target protein structures co-complexed with small molecules were carefully selected from DrugBank (21 (link)), BindingDB (22 (link)), PDBBind (23 (link)) and our PDTD (18 (link)) databases. DrugBank hosts a complete list of known targets with appropriate annotations, while BindingDB and PDBBind provide public, web-accessible databases of measured binding affinities, focusing chiefly on the interactions of those proteins considered to be drug targets with small or drug-like molecules. Only those proteins with available 3D crystal structures were selected and used for pharmacophore model extraction. LigandScout, which is a software tool that allows rapid extraction of 3D pharmacophores from structural data of macromolecule–ligand complexes in a fully automated and convenient way (19 (link)), was used in the process of pharmacophore model derivation. Six primary types of pharmacophore features were adopted in this process: hydrophobic center (H), positive-charged center (P), negative-charged center (N), hydrogen bond acceptor vector (HBA), hydrogen bond donor vector (HBD) and aromatic plane (AR) and one optional feature [metal interaction center (M)]. Each ligand binding site was manually analyzed after generation of corresponding pharmacophore model and the corresponding shape was characterized by several excluded volumes centered at each residue of the binding pocket. All the small ligands with molecular weight lower than 100, such as solvents, buffers and metal cations, and all the cofactors with molecular weight over 600, such as CoAs, polypeptides and nucleic acids were regarded as ‘environment atoms’ instead of binding ligands. In this context, the corresponding pharmacophore models were not generated. For the proteins existing as homopolymers, only one monomer was reserved for analysis. For the proteins determined by NMR with multiple structure models, only the first model was selected for pharmacophore generation. As a result, we generated 7302 pharmacophore models (2241 entries are annotated as ‘Human protein targets’) and deposited them in PharmTargetDB. The target annotations were extracted from DrugBank, PDBSum (24 (link)), UniProt (25 (link)) and in-house TargetBank (our unpublished data) and were categorized as follows: UniProt access ID, target name, target function and indication/disease involved.
Liu X., Ouyang S., Yu B., Liu Y., Huang K., Gong J., Zheng S., Li Z., Li H, & Jiang H. (2010). PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Research, 38(Web Server issue), W609-W614.
The total energy function consists of a weighted sum of three terms where Egeometric is a “geometric” or stereochemical energy function commonly used for macromolecular crystal structure refinement21 , EML is a maximum likelihood target function that incorporates experimental X-ray amplitude (and optionally phase information) 22 –24 (link), EDEN (γ) is the DEN potential (Online Methods), and wa and wDEN are relative weights. Such combination energy functions have been used for refinement of macromolecules since their first introduction for energy refinement 25 (link) and application to X-ray refinement9 . The refinement protocol uses repeats of torsion angle dynamics26 (link) against Etotal and B-factor refinement (Online Methods). For DEN, the target sequence must be sufficiently close to an homologous sequence (sequence identity at least 30%), which means that the target and homolog will be structurally similar. It also requires that the homolog structure was determined at sufficiently high resolution (at least 3.5 Å resolution), so that it will contain useful specific high-resolution information about the target. Homology models for the target sequence were constructed using standard well-accepted methods such as SegMod27 (link) or MODELLER28 (link). Often, multiple homology models were combined to cover the entire target structure even when it consists of multiple domains and polypeptide chains. Our approach is a major advance over conventional modeling of low resolution X-ray diffraction data by fitting rigid bodies29 since it accounts for deformations of the models while at the same time using a minimal set of variables (the single-bond torsion angles) (for five cases, our re-refinement achieved a substantial improvement in Rfree over rigid-body refined structures, Supplementary Table 1). Optionally, we turn off the DEN potential during the last refinement repeats to assess the robustness of the improvement achieved by DEN. The radius of convergence of DEN refinement is very large: in tests, automatic correction of polypeptide chain register in α-helices was observed, a notoriously difficult problem for macromolecular refinement.
Schröder G.F., Levitt M, & Brunger A.T. (2010). Super-resolution biomolecular crystallography with low-resolution data. Nature, 464(7292), 1218-1222.
