Improved predictions by Pcons.net using multiple templates

Summary: Multiple templates can often be used to build more accurate homology models than models built from a single template. Here we introduce PconsM, an automated protocol that uses multiple templates to build protein models. PconsM has been among the top-performing methods in the recent CASP experiments and consistently perform better than the single template models used in Pcons.net. In particular for the easier targets with many alternative templates with a high degree of sequence identity, quality is readily improved with a few percentages over the highest ranked model built on a single template. PconsM is available as an additional pipeline within the Pcons.net protein structure prediction server

SP5: Improving Protein Fold Recognition by Using Torsion Angle Profiles and Profile-Based Gap Penalty Model

How to recognize the structural fold of a protein is one of the challenges in protein structure prediction. We have developed a series of single (non-consensus) methods (SPARKS, SP2, SP3, SP4) that are based on weighted matching of two to four sequence and structure-based profiles. There is a robust improvement of the accuracy and sensitivity of fold recognition as the number of matching profiles increases. Here, we introduce a new profile-profile comparison term based on real-value dihedral torsion angles. Together with updated real-value solvent accessibility profile and a new variable gap-penalty model based on fractional power of insertion/deletion profiles, the new method (SP5) leads to a robust improvement over previous SP method. There is a 2% absolute increase (5% relative improvement) in alignment accuracy over SP4 based on two independent benchmarks. Moreover, SP5 makes 7% absolute increase (22% relative improvement) in success rate of recognizing correct structural folds, and 32% relative improvement in model accuracy of models within the same fold in Lindahl benchmark. In addition, modeling accuracy of top-1 ranked models is improved by 12% over SP4 for the difficult targets in CASP 7 test set. These results highlight the importance of harnessing predicted structural properties in challenging remote-homolog recognition. The SP5 server is available at http://sparks.informatics.iupui.edu

Predicting protein structures and structural annotation of proteomes

Protein structure prediction methods aim to predict the structures of proteins from their amino acid sequences, utilizing various computational algorithms. Structural genome annotation is the process of attaching biological information to every protein encoded within a genome via the production of three-dimensional protein models

Size does not matter: a molecular insight into the biological activity of chemical fragments utilizing computational approaches.

Masters Degree. University of KwaZulu-Natal, Durban.Insight into the functional and physiological state of a drug target is of essential importance in the drug discovery process, with the lack of emerging (3D) drug targets we propose the integration of homology modeling which may aid in the accurate yet efficient construction of 3D protein structures. In this study we present the applications of homology modeling in drug discovery, a conclusive route map and detailed technical guideline that can be utilised to obtain the most accurate model. Even with the presence of available drug targets and substantial advancements being made in the field of drug discovery, the prevalence of incurable diseases still remains at an all-time high. In this study we explore the biological activity of chemically derived fragments from natural products utilising a range of computational approaches and implement its use in a new route towards innovative drug discovery. A potential avenue referred to as the reduce to maximum concept recently proposed by organic chemists, entails reducing the size of a chemical compound to obtain a structural analogs with retained or enhanced biological activity, better synthetic approachability and reduced toxicity. Displaying that size may not in fact matter. Molecular dynamic simulations along with toxicity profiling were comparatively performed, on natural compound Anguinomycin D and its derived analog SB 640 each in complex with the CRM1 protein which plays an avid role in cancer pathogenesis. Each system was post-dynamically studied to comprehend structural dynamics adopted by the parent compound to that exhibited by the analog. Although being reduced by 60% the analog SB 640 displayed an overall exhibition of attractive pharmacophore properties which include minimal reduction in binding affinity, enhanced synthetic approachability and reduced toxicity in comparison to the parent compound. Potent inhibitor of CRM1, Leptomycin B (LMB) displayed substantial inhibition of the CRM1 export protein by binding to four of the PKIαNES residues (ϕ0, ϕ1, ϕ2, ϕ3, and ϕ4) present within the hydrophobic binding groove of CRM1. Although being drastically reduced in size and lacking the presence of the polyketide chain present in the parent compound Anguinomycin D and LMB the analog SB 640 displaced three of these essential NES residues. The potential therapeutic activity of the structural analog remains undeniable, however the application of this approach in drug design still remains ambiguous as to which chemical fragments must be retained or truncated to ensure retention or enhanced pharmacophore properties. In this study we aimed to the use of thermodynamic calculations, which was accomplished by incorporating a MM/GBSA per-residue energy contribution footprint from molecular dynamics simulation. The proposed approach was generated for each system. Anguinomycin D and analog SB 640 each in complex with CRM1 protein, each system formed interactions with the conserved active site residues Leu 536, Thr 575, Val 576 and Lys 579. These residues were highlighted as the most energetically favourable amino acid residues contributing substantially to the total binding free energy. Thus implying a conserved selectivity and binding mode adopted by both compounds despite the omission of the prominent polyketide chain in the analog SB 640, present in the parent compound. A strategic computational approach presented in this study could serve as a beneficial tool to enhance novel drug discovery. This entire work provides an invaluable contribution to the understanding of the phenomena underlying the reduction in the size of a chemical compound to obtain the most beneficial pharmacokinetic properties and could largely contribute to the design of potent analog inhibitors for a range of drug targets implicated in the orchestration of diseases

