01754nas a2200241 4500008004100000022001400041245010500055210006900160260000900229300001100238490000700249520094300256653001601199653002101215653001801236653002501254653002201279100002101301700002601322700002001348700002401368856012001392 2021 eng d a0960-894X00aMachine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics0 aMachine learning classifiers aid virtual screening for efficient cAPR  a1278520 v383 a

De novo design of mini-proteins (4-12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. In this study we trained machine learning classifiers which can distinguish, with 90% accuracy and 80% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.

10aDrug design10amachine learning10aMini-proteins10aProtein therapeutics10avirtual screening1 aGaur, Neeraj, K.1 aGoyal, Venuka, Durani1 aKulkarni, Kiran1 aMakde, Ravindra, D. uhttps://library.ncl.res.in/content/machine-learning-classifiers-aid-virtual-screening-efficient-design-mini-protein02551nas a2200253 4500008004100000022001400041245013600055210006900191260000900260300001400269490000700283520166300290653002601953653001701979653002001996653002302016653001802039100002102057700001802078700002602096700002002122700002402142856013102166 2023 eng d a0739-110200aEvolutionary conservation of protein dynamics: insights from all-atom molecular dynamics simulations of `peptidase' domain of Spt160 aEvolutionary conservation of protein dynamics insights from alla cMAR  a1445-14570 v413 a

Protein function is encoded in its sequence, manifested in its three-dimensional structure, and facilitated by its dynamics. Studies have suggested that protein structures with higher sequence similarity could have more similar patterns of dynamics. However, such studies of protein dynamics within and across protein families typically rely on coarse-grained models, or approximate metrics like crystallographic B-factors. This study uses mu s scale molecular dynamics (MD) simulations to explore the conservation of dynamics among homologs of similar to 50 kDa N-terminal module of Spt16 (Spt16N). Spt16N from Saccharomyces cerevisiae (Sc-Spt16N) and three of its homologs with 30-40% sequence identities were available in the PDB. To make our data-set more comprehensive, the crystal structure of an additional homolog (62% sequence identity with Sc-Spt16N) was solved at 1.7 angstrom resolution. Cumulative MD simulations of 6 mu s were carried out on these Spt16N structures and on two additional protein structures with varying degrees of similarity to it. The simulations revealed that correlation in patterns of backbone fluctuations vary linearly with sequence identity. This trend could not be inferred using crystallographic B-factors. Further, normal mode analysis suggested a similar pattern of inter-domain (inter-lobe) motions not only among Spt16N homologs, but also in the M24 peptidase structure. On the other hand, MD simulation results highlighted conserved motions that were found unique for Spt16N protein, this along with electrostatics trends shed light on functional aspects of Spt16N. Communicated by Ramaswamy H. Sarma.

10aBackbone fluctuations10aFACT complex10ahistone binding10ainterdomain motion10aM24 peptidase1 aGaur, Neeraj, K.1 aGhosh, Biplab1 aGoyal, Venuka, Durani1 aKulkarni, Kiran1 aMakde, Ravindra, D. uhttps://library.ncl.res.in/content/evolutionary-conservation-protein-dynamics-insights-all-atom-molecular-dynamics-simulations02504nas a2200217 4500008004100000022001400041245010900055210006900164260000800233520174600241653001601987653001602003653001902019653001502038653001702053653002302070100002102093700002302114700002402137856012502161 2024 eng d a0739-110200aDesign of human ACE2 mimic miniprotein binders that interact with RBD of SARS-CoV-2 variants of concerns0 aDesign of human ACE2 mimic miniprotein binders that interact wit cJAN3 a