Complement Factor B Helix (Snails) Homologous Sequences Human Body Molecular Structure Muscle Rigidity Polypeptides Radiography Radius single bond X-Ray Diffraction
The total energy function consists of a weighted sum of three terms where Egeometric is a “geometric” or stereochemical energy function commonly used for macromolecular crystal structure refinement21 , EML is a maximum likelihood target function that incorporates experimental X-ray amplitude (and optionally phase information) 22 –24 (link), EDEN (γ) is the DEN potential (Online Methods), and wa and wDEN are relative weights. Such combination energy functions have been used for refinement of macromolecules since their first introduction for energy refinement 25 (link) and application to X-ray refinement9 . The refinement protocol uses repeats of torsion angle dynamics26 (link) against Etotal and B-factor refinement (Online Methods). For DEN, the target sequence must be sufficiently close to an homologous sequence (sequence identity at least 30%), which means that the target and homolog will be structurally similar. It also requires that the homolog structure was determined at sufficiently high resolution (at least 3.5 Å resolution), so that it will contain useful specific high-resolution information about the target. Homology models for the target sequence were constructed using standard well-accepted methods such as SegMod27 (link) or MODELLER28 (link). Often, multiple homology models were combined to cover the entire target structure even when it consists of multiple domains and polypeptide chains. Our approach is a major advance over conventional modeling of low resolution X-ray diffraction data by fitting rigid bodies29 since it accounts for deformations of the models while at the same time using a minimal set of variables (the single-bond torsion angles) (for five cases, our re-refinement achieved a substantial improvement in Rfree over rigid-body refined structures, Supplementary Table 1). Optionally, we turn off the DEN potential during the last refinement repeats to assess the robustness of the improvement achieved by DEN. The radius of convergence of DEN refinement is very large: in tests, automatic correction of polypeptide chain register in α-helices was observed, a notoriously difficult problem for macromolecular refinement.
Schröder G.F., Levitt M, & Brunger A.T. (2010). Super-resolution biomolecular crystallography with low-resolution data. Nature, 464(7292), 1218-1222.
Complement Factor B Helix (Snails) Homologous Sequences Human Body Molecular Structure Muscle Rigidity Polypeptides Radiography Radius single bond X-Ray Diffraction
PIC server accepts atomic coordinate set of a protein structure in the standard Protein Data Bank (PDB) format. The user is prompted with selecting one or more of the following interaction types: Interaction between apolar residues, disulphide bridges, hydrogen bond between main chain atoms, hydrogen bond between main chain and sidechain atoms, hydrogen bond between two sidechain atoms, interaction between oppositely charged amino acids (ionic interactions), aromatic–aromatic interactions, aromatic–sulphur interactions and cation–π interactions. The input coordinate set is accepted, under each section of the page, for recognition of interactions within a polypeptide chain. If an ensemble of NMR-derived structures is input then the first model in the file is taken as a representative and is used by the PIC server. The output corresponds to the list of residues involved in interaction type of interest. An option is provided, using RasMol (25 (link)) interface and Jmol interface, for enabling visualization of structure in the graphics with interactions highlighted. It is possible to get the results by e-mail. It is also possible to download the output files of the original programs. A separate panel is available for identification of various types of interactions between polypeptide chains when a multi-chain PDB file is subjected to the analysis. All the said interactions could be explored for their occurrence across the inter-polypeptide chain interface. Thus this panel facilitates recognition of interactions between different subunits in a multimeric protein structures or between proteins in a protein–protein complex structure. Figure 1 show ionic interactions between oppositely charged sidechains across the interface, formed between cyclin-dependent protein kinase and bound cyclin (26 (link)), recognized using PIC server.
Interactions between oppositely charged amino acid sidechains in the interaction interface of cyclic dependent protein kinase 2 (CDKs) and cyclin identified using PIC server. The folds of CDK2 and cyclin and the charged residues in the interface formed by the two proteins are represented in different colours. The ion pairs are highlighted by black dotted lines. This figure is produced using SETOR (35 ).