Mass & secondary structure propensity of amino acids explain their mutability and evolutionary replacements

Why is an amino acid replacement in a protein accepted during evolution? The answer given by bioinformatics relies on the frequency of change of each amino acid by another one and the propensity of each to remain unchanged. We propose that these replacement rules are recoverable from the secondary structural trends of amino acids. A distance measure between high-resolution Ramachandran distributions reveals that structurally similar residues coincide with those found in substitution matrices such as BLOSUM: Asn Asp, Phe Tyr, Lys Arg, Gln Glu, Ile Val, Met → Leu; with Ala, Cys, His, Gly, Ser, Pro, and Thr, as structurally idiosyncratic residues. We also found a high average correlation (\overline{R} R = 0.85) between thirty amino acid mutability scales and the mutational inertia (I X ), which measures the energetic cost weighted by the number of observations at the most probable amino acid conformation. These results indicate that amino acid substitutions follow two optimally-efficient principles: (a) amino acids interchangeability privileges their secondary structural similarity, and (b) the amino acid mutability depends directly on its biosynthetic energy cost, and inversely with its frequency. These two principles are the underlying rules governing the observed amino acid substitutions. © 2017 The Author(s)

Protein kinases: Structure modeling, inhibition, and protein-protein interactions

Human protein kinases belong to a large and diverse enzyme family that contains more than 500 members. Deregulation of protein kinases is associated with many disorders, and this is why protein kinases are attractive targets for drug discovery. Due to the high conservation of the ATP binding pocket among this family, designing specific and/or selective inhibitors against certain member(s) is challenging. Several studies have been conducted on protein kinases to validate them as suitable drug targets. Although there are numerous target-validated protein kinases, the efforts to develop small molecule inhibitors have so far led to only a limited number of therapeutic agents and drug candidates. In our studies, we tried to understand the basic structural features of protein kinases using available computational tools. There are wide structural variations between different states of the same protein kinase that affect the enzyme specificity and inhibition. Many protein kinases do not yet have an available X-ray crystal structure and have not yet been validated to be drug targets. For these reasons, we developed a new homology modeling approach to facilitate modeling non-crystallized protein kinases and protein kinase states. Our homology modeling approach was able to model proteins having long amino acid sequences and multiple protein domains with reliable model quality and a manageable amount of computational time. Then, we checked the applicability of different docking algorithms (the routinely used computational methodology in virtual screening) in protein kinase studies. After performing the basic study of kinase structure modeling, we focused our research on cyclin dependent kinase 2 (CDK2) and glycogen synthase kinase-3β (GSK-3β). We prepared a non-redundant database from 303 available CDK2 PDB structures. We removed all structural anomalies and proceeded to use the CDK2 database in studying CDK2 structure in its different states, upon ATP, ligand and cyclin binding. We clustered the database based on our findings, and the CDK2 clusters were used to generate protein ligand interaction fingerprints (PLIF). We generated a PLIF-based pharmacophore model which is highly selective for CDK2 ligands. A virtual screening workflow was developed making use of the PLIF-based pharmacophore model, ligand fitting into the CDK2 active site and selective CDK2 shape scoring. We studied the structural basis for selective inhibition of CDK2 and GSK-3β. We compared the amino acid sequence, the 3D features, the binding pockets, contact maps, structural geometry, and Sphoxel maps. From this study we found 1) the ligand structural features that are required for the selective inhibition of CDK2 and GSK-3β, and 2) the amino acid residues which are essential for ligand binding and selective inhibition. We used the findings of this study to design a virtual screening workflow to search for selective inhibitors for CDK2 and GSK-3β. Because protein–protein interactions are essential in the function of protein kinases, and in particular of CDK2, we used protein–protein docking knowledge and binding energy calculations to examine CDK2 and cyclin binding. We applied this study to the voltage dependent calcium channel 1 (VDAC1) binding to Bax. We were able to provide important data relevant to future experimental researchers such as on the possibility of Bax to cross biological membranes and the most relevant amino acid residues in VDAC1 that interact with Bax