The world of medicine demands from the research community solutions to the emerging problem of SARS-CoV-2 variants and other such potential global pandemics. With advantages of specificity over small molecule drugs and designability over antibodies, miniprotein therapeutics offers a unique solution to the threats of rapidly emerging SARS-CoV-2 variants. Unfortunately, most of the promising miniprotein binders are de novo designed and it is not viable to generate molecules for each new variant. Therefore in this study, we demonstrate a method for design of miniprotein mimics from the interaction interphase of human angiotensin converting enzyme 2 (ACE2). ACE2 is the natural interacting partner for the SARS-CoV-2 spike receptor binding domain (RBD) and acts as a recognition molecule for viral entry into the host cells. Starting with ACE2 N-terminal triple helix interaction interphase, we generated more than 70 miniprotein sequences. Employing Rosetta folding and docking scores we selected 10 promising miniprotein candidates amongst which 3 were found to be soluble in lab studies. Further, using molecular mechanics (MM) calculations on molecular dynamics (MD) trajectories we test interaction of miniproteins with RBD from various variants of concern (VOC). Presently, we report two key findings; miniproteins in this study are generated using less than 10 lab testing experiments, yet when tested through in-vitro experiments, they show submicro to nanomolar affinities towards SARS-CoV-2 RBD. Also in simulation studies, when compared with previously developed therapeutics, our miniproteins display remarkable ability to mimic ACE2 interphase; making them an ideal solution to the ever evolving problem of VOCs.

10aACE2 mimics10aminiprotein10aprotein design10aSARS-CoV-210atherapeutics10aVariant of concern1 aGaur, Neeraj, K.1 aKhakerwala, Zeenat1 aMakde, Ravindra, D. uhttps://library.ncl.res.in/content/design-human-ace2-mimic-miniprotein-binders-interact-rbd-sars-cov-2-variants-concerns02559nas a2200265 4500008004100000022001400041245014200055210006900197260000900266300001100275490000800286520163300294653002301927653002201950653003401972653002102006653002602027100002002053700002102073700001902094700002202113700001702135700002002152856012102172 2024 eng d a0006-291X00aStructure of epoxide hydrolase 2 from Mangifera indica throws light on the substrate specificity determinants of plant epoxide hydrolases0 aStructure of epoxide hydrolase 2 from Mangifera indica throws li cNOV  a1504440 v7333 a

Epoxide hydrolases (EHs) are a group of ubiquitous enzymes that catalyze hydrolysis of chemically reactive epoxides to yield corresponding dihydrodiols. Despite extensive studies on EHs from different clades, generic rules governing their substrate specificity determinants have remained elusive. Here, we present structural, biochemical and molecular dynamics simulation studies on MiEH2, a plant epoxide hydrolase from Mangifera indica. Comparative structure-function analysis of nine homologs of MiEH2, which include a few AlphaFold structural models, show that the two conserved tyrosines (MiEH2Y152 and MiEH2Y232) from the lid domain dissect substrate binding tunnel into two halves, forming substrate-binding-pocket one (BP1) and two (BP2). This compartmentalization offers diverse binding modes to their substrates, as exemplified by the binding of smaller aromatic substrates, such as styrene oxide (SO). Docking and molecular dynamics simulations reveal that the linear epoxy fatty acid substrates predominantly occupy BP1, while the aromatic substrates can bind to either BP1 or BP2. Furthermore, SO preferentially binds to BP2, by stacking against catalytically important histidine (MiEH2H297) with the conserved lid tyrosines engaging its epoxide oxygen. Residue (MiEH2L263) next to the catalytic aspartate (MiEH2D262) modulates substrate binding modes. Thus, the divergent binding modes correlate with the differential affinities of the EHs for their substrates. Furthermore, long-range dynamical coupling between the lid and core domains critically influences substrate enantioselectivity in plant EHs.

10aEnantioselectivity10aEpoxide hydrolase10amolecular dynamics simulation10aRegioselectivity10aX-ray crystallography1 aBhoite, Ashwini1 aGaur, Neeraj, K.1 aPalange, Megha1 aKontham, Ravindar1 aGupta, Vidya1 aKulkarni, Kiran uhttps://library.ncl.res.in/content/structure-epoxide-hydrolase-2-mangifera-indica-throws-light-substrate-specificity