Solvent accessibility calculations could be used to identify different kinds of interactions between buried or between solvent exposed residues. Solvent accessibility calculations are performed using NACCESS program (Hubbard, S.J. and Thornton, J.M., 1993, NACCESS Computer Program, Department of Biochemistry and Molecular Biology, University College London.). The exposed and buried residues are identified by >7% and ⩽7% residue accessibility, respectively. Under this facility list of all the interaction types are displayed prompting the user to select list of interaction types of interest. For example, a user may prefer to identify interactions between apolar residues that are exposed. Figure 2 shows interactions between solvent exposed apolar residues, in crambin (27 (link)), recognized using PIC server.
Structure of crambin with solvent exposed and interacting apolar sidechains, recognized using PIC. Interactions between apolar sidechains is shown by green dots. Disulphide bonds are shown in yellow. This figure is produced using SETOR (35 ).
Depth of an atom in a protein is defined as the distance from the nearest atom in the surface of the protein structure. Mean depths of atoms of a residue defines the residue depth (28 (link),29 (link)). Analogous to the panel on solvent accessibility, panel on residue depth enables the users to identify specific types of interactions near the protein surface or deep inside the core of the structure. Based on the analysis of residue depth parameter by Chakravarty and Varadarajan (28 (link)) we consider those residues with depths ⩽5 Å as close to the protein surface and others as deep inside. Using this part of the PIC server it is possible to identify interactions between, say, aromatic residues near the protein structural surface. As calculation of residue depths takes a few minutes for most protein structures, results involving depth calculation are sent by e-mail to the user if a valid e-mail address is provided.
Tina K.G., Bhadra R, & Srinivasan N. (2007). PIC: Protein Interactions Calculator. Nucleic Acids Research, 35(Web Server issue), W473-W476.
Amino Acids Amino Acids, Cyclic CDK2 protein, human crambin protein, Crambe abyssinica Cyclin-Dependent Kinases Cyclins Disulfides Hydrogen Bonds Membrane Proteins Polypeptides Protein Kinases Proteins Protein Subunits SET protein, human Solvents Staphylococcal Protein A Sulfur
TbpB and NMB0313 genes were amplified from the genome of Neisseria meningitidis serotype B strain B16B6. The LbpB gene was amplified from Neisseria meningitidis serotype B strain MC58. Full length TbpB was inserted into Multiple Cloning Site 2 of pETDuet using restriction free cloning ((F van den Ent, J. Löwe, Journal of Biochemical and Biophysical Methods (Jan. 1, 2006)).). NMB0313 was inserted into pET26, where the native signal peptide was replaced by that of pelB. Mutations and truncations were performed on these vectors using site directed mutagenesis and restriction free cloning, respectively. Pairs of vectors were transformed into E. coli C43 and were grown overnight in LB agar plates supplemented with kanamycin (50 μg/mL) and ampicillin (100 μg/mL).
tbpB genes were amplified from the genomes of M. catarrhalis strain 035E and H. influenzae strain 86-028NP and cloned into the pET52b plasmid by restriction free cloning as above. The corresponding SLAMs (M. catarrhalis SLAM 1, H. influenzae SLAM1) were inserted into pET26b also using restriction free cloning. A 6His-tag was inserted between the pelB and the mature SLAM sequences as above. Vectors were transformed into E. coli C43 as above.
Cells were harvested by centrifugation at 4000 g and were twice washed with 1 mL PBS to remove any remaining growth media. Cells were then incubated with either 0.05-0.1 mg/mL biotinylated human transferrin (Sigma-aldrich T3915-5 MG), α-TbpB (1:200 dilution from rabbit serum for M. catarrhalis and H. influenzae; 1:10000 dilution from rabbit serum for N. meningitidis), or α-LbpB (1:10000 dilution from rabbit serum-obtained a gift from J. Lemieux) or α-fHbp (1:5000 dilution from mouse, a gift from D. Granoff) for 1.5 hours at 4° C., followed by two washes with 1 mL of PBS. The cells were then incubated with R-Phycoerythrin-conjugated Streptavidin (0.5 mg/ml Cedarlane) or R-phycoerythrin conjugated Anti-rabbit IgG (Stock 0.5 mg/ml Rockland) at 25 ug/mL for 1.5 hours at 4° C. The cells were then washed with 1 mL PBS and resuspended in 200 uL fixing solution (PBS+2% formaldehyde) and left for 20 minutes. Finally, cells were washed with 2×1 mL PBS and transferred to 5 mL polystyrene FACS tubes. The PE fluorescence of each sample was measured for PE fluorescence using a Becton Dickinson FACSCalibur. The results were analyzed using FLOWJO software and were presented as mean fluorescence intensity (MFI) for each sample. For N. meningtidis experiments, all samples were compared to wildtype strains by normalizing wildtype fluorescent signals to 100%. Errors bars represent the standard error of the mean (SEM) across three experiments. Results were plotted statistically analysed using GraphPad Prism 5 software. The results shown in FIG. 6 for the SLPs, TbpB (FIG. 6A), LbpB. (FIG. 6B) and fHbp (FIG. 6C) demonstrate that SLAM effects translocation of all three SLP polypeptides in E. coli. The results shown in FIG. 10 demonstrate that translocation of TbpB from M. catarrhalis (FIG. 10C) and in H. influenzae (FIG. 10D) in E. coli require the co-expression of the required SLAM protein (Slam is an outer membrane protein that is required for the surface display of lipidated virulence factors in Neisseria. Hooda Y, Lai C C, Judd A, Buckwalter C M, Shin H E, Gray-Owen S D, Moraes T F. Nat Microbiol. 2016 Feb. 29; 1:16009).
US11872275B2. SLAM polynucleotides and polypeptides and uses thereof (2024-01-16). ENGINEERED ANTIGENS INC. [CA]. Inventors: Trevor F. Moraes [CA], Christine Chieh-Lin Lai [CA], Yogesh Hooda [CA], Andrew Judd [CA], Anthony B. Schryvers [CA], Scott D. Gray-Owen [CA].
VEGF-A Protein Expression after Modified RNA Injection to the Heart with Citrate Saline Buffer is Saturable and has Similar Pharmacokinetics Across Multiple Species
To compare VEGF-A protein production, 150 μg of VEGF-A modified RNA in a citrate saline buffer and 100 μg of VEGF-A modified RNA using RNAiMax (a lipid-based formulation) as the delivery carrier were injected into a rat heart. After 24 hours, VEGF-A protein levels in the rats with the citrate saline buffer (NTB) was at a comparable level to rats injected with RNAiMax and the pharmacokinetic profile were similar (FIG. 12A). The protein expression was dose limited and saturable, which was seen across species (FIG. 12B). With a ten-fold increase in dose, there was only a 1.6-fold increase in the area under the curve (FIG. 12C).
US11866475B2. Modified RNA encoding VEGF-A polypeptides, formulations, and uses relating thereto (2024-01-09). MODERNATX, INC [US]. Inventors: Leif Karlsson Parinder [SE], Regina Desirée Fritsche Danielson [SE], Kenny Mikael Hansson [SE], Li Ming Gan [SE], Jonathan Clarke [SE], Ann-Charlotte Eva Egnell [SE], Kenneth Randall Chien [SE].
For the production of gB1666, the PSB1666 construct was transiently transfected into Expi293F cells. The cell pellets were harvested 96 hours after transfection. The PSB1666 protein was purified in 25 mM HEPES pH 7.5, 250 mM NaCl, 0.02% DDM, 0.002% CHS, 3 μg/ml WAY-174865 (inhibitor, see FIG. 5D) through a series or processes of solubilization, affinity column and size exclusion chromatography. The protein was analyzed on SDS-PAGE and by EM with negative staining to ensure at least 50% of the proteins displaying prefusion conformation. The PSB1666 protein is expressed efficiently in transfection of Expi293F cells and 1L expression would generate ˜0.1 mg of purified PSB1666 in high quality.
The polypeptide gB1666 (PSB1666) (SEQ ID NO: 57) includes a mutation in Domains I and IV. The polypeptide includes the following mutations, D217C and Y589C, relative to the corresponding wild-type gB (Towne) set forth in SEQ ID NO: 1.
US11857622B2. Human cytomegalovirus GB polypeptide (2024-01-02). Pfizer Inc. [US]. Inventors: Yuhang Liu [US], Ye Che [US], Xiaoyuan Sherry Chi [US], Philip Ralph Dormitzer [US], Jennifer Anne Nicki [US], Xiaojie Yao [US].
An exemplary fusion protein construct was designed, comprising an exemplary anti-C3d antibody (3d8b) connected to a CR1 (1-10) complement modulator polypeptide, illustrated in FIG. 13. Numbering of amino acid positions mentioned in the below exemplary design is according to the EU index as in Kabat. The anti-C3d antibody comprises a light chain (domains VL and CK) comprising the sequence in SEQ ID NO: 69, a first heavy chain (domains VH-hinge-CH1-CH2-CH3; as in the sequence in SEQ ID NO: 89) comprising amino acid substitutions Thr366Ser, Met368Ala, and Tyr407Val, forming an Fc region comprising a hole, which pairs with a second heavy chain (domains hinge-CH2-CH3) Fc region comprising amino acid substitution Thr366Trp, forming an Fc region with a knob, the second heavy chain is connected to the CR1 (1-10) complement modulator polypeptide at the hinge region, via the linker (G4SG4S) (SEQ ID NO: 242), as in the sequence of SEQ ID NO: 90.
US11879008B2. Methods of treating complement mediated diseases with fusion protein constructs comprising anti-C3d antibody and a complement modulator (2024-01-23). Q32 Bio Inc. [US]. Inventors: Michael Steven Curtis [US], Michael Storek [US], Shelia Marie Violette [US], Susan L. Kalled [US], Kelly C. Fahnoe [US], Cheng Ran Huang [US], Ellen Garber Stark [US], Frederick Robbins Taylor [US], Justin Andrew Caravella [US], Vernon Michael Holers [US].
Different EBPs having a pentapeptide repeat unit of Val-Pro-(Gly or Ala)-Xaa-Gly[VP (G or A)XG] (SEQ ID NO: 43) are named as follows. Xaa may be any amino acid except Pro. First, pentapeptide repeats of Val-Pro-Ala-Xaa-Gly (VPAXG (SEQ ID NO: 2)) with plasticity are defined as an elastin-based polypeptide with plasticity (EBPP). On the other hand, pentapeptide repeats of Val-Pro-Gly-Xaa-Gly (VPGXG (SEQ ID NO: 1)) are called an elastin-based polypeptide with elasticity (EBPE). Second, in [XiYjZk]n, the capital letters in the parentheses represent the single letter amino acid codes of guest residues, i.e., amino acids at the fourth position (Xaa or X) of an EBP pentapeptide, and subscripts corresponding to the capital letters indicate the ratio of the guest residues in an EBP monomer gene as a repeat unit. The subscript number n of [XiYjZk]n represents the total number of repeats of an EBP. For example, EBPP[G1A3F2]12 is an EBPP block including 12 repeats of the Val-Pro-Gly-Xaa-Gly (SEQ ID NO: 1) pentapeptide unit, in which a ratio of Gly, Ala, and Phe at the fourth guest residue position (Xaa) is 1:3:2. Finally, the EBP-CalM-EBP triblock polypeptides are named by the composition of each block in square brackets with a hyphen between blocks such as EBPP[G1A3F2]12-CalM-EBPP[G1A3F2]12.
US11866470B2. Polypeptides with phase transition, triblock polypeptides of the polypeptide-calmodulin-polypeptide with multi-stimuli responsiveness, hydrogel of the triblock polypeptides, and its uses (2024-01-09). INDUSTRY-UNIVERSITY COOPERATION FOUNDATION HANYANG UNIVERSITY ERICA CAMPUS [KR]. Inventors: Dong Woo Lim [KR], Jae Sang Lee [KR], Min Jung Kang [KR].
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TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
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Puromycin is a laboratory product manufactured by Merck Group. It functions as an antibiotic that inhibits protein synthesis in eukaryotic cells.
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Sodium acetate is a chemical compound with the formula CH3COONa. It is a common salt that is widely used in various laboratory and industrial applications. Sodium acetate functions as a buffer solution, helping to maintain a specific pH level in chemical reactions and processes.
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HEPES is a buffering agent commonly used in cell culture and biochemical applications. It helps maintain a stable pH environment for biological processes.
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The RNeasy Mini Kit is a laboratory equipment designed for the purification of total RNA from a variety of sample types, including animal cells, tissues, and other biological materials. The kit utilizes a silica-based membrane technology to selectively bind and isolate RNA molecules, allowing for efficient extraction and recovery of high-quality RNA.
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TRIzol is a monophasic solution of phenol and guanidine isothiocyanate that is used for the isolation of total RNA from various biological samples. It is a reagent designed to facilitate the disruption of cells and the subsequent isolation of RNA.
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
NaCNBH3 is a reducing agent commonly used in organic synthesis. It functions as a mild reducing agent for the selective reduction of nitro, azido, and carbonyl groups.
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The Protease Inhibitor Cocktail is a laboratory product designed to inhibit the activity of proteases, which are enzymes that can degrade proteins. It is a combination of various chemical compounds that work to prevent the breakdown of proteins in biological samples, allowing for more accurate analysis and preservation of protein integrity.
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PVDF membranes are a type of laboratory equipment used for a variety of applications. They are made from polyvinylidene fluoride (PVDF), a durable and chemically resistant material. PVDF membranes are known for their high mechanical strength, thermal stability, and resistance to a wide range of chemicals. They are commonly used in various filtration, separation, and analysis processes in scientific and research settings.
Polypeptides can vary greatly in their length, complexity, and the specific amino acid sequences that compose them. Shorter polypeptides are often referred to as peptides, while longer chains are considered proteins. The diversity of polypeptides allows them to play a wide range of biological roles, from structural support and enzymatic catalysis to cellular signaling and immune response. Understanding the unique characteristics of different polypepptides is crucial for advancing research in areas like protein engineering and drug development.
One of the key challenges in polypeptide research is ensuring reproducibility and accuracy of results. Polypeptides can be sensitive to factors like temperature, pH, and buffer composition, which can impact their structure and function. Researchers must carefully optimize protocols to account for these variables and ensure consistent, reliable data. Additionaly, the sheer volume of literature on polypeptide-related protocols can make it difficult to identify the most effective approaches for specific research goals.
PubCompare.ai is an AI-driven platform that can greatly enhance the efficiency and reliabiltiy of polypeptide research. The tool allows researchers to quickly screen protocol literature from a wide range of sources, including published papers, pre-prints, and patents. Moreoverr, the platform's AI-driven analysis can pinpoint critical insights, helping researchers identify the most effective protocols related to their specific polypeptide research goals. By leveraging AI to compare protocol effectiveness, PubCompare.ai enables researchers to choose the best options for reproducibility and accuracy, ultimately supporting advancements in this important field of study.
More about "Polypeptides"
Polypeptides, also known as peptides or protein subunits, are essential biomolecules that serve as the building blocks of proteins.
These amino acid chains are linked together by peptide bonds, forming a diverse range of structures and functions.
Understanding the intricate details of polypeptides is crucial for advancements in various fields, including protein engineering, drug development, and disease diagnosis.
Polypeptides play pivotal roles in numerous biological processes, such as enzyme catalysis, structural support, and cellular signaling.
Their length and complexity can vary, contributing to the vast array of proteins found in living organisms.
Techniques like TRIzol reagent extraction, Puromycin selection, and the use of buffers like Sodium acetate and HEPES are commonly employed in polypeptide research to isolate, purify, and analyze these important biomolecules.
The RNeasy Mini Kit and TRIzol reagent are often utilized to extract and purify RNA, which can then be used for the synthesis of polypeptides.
Additionally, the presence of serum components like FBS (Fetal Bovine Serum) and the use of reducing agents like NaCNBH3 (Sodium cyanoborohydride) can influence the structure and function of polypeptides.
Protease inhibitor cocktails are also essential in preserving the integrity of polypeptides during research and analysis.
PubCompare.ai, an AI-driven platform, enhances the reproducibility and accuracy of polypeptide research by providing researchers with the ability to easily locate protocols from literature, pre-prints, and patents, and leverage AI-driven comparisons to identify the best protocols and products.
This tool can improve the effciency and reliabiltiy of polypeptide research, supporting advancements in this critical field of study